**Clinical Research on Type 2 Diabetes and Its Complications**

Editor

**Fernando G ´omez-Peralta**

Basel • Beijing • Wuhan • Barcelona • Belgrade • Novi Sad • Cluj • Manchester

*Editor* Fernando Gomez-Peralta ´ Segovia General Hospital Segovia, Spain

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Journal of Clinical Medicine* (ISSN 2077-0383) (available at: https://www.mdpi.com/journal/jcm/ special issues/Type 2 Diabetes Complications).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

Lastname, A.A.; Lastname, B.B. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-9418-7 (Hbk) ISBN 978-3-0365-9419-4 (PDF) doi.org/10.3390/books978-3-0365-9419-4**

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## **Contents**

Reprinted from: *J. Clin. Med.* **2021**, *10*, 5755, doi:10.3390/jcm10245755 ................ **125**

### **Ricardo G ´omez-Huelgas and Fernando G ´omez-Peralta**

## *Editorial* **Clinical Research on Type 2 Diabetes: A Promising and Multifaceted Landscape**

**Fernando Gómez-Peralta \* and Cristina Abreu**

Unidad de Endocrinología y Nutrición, Hospital General de Segovia, 40002 Segovia, Spain

**\*** Correspondence<EMAIL_ADDRESS>
Type 2 diabetes constitutes an imposing epidemiological, economic, and scientific global challenge. The chronic complications of type 2 diabetes are a major cause of mortality and disability worldwide [1,2]. Clinical research is the main way to gain knowledge about longterm diabetic complications and reduce the burden of diabetes. This allows for designing effective programs for screening and follow-up and fine-targeted therapeutic interventions. However, new research methodologies are needed to obtain more accurate and useful insights into the biological and clinical processes involved in diabetic complication development.

During the last few years, new approaches for clinical research have incorporated digital tools to analyze the complex physiopathological background of type 2 diabetes. In this *Special Issue,* entitled "*Clinical Research on Type 2 Diabetes and Its Complications*" and published in the *Journal of Clinical Medicine* (https://www.mdpi.com/journal/jcm/ special\_issues/Type\_2\_Diabetes\_Complications), some valuable digital methodologies were used in different studies focusing on the type 2 diabetes syndrome. Novel machine learning techniques for predicting long-term complications are one of these approaches, as the studies of Huang, Rashid, and Shin et al. depict [3–5]. The data presented by these authors suggest that machine learning may be more accurate in predicting diabetic microvascular complications than traditional methods. Additionally, digital tools such as artificial intelligence and machine learning can be implemented through an automated and rapid process.

Among the frequent causes of frustration for people with diabetes and the health care providers involved in their management is the delayed detection of diabetic complications. The outlook of clinical research appears promising in the near future owing to the development and implementation of advanced methods for the detection of early alterations in the micro- and macrovascular complications associated with diabetes. Two papers in this *Special Issue* cover the use of specific biomarkers tracing the progress of diabetic cardiovascular complications [6,7]. In another contribution, Lee et al. revisit the long-term glycemic variability and its relationship with end-stage kidney disease [8].

Besides the genetic approach, the application of digital techniques, including machine learning and artificial intelligence, and novel biomarkers could be crucial for individualized type 2 diabetes management, which is the backbone of precision medicine.

Two review papers address the complications that are non-traditionally linked to type 2 diabetes, although currently under exhaustive research: bone health and non-alcoholic fatty liver disease [9,10]. The multifaceted nature of type 2 diabetes is clearly visualized owing to the holistic angle used by these approaches.

The efficacy and safety of new type 2 diabetes pharmacological treatment are covered by three original papers [11–13]. The Yu-Chuan Kang et al. study includes a large population sample and an extended follow-up to evaluate the association between dipeptidyl peptidase-4 inhibitors and diabetic retinopathy [13]. This could be the first signal for a new safety risk of a pharmacological class of drugs used by millions worldwide.

The COVID-19 pandemic was first reported in China in December 2019 and continues to be a devastating condition for global health and economy. The COVID-19 disease has

**Citation:** Gómez-Peralta, F.; Abreu, C. Clinical Research on Type 2 Diabetes: A Promising and Multifaceted Landscape. *J. Clin. Med.* **2022**, *11*, 6007. https://doi.org/10.3390/ jcm11206007

Received: 27 September 2022 Accepted: 5 October 2022 Published: 12 October 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

immediate implications for common chronic metabolic disorders such as type 2 diabetes. Both direct infection and the associated distress due to preventive measures in the general population have worsened the control of type 2 diabetes. Some factors indicate that COVID-19 or other coronavirus-caused diseases can be seasonal or persistent in the future. Type 2 diabetes has a strong negative effect on the prognosis of patients with COVID-19. Three papers in this *Special Issue* review the implications of this disease in relation to diabetes [14–16].

Finally, the aim of researchers in this field should be to make all these remarkable advances accessible to those populations experiencing more difficulties due to sociodemographic factors such as cultural deprivation, sex discrimination, or limited income [17–19].

**Author Contributions:** Conceptualization, writing—original draft preparation, writing—review and editing were equally done by F.G.-P. and C.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors acknowledge the continuous editorial assistance of Nicole Quinn, Always English S.L.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

## *Article* **Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles**

**Mamunur Rashid 1,†, Mohanad Alkhodari 2,\*,†, Abdul Mukit 1,3, Khawza Iftekhar Uddin Ahmed 1, Raqibul Mostafa 1, Sharmin Parveen 4 and Ahsan H. Khandoker 2**

**Abstract:** Microvascular complications are one of the key causes of mortality among type 2 diabetic patients. This study was sought to investigate the use of a novel machine learning approach for predicting these complications using only the patient demographic, clinical, and laboratory profiles. A total of 96 Bangladeshi participants with type 2 diabetes were recruited during their routine hospital visits. All patient profiles were assessed by using a chi-squared (χ2) test to statistically determine the most important markers in predicting three microvascular complications: cardiac autonomic neuropathy (CAN), diabetic peripheral neuropathy (DPN), and diabetic retinopathy (RET). A machine learning approach based on logistic regression, random forest (RF), and support vector machine (SVM) algorithms was then developed to ensure automated clinical testing for microvascular complications in diabetic patients. The highest prediction accuracies were obtained by RF using diastolic blood pressure, albumin–creatinine ratio, and gender for CAN testing (98.67%); microalbuminuria, smoking history, and hemoglobin A1C for DPN testing (67.78%); and hemoglobin A1C, microalbuminuria, and smoking history for RET testing (84.38%). This study suggests machine learning as a promising automated tool for predicting microvascular complications in diabetic patients using their profiles, which could help prevent those patients from further microvascular complications leading to early death.

**Keywords:** microvascular complications; cardiac autonomic neuropathy; diabetic peripheral neuropathy; diabetic nephropathy; diabetic retinopathy; patient profiles; machine learning

### **1. Introduction**

Diabetes is called a 'silent killer' that is killing around 1.6 million people each year, making it the 5th leading cause of death worldwide [1]. There are two types of diabetes, type 1 and type 2. Type 2 is a chronic metabolic disorder and an expanding global health problem in the past decades. It results in hyperglycemia, which reduces the ability of the body's cells to respond fully to insulin. This situation is called 'insulin resistance'. In this state, insulin production increases, due to the inaction of the hormone. The global prevalence of type 2 diabetes in low- and middle-income countries was estimated to be 415 million in 2015 and is predicted to rise to 642 million by 2040 [2]. Type 2 diabetes mellitus has been rapidly rising worldwide over the past three decades, particularly in developing countries, including Bangladesh [3]. The prevalence of type 2 diabetes in Bangladesh will

**Citation:** Rashid, M.; Alkhodari, M.; Mukit, A.; Ahmed, K.I.U.; Mostafa, R.; Parveen, S.; Khandoker, A.H. Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles. *J. Clin. Med.* **2022**, *11*, 903. https://doi.org/ 10.3390/jcm11040903

Academic Editors: Fernando Gómez-Peralta and José Luis Lázaro-Martínez

Received: 15 November 2021 Accepted: 30 January 2022 Published: 9 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

be more than 50% within the next 15 years, placing Bangladesh as the country with the 8th largest diabetic population in the world [4]. A study suggests that diabetic prevalence will more than double between 2020 and 2030 [5]. The IDF (International Diabetes Federation) Diabetes Atlas has estimated that if nothing is done, the number of diabetes patients may rise to 629 million in 2045 [6] and cases may double from 151 million [7] from 2000 to 2025 [8]. The prevalence of diabetes is higher in rural areas [9], but it was high for males in urban areas, whereas it was lower in rural areas compared to females in Bangladesh [10,11].

Neuropathies are a common persistent complication of both types of diabetes mellitus that confer morbidity and mortality to diabetic patients. Cardiac autonomic neuropathy (CAN) is associated with an increased risk of mortality [12,13]. A study including 1171 patients with type 1 and type 2 diabetes mellitus using a predefined HRV and spectral analysis of R-R intervals reported abnormal findings for 34.3% of type 2 patients [14]. Neuropathy is the most common microvascular complication of both type 1 and type 2 diabetes mellitus [15–17]. A study conducted in the outpatient section of BIRDEM Hospital, Dhaka, Bangladesh found that 19.7% of all registered type 2 patients have diabetic peripheral neuropathy (DPN) [18]. The prevalence of DPN among type 2 diabetic patients is much higher in Europe. A study concludes that 32.1% of the diabetic patients in the United Kingdom, 17.6% in Turkey, and 35.4% in Spain have DPN [19]. The prevalence of DPN increases with the age of the patient and also with the diabetic duration [18,20]. A multi-country study conducted in Asia shows a 58.6% prevalence of micro or macroalbuminuria, indicating an impending pandemic of diabetic renal (i.e., nephropathy) and cardiovascular diseases in Asia [21]. A cross-sectional study with 836 rural Bangladeshi patients showed a high prevalence of retinopathy in Bangladesh [22]. Results from 35 studies from 1980 to 2008 with 22,896 subjects with diabetes showed that the global prevalence for any RET was 34.6% (95% CI 34.5–34.8) [23]. Analyses of the exponential trend revealed an increase in diabetes prevalence among the urban and rural populations at a rate of 0.05% and 0.06% per year, respectively [24]. Increasing age, hypertension, and higher BMI were found to be significant risk factors in the urban and rural communities of Bangladesh [25]. However, the patients with type 2 diabetes in Bangladesh have limited knowledge of its risk factors, cause, and management [26,27]. Depressive diabetic symptoms were found in 29% of males and 30.5% of female participants with diabetes and 6.0% of males and 14.6% of female subjects without diabetes [28].

Most recently, machine learning has emerged in many biomedical applications as a promising tool to aid in decision-making regarding many diseases, including diabetes. In [29], the authors managed to implement a machine learning approach based on decision trees to identify the diabetic patients with or without treatment procedures from their lipid profiles. In addition, Koren et al. [30] developed a trained model capable of diagnosing diabetic patients with drugs that lower blood glucose levels. Moreover, in [30,31], the authors proposed a deep neural network to diagnose diabetic patients from clinical profiles. To recognize patterns among diabetic patients, Alloghani et al. [31] presented several machine learning models that were able of characterizing patients and explain the readmission procedures. Several other studies [32–35] utilized machine learning and deep neural networks in many other applications in diabetes diagnostics. However, even though the implementation of machine learning models for diabetes diagnostics showed high levels of performance, there is still a lack of knowledge about its impact on discriminating between the various microvascular complications. In addition, it is essential to be able to determine, both statistically as well as from a machine perspective, which features play a critical role in characterizing these complications in type 2 diabetic patients.

In this paper, a study is conducted to investigate the efficiency of applying a machinelearning-based approach in discriminating between diabetic patients, according to their microvascular complication status (Figure 1). The novelty of the presented approach lies in utilizing only the demographic, laboratory, and clinical information of patients within the framework of machine learning for diabetes diagnostics. Therefore, time-consuming clinical testing using advanced medical equipment can be avoided, which is essential in communities with economic hardship or a lack of clinical expertise. In addition, the proposed study allows for elaborating on the most important information within patient profiles when testing for each microvascular complication. To the best of the authors' knowledge, there have been very limited attempts towards identifying certain types of microvascular complications using machine learning. Therefore, a gap still exists in the literature about how certain patient information impacts the discrimination between diabetes complications. The present study provides a complete clinical testing approach for CAN-, DPN-, and RET-positive cases by looking into patient information from a machine-based perspective. NEP cases were not used in a separate machine-learning-based testing scenario because they can be easily identified from their patient profile information. Further, with a focus on CAN cases, the study investigates the ability of trained models to deeply discriminate between CAN-only patients and patients with additional complications alongside CAN.

**Figure 1.** A graphical view of the complete research work in this study, including patient enrollment; demographic, clinical, and laboratory information acquisition; machine learning modeling; and

### **2. Materials and Methods**

performance evaluation of the model.

