Lipid Levels and Risk of Diabetic Polyneuropathy in 2 Danish Type 2 Diabetes Cohorts.

BACKGROUND AND OBJECTIVES: Reduction of blood lipids may aid in preventing diabetic polyneuropathy (DPN), but evidence remains conflicting. We investigated the association between lipid parameters and DPN risk in individuals with type 2 diabetes mellitus (T2DM). METHODS: We conducted a population-based cohort study of individuals with newly diagnosed T2DM and a cross-sectional study using a clinically recruited T2DM cohort. Triglycerides, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and non-HDL cholesterol were measured in routine diabetes care. Each lipid parameter was categorized according to the latest cutoffs in clinical guidelines on dyslipidemia. DPN was assessed with validated hospital diagnosis codes in the population-based cohort and with the Michigan Neuropathy Screening Instrument questionnaire in the clinical cohort. We calculated hazard ratios (HRs) using Cox regression and prevalence ratios (PRs) using Poisson regression. RESULTS: We included 61,853 individuals in the population-based cohort (median age 63 [quartiles 54-72] years) and 4,823 in the clinical cohort (median age 65 [quartiles 57-72] years). The incidence rate of hospital-diagnosed DPN in the population-based cohort was 3.6 per 1000 person-years during a median follow-up of 7.3 years. Achieving guideline targets for HDL, LDL, and non-HDL cholesterol showed no association with DPN risk. By contrast, adjusted HRs (95% CI) for DPN were 1.02 (0.89-1.18) for triglyceride levels between 150 and 204 mg/dL (1.7-2.3 mmol/L) and 1.28 (1.13-1.45) for levels >204 mg/dL (2.3 mmol/L). In the clinical cohort with a DPN prevalence of 18%, DPN associated strongly with triglycerides >204 mg/dL (2.3 mmol/L) with an adjusted PR (95% CI) of 1.40 (1.21-1.62). The prevalence of DPN was modestly elevated for individuals with HDL cholesterol <39 mg/dL (1.0/1.3 mmol/L) in men and <50 mg/dL (1.3 mmol/L) in women (PR 1.13 [0.99-1.28]) and for individuals with non-HDL cholesterol >131 mg/dL (3.4 mmol/L) (PR 1.27 [1.05-1.52]). In both cohorts, spline models showed an increasing risk of DPN starting from triglyceride levels >124 mg/dL (1.4 mmol/L). All results were similar among statin users. DISCUSSION: High triglyceride levels are a strong DPN risk factor. Future intervention studies shall determine whether triglyceride reduction is more important for DPN prevention than reduction of other lipids.

[To determine the value of iMAX, a new electrodiagnostic method, and its application in the evaluation of peripheral motor nerve excitability].

Objective: To test the new method of iMAX (the minimum stimulus current that elicits the maximum compound muscle action potential amplitude) electrodiagnosis, verify the feasibility of this method in evaluating the excitability of peripheral motor axons, and preliminarily explore the clinical application value. Methods: This study was a cross-sectional study. A total of 50 healthy subjects were recruited from the outpatient department of Peking University Third Hospital from June 2022 to March 2023, including 25 males and 25 females, aged 25-68 (48±8) years. Eleven patients with Charcot-Marie-Pain-1A (CMT1A), 7 males and 4 females, aged 19-55 (41±13) years and 21 patients with diabetic peripheral neuropathy (DPN), 10 males and 11 females, aged 28-79 (53±16) years were enrolled in this study. iMAX of bilateral median nerves, ulnar nerves and peroneal nerves were detected in all patients. Repeatable motor responses with minimum motor threshold and amplitude of at least 0.1 mV and the minimum stimulus current intensity, at which the maximum compound muscle action potential amplitude is elicited, were measured respectively [1 mA increment is called (iUP) and, 0.1 mA adjustment is called (iMAX)].Comparison of the parameters: the parameters of threshold, iUP and iMAX were compared among different age groups, genders and sides, body mass index(BMI) values and detection time , as well as between CMT1A patients, DPN patients and healthy subjects. Results: In healthy subjects, the threshold, iUP value and iMAX value were (1.8±0.7) mA, (4.4±1.2) mA, and (4.2±1.3) mA respectively; ulnar nerve (3.1±1.6) mA, (6.8±3.2) mA, (6.4±3.2) mA; peroneal nerve (3.7±2.0) mA, (7.8±2.8) mA, (7.4±2.9) mA. There were statistically significant differences in threshold, iUP value and iMAX value among different age groups (all P<0.001).With the increase of age, there was a trend of increasing threshold, iUP, and iMAX values in different nerves, and the differences are statistically significant (all P<0.001). There were no significant differences in gender, side and detection time threshold, iUP value and iMAX value (all P>0.05). The parameters of healthy subjects with high BMI value were higher than those of healthy subjects with low BMI value(all P<0.05). Compared with the healthy subjects, the parameters of 11 CMT1A patients were significantly increased (all P<0.05), and the parameters of 21 DPN patients were slightly increased (P<0.05). Conclusion: The new iMAX method reflects the excitability of motor axons and early axonal dysfunction, which is an important supplement to the traditional nerve conduction, and can be used to monitor motor axon excitability disorders.