### *2.1. Study Type*

This is a cross-sectional study of Bangladeshi patients from Dhaka who have had type 2 diabetes mellitus for more than 10 years. We followed the STROBE cross-sectional reporting guidelines [36]. The study was approved by the ethical review committee of the Bangladesh University of Health Sciences (BUHS/BIO/EA/17/01) and conforms to the ethical principles outlined in the declaration of Helsinki and the Ministry of Health and Family Welfare of Bangladesh.

### *2.2. Inclusion and Exclusion Criteria*

The parameters that were included in the inclusion criteria: Bangladeshi national, diagnosis of type 2 diabetes mellitus, above 40 years of age, able to give written consent, and the diabetes duration was 10 years or more. The exclusion criteria included: stroke history, having any heart disease, not being able to give consent, diabetes duration of less than 10 years, and the presence of any other pathophysiology that may lead to one or more similar complications, such as cancer.

### *2.3. Participants and Complications*

One hundred and three (47 males and 56 females) unrelated patients of more than 40 years of age that had type 2 diabetes for 10 years or more were randomly selected and enrolled in the study during routine visits to the BIHS [37] Hospital between 18 December 2017 and 26 April 1018. This hospital is one of the most visited hospitals for diabetic patients in Bangladesh.

In this study, the recruited patients were diagnosed with complications, such as CAN, DPN, NEP, and RET (Table 1). The presence of these complications was confirmed by a qualified physician, based on the criteria outlined by the report of the WHO consultation group [38]. A diagnosis of cardiac autonomic neuropathy (CAN) was obtained from the Ewing test, which included five tests: deep breathing, lying to standing, the Valsalva maneuver, lying to standing BP, and sustained handgrip BP [39]. A diagnosis of diabetic peripheral neuropathy (NCV) was obtained using a nerve conduction velocity (NCV) test. There were several tests for recognizing polyneuropathy, CTS (carpal tunnel syndrome), peroneal neuropathy, and other types of neuropathies. A diagnosis of nephropathy (NEP) was determined by the ACR (albumin–creatinine ratio) level >30 mg/mmol for microalbuminuria, and >300 mg/mmol for macroalbuminuria [40]. A diagnosis of retinopathy (RET) was obtained from the fundus image test and classified according to the WHO criteria [41]. Fundus imaging is a process where 3-D retinal semi-transparent tissues are projected onto the imaging plane using reflected light and represented in 2-D [42].

**Table 1.** Types of complications of patients included in this study.

Among these subjects, 70 were able to complete the diagnostic tests for all three complications (CAN, DPN, and RET). There were several combined complications found in some patients. The frequency of complications is shown in Table 2. To observe the importance of demographic, clinical, and laboratory profiles, a multiclass analysis (3-class analysis) was conducted using the classes marked in bold in Table 2 (CAN vs. CAN + DPN vs. CAN + DPN + Others). CAN + DPN + Others are the combinations of CAN + DPN + NEP, CAN + DPN + RET, and CAN + DPN + NEP + RET. These three classes were selected from Table 2 with higher numerals.

**Table 2.** Types and frequency of complications of diabetes patients.

### *2.4. Types of Variables*

### 2.4.1. Demographic and Clinical Variables

The demographic data were collected from the patients at the time of enrollment. We measured the waist circumference, height, and weight at the time of enrollment and listed the value for the diabetic duration, age, gender, smoking history, and smokeless tobacco history. All of these data were verified from the necessary and relevant documents. The clinical data were measured at the time of enrollment. The blood pressure was measured on the first day before starting their Ewing test. If the systolic blood pressure was >130 mm Hg and diastolic blood pressure was >80 mm Hg or they were taking antihypertensive medications, it was called hypertension. Dyslipidemia was diagnosed from the medications of the patient or by checking the history of dyslipidemia of that patient. The data and its basic analysis are shown in Table 3.

**Table 3.** Demographic and clinical variables of patients.

**Table 3.** *Cont.*

### 2.4.2. Laboratory Data

The laboratory data were taken from the laboratory of the hospital after the enrollment. The laboratory test parameters were hemoglobin A1c (HbA1c), microalbuminuria, urinary creatinine, and the albumin–creatinine ratio. The data and its basic analysis are shown in Table 4.

**Table 4.** Laboratory variables of patients.

Microalbuminuria: 30–300 9 (75.00) 13 (81.25) 22 (78.57)

### **Table 4.** *Cont.*

### **Table 4.** *Cont.*

### *2.5. Machine Learning Modeling*

### 2.5.1. Clinical Testing Approach

To provide a complete diagnosis of a type 2 diabetes patient, four tests in two steps were applied sequentially (Figure 2) on patients' demographic, clinical, and laboratory (DCL) information. This study supports type 2 diabetic patients with microvascular complications having a better screening from their DCL information. The approach combines a single-class binary classification model with three different classifiers and a multiclass classification model. The single-class classification model can run three tests in parallel to classify CAN, DPN, and RET separately. If all three tests result in a negative class, it means the patient with type 2 diabetes has no microvascular complications. If the test shows positive results, the patient goes for that specific complication treatment. However, obtaining a positive class from the CAN test leads to a multiclass classification model. This model can determine whether the patient has other microvascular complications along with CAN. Thus, this results of this model include: CAN (having only CAN), CANDPN (having DPN with CAN), CANDPN+ (having NEP or RET with CAN and DPN). The resulting class determines the treatment that should be provided to the patient.

### 2.5.2. Analysis of the Demographic Clinical and Laboratory Profiles

The demographic variables (such as gender, height, age, weight, smoking history, tobacco history, and diabetes duration), clinical measurements (waist circumference, BMI, systolic blood pressure, and diastolic blood pressure), and measured laboratory values (such as HbA1c, microalbuminuria, urinary creatinine, and albumin–creatinine ratio) were selected for further analysis as patient information.

A feature selection approach was then followed based on the univariate chi-squared test to choose the foremost critical factors among all the demographic, clinical, and laboratory variables. In this test, a statistical hypothesis investigation is performed for each DCL feature to test whether the observed calculations coordinate with the anticipated ones, i.e., patient's complication type. Moreover, it gives a noteworthy distinction *p*-value measure (*p*-value < 0.05) between categories based on the statistical calculations and desire [43]. A feature with a lower *p*-value signifies that this variable is most likely dependent on the complication label. Hence, it is vital for anticipating the complication and has discriminatory characteristics. In this way, a score of significance is returned for each DCL profile utilized within the test as score = −log (*p*). In this work, we call this score importance. We calculated importance using a function called fscchi2 () in MATLAB 2021a.

**Figure 2.** The proposed procedure for screening diabetic patients. Every patient initially goes through the information acquisition of this clinical diagnosis flowchart. Five tests are then applied in two stages. The second stage (multiclass class is only for the patients who go through the CAN test and have a positive CAN. A single-class classification can predict the presence of microvascular complications (CAN, DPN, or RET) and can predict whether there is any presence of complications. Multiple complications with CAN could be classified using the multiclass classifier.

### 2.5.3. Support Vector Machine (SVM)

SVM is an exceedingly popular machine learning algorithm used in classification and regression problems. It is one of the classic machine learning techniques that can help to solve big data classification problems. SVM allows the classification of single-class as well as multiclass classification problems. It is commonly utilized as an exception finder, where the model is prepared to recognize training data from any other irrelevant information [44]. The model tends to distinguish which unused objects are closely representing the selected class in the training phase, which is generally called a positive class [45]. A set of probabilities has been returned by the model to show the degree of matching between the testing and training samples. In this paper, a single-class SVM was used for the training model in the CAN, DPN, and RET tests. Having the complication has been considered as the positive class in the single-class classification. However, a multiclass SVM was for training in the CANDPNOthers test. To guarantee the highest performance from the model, a non-linear RBF (radial basis function) kernel was used with fine-tuned hyper-parameters.

### 2.5.4. Random Forest (RF)

Random forest (RF), also known as classification and regression tree (CART), is a form of decision trees, where a set of tree-like trait nodes is associated with a set of subtrees of decision nodes [46,47]. This algorithm is considered a conglomeration strategy that employs the concepts of bagging. All the decision trees are calculated based on the corresponding resource cost, outcome chances, and utility to provide a prediction. The prediction preparation begins by doling out an occasion at each tree to its root node. At that point, for each of the subsequent sub-nodes, the results are calculated successively. Once a

leaf is experienced, the tree-like nodes halt and an occasion is relegated with a prediction. All of the occasions and predictions shape the ultimate choice made by the tree model [48]. In this work, 20–120 decision trees were utilized to construct the model. The choice of the number of trees for each single-class test, as well as the multiclass test, was fine-tuned to guarantee the greatest conceivable performance from the model.

### 2.5.5. Logistic Regression

Logistic regression is one of the most commonly used machine learning algorithms in statistics. It is a statistical model that uses a logistic function to represent a binary dependent variable in its most basic form, though there are many more advanced variants. Logistic regression is a technique for estimating the parameters of a logistic model in regression analysis. The natural logarithm of the odds is used as a regression function of the predictors in the logistic regression model. The expression for a one predictor (X) one outcome (Y) logistic regression model is ln [odds (Y = 1)] = β0 + β1X, where ln is the natural algorithm, Y = 1 or Y = 0 refers to the event occurrence of the event, β0 is the intercept term, and β1 is the regression coefficient that refers to the change in the logarithm of the event's odds with a 1-unit change in the predictor X [49].

### 2.5.6. Training and Testing

A leave-one-out scheme was followed in the single-class models, as well as in the multiclass model, to ensure the incorporation of the highest possible number of samples within the prepared models. Besides, it was fundamental to supply a prediction for each and every patient. An iterative process was applied in this scheme by selecting one subject as testing data, whereas the remaining subjects were used for training. The method repeated on each cycle until a prediction was given for every subject.

### 2.5.7. Parameter Optimization

In each test, several model parameters were fine-tuned to ensure the highest acquirable model performance. Performance was measured in the form of accuracy, sensitivity, specificity, precision, f1-score, and area under the curve (AUC). To handle data imbalance (65 positive classes vs. 10 negative classes in the CAN test and 7 positive classes vs. 89 negative classes in the RET test), a model parameter called 'prior probability' was introduced in the algorithm during the training phase. The prior probabilities were found observationally, where the initial weight was set to each class that was equal to its number of samples relative to the whole number of samples [50]. Prior probability was not used in the DPN test, as it had balanced classes. The minimum leaf size and bag fraction value were used as per the behavior of the RF model, on an iterative basis and keeping the optimum value.

### **3. Results**

### *3.1. Demographic, Clinical, and Laboratory Profiles*

Demographic and clinical data, along with major comorbidities with type 2 diabetes, are shown in Table 3, and laboratory profiles are shown in Table 4. There were 47 (45.63%) male patients and 56 (54.37%) female patients. The mean age of the patients was 56 years (±8.913), the mean ages of the male and female patients were 57.1 years (±9.78) and 54.6 years (±7.93), respectively. This is consistent with the finding that the diabetic population in Bangladesh, as well as south Asia, are comparatively younger than in the west [51,52]. The sub-variables under 'Age' show that 46.8% of the male subjects were greater than 60 years old, but about 40% female subjects were between 40 and 50, though, overall, the patients showed an increasing prevalence for a higher age. A study in Spain also showed that an increase in patient age increases the prevalence of diabetic complications [19]. In this study, 27 (57.45%) males, 35 (62.50%) females, and a total of 62 (60.19%) patients had a history of hypertension (mean systolic blood pressure was 138.4 mm Hg). A total of 35 (33.98%) patients had dyslipidemia, where 14 (29.79%) were male and 21 (37.5%) were female. Only nine (8.74%) patients had a history of smoking, and they were all male. In addition, the overweight condition (42.86%) was common for female

diabetic patients, with more than 98% female subjects having a waist circumference higher than 80 cm, while 57.45% of the male subjects had a normal weight. Though obesity was relatively common for female patients (27%), a total of 20 (19.42%) patients were obese (mean BMI (body mass index) = 33.94 kg/m2 and mean waist circumference = 90.84 cm for males and 97.38 cm for females). For the retinopathy patients, the waist circumference was 89.91 cm for males and 93.98 cm for females, where 15 (26.79%) were female and 5 (10.638%) were males.

More than 67% of the patients for any type of complication had a high HbA1c (mean HbA1c = 8.824, male mean HbA1c = 9.066, and female mean HbA1c = 8.621 for the patients with CAN). The retinopathy patients had very high HbA1c (mean HbA1c = 10.829, male mean HbA1c = 10.720, and female mean HbA1c = 11.100). Microalbuminuria was found in 25 (24.51%) patients, where 10 were male and 15 were female. In the case of nephropathy, a total of 22 (78.57%) patients had microalbuminuria. All the retinopathy patients had a creatinine level of 20 to 320 mg/dL. The mean ACR (albumin–creatinine ratio) for the patients was 35.967 mg/mmol, where 47 (46.08%) males had a mean ACR of 32.092 mg/mmol, and 55 (53.92%) females had a mean ACR of 39.280 mg/mmol. Neuropathy was the most common complication in Bangladeshi diabetic type 2 patients of more than 40 years' old who had diabetes for more than 10 years. Besides, there were very few retinopathy patients, so it implies that the rate of retinopathy in Bangladeshi type 2 diabetes patients is very low.