Associations between multiple neurological biomarkers and distal sensorimotor polyneuropathy: KORA F4/FF4 study.

AIMS: The aim of this study was to assess associations between neurological biomarkers and distal sensorimotor polyneuropathy (DSPN). MATERIALS AND METHODS: Cross-sectional analyses were based on 1032 participants aged 61-82 years from the population-based KORA F4 survey, 177 of whom had DSPN at baseline. The prevalence of type 2 diabetes was 20%. Prospective analyses used data from 505 participants without DSPN at baseline, of whom 125 had developed DSPN until the KORA FF4 survey. DSPN was defined based on the examination part of the Michigan Neuropathy Screening Instrument. Serum levels of neurological biomarkers were measured using proximity extension assay technology. Associations between 88 biomarkers and prevalent or incident DSPN were estimated using Poisson regression with robust error variance and are expressed as risk ratios (RR) and 95% CI per 1-SD increase. Results were adjusted for multiple confounders and multiple testing using the Benjamini-Hochberg procedure. RESULTS: Higher serum levels of CTSC (cathepsin C; RR [95% CI] 1.23 (1.08; 1.39), pB-H = 0.044) and PDGFRα (platelet-derived growth factor receptor A; RR [95% CI] 1.21 (1.08; 1.35), pB-H = 0.044) were associated with prevalent DSPN in the total study sample. CDH3, JAM-B, LAYN, RGMA and SCARA5 were positively associated with DSPN in the diabetes subgroup, whereas GCP5 was positively associated with DSPN in people without diabetes (all pB-H for interaction <0.05). None of the biomarkers showed an association with incident DSPN (all pB-H>0.05). CONCLUSIONS: This study identified multiple novel associations between neurological biomarkers and prevalent DSPN, which may be attributable to functions of these proteins in neuroinflammation, neural development and myelination.

Can the 128-Hz tuning fork be an alternative to the biothesiometer for diabetic peripheral neuropathy screening? A cross-sectional study in a tertiary hospital in East India.

INTRODUCTION: Diabetic neuropathy is frequently underdiagnosed and undertreated. Logistic problems accompany the routine use of the biothesiometer. Hence, we attempted to find a more easily available alternative. RESEARCH DESIGN AND METHODS: 149 patients with diabetes visiting the outpatient endocrinology clinic were assessed for vibration sense using a 128-Hz tuning fork (absolute timing method) and a biothesiometer. A reading of >25 V on the biothesiometer (known as vibration perception threshold or VPT) was taken as the diagnostic criterion for severe neuropathy while >15 V was used as an indicator of the mild form. The sensitivity and specificity were calculated by constructing the receiver operating characteristic curve (ROC). A p value of <0.05 was considered as statistically significant. RESULTS: The timed tuning fork (TTF) test showed a statistically significant correlation with the VPT measurements (r=-0.5, p=0.000). Using the VPT findings as a reference, a timed tuning fork cut-off of 4.8 s was 76% sensitive and 77% specific in diagnosing mild neuropathy while absent tuning fork sensation demonstrated 70% sensitivity and 90% specificity in detecting severe neuropathy. CONCLUSIONS: The tuning fork test demonstrated significant sensitivity and specificity in diagnosing diabetic peripheral neuropathy when compared against the biothesiometer. A cut-off of 4.8 s can be a useful indicator of the early stages of onset of the condition.

Undiagnosed and Untreated Peripheral Complications of Diabetes: Findings from a Pilot Study on Diabetes-Related Foot Diseases (DFD) in Patients with Glycemic Disorders.