### *3.2. Complications of Type 2 Diabetes*

Overall, more than one clinically diagnosed complication was present in 99 subjects out of the cohort of 103 diabetics included in this study. Most of the subjects had CAN (66.02%), followed by diabetic peripheral neuropathy (43.69%), nephropathy (27.18%), and retinopathy (6.8%). Those patients who had retinopathy also had CAN and DPN. The rate of retinopathy complication was very low. Only seven retinopathy patients were found, and five patients out of them had all types of complication, while the other two had CAN and DPN. This trend suggests that RET should be the final stage of the above four diabetes microvascular complications in Bangladesh. We did not find any subject with only NEP or only RET. If a patient had RET, we can say that he/she had CAN and DPN both or CAN, DPN, and NEP, i.e., all the complications. The average diabetic duration of the male patients with CAN and DPN was high (17.33 years for CAN and 18.91 years for DPN) and comparatively lower for RET (13 years). The female patients with retinopathy had a high diabetic duration of 17.5 years. They did not check for DM until they became very ill, so their reported DM duration is from the day they first found out, not from the actual moment of DM development. The overall result indicates a high prevalence of complications in Bangladeshi type 2 diabetes patients.

### *3.3. Classification of Cardiac-Related Microvascular Complications*

To assess the association between any complication (as an outcome) and significant demographic, clinical, and laboratory variables of the patients, several machine learning models (logistic regression, RF, and SVM) were trained by changing the model parameters in an iterative way and observing the sensitivity, specificity, precision, f1-score, and accuracy of the model. The chi-squared (χ2) test was used to choose significant variables and we use only these significant variables to determine the classification accuracy. The threshold for a significant importance level was different for each test.

### 3.3.1. CAN

We found diastolic BP (importance 2.1), albumin–creatinine ratio (importance 1.6), and gender (importance 1) to be the significant predictors for screening CAN, which is the most common complication among Bangladeshi patients with type 2 diabetes. We had 65 positive and 10 negative CAN patients in our study. To find the best suitable result and to maximize the model performance, we used prior probability in the classification model of CAN. We found that RF was the best model at the weight of (1.05 and 0.9). The

performance (shown in Table 5) of the model was obtained as accuracy 98.68%, sensitivity 98.48%, and specificity 100%. The performance is shown in Figure 3. In Table 6, the 95% confidence intervals, including the mean values of the features, are provided for CAN patients to represent the true mean of the population.

**Table 5.** Comparison between two machine learning models for each test.

**Figure 3.** (**a**) Chi-squared test result. The importance of different marked features was used in the model as an identifier; (**b**) confusion matrix of the CAN test (pClass vs. nClass); (**c**) performance evaluation matrices; (**d**) TPR vs. FPR, graphical view of the CAN classifier model performance.

**Table 6.** 95% Confidence intervals for cardiac autonomic neuropathy patients (categorical features, such as gender, smoking history, and smokeless tobacco history, have been omited from the table);. The subject count is 75 (65 pCAN, and 10 nCAN patients), and the features that are used in the model classifier are marked in bold text.

### 3.3.2. DPN

Similarly, microalbuminuria (importance 5.1), smoking history (importance 2.9), smokeless tobacco history (importance 2.7), HbA1c (importance 2.4), albumin–creatinine ratio (importance 1.9), systolic BP (importance 1.8), diastolic BP (importance 1.4), and urinary creatinine (importance 1.4) were found to be the most significant predictors for determining DPN from type 2 diabetes patients in Bangladesh. This is consistent with other findings that age and diabetic duration are insignificant [15,53–58] here, since all the patients were more than 40 years of age and the diabetic duration was a minimum of 10 years. Both the RF and SVM models showed the highest accuracy for classifying DPN in the patients with type 2 diabetes mellitus from Bangladesh. Figure 4 illustrates the result of classifying DPN, and the numeric values are stored in Table 5. Table 7 shows the true means and 95% confidence intervals of the populations included in the DPN test.

**Table 7.** 95% confidence intervals for diabetic peripheral neuropathy patients (categorical features, such as gender, smoking history, and smokeless tobacco history, have been omited from the table). The subject count is 90 (44 pDPN, and 46 nDPN patients), and the features that are used in the model classifier are marked in bold text.

**Figure 4.** (**a**) Chi-squared test result. The importance of different marked features was used in the model as an identifier; (**b**) confusion matrix of the DPN test (pClass vs. nClass); (**c**) performance evaluation matrices; (**d**) TPR vs. FPR, graphical view of the DPN classifier model performance.

### 3.3.3. RET

In the case of diabetic retinopathy (RET), HbA1c (importance 6.1), microalbuminuria (importance 4.7), smokeless tobacco history (importance 2.8), weight (importance 1.9), gender (importance 1.8), urinary creatinine (importance 1.7), and albumin–creatinine ratio (importance 1.7) were found to be significant predictors to classify whether a type 2 diabetes mellitus patient has retinopathy (Figure 5). A previous study in Bangladesh showed a 5.4% prevalence of retinopathy patients [22], and in our study, we had 6.8% of type 2 diabetes patients with retinopathy. The accuracy (shown in Table 5) of the RF model was 84.38%. To show the true mean of the features in diabetic retinopathy test, Table 8 is added with means and 95% confidence interval information.

**Figure 5.** (**a**) Chi-squared test result. The importance of different marked features was used in the model as an identifier; (**b**) confusion matrix of the RET test (pClass vs. nClass); (**c**) performance evaluation matrices; (**d**) TPR vs. FPR, graphical view of the RET classifier model performance.

**Table 8.** 95% confidence interval for diabetic retinopathy patients (categorical features, such as gender, smoking history, and smokeless tobacco history, have been omited from the table). The subject count is 96 (7 pRet and 89 nRet patients) and the features that are used in the model classifier are marked in bold text.

### 3.3.4. CANDPNOthers

The multiclass analysis test provides a comprehensive picture of patients who have other diabetic neuropathies in addition to CAN. A total of 55 patients were considered as training and testing inputs for machine learning models, with 16 suffering from CANDPN and 14 suffering from CANDPN+, where 'Others' included NEP and RET. In the CANDP-NOther test, three classes were assigned to the model, i.e., CAN vs. CANDPN (the patients with both CAN and DPN complications) vs. CANDPN+ (the patients with CAN, DPN, and NEP; CAN, DPN, and RET; or CAN, DPN, NEP, and RET). We only included these classes due to the insufficient number of patients in the other classes. SVM performed better in this multiclass classification rather than RF. The confusion matrix (Figure 6) illustrates that the CAN and CANDPN+ classes could be classified effectively. However, identifying CANDPN patients using this model might be inefficient. The features used in this model were the albumin–creatinine ratio and microalbuminuria. These two features are common for all the binary tests that have been performed in this study.

**Figure 6.** Performance comparison (confusion matrix) between the multiclass SVM classifier and the multiclass RF classifier. (**a**) Confusion matrix of SVM classifier; (**b**) confusion matrix of RF classifier; classes: 1. CAN, patients with CAN; 2. CANDPN, with DPN and CAN; 3. CANDPN+, patients with NEP and/or RET with CAN and DPN.

### **4. Discussion**

This study demonstrated the importance of demographic, clinical, and laboratory profiles in the machine learning domain for the classification of diabetic microvascular complications. Moreover, this study illustrated a complete machine-learning-based clinical approach to screen diabetic patients suffering from diabetic microvascular complications. It also provides an association of other microvascular complications along with CAN. It provides a stepwise clinical approach to screen diabetes microvascular complications for the Bangladeshi type 2 diabetic cohort. The high performance achieved in each test strongly suggests that DCL profiles should be included as features in the machine learning approach to ensure a high classification accuracy. In our study, we also showed the DCL profiles that are highly associated with a kind of complication. Thus, the clinician can easily coordinate the physiological grounds.

### *4.1. Demographic, Clinical, and Laboratory Profiles*

In this study, we demonstrated the significance of DCL profiles to screen a microvascular complication of the type 2 diabetic population in Bangladesh. The profiles that were used in this study are easily collectible by any hospital in Bangladesh. Moreover, gathering this information from a patient is not costly. Furthermore, all the DCL profiles used in this study are not required to be collected for screening using the proposed method. Only the significant features that are listed (Section 3.3) for each test will be needed to execute the test. However, to execute all the tests proposed in this study, a mathematical union of all the significant features that are used in each test would be required.

Diastolic BP, Albumin–Creatinine Ratio, and Gender were highly associated with CAN in our CAN test. Thus, we have found the highest accuracy for the CAN classifier by using these three predictors. In [59,60], the authors showed the influence of hypertension on diabetic complications, and our study we found that diastolic BP was a good predictor variable to classify CAN. However, we have found that HbA1c was not required for testing CAN. On the other hand, microalbuminuria was significantly associated with peripheral neuropathy, nephropathy, and retinopathy. This finding supports both the studies in [61,62], where the authors showed the association of microalbuminuria with nephropathy and retinopathy. Moreover, in [63], Bell et. al. observed the significant association of microalbuminuria with diabetic neuropathy. HbA1c was significantly associated with retinopathy in our study. It was also associated with peripheral neuropathy. In [64,65], the authors established the relationship between HbA1c and microvascular complications. Many authors showed the significance and association of different demographic, clinical, and laboratory parameters with diabetic microvascular complications. However, in our study, we found significant DCL profiles using a statistical model and used these significant profiles with a machine learning model to show the performance.

### *4.2. Machine Learning as a Screening Tool*

This work describes the application of a modern machine learning model, combining the use of statistically significant features to exploit demographic, clinical, and laboratory data to extract a classifier that can classify type 2 diabetes microvascular complications. To address the class imbalance, a machine learning hyper-parameter 'prior probability' was used. Picking up the benefits of the recent advances of machine learning in the area of diabetes diagnosis is considered to be fundamental. It makes a difference within the investigation of colossal healthcare records and changes them into clinical experiences that can help healthcare experts in prompt and intelligent decision-making. Even though the involvement of a clinician within the diagnosis and treatment of diabetic patients may be necessary, machine learning models might be able to provide an early-stage screening that can avoid numerous complications from further development. Besides, when there is a tremendous request for medical specialists or unbounded data available, it is quite hard to provide a complete diagnostic for each quite effectively. In this manner, pre-trained machine learning models can make the process faster and less rigorous for healthcare suppliers and practitioners. Different sorts of machine learning algorithms, such as support vector machines (SVM), K-nearest neighbor (KNN), choice trees, etc., have been utilized broadly within the research associated with type 2 diabetes microvascular complications [66].

CAN plays a major role in myocardial ischemia and infarction, heart arrhythmias, hypertension, and heart disappointment and it increases the risk of sudden cardiac death. Jelinek and Cornforth [67] proposed a novel clustering technique using a graph-based machine learning system that enables the identification of severe diabetic neuropathies in 2016. This proposed model outperforms SVM, RF, and KNN. Cho et al. [68] showed an accuracy of 88.7% (AUC 0.969 and specificity 0.85) using SVM classifiers along with a feature selection method for the prediction of diabetic nephropathy from the data of 4321 patients. Reedy et. al. [69] proposed a multi-model ensemble-based machine learning algorithm to classify diabetic retinopathy. The authors included several machine learning classifiers in their research and concluded that the ensemble model provides better accuracy with better sensitivity and specificity. Sambyal et. al. [66] provided a review of using machine learning models to classify diabetes microvascular complications in 2020. The authors showed that most of the work for classifying RET had been conducted using fundus image as the input, and he compared the different achieved accuracies of the different classifiers by different authors. By only using the demographic, clinical, and laboratory profiles, our model outperforms all the models reviewed by the author in terms of classifying diabetic retinopathy. The authors also have reviewed several machine learning models proposed by different authors for classifying cardiac autonomic neuropathy and nephropathy. However, we only used DCL profiles as independent features to classify microvascular complications.

### *4.3. Clinical Relevance*

The test schemes followed in this work offer physicians an important clinical diagnostic method in the evaluation and diagnosis of type 2 diabetes microvascular complications. The single-class classifiers can operate individually in parallel or sequentially. However, for finding combined complications with CAN, the model works sequentially with the CAN testing classifier. Since this model is sequential, this digs deeper by analyzing diabetics with single and combined complications. Single-class classifiers identify any microvascular complication that is present in a patient, regardless of whether the multiclass classifier predicts the presence of other complications with CAN. Such a clinical test will ensure a better diagnose of type 2 diabetes microvascular complications by distinguishing the cause of single CAN and other related complications. Furthermore, the silent nature of these complications makes it difficult to diagnosis correctly, especially when combined with other microvascular complications. The performance achieved through machine learning using only DCL profiles in this study provides a path to prevent many undiagnosed CANonly cases. Since a CAN-only medical procedure may not provide effective treatment if additional complications are not properly identified. It is vital to know about combined complications. The multiclass classification test helps to identify multiple complications with autonomic neuropathy.

### *4.4. Key Message to the Health Community of Bangladesh*

Globally, healthcare stakeholders are entering a new era of data-driven clinical detection and prognostication. The application of modern machine-learning-based approaches offers great promises for early diagnosis or prognosis of various health complications. The early identification of patients at risk of microvascular complications due to type 2 diabetes can mitigate the burden on the healthcare system, especially in the context of a resourcelimited setup. As the present study shows that screening is feasible from the demographic, clinical, and laboratory (DCL) variables using a proper machine learning classification model, the health community can utilize this benefit for screening that can avoid numerous complications from further development. It also can help healthcare experts in prompt and intelligent decision-making and save the patients from incurring greater healthcare costs.

### **5. Conclusions**

This study explored the present status of microvascular complications in a cohort of type 2 diabetes patients in Bangladesh. Higher comorbidities and microvascular complications were found as compared with neighboring countries, most likely due to the increased levels of hypertension in this cohort. This study also suggests that a high diastolic BP and albumin-creatinine ratio are related to CAN; high microalbuminuria, HbA1c, and blood pressure are related to DPN; high HbA1c and microalbuminuria are related to RET. These findings may be useful in finding risk factors for the development of diabetic complications. Using these risk factors as the independent features, a machine learning model could be designed to screen microvascular complications. This study shows a machine learning model could be utilized to identify diabetes complications in Bangladesh, where the majority of its population is poor. We believe this study could contribute to more effective and affordable screening techniques [70] for diabetes-related microvascular complications.

It is worth noting that the proposed study should be further validated on a wider patient cohort to strengthen the observations. Although the findings of this study were promising and correlate with the observations found in the literature, one limitation to the current work was the small sample size, which is a common situation in biomedical studies that rely on patient data. Overall, RF and SVM are known to handle small sample sizes with high performance capabilities [71–73], especially when compared to other artificial intelligence algorithms, such as deep neural networks, that require large datasets. Therefore, an essential future direction to the current study is to be tested on large clinical data and with additional machine/deep learning algorithms.

**Author Contributions:** Conceptualization, M.R., A.M., K.I.U.A., and A.H.K.; data curation, M.R. and A.M.; formal analysis, M.R. and M.A.; investigation, M.R. and M.A.; methodology, M.R., M.A., A.M., K.I.U.A., and A.H.K.; project administration, K.I.U.A., R.M., and A.H.K.; software, M.R. and M.A.; supervision, K.I.U.A., R.M., and A.H.K.; validation, M.R., M.A., A.M., K.I.U.A., R.M., S.P., and A.H.K.; visualization, M.R. and M.A.; writing—original draft, M.R. and M.A.; writing—review and editing, M.A., A.M., K.I.U.A., R.M., S.P., and A.H.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by United International University [UIU-RG 162013] and was partially supported by another grant (award number<PHONE_NUMBER>) from the Healthcare Engineering Innovation Center (HEIC) at Khalifa University, Abu Dhabi, UAE.

**Institutional Review Board Statement:** The study was approved by the ethical review committee of Bangladesh University of Health Sciences (BUHS/BIO/EA/17/01) and conforms to the ethical principles outlined in the declaration of Helsinki and the Ministry of Health and Family Welfare of Bangladesh.

**Informed Consent Statement:** A consent form was taken from every participant to be eligible for enrollment in the study.

**Data Availability Statement:** Data and models could be shared under research agreement with any other researchers working in non-profit organizations.

**Acknowledgments:** This work would not have been possible without the research funding from Institute of Advance Research (IAR) and United International University. Their support for this study is greatly appreciated.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **Abbreviations**

### **References**

**Dae Youp Shin 1, Bora Lee 2, Won Sang Yoo 3, Joo Won Park 4 and Jung Keun Hyun 1,5,6,\***

**Abstract:** Diabetic sensorimotor polyneuropathy (DSPN) is a major complication in patients with diabetes mellitus (DM), and early detection or prediction of DSPN is important for preventing or managing neuropathic pain and foot ulcer. Our aim is to delineate whether machine learning techniques are more useful than traditional statistical methods for predicting DSPN in DM patients. Four hundred seventy DM patients were classified into four groups (normal, possible, probable, and confirmed) based on clinical and electrophysiological findings of suspected DSPN. Three ML methods, XGBoost (XGB), support vector machine (SVM), and random forest (RF), and their combinations were used for analysis. RF showed the best area under the receiver operator characteristic curve (AUC, 0.8250) for differentiating between two categories—criteria by clinical findings (normal, possible, and probable groups) and those by electrophysiological findings (confirmed group)—and the result was superior to that of linear regression analysis (AUC = 0.6620). Average values of serum glucose, International Federation of Clinical Chemistry (IFCC), HbA1c, and albumin levels were identified as the four most important predictors of DSPN. In conclusion, machine learning techniques, especially RF, can predict DSPN in DM patients effectively, and electrophysiological analysis is important for identifying DSPN.

**Keywords:** machine learning; diabetes mellitus; diabetic sensorimotor polyneuropathy; random forest; prediction; electrophysiology

### **1. Introduction**

Type 2 diabetes mellitus (T2DM), the most common form of diabetes, is a major disease in humans worldwide [1], and its incidence is increasing with aging and lifestyle changes [2]. There is evidence that half of T2DM patients experience neurological disorders and a progressive disability of nerve fibers in the course of diabetes, and serious neurological symptoms lead to poor quality of life [3]. Diabetic sensorimotor polyneuropathy (DSPN) is a common neurological complication resulting from neuroinflammation, mitochondrial dysfunction, and apoptosis due to hyperglycemia, dyslipidemia, and altered insulin signaling, and leads to various symptoms and signs, including neuropathic pain, decreased sensation, and foot ulceration [4,5]. The management of DSPN is not limited to controlling hyperglycemia, and multidisciplinary programs, such as patient education, lifestyle modification, and physical activity, are required to control various physical and psychological symptoms and foot complications [6]. Therefore, early detection and prediction of DSPN is very important in DM patients.

**Citation:** Shin, D.Y.; Lee, B.; Yoo, W.S.; Park, J.W.; Hyun, J.K. Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques. *J. Clin. Med.* **2021**, *10*, 4576. https://doi.org/ 10.3390/jcm10194576

Academic Editor: Fernando Gómez-Peralta

Received: 7 August 2021 Accepted: 30 September 2021 Published: 2 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

The classification of DSPN has been defined in previous studies [7–10]. Typical DSPN is the most common form in DM patients and chronic, symmetrical, and length-dependent sensorimotor polyneuropathy [11]. Tesfaye et al. defined the minimal criteria for typical DSPN to estimate severity: possible, probable, confirmed, and subclinical based on clinical symptoms and signs and electrophysiology [7]. Numerous staging and scoring systems have been developed to assess the severity of DSPN; however, choosing the optimal scoring system is confusing because the results of previous studies are different regarding which system is effective [12–14]. Electrophysiological assessments, including nerve conduction studies (NCS), are important for diagnosing DSPN objectively [15,16]; however, special equipment is needed, and these assessments cannot be performed routinely for patients without clinical symptoms or signs because of the discomfort caused by electrical stimulation or needle insertion. Because the pathophysiology of diabetic neuropathy reveals a broad spectrum of axonal involvement and segmental demyelination, electrophysiological findings also indicate both axonal degeneration and demyelination [17]. Numerous predisposing factors for the development of DSPN have been found [18–21]. DSPN is significantly correlated with poor glucose control [18,19], longer duration of diabetes, poor metabolic management, smoking and the presence of cardiovascular disease, and DSPN severity is correlated with hypertension, dyslipidemia, microalbuminuria, alcohol consumption, and body mass index [20,21]. Most previous studies on the prediction of DSPN used various statistical methods. While traditional statistical methods draw only population inferences from clinical information, recently developed machine learning (ML) methods focus on developing predictive models from general-purpose learning algorithms [22]. Therefore, ML is considered to be a better way to predict DSPN in DM patients.

ML is a computationally broad and powerful data mining technique that can accommodate a large set of proposed variables as inputs to identify factors related to the results of interest [23], and ML develops algorithms that can learn patterns and decision rules, such as early detection, prediction and diagnosis, from data that are attributable to the medical field. Recent studies have used various ML techniques to predict complications, including retinopathy, nephropathy, foot ulceration and DSPN, in T2DM patients [24–28], and ML was effective for prediction of DSPN severity [24], 3-year complication developments [25], high-risk retinopathy, and numerous complications in nonadherent T2DM [27]. Haque et al. found that machine learning algorithms, especially random forest (RF), were effective in predicting DSPN severity based on the scoring system using Michigan Neuropathy Screening Instrumentation [29], which is not used widely, and that study assessed only type 1 diabetes mellitus (T1DM) patients.

The purpose of the current study was to delineate whether machine learning techniques are more useful than traditional statistical methods for predicting DSPN in type 2 DM patients, and whether the widely used classification for DSPN, which is based on clinical and electrophysiological findings, is amenable to the use of predictive models.

### **2. Materials and Methods**

### *2.1. Subjects*

Medical records of patients with T2DM who visited Dankook University Hospital for the management of DM were collected, and 746 subjects were initially enrolled (Figure 1). Patients were diagnosed with T2DM by a physician at the Department of Endocrinology, based on the guideline of the American Diabetes Association [30]. Patients who did not undergo electrophysiological studies (*n* = 206) or had incomplete clinical data (*n* = 53) were excluded at first, and then patients who had other types of polyneuropathies, including heavy alcohol use (*n* = 3), hepatic failure (*n* = 2), renal failure (*n* = 4), chemotherapy for malignancy (*n* = 7), and typical musculoskeletal anomalies (*n* = 1), were subsequently excluded. As a result, 470 patients were included in the study (Figure 1). This study was approved by the Dankook University Hospital Institutional Review Board (IRB No. 2019-12-009).

**Figure 1.** Flow and grouping of patients.

### *2.2. Classification*

Subjects were classified into 4 groups according to definitions of minimal criteria for typical DSPN based on the area of clinical care by Tesfaye et al. [7]: normal, possible, probable, and confirmed. The normal group (*n* = 93) consisted of subjects without any neurological symptoms or signs as previously described [7], and the possible group (*n* = 91) comprised subjects with one of the neurological symptoms or signs. The probable group (*n* = 13) comprised subjects with two or more neurological symptoms or signs. The confirmed group (*n* = 273) consisted of subjects with abnormal electrophysiological findings

and neurological symptoms or signs. Electrophysiological assessments were performed according to the guidelines of the American Academy of Neurology [16], and NCS and electromyography of the upper and lower extremities were conducted. According to electrophysiological findings, the confirmed group was divided into two subgroups: A demyelinated subgroup (*n* = 87) with subjects who predominantly showed demyelination and a mixed subgroup (*n* = 186) with subjects who showed abnormal spontaneous activities during needle electromyography and demyelination (Figure 1).

### *2.3. Clinical Data*

All subjects' clinical information, such as baseline characteristics, past medical history, current health status, diabetic complications, and medications, was analyzed. Baseline characteristics included age, sex, weight, height, body mass index (BMI), disease duration (from initial diagnosis of T2DM to the date of the last follow-up at the hospital), smoking (current smoking, past smoking, or nonsmoking), family history of T2DM, and diabetes education. Past medical history included hypertension (HTN), dyslipidemia, and history of stroke and coronary artery disease. HTN was defined as systolic blood pressure > 140 mmHg, diastolic blood pressure > 90 mmHg or the use of antihypertensive medications. Diabetic retinopathy was included in diabetic complications. Medications for DM, HTN and dyslipidemia were included; medications for DM were metformin, sulfonylureas, thiazolidinediones (TZDs), dipeptidyl peptidase-4 inhibitors (DPP4is), sodium-glucose cotransporter-2 inhibitors (SGLT2is), and insulin; medications for HTN were calcium channel blockers (CCBs), angiotensin-converting-enzyme inhibitors (ACEis), angiotensin II receptor blockers (ARBs), beta blockers (BBs) and thiazides; and medications for dyslipidemia were statins. BMI was calculated as weight in kilograms divided by the square of height in meters.

### *2.4. Laboratory Data*

A total of 432 laboratory codes from blood and urine tests were obtained from all subjects, and we divided subjects into a control group (*n* = 197) with normal electrophysiological findings and a test group (*n* = 273) with abnormal electrophysiological findings within the criteria of DSPN to identify the optimal number of laboratory codes (Figure 2). Forty-eight codes could be obtained for more than half of the subjects (*n* = 98) in the control group, and 62 codes could be obtained for more than half of the subjects (*n* = 135) in the test group (Figure 2a). When the results of the two groups were combined, 39 laboratory codes were ultimately selected (Figure 2b). Each laboratory code was assessed several times during the follow-up periods (range: 31–18368 days, mean value: 5202.9 days), and various changes in the values were observed within the period (Figure 2c).