BACKGROUND Diabetes-related foot disease (DFD) is a serious complication of diabetes, increasing the risk of amputation. Coimplications are preventable, but most diabetics do not receive proper screening and treatment, despite indications. This study was a pilot screening of diabetes-related foot disease in a group of people with glycemic disorders. MATERIAL AND METHODS We recruited 143 volunteers over 40 years of age. In the final analysis, we included 85 people diagnosed with glycemic disorders (diabetes or prediabetes), for whom we performed a total of 170 foot measurements. We screened for peripheral artery disease using: foot pulse, ankle-brachial index (manual and automatic), toe-brachial index, and transcutaneous oxygen pressure (TcPO2). To screen for diabetic peripheral neuropathy, we used indicators of loss of protective sensation: pressure perception and temperature perception, and plantar pressure distribution. RESULTS A history of diabetes was reported by 26 (30.6%) of the subjects. Disorders of at least 1 foot occurred in 20 (66.7%) subjects with diagnosed diabetes and in 10 (17%) subjects declaring no diabetes. Higher risk and DFD category were correlated with duration of diabetes (r=0.68, p=0.007), glycemic levels (r=0.56, p=0.001), age (r=0.57, p=0.007), and the presence of other diabetes complications. The best predictor of risk in DFD was manual ABI, p=0.001; followed by automatic ABI, p=0.006. CONCLUSIONS Our results showed that peripheral complications of diabetes, such as DFD, often remain undiagnosed and untreated despite the high risk of developing ulcers. There is a need for multi-center screening studies.

Correlation of serum Meteorin-like (Metrnl) level with type 2 diabetic peripheral neuropathy.

OBJECTIVE: Meteorin-like (Metrnl), a secreted myokine, is a newly discovered neurotrophic factor. The aim of this study was to determine if there is a correlation between the Metrnl level and diabetic peripheral neuropathy (DPN). METHODS: The investigation was conducted on a sample of 80 patients with type 2 diabetes mellitus (T2DM) and 60 healthy controls. The T2DM patients were categorized into two subgroups based on skin biopsy: the DPN subgroup (n = 20) and the diabetes without neuropathy subgroup (n = 60). RESULTS: The T2DM groups had higher serum Metrnl concentrations compared with the controls. The serum Metrnl concentration was significantly lower in the DPN group than in T2DM patients without neuropathy. Logistic regression analysis demonstrated a notable correlation between serum Metrnl and DPN (OR: 0.997, 95% CI: 0.995-1.000, P < 0.05). Serum Metrnl level was negatively correlated with age and SBP after a simple logistic regression analysis. CONCLUSION: Serum Metrnl concentration is independently correlated with DPN.

Implementation of surface mechanomyography as a novel approach for objective evaluation of phasic muscle stretch reflexes in people with type 2 diabetes.

INTRODUCTION: Diabetic peripheral neuropathy is the most common complication of diabetes producing metabolic disruptions in the peripheral nervous system. Alteration in the predictable nature of tendon reflexes is the most common indicator suggesting the possibility of diabetic neuropathy. Evaluation of tendon reflexes is a part of various clinical scoring systems that assess neuropathy. The conventional reflex grading scales are subjective, lack temporal data, and have high inter-rater variability. Hence, an indigenous quantification tool was developed to evaluate the tendon reflexes in order to assess diabetic peripheral neuropathy. MATERIALS AND METHODS: A cross-sectional study was carried out in 140 healthy volunteers and 140 patients with type 2 diabetes. The mean age of controls and diabetics (49.1 ± 8.9, 50.7 ± 7.5) years, weight (66.9 ± 9.4, 69.8 ± 11.5) kilograms and BMI (24.5 ± 3.8, 26.1 ± 4.7), respectively. All of them are subjected to evaluation of tendon reflexes using the reflex quantification tool comprised of surface mechanomyography and electrogoniometry that can provide various static and dynamic variables of tendon reflex. RESULTS: The dynamic variables such as reflex amplitude, muscle velocity and angular velocity were significantly low in diabetic patients (p: <0.001) whereas latency and duration (p: <0.001) were prolonged. Furthermore, no significant difference was observed in the application of tendon striking force (p: 0.934) among the participants. CONCLUSION: The current study demonstrates that the proposed reflex quantification tool provides several dynamic variables of patellar tendon reflex, which are significantly affected and altered in diabetic patients suggesting the involvement of peripheral neurons.

Contemporary prevalence of diabetic neuropathies in individuals with type 1 and type 2 diabetes in a Danish tertiary outpatient clinic.

BACKGROUND: Population-based prevalence estimates of distal symmetric polyneuropathy (DPN) and diabetic autonomic neuropathy (DAN) are scares. Here we present neuropathy estimates and describe their overlap in a large cohort of people with type 1 and type 2 diabetes. METHODS: In a large population of outpatient participants, DPN was assessed using vibration perception threshold, sural nerve function, touch, pain and thermal sensation. Definite DPN was defined by the Toronto Consensus Criteria. Painful DPN was defined by Douleur Neuropathique 4 Questions. DAN measures were: cardiovascular reflex tests, electrochemical skin conductance, and gastroparesis cardinal symptom index. RESULTS: We included 822 individuals with type 1 (mean age (±SD) 54 ± 16 years, median [IQR] diabetes duration 26 [15-40] years) and 899 with type 2 diabetes (mean age 67 ± 11 years, median diabetes duration 16 [11-22] years). Definite DPN was prevalent in 54 % and 68 %, and painful DPN was in 5 % and 15 % of type 1 and type 2 participants, respectively. The prevalence of DAN varied between 6 and 39 % for type 1 and 9-49 % for type 2 diabetes. DPN without other neuropathy was present in 45 % with T1D and 50 % with T2D. CONCLUSION: The prevalence of DPN and DAN was high. DPN and DAN co-existed in only 50 % of cases.

Genetic markers of cardiac autonomic neuropathy in the Kazakh population.

BACKGROUND: Cardiac autonomic neuropathy (CAN) is a complication of diabetes mellitus (DM) that increases the risk of morbidity and mortality by disrupting cardiac innervation. Recent evidence suggests that CAN may manifest even before the onset of DM, with prediabetes and metabolic syndrome potentially serving as precursors. This study aims to identify genetic markers associated with CAN development in the Kazakh population by investigating the SNPs of specific genes. MATERIALS AND METHODS: A case-control study involved 82 patients with CAN (cases) and 100 patients without CAN (controls). A total of 182 individuals of Kazakh nationality were enrolled from a hospital affiliated with the RSE "Medical Center Hospital of the President's Affairs Administration of the Republic of Kazakhstan". 7 SNPs of genes FTO, PPARG, SNCA, XRCC1, FLACC1/CASP8 were studied. Statistical analysis was performed using Chi-square methods, calculation of odds ratios (OR) with 95% confidence intervals (CI), and logistic regression in SPSS 26.0. RESULTS: Among the SNCA gene polymorphisms, rs2737029 was significantly associated with CAN, almost doubling the risk of CAN (OR 2.03(1.09-3.77), p = 0.03). However, no statistically significant association with CAN was detected with the rs2736990 of the SNCA gene (OR 1.00 CI (0.63-1.59), p = 0.99). rs12149832 of the FTO gene increased the risk of CAN threefold (OR 3.22(1.04-9.95), p = 0.04), while rs1801282 of the PPARG gene and rs13016963 of the FLACC1 gene increased the risk twofold (OR 2.56(1.19-5.49), p = 0.02) and (OR 2.34(1.00-5.46), p = 0.05) respectively. rs1108775 and rs1799782 of the XRCC1 gene were associated with reduced chances of developing CAN both before and after adjustment (OR 0.24, CI (0.09-0.68), p = 0.007, and OR 0.43, CI (0.22-0.84), p = 0.02, respectively). CONCLUSION: The study suggests that rs2737029 (SNCA gene), rs12149832 (FTO gene), rs1801282 (PPARG gene), and rs13016963 (FLACC1 gene) may be predisposing factors for CAN development. Additionally, SNPs rs1108775 and rs1799782 (XRCC1 gene) may confer resistance to CAN. Only one polymorphism rs2736990 of the SNCA gene was not associated with CAN.

The Relationship between the Ewing Test, Sudoscan Cardiovascular Autonomic Neuropathy Score and Cardiovascular Risk Score Calculated with SCORE2-Diabetes.

Background and Objectives: Cardiac autonomic neuropathy (CAN) is a severe complication of diabetes mellitus (DM) strongly linked to a nearly five-fold higher risk of cardiovascular mortality. Patients with Type 2 Diabetes Mellitus (T2DM) are a significant cohort in which these assessments have particular relevance to the increased cardiovascular risk inherent