Three methods were used to standardize the values of laboratory codes for ML analysis. Method 1 refers to the average value of each laboratory code during the follow-up period, method 2 is the first value of each laboratory code when T2DM was initially diagnosed while visiting the hospital, and method 3 refers to the pattern of laboratory code changes. The pattern was defined as −1, 0, and 1 as follows. If the initial value was 10% or more lower than the overall average of the values excluding the initial value, it was considered −1; if the change was less than 10%, it was regarded as 0; and if the initial value was greater than 10% of the overall average of the values excluding the initial value, it was regarded as 1.

**Figure 2.** Selection of laboratory codes for machine learning analysis. (**a**) The distribution of laboratory codes according to tested subject numbers in the control and test groups, (**b**) lists of 39 selected laboratory codes, (**c**) graphs showing the changes

in 39 selected laboratory codes at the initial and follow-up periods. Abbreviations: ALP = alkaline phosphatase; ALT (GPT) = alanine aminotransferase (glutamic pyruvate transaminase); AST (GOT) = aspartate aminotransferase (glutamic oxaloacetic transaminase); BST = blood sugar test; BUN = blood urea nitrogen; Diff = differential; T4 = thyroxine; Hb = hemoglobin; HbA1c = hemoglobin A1c; HCT = hematocrit; HDL = high-density lipoprotein cholesterol; IFCC = International Federation of Clinical Chemistry; LDL = low-density lipoprotein cholesterol; MCH = mean corpuscular hemoglobin; MCHC = mean corpuscular hemoglobin concentration; MCV = mean cell volume; PLT = platelet; RBC = red blood cell; TG = triglyceride; TSH = thyroid-stimulating hormone; SG = specific gravity; WBC = white blood cell.

### *2.5. Machine Learning Analysis*

First, to define which variable set will be used for the classification model, a random forest (RF) model trained by different variable combinations was tested. As described above, there are four different variable sets: clinical data and methods 1, 2, and 3 for laboratory data. RF was trained with all possible combinations of four variable sets. Because of the limitation of the sample size, the sample was divided into ten groups, and each group was used as the test set. For each test set, the remainder of the samples were divided into a training set and a validation set at a 4:1 ratio by preserving the percentage of samples for each class. Fivefold cross-validation was performed for each test set, and the final performance was defined as the average of the performance over 10 iterations [31]. The combination set of clinical data and methods 1 and 3 for laboratory data (total, 105 variables) showed the best performance in cases of classifying patients [area under the curve (AUC) = 0.8350 and accuracy = 74.85%, Table 1; therefore, the combination set was used as an input variable for model training.

**Table 1.** Identification of the selection of data and methods for machine learning analysis of subjects.

Note: method 1 = average value of each laboratory code during the follow-up period; method 2 = the first value of each laboratory code when T2DM was diagnosed initially; method 3 = the pattern of laboratory code changes (−1, 0, or 1), Abbreviations: AUC = area under the curve.

> The DSPN predictor model was trained with the input variables identified above. The model performance was tested with the same method used when identifying the input variables. Three ML algorithms were used: XGBoost (XGB) [32], support vector machine (SVM) [33], and random forest (RF) [23], which were used alone or in combinations of two or more, that is, an ensemble of models for improvement of the model performance by fusion of the contents learned by different models and reduction of overfitting problems [34]. Among the various methods, the model averaging method for averaging the predicted values of several models was used in this work. AUC, accuracy, sensitivity, and specificity were used as performance metrics.

> Finally, the feature importance of the best model among 7 models (XGB, SVM, RF, ensemble of XGB and SVM, ensemble of XGB & RF, ensemble of SVM and RF and ensemble of XGB and SVM and RF) was extracted from each model. If the best model was an ensemble of more than two models, the average feature importance obtained from each

model was used as the feature importance of the ensemble model. Next, the models were retrained and evaluated with input features by adding features one by one, from the most to the least important. This was done to select the best set of features for DSPN prediction based on feature importance, and the performance was better when using the top 69 features for AUC and top 38 features for accuracy rather than all 105 features.

### *2.6. Statistics*

To compare the predictability of ML results, traditional statistical methods were also carried out. All statistical analyses were performed with SPSS 26 (IBM, Armonk, NY, USA). The Shapiro-Wilk test was performed to assess the normal distribution of all quantified histological and functional data from each group. Categorical parameters were compared by likelihood ratio, and numerical parameters among groups were compared by one-way analysis of variance (ANOVA) and the Games–Howell post hoc test. Logistic regression was performed using statistically significant parameters and parameters that were identified to be important in previous studies, and the AUC, accuracy, sensitivity, and specificity were analyzed. *p*-values less than 0.05 were considered to indicate statistical significance.

### **3. Results**

### *3.1. Baseline Characteristics among the Four Groups*

When comparing baseline characteristics among the four groups, disease duration was significantly longer in the confirmed group than in the normal and possible groups (4543.18 ± 2849.75 days and 4464.03 ± 2934.87 days vs. 5686.67 ± 3648.57 days and in the normal, possible, and confirmed groups, respectively), and height was higher in the confirmed group than in the normal group (1.61 ± 0.09 m vs. 1.64 ± 0.09 m in the normal and confirmed groups, respectively). BMI and the initial values of BST and HbA1c were also different between the confirmed group and normal group and between the confirmed group and possible group (Table 2). The incidence of diabetic retinopathy was higher in the confirmed group (51.6%) than in the other groups (23.1–28.6%). Age; sex; weight; incidence of hypertension and dyslipidemia; smoking habit; past medical history of coronary artery disease, cerebrovascular disease, and stroke; and number of subjects who received diabetes education were not different among the groups (Table 2). Medications for diabetes control were different among groups; metformin (89.2–94.5%), sulfonylureas (68.1– 68.8%), dipeptidyl peptidase-4 inhibitors (66.7–71.4%), and sodium-glucose cotransporter-2 inhibitors (17.2–20.9%) were used by a higher proportion of subjects in the normal and possible groups, whereas the proportion of subjects in the confirmed group who used insulin (65.6%) was higher than that in other groups (Table 2).

**Table 2.** *Cont.*

Note: Values are presented as the mean ± standard deviation or number of subjects (%). *p* < 0.05 among the four groups by one-way ANOVA for continuous data or likelihood ratio for categorical data. Post hoc testing was performed using the Games–Howell test. Abbreviations: BMI = body mass index; BST = blood sugar test; HbA1c = hemoglobin A1c; DM = diabetes mellitus; Hx = history; CAD = coronary artery disease; CVD = cerebrovascular disease; TZDs = thiazolidinediones; DPP4is = dipeptidyl peptidase-4 inhibitors; SGLT2is = sodium-glucose cotransporter-2 inhibitors; CCBs = calcium channel blockers, ACEis = angiotensin-converting-enzyme inhibitors; ABRs = angiotensin II receptor blockers; BBs = beta blockers.

### *3.2. Identification of an Appropriate Classification for Prediction Using Machine Learning Analysis*

Using ML algorithms, four groups of normal (A), possible (B), probable (C), and confirmed (D) samples were analyzed with various combinations. When comparing all groups separately (A vs. B vs. C vs. D) using the combined analysis of XGB and RF, the AUC was 0.8546, and the accuracy was 60.85% (Table 3). One of the classifications set to three groups (combination of A and B vs. C vs. D) showed the highest AUC value (0.8925) using the same analysis (XGB + RF); however, this classification was not appropriate because the number of group C patients was small (*n* = 13), which can result in imbalanced results [35]. When looking at the classification that combined group C with other groups, rather than alone, the classification with the combination of A, B and C vs. D showed a higher value of AUC (0.8250) than the other classifications and the highest value of accuracy (74.47%) (Table 3). Therefore, we performed all ML analyses and statistics based on this classification (A + B + C vs. D).

**Table 3.** Values of AUC and accuracy of machine learning analysis when comparing each group or their combinations.

Note: A = normal group, B = possible group, C = probable group, D = confirmed group. Abbreviations: AUC = area under the curve; XGB = XGBoost; RF = random forest; SVM = support vector machine.

### *3.3. Identification of an Appropriate ML Algorithm for the Prediction of DSPN and Analysis of Predictive Values*

When we compared various ML techniques (XGB, SVM, RF, and their combinations), RF showed the best AUC (0.8250) and accuracy (74.47%), and the sensitivity and specificity were also higher (0.7940 and 0.6720, respectively) than those of any other single algorithm or their combination (Table 4). Logistic regression analysis was performed to compare the

combination of normal, possible, and probable groups with the confirmed group using meaningful parameters of the following basic characteristics and laboratory data: disease duration, initial value of HbA1c, DM retinopathy, family history of DM, use of metformin and insulin, serum levels of glucose, HDL cholesterol, albumin, and creatinine. The results of logistic regression analysis showed lower AUC (0.6620) and specificity (0.3519) values than RF. The receiver operating characteristic (ROC) curves of each ML algorithm and logistic regression analysis are shown in Figure 3. The AUC of RF was the highest (0.8250) among the 7 ML models, as described earlier, whereas the AUC of logistic regression was the lowest AUC value (0.6620).

**Table 4.** Values of machine learning and logistic regression analysis using the classification of the combination of the normal, possible, and probable groups versus the confirmed group.

Abbreviations: AUC = area under the curve; XGB = XGBoost; RF = random forest; SVM = support vector machine.

**Figure 3.** Receiver operating characteristic (ROC) curve for single or combinations of various machine learning algorithms and logistic regression analysis in the classification of the combination of the normal, possible, and probable groups versus the confirmed group. Abbreviations: AUC = area under the curve; XGB = XGBoost; RF = random forest; SVM = support vector machine.

### *3.4. Development of a Decision-Making Model Using Influential Features from the RF Algorithm*

RF analysis using the classification of the combination of the normal, possible, and probable groups versus the confirmed group was used to derive influential features, which consisted of clinical data and methods 1 and 3 for laboratory data. When these features are accumulated in the order of the importance score, the AUC and accuracy increase and then reach a maximum value at a certain moment (Figure 4a,b). In the case of AUC, the maximum value was reached when the number of parameters reached 69 (0.8302), and in the case of accuracy, the maximum value was reached when the number of parameters was 38 (76.17%) (Figure 4a,b). From this classification, the average value of HbA1c was identified as the first single discriminator for group determination between the combination of the normal, possible, and probable groups and the confirmed group (Figure 4c). The top 69 influential features are shown in Table 5. The average serum glucose level during the follow-up period was the most important feature (importance score = 0.997768) for determining the group in the classification, and the average values of the International Federation of Clinical Chemistry (IFCC; 0.794161), HbA1c (0.789265), and albumin levels (0.731579) during the follow-up period are shown in order of importance score (Table 5).

**Table 5.** Top 69 influential features in the classification of the combination of the normal, possible, and probable groups versus the confirmed group.

Abbreviations: Avg = average; IFCC = International Federation of Clinical Chemistry; HbA1c = hemoglobin A1c; Diff = differential; HCT = hematocrit; ALT (GPT) = alanine aminotransferase (glutamic pyruvate transaminase); BST = blood sugar test; RBC = red blood cell; Hb = Hemoglobin; BMI= body mass index; HDL = high-density lipoprotein cholesterol; BUN = blood urea nitrogen; AST (GOT) = aspartate aminotransferase (glutamic oxaloacetic transaminase); ALP = alkaline phosphatase; TG = triglyceride; LDL = low-density lipoprotein cholesterol; TSH = thyroid-stimulating hormone; CP = change pattern; SG = specific gravity; WBC = white blood cell; PLT = platelet; MCV = mean cell volume; MCH = mean corpuscular hemoglobin; MCHC = mean corpuscular hemoglobin concentration; T4 = thyroxine.

**Figure 4.** Application of random forest algorithm and process of extraction of important features in the classification of the combination of the normal, possible, and probable groups versus the confirmed group. (**a**) Model performance according to the number of input features sorted by importance, (**b**) the result of arranging input features in order of importance score, (**c**) a decision tree using the random forest algorithm with the classification of the combination of the normal, possible, and probable groups versus the confirmed group. Note: Group 1 = a group in which the normal, possible, and probable groups are combined, Group 2 = the confirmed group. Black arrow = positive results for the above features, red arrow = negative results for the above features, gini = gini index. Abbreviations: AUC = area under the curve; ALP = alkaline phosphatase; ALT (GPT) = alanine aminotransferase (glutamic pyruvate transaminase); AST (GOT) = aspartate aminotransferase (glutamic oxaloacetic transaminase); Avg = average; BST = blood sugar test; BUN = blood urea nitrogen; CP = change pattern; Diff = differential; T4 = thyroxine; Hb = hemoglobin; HbA1c = hemoglobin A1c; HCT = hematocrit; HDL = high-density lipoprotein cholesterol; IFCC = International Federation of Clinical Chemistry; LDL = low-density lipoprotein cholesterol; MCH = mean corpuscular hemoglobin; MCHC = mean corpuscular hemoglobin concentration; MCV = mean cell volume; PLT = platelet; RBC = red blood cell; TG = triglyceride; TSH = thyroid-stimulating hormone; SG = specific gravity; WBC = white blood cell.

### *3.5. ML Analysis of the Confirmed Group to Identify Demyelinated and Mixed Types of DSPN*

We compared the demyelinated subgroup with the mixed subgroup, as shown in electrophysiological studies of the confirmed group, using various ML algorithms and logistic regression analysis (Table 6). ML analysis revealed that the combination of XGB and SVM models showed the highest AUC and accuracy values of 0.5698 and 67.78%, respectively, whereas the statistical method using logistic regression showed a higher AUC value (0.6350). However, the overall AUC values of all ML algorithms and logistic regression analysis were much lower than the AUC value (0.8250) when RF was used to compare the combination of the normal, possible, and probable groups versus the confirmed group, and the specificity was quite low (0 and 0.3889 for RF and logistic regression, respectively) to predict the two subgroups within the confirmed group (Table 6).

**Table 6.** Machine learning and logistic regression results analyzing the demyelinated type vs. mixed type.

Abbreviations: AUC = area under the curve; XGB = XGBoost; RF = random forest; SVM = support vector machine.

### **4. Discussion**

Interest in machine learning algorithms is widely increasing in the medical field because they can be used to predict disease development and generate semantic interpretations [36]. In the field of endocrinology, the prediction of diabetes is expected to be very useful for preventing disease progression and complications [37]. In this study, we have performed conventional statistics, as well as various ML algorithms to compare predictive power expressed in AUC and accuracy. Logistic regression analysis, a traditional statistical method, has an obvious limitation compared to the ML analysis. Only a small number of clinical and laboratory data (9 variables among over 400 data) were used during the statistical processing, which inevitably resulted in poor AUC whereas ML analysis could include over 100 meaningful data. Classical statistics usually draw population inferences, but become less precise when input variables that exceed the number of subjects, therefore appropriate ML method can help overcome this limitation [22].

As in all other fields, for the results of ML analysis to be more accurate, the input data must have extensive and accurate information. Laboratory data are usually obtained numerous times for a single subject during the follow-up period, and effective processing of meaningful data can have a significant impact on the establishment of predictive models. In this study, we tried various methods to optimize input data during the preprocessing step, especially for standardization of laboratory tests conducted at various time points. First, from the 432 types of laboratory data received for all patients, only 39 datapoints repeatedly obtained for more than half of all patients were filtered out. Then, depending on the timing of the laboratory data received, data were classified into average, initial, and change patterns of each value, and we found that average and changed patterns were meaningful parameters for ML analysis. Through these preprocesses, we are confident that we have increased the reliability of laboratory data and created a more accurate predictive model. When compared to previous studies that made predictive models of DSPN using ML algorithms in diabetic patients (Table 7), they did not explain what time point was used or whether there was any consideration of the amount of change in the laboratory data in addition to the data imputation process that handles missing data [24,25,27,38]. In

addition, they did not provide any diagnostic tools, such as decision tree or nomogram, except Dagliati et al. [25].

**Table 7.** Comparison of previous studies that used machine learning algorithms to predict DPSN in type 2 diabetes mellitus patients.

Abbreviations: ML = machine learning, AUC = area under the curve; MSVM = multicategory support vector machine; LR = logistic regression; EM = ensemble model; RF = random forest; UC = uncheckable; N = none.

> Various criteria for defining DSPN have been developed, and many of them have been designed to classify the severity of DSPN based on clinical signs and symptoms alone [39] or in combination with physical examination [40,41] or electrophysiological findings [7,10]. Neurological signs, especially sensory abnormalities, are sensitive and specific findings for diagnosing DSPN and have been correlated with electrophysiological findings in previous studies [12,42,43]; however, we found that clinical data alone, which was categorized as normal, possible and probable groups defined in a previous study [7], was not effective in predicting DSPN in T2DM patients. Other studies have revealed that clinical symptoms and signs are too variable and inaccurate [44] and do not correlate well with the development of pathophysiological changes in the peripheral nervous system [13]. On the basis of our results, we confirmed that severity grading based on clinical symptoms and signs is not helpful and that electrophysiological assessment is essential in predicting DSPN. However, small fiber involvement, which is frequently occurs in early DSPN, is not identified by conventional NCS. Therefore, more specialized diagnostic tools such as quantitative sensory testing, skin biopsy, and corneal confocal microscopy are needed to identify small fiber damage [45,46].

> We failed to classify the demyelinated and mixed types in the confirmed group in this study. Axonal involvement is frequently observed in DSPN, as is demyelination [17], and even axonal loss, which precedes demyelination, in sural nerves or plantar nerves of DSPN patients might be a primary finding [47,48]. Electrophysiological analysis, which shows decreased conduction velocity of sensory and motor nerves, decreased compound muscle action potential, and prolonged latency of F-wave, is considered to be highly sensitive for early diagnosis of DSPN [16,49], but NSC cannot be used to assess therapeutic effects in diabetic patients [49]. Electromyography can be useful for detecting abnormal spontaneous activities in distal muscles in moderate to severe DSPN [50], although this test is also useful for ruling out other neuropathies, such as radiculopathies, mononeuropathies, or myopathies. In this study, we could not find axonal involvement without demyelination within DSPN patients. In T2DM, segmental demyelination is prominent with a milder axonal involvement whereas axonal loss is more severe in T1DM [51,52]. Initially, we considered abnormal electromyographic findings with abnormal NCS (mixed type) to be advanced or severe type DSPN, and diabetic patients with mixed type DSPN might show abnormal clinical and laboratory findings more frequently than those with demyelinated type DSPN. However, ML analysis and logistic regression did not effectively suggest any difference between the demyelinated and mixed types. Therefore, electrophysiological analysis is necessary to differentiate these two types of diabetic patients.

Numerous ML algorithms have been used to predict DM and diabetic complications such as retinopathy, nephropathy, foot ulceration and DSPN [24–29]. XGB is a scalable end-to-end tree boosting system [32] and is more suitable for small sample sizes unless the data are not highly dispersed when predicting glucose variability in T2DM patients [53]. SVM was used for microarray or high-dimensional data and is suitable for predicting DSPN in DM patients with a clinical data-based classification [24] and distinguishing retinopathy between diabetic patients and normal controls [26]. RF is an ensemble of decision trees and can minimize the individual error of trees [23]. RF has shown good performance in predicting the development and classification of DSPN based on clinical symptoms and examinations of type 1 diabetic patients [29]. Logistic regression analysis is a common statistical method used to develop a model for binary outcomes in the medical field [54] and can also be used as a supervised learning technique in ML methods. Even though various ML algorithms have been successfully developed as predictive models for the purpose of preventing the occurrence of diseases or their complications, some recent studies have shown that logistic regression has similar results to ML analysis [55,56], and attempts to combine logistic regression and ML methods also appear to enhance the performance of statistical methods in an automated manner [57]. In our study, the AUC of RF was superior to that of logistic regression when subjects were classified into two groups: confirmed vs. other combinations (Table 4), but the AUC of logistic regression was higher than that of ML algorithms for comparison between the demyelinated and mixed subgroups within the confirmed group (Table 6). The development of proper hybrid models for statistical and ML algorithms might increase the power of DSPN prediction in future studies.

In previous studies, numerous predisposing factors have been associated with DSPN in diabetic patients, particularly, duration of diabetes and HbA1c in T2DM patients [21,58]; moreover, old age, increased height, obesity, higher body mass index, poor glucose control, alcohol abuse, smoking, hypertension, cardiovascular disease, low level of HDL, dyslipidemia, hypertriglyceridemia, and microalbuminuria have also been shown to be risk factors in previous studies [18–21,58–61]. We found that the average values of numerous laboratory datapoints during the follow-up period (serum glucose, IFCC, HbA1c, albumin, and differential counts of lymphocytes and neutrophils) were important predisposing factors, as were clinical data such as height and disease duration (Table 5). The albumin has important antioxidant and anti-inflammatory properties, and the lower level of serum albumin was associated with the prevalence of DSPN or peripheral nerve dysfunctions in T2DM patients in previous studies [62,63] In our study, average value of HbA1c is the most sensitive node of a decision tree among the influence features, and average differential counts of lymphocytes and neutrophils are the second node (Figure 4c). Although there is no standardized decision-making algorithm for DSPN diagnosis, HbA1c qualifies as an important diagnostic criterion for DPSN because HbA1c a major risk factor for microvascular complications and closely associated with DSPN in T2DM [64] The neutrophil-lymphocyte ratio is an inflammatory marker and an important factor that predicts cardiovascular disease [65] and foot ulcer infection [66] in diabetic patients. Neutrophil level was also the most sensitive node for decision making of DPSN prediction in a previous study [67], and higher neutrophil-lymphocyte ratio might be related to chronic inflammatory process and increase the risk of DSPN [68].

In this study, we analyzed a small-sized sample, especially the probable group (*n* = 13), which might cause problems for pattern recognition and poor accuracy [69]. Many studies in the medical field often have only a small number of patients. In this study, we tried to increase the accuracy by dividing the patients into ten groups for use as a test set and a tenfold stratified cross validation set to compensate for the small sample size [31], but a more accurate prediction might be achieved with a larger number of diabetic patients. We further plan to perform ML analysis to predict various complications in diabetic patients in a prospective multicenter study and develop an application attached to an existing electronic health record system for easier transfer of patient data that can assist in predicting complications in diabetic patients. In addition, it was difficult to use deep

learning model because insufficient sample size can lead to overfitting. If sufficient data is accumulated, it is possible to build deep learning model using time-series laboratory data or to apply a method of transfer learning with DSPN patient using pre-trained models for all diabetic patients.

### **5. Conclusions**

In this study, we revealed that the ML algorithms, whose AUC values were superior to logistic regression, can be applied to type 2 DM patients to predict DSPN and that the classification depending only on clinical symptoms and signs of suspected DSPN was not appropriate for the application of ML algorithms to develop prediction models. In addition, ML algorithms cannot predict the type of electrophysiological features in DSPN, namely, demyelinated and mixed subgroups. We concluded that ML techniques, especially RF, can predict DSPN effectively when comparing the combination of the normal, possible, and probable groups with the confirmed group of DM patients and that electrophysiological analysis is important for identifying DSPN.

**Author Contributions:** Conceptualization, J.K.H.; data curation, D.Y.S., W.S.Y. and J.W.P.; formal analysis, D.Y.S., B.L., W.S.Y., J.W.P. and J.K.H.; funding acquisition, J.K.H.; investigation, D.Y.S., B.L. and J.K.H.; methodology, D.Y.S., B.L. and J.K.H.; project administration, J.K.H.; resources, W.S.Y. and J.W.P.; software, D.Y.S. and B.L.; supervision, J.K.H.; validation, D.Y.S., B.L. and J.K.H.; visualization, B.L. and J.K.H.; writing—original draft, D.Y.S., B.L. and J.K.H.; writing—review and editing, D.Y.S. and J.K.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by grants (2019R1A6A1A11034536, 2020R1A2C2004764) through the National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) and a Korea Medical Device Development Fund grant (202017D01) funded by the Korean government (MSIT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, and the Ministry of Food and Drug Safety).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Dankook University Hospital (IRB No. 2019-12-009).

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available from the corresponding author upon reasonable request.

**Conflicts of Interest:** Bora Lee is an employee of Deargen, Co., Ltd., Daejeon 34051, Korea.

### **References**

## *Article* **Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study**

**Li-Ying Huang 1, Fang-Yu Chen 1, Mao-Jhen Jhou 2, Chun-Heng Kuo 1, Chung-Ze Wu 3,4, Chieh-Hua Lu 5, Yen-Lin Chen 6, Dee Pei 1, Yu-Fang Cheng 7 and Chi-Jie Lu 2,8,9,\***

**Abstract:** The urine albumin–creatinine ratio (uACR) is a warning for the deterioration of renal function in type 2 diabetes (T2D). The early detection of ACR has become an important issue. Multiple linear regression (MLR) has traditionally been used to explore the relationships between risk factors and endpoints. Recently, machine learning (ML) methods have been widely applied in medicine. In the present study, four ML methods were used to predict the uACR in a T2D cohort. We hypothesized that (1) ML outperforms traditional MLR and (2) different ranks of the importance of the risk factors will be obtained. A total of 1147 patients with T2D were followed up for four years. MLR, classification and regression tree, random forest, stochastic gradient boosting, and eXtreme gradient boosting methods were used. Our findings show that the prediction errors of the ML methods are smaller than those of MLR, which indicates that ML is more accurate. The first six most important factors were baseline creatinine level, systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose. In conclusion, ML might be more accurate in predicting uACR in a T2D cohort than the traditional MLR, and the baseline creatinine level is the most important predictor, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose in Chinese patients with T2D.

**Keywords:** type 2 diabetes; nephropathy; urine albumin-creatinine ratio; machine learning

### **1. Introduction**

Type 2 diabetes (T2D) has become a growing global issue in recent decades. According to the 2021 Atlas of the International Diabetes Federation, it is estimated that there are 5.37 billion patients worldwide, and this trend will further increase to 6.0 billion by 2045 [1]. Not surprisingly, a similar endemic was noted in Taiwan. According to the data bank of the

**Citation:** Huang, L.-Y.; Chen, F.-Y.; Jhou, M.-J.; Kuo, C.-H.; Wu, C.-Z.; Lu, C.-H.; Chen, Y.-L.; Pei, D.; Cheng, Y.-F.; Lu, C.-J. Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study. *J. Clin. Med.* **2022**, *11*, 3661. https://doi.org/10.3390/ jcm11133661

Academic Editor: Fernando Gómez-Peralta

Received: 29 April 2022 Accepted: 22 June 2022 Published: 24 June 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

National Health Insurance Company, the total number of diabetic patients increased from 1.32 million to 2.2 million within 10 years (2005 to 2014). This represents an astonishing 66% increase [2]. It is now the 5th highest cause of death. In 2020, the cost spent on T2D was over 10 billion USD, which is approximately 4.66% of the budget of the National Health Insurance Company in one year. The accompanying complications, such as micro- and macrovascular diseases, impose heavy burdens on individuals and their families, as well as health providers and society [3,4]. It is important to note that this trend is particularly prominent among people aged <40 and ≥80 years [5].

Among all the complications, diabetic nephropathy is the leading cause of chronic kidney disease and end-stage renal disease (ESRD) [6], which are associated with high morbidity and mortality rate. According to the annual report of the US Renal Data System, Taiwan has the highest incidence (523 per million population) and prevalence of treated ESRD requiring renal replacement therapy [7]. In 2019, there were 84,615 dialysis patients and the National Health Insurance spent 1.54 billion, which is approximately 8.7–9.3% of the annual budget [8,9]. Therefore, its early detection and prevention are urgently required.

It is well known that urine albumin–creatinine ratio (uACR) is a strong predictor of the subsequent decline of the glomerular filtration rate in T2D, with an average of 0.93 mL per minute per month in approximately 35% of the subjects [10]. The underlying pathophysiology is due to the increased glomerular pressure, which is independent of hyperfiltration or hyperglycemia [11–13].

Traditionally, most studies have used multiple linear regression (MLR) to explore the relationships between risk factors and outcomes (complications) in medical research. Nevertheless, artificial intelligence using machine learning (ML), which enables machines to learn from past data or experiences without being explicitly programmed, has now become a new modality for data analysis that is competitive with MLR [14–16]. Because ML can capture nonlinear relationships in data and complex interactions among multiple predictors, it has the potential to outperform conventional MLR in disease prediction [17].

To our knowledge, only one study has attempted to predict the uACR in a T2D cohort. Thus, in the present study, we applied four different ML methods and attempted to answer the following questions in a diabetic cohort that was followed up for four years.

### **2. Methods**

### *2.1. Participant and Study Design*

Data for this study were obtained from the diabetic outpatient clinic of the Cardinal Tien Hospital in Taiwan from 2013 to 2019. This study is a prospective study, as we have collected our patients from 2013 to 2016. We designated this cohort as the Cardinal Tien Diabetes Study Cohort. Informed consent was obtained from all participants, and data were collected anonymously. The study protocol was approved by the Institutional Review Board of the hospital. In total, 1682 T2D patients were enrolled. After excluding subjects with different causes, 1147 subjects remained for analysis (women: 608, men: 539), as shown in Figure 1. They were followed up for 4 years. The following were the criteria for inclusion: (1) type 2 diabetes; (2) age between 50 and 75 years; (3) body mass in the range of 22–30 kg/m2; (4) glycated hemoglobin level between 6.5 and 10.5%; (5) the patients did not undergo regular dialysis. A flowchart of participant selection is displayed in Figure 1.

On the day of the study, senior nursing staff recorded the subject's medical history, including information on any current medications, and a physical examination was performed. The waist circumference was measured horizontally at the level of the natural waist. The body mass index (BMI) was calculated as the participant's body weight (kg) divided by the square of the participant's height (m). The systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured using standard mercury sphygmomanometers on the right arm of each subject while seated.

**Figure 1.** Flowchart of sample selection from the Cardinal Tien Hospital Diabetes Study Cohort.

As previously published, the procedures for collecting demographic and biochemical data are as follows [18]. After fasting for 10 h, blood samples were collected for biochemical analyses. Plasma was separated from the blood within 1 h of collection and stored at 30 ◦C until the analysis of fasting plasma glucose (FPG) and lipid profiles. FPG was measured using the glucose oxidase method (YSI 203 glucose analyzer; Yellow Springs Instruments, Yellow Springs, OH, USA). The total cholesterol and triglyceride (TG) levels were measured using the dry multilayer analytical slide method with a Fuji Dri-Chem 3000 analyzer (Fuji Photo Film, Tokyo, Japan). The serum high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) concentrations were analyzed using an enzymatic cholesterol assay, following dextran sulfate precipitation. A Beckman Coulter AU 5800 biochemical analyzer was used to determine the urine ACR by turbidimetry.

Table 1 lists the definitions of the 15 baseline clinical variables (independent variables, sex, age, BMI, duration of diabetes, smoking, alcohol use, FPG, glycated hemoglobin, triglyceride, HDL-C, LDL-C, alanine aminotransferase, creatinine (Cr), SBP, and DBP) used in this study. The uACR at the end of the follow-up was a numerical variable, which was used as a dependent (target) variable, while the remaining 15 variables were used as predictor variables in this study.

### **Table 1.** Variable definition.

uACR: urine albumin–creatinine ratio.

### *2.2. Proposed Scheme*

This research proposed a scheme based on four machine learning methods, namely classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB), and eXtreme gradient boosting (XGBoost), to construct predictive models for predicting diabetic uACR and to identify the importance of these risk factors. These ML methods have been applied in various healthcare applications and do not have prior assumptions regarding data distribution [19–28]. MLR was used as the benchmark for comparison.

The first method, CART, is a tree-structure method [29]. It is composed of root nodes, branches, and leaf nodes that grow recursively based on the tree structures from the root nodes and split at each node based on the Gini index to produce branches and leaf nodes with the rule. Then, the pruning node in the overgrown tree for optimal tree size using the cost-complexity criterion generates different decision rules to compose a complete structure tree [30,31].

RF, the second method in this study, is an ensemble learning decision tree algorithm that combines bootstrap resampling and bagging [32]. RF's principle entails randomly generating many different and unpruned CART decision trees, in which the decrease in Gini impurity is regarded as the splitting criterion, and all generated trees are combined into a forest. Then, all the trees in the forest are averaged or voted to generate output probabilities and a final model that generates a robust model [33].

The third method, SGB, is a tree-based gradient boosting learning algorithm that combines both bagging and boosting techniques to minimize the loss function to solve the overfitting problem of traditional decision trees [34,35]. In SGB, many stochastic weak learners of trees are sequentially generated through multiple iterations, in which each tree concentrates on correcting or explaining errors of the tree generated in the previous iteration, that is, the residual of the previous iteration tree is used as the input for the newly generated tree. This iterative process is repeated until the convergence condition or a stopping criterion is reached for the maximum number of iterations. Finally, the cumulative results of many trees are used to determine the final robust model.

XGBoost, the fourth method of this study, is a gradient boosting technology based on an SGB optimized extension [36]. Its principle is to train many weak models sequentially to ensemble them using the gradient boosting method of outputs, which achieves a better prediction performance. In XGBoost, Taylor binomial expansion is used to approximate the objective function and arbitrary differentiable loss functions to accelerate the model construction convergence process [37]. Then, XGBoost applies a regularized boosting technique to penalize the complexity of the model and correct overfitting, thus increasing model accuracy [36].

A flowchart of the proposed prediction and important variable identification scheme that combines the four ML methods is shown in Figure 2. First, patient data were collected using the proposed method to prepare the dataset. The dataset was then randomly divided into an 80% training dataset for model building and a 20% testing dataset for model testing. In the training process, each ML method has its hyperparameters that must be tuned to construct a relatively well-performed model. In this study, a 10-fold crossvalidation (CV) technique for hyperparameter tuning was used. The training dataset was further randomly divided into a training dataset to build the model with a different set of hyperparameters and a validation dataset for model validation. All possible combinations of the hyperparameters were investigated using a grid search. The model with the lowest root mean square error for the validation dataset was viewed as the best model for each ML method. The best turned RF, SGB, CART, and XGBoost models were generated, and the corresponding variable importance ranking information was obtained.

During the testing process, the testing dataset was used to evaluate the predictive performance of the best RF, SGB, CART, and XGBoost models. As the target variable of the models built in this study is a numerical variable, the metrics used for model performance comparison are the mean absolute percentage error (MAPE), symmetric MAPE (SMAPE), and relative absolute error (RAE), which are shown in Table 2.

**Table 2.** Equation of Performance Metrics.

where *y*ˆ*i* and *yi* represent predicted and actual values, respectively; *n* stands the number of instances.

To provide a more robust comparison, the training and testing processes mentioned above were randomly repeated 10 times. The averaged metrics of the RF, SGB, CART, and XGBoost models were used to compare the model performance of the benchmark MLR model that used the same training and testing dataset as the ML methods. An ML model with an average metric lower than that of MLR was considered a convincing model.

Because all of the ML methods used can produce the importance ranking of each predictor variable, we defined that the priority demonstrated in each model ranked 1 as the most critical risk factor and 15 as the last selected risk factor. The different ML methods may produce different variable importance rankings because they have different modeling characteristics; therefore, we integrated the variable importance ranking of the convincing ML models to enhance the stability and integrity of re-ranking the importance of risk factors. In the final stage of the proposed scheme, we summarize and discuss our significant findings regarding the convincing ML models and identify important variables.

**Figure 2.** Proposed ML prediction scheme.

In this study, all methods were performed using R software version 4.0.5 and RStudio version 1.1.453 with the required packages installed (http://www.R-project.org, accessed on 1 February 2022; https://www.rstudio.com/products/rstudio/, accessed on 1 February 2022). The implementations of RF, SGB, CART, and XGBoost were the "randomForest" R package version 4.6-14 [38], "gbm" R package version 2.1.8 [39], "rpart" R package version 4.1-15 [40], and "XGBoost" R package version <IP_ADDRESS>, respectively [41]. In addition, to estimate the best hyperparameter set for the developed effective CART, RF, SGB, and XGBoost methods, the "caret" R package version 6.0–90 was used [42]. The MLR was implemented using the "stats" R package version 4.0.5, and the default setting was used to construct the models.

### **3. Results**

A total of 1147 participants were enrolled in the study (men: 539, women: 608). The demographic data are shown in Table 3 (mean ± standard deviation). The results of the comparison between the traditional MLR and the four ML methods (i.e., RF, SGB, CART, and XGBoost) in predicting diabetic uACR in a 4-year follow-up cohort are shown in Table 4. From the table, it can be seen that all four ML methods yielded lower prediction errors than the MLR method and were all convincing ML models. To determine whether the four ML methods significantly outperformed the MLR method, the Wilcoxon signed-rank test was used. The Wilcoxon signed-rank test is one of the most popular distributionfree, non-parametric statistical tests for evaluating the performance of two prediction models [43]. Table 5 shows the test results of the four ML methods and the MLR method. It can be observed from the table that the prediction error values of all ML methods were significantly different from those of the MLR method. Therefore, it can be determined that the ML methods used in this study significantly outperformed traditional MLR in predicting uACR at the end of the follow-up in terms of prediction error.

**Table 3.** Participant demographics.

BMI: body mass index. uACR: urine albumin–creatinine ratio.

**Table 4.** The average performance of the MLR, RF, SGB, CART, and XGBoost methods.

MLR: multiple linear regression; RF: random forest; SGB: stochastic gradient boosting; CART: classification and regression tree; XGBoost: eXtreme gradient boosting; MAPE: mean absolute percentage error; SMAPE: symmetric mean absolute percentage error; RAE: relative absolute error.

**Table 5.** Wilcoxon sign-rank test between four ML methods and MLR method.

Table 6 presents the average importance ranking of each factor generated by the RF, SGB, CART, and XGBoost methods. It can be observed from the figure that the different ML methods generated different relative importance rankings for each factor. The darkness of the blue color indicates the importance of risk factors. The darker the blue color, the more important the risk factor. For instance, in the RF method, the first three important factors were baseline Cr, age, and baseline SBP. The most important feature of the SGB method was baseline Cr, which was followed by baseline HDL-C and baseline DBP. To fully integrate the importance rankings of each factor in all the four ML methods, the average importance ranking of each risk factor was obtained by averaging the ranking values of each variable in each method.

**Table 6.** Importance ranking of each risk factor using the four convincing methods.

Note: Different blue colors indicate different rank values of risk factors. The darker the blue color, the more important the risk factor.

Figure 3 depicts the risk factors based on the increasing order of the averaged ranking values. It can be noted from the figure that the first six important risk factors in predicting diabetic uACR in a 4-year follow-up cohort are baseline Cr, baseline SBP, baseline DBP, baseline HDL-C, baseline glycated hemoglobin, and baseline FPG.

**Figure 3.** Integrated importance ranking of all risk factors. Note: The darker color indicates the first six important risk factors of this study.

### **4. Discussion**

As mentioned in the Introduction, the present study has two goals. The first was to compare the accuracy between ML methods and MLR, and the second was to identify the rank of different risk factors for predicting uACR. Our study showed that all four ML methods outperformed the MLR. We also found that baseline Cr, blood pressure, HDL-C, glycated hemoglobin, and FPG were the most important factors.

Traditionally, MLR has been widely used to analyze medical research to deal with continuous variables. However, it is difficult to describe the nonlinear data patterns of MLR, and the effective use of MLR requires fitting its strong assumptions during modeling. Unlike MLR, ML does not require strong model assumptions and can capture the delicate underlying nonlinear relationships contained in empirical data [19]. Our present data showed that all four ML methods are superior to MLR because the MAPE and RAE of the ML methods all have lower values (Table 4). Our results suggest that ML might have a great potential for medical studies and applications.

Because diabetic nephropathy causes a serious burden on individuals and consumes a large portion of the government health budget, extensive studies have focused on this topic [6,44–47]. From these previous studies, it could be concluded that sex, high blood glucose and blood pressure, smoking, dyslipidemia, decreased glomerular filtration rate, BMI, and uACR are common risk factors for future uACR. However, in the present study, our data showed that baseline Cr, DBP, SBP, HDL-C, glycated hemoglobin, and FPG were the most important risks. Additionally, the roles of diabetes duration, glycated hemoglobin, BMI, HDL-cholesterol, triglyceride, sex, smoking, and alcohol use were less important.

Our data suggest that the most important predictor of albuminuria is baseline Cr. This is not surprising because albuminuria occurs early in the course of diabetic nephropathy [48]. According to the majority of previous studies, a summary of this relationship could be depicted as follows: diabetic patients with albuminuria are at a higher risk of

end-stage renal and cardiovascular diseases [49,50]. This indicates that albuminuria is the cause of end-stage renal disease, which differs from the findings of the present study. Our results show that an increase in serum Cr level could predict albuminuria four years later, which is an opposite cause–effect relationship to the majority of the other studies. However, our finding can be supported by the cornerstone study conducted by Gansevoort et al. [51]. This meta-analysis clearly showed that there are independent, continuous, and negative associations between serum Cr and albuminuria. Thus, it could be postulated that each of these factors could affect the other at the same time. Further research is required to explore this area.

Both diastolic and systolic blood pressures were identified as the second and third important factors for predicting albuminuria. Their relationships are well known and have been extensively studied [52]. Similar to the role of increased serum Cr levels, kidney disease causes an increase in BP, which could further deteriorate renal function. More specifically, the change in BP is in concordance with and even precedes albuminuria [53]. By controlling BP, the speed of end-stage renal disease progression can be slowed down [54].

Interestingly, HDL cholesterol level was the only lipid found to be correlated with albuminuria. However, few studies have focused on this topic. Most previous studies have demonstrated that different stages of diabetic kidney disease (DKD) have different influences on blood lipid levels [55,56]. Other studies measured apolipoproteins and the size of LDL-cholesterol, which all showed positive correlations with DKD, including albuminuria [57]. To our knowledge, only two studies are relatively close to the present findings. The first study was performed by Sacks et al. In a group of 2535 T2D patients, they evaluated the impact of HDL-C levels on uACR. Furthermore, kidney disease was defined as albuminuria, proteinuria, or decreased eGFR. The data showed that the odds ratio of having kidney disease decreased by 0.86 (0.82–0.91) for every 0.2 mmol/L (approximately 1 quintile) increase in HDL-C [58]. The second study was conducted on a cohort of 524 Chinese patients. Using multiple logistic regression, after adjusting for the available confounding factors, they suggested that subjects with the highest quartile HDL-C had a lower odds ratio (OR = 0.17, 95% confidence interval 0.15–0.52) of having uACR than the lowest quartile. However, a limitation of this study was that it was cross-sectional. Thus, it was unable to infer the causation or directionality of this relationship [59]. This study responds to this limitation in its longitudinal design. The causative influence of HDL-C level can be explained by several assumptions. First, the glomerular and renal tubules could be injured by impaired HDL-C function, which hinders the reversal of the cholesterol transport process [60]. Second, the antioxidative ability of the HDL-C is reduced and oxidative stress is increased, which further influences the immune-mediated diabetic nephropathy [61]. Finally, it is well known that low HDL-C levels are associated with insulin resistance, hyperinsulinemia, and hyperglycemia. All these untoward derangements can damage endothelial cells in the glomerulus [62,63].

The last two factors affecting albuminuria are glycated hemoglobin and FPG levels. This finding is compatible with the results of the Diabetes Control and Complication Trial (DCCT) [64]. The data showed positive relationships between glucose control and albuminuria. Moreover, after controlling for blood glucose levels, albuminuria also improved [65]. Because DCCT enrolled patients with type 1 diabetes, its pathophysiology is different from that of the present study. Regarding T2D, few studies have been conducted in this area. A comprehensive meta-analysis conducted by Lo et al. [66] showed that for intensive control (glycated hemoglobin < 7% and FPG < 6.6 mmol/L), the relative risk of having uACR was 0.59 (confidence interval: 0.38–0.93). As this study enrolled 11 studies (29,141 subjects) and follow-ups were conducted for an average of 56.7 months, their conclusion is convincing. The underlying pathophysiology to support this result is that high blood glucose concentration could involve mesangial cell damage in nephrons [67]. However, it is worth noting that both A1c and FPG were classified as important predictors. This might indicate that because FPG is only one blood glucose measurement within 90 days

compared to A1c, it is less accurate than A1c. Our results show that they are 'independent' of each other.

Interestingly, in the present study, the duration of diabetes, body mass index, sex, smoking, and alcohol use were less important. This finding could be attributed to the nature of the ML. ML methods are data-driven, non-parametric models. They can map any nonlinear function without an a priori assumption about the properties of the data and have the ability to capture subtle functional relationships among the empirical data, even though the underlying relationships are unknown or difficult to describe [68–70]. These factors may contain richer linear pattern information and less important nonlinear information than baseline creatinine, blood pressure, albuminuria level, and age. Thus, they were ranked as less important risk factors using ML methods.

This study had some limitations. First, the smoking and alcohol details need to be more defined because some other reports have shown that they have an important impact on the occurrence of diabetic nephropathy. Second, we did not collect information on the use of angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, sodium-glucose cotransporter 2 inhibitors, and glucagon-like peptide-1 agonists. All these medications would have beneficial effects on DKD. Third, some of the data, such as uACR and blood pressure, were collected only once. For some of the participants, we did have data more than once. However, because the number is less than the present number, we still chose to enroll subjects with only one value. Even though these drawbacks do exist, our large n number and the characteristics of ML (alleviating the effects of extremes) could at least partially adjust.

### **5. Conclusions**

ML might be more accurate in predicting uACR in T2D than the traditional MLR, and the baseline creatinine level is the most important factor to predict uACR in a T2D cohort, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose.

**Author Contributions:** Developed the theory and wrote the draft, L.-Y.H.; Conceived and planned the experiment, F.-Y.C.; perform the machine learning analysis, M.-J.J.; helped to do the figures and tables, C.-H.K.; supervised the project, C.-Z.W.; discuss the results and contributed to the final manuscript, C.-H.L.; discuss the results and contributed to the final manuscript, Y.-L.C. and Y.-F.C.; collecting the medical records, D.P.; designed the data analysis scheme and wrote the draft, C.-J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was approved by the Research Ethics Review Committee at the Cardinal Tien Hospital (IRB No. CTH-100-2-5-036).

**Informed Consent Statement:** This manuscript contains no person's details, images, or videos.

**Data Availability Statement:** Data available on request due to privacy/ethical restrictions.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

## *Article* **Measuring of Advanced Glycation End Products in Acute Stroke Care: Skin Autofluorescence as a Predictor of Ischemic Stroke Outcome in Patients with Diabetes Mellitus**

**Alexandra Filipov \*, Heike Fuchshuber, Josephine Kraus, Anne D. Ebert, Vesile Sandikci and Angelika Alonso**

Department of Neurology, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany<EMAIL_ADDRESS>(H.F<EMAIL_ADDRESS>(J.K<EMAIL_ADDRESS>(A.D.E<EMAIL_ADDRESS>(V.S<EMAIL_ADDRESS>(A.A.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +49-621-383-3628; Fax: +49-621-383-3807

**Abstract:** *Background:* Patients with diabetes mellitus (DM) are known to show poor recovery after stroke. This specific burden might be due to acute and chronic hyperglycemic effects. Meanwhile, the underlying mechanisms are a cause of discussion, and the best measure to predict the outcome is unclear. Skin autofluorescence (SAF) reflects the in-patient load of so-called advanced glycation end products (AGEs) beyond HbA1c and represents a valid and quickly accessible marker of chronic hyperglycemia. We investigated the predictive potential of SAF in comparison to HbA1c and acute hyperglycemia on the functional outcome at 90 days after ischemic stroke in a cohort of patients with DM. *Methods:* We prospectively included 113 patients with DM type 2 hospitalized for acute ischemic stroke. SAF was measured on each patient's forearm by a mobile AGE-Reader mu© in arbitrary units. HbA1c and the area under the curve (AUC) of the blood sugar profile after admission were assessed. Functional outcome was assessed via phone interview after 90 days. A poor outcome was defined as a deterioration to a modified Rankin Scale score ≥ 3. A good outcome was defined as a modified Rankin Scale score < 3 or as no deterioration from premorbid level. *Results:* Patients with a poor outcome presented with higher values of SAF (mean 3.38 (SD 0.55)) than patients with a good outcome (mean 3.13 (SD 0.61), *p* = 0.023), but did not differ in HbA1c and acute glycemia. In logistic regression analysis, age (*p* = 0.021, OR 1.24 [1.12–1.37]) and SAF (*p* = 0.021, OR 2.74 [1.16–6.46]) significantly predicted a poor outcome, whereas HbA1c and acute glycemia did not. Patients with a poor 90-day outcome and higher SAF experienced more infections (4.2% vs. 33.3% (*p* < 0.01)) and other various in-hospital complications (21.0% vs. 66.7% (*p* < 0.01)) than patients with a good outcome and lower SAF levels. *Conclusions:* SAF offers an insight into glycemic memory and appears to be a significant predictor of poor stroke outcomes in patients with DM exceeding HbA1c and acute glycemia. Measuring SAF could be useful to identify specifically vulnerable patients at high risk of complications and poor outcomes.

**Keywords:** stroke outcome; diabetes mellitus; hyperglycemia; skin autofluorescence; advanced glycation end products; poststroke complications

### **1. Introduction**

Around 30% of patients in ischemic stroke care suffer from diabetes mellitus (DM). Concomitantly, due to acute and chronic hyperglycemic effects, patients with DM show poor recovery after stroke [1]. HbA1c from nonenzymatic glycation of hemoglobin represents the best-established marker of chronic hyperglycemia regarding the last three months. Meanwhile, different long-lasting molecules underlie similar transformations and form the group of advanced glycation end products (AGEs), also known as glycemic memory [2]. Skin autofluorescence (SAF) represents a valid, quick and noninvasive approach to measure AGEs in vivo [3] and is a marker of vasculopathy in DM type 2 [4]. We aimed to investigate the predictive potential of SAF as a surrogate of long-term hyperglycemia in comparison to

**Citation:** Filipov, A.; Fuchshuber, H.; Kraus, J.; Ebert, A.D.; Sandikci, V.; Alonso, A. Measuring of Advanced Glycation End Products in Acute Stroke Care: Skin Autofluorescence as a Predictor of Ischemic Stroke Outcome in Patients with Diabetes Mellitus. *J. Clin. Med.* **2022**, *11*, 1625. https://doi.org/10.3390/jcm11061625

Academic Editor: Fernando Gómez-Peralta

Received: 22 February 2022 Accepted: 10 March 2022 Published: 15 March 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

HbA1c as marker of intermediate glycemia and acute hyperglycemia on stroke outcome in a cohort of patients with DM.

### **2. Materials and Methods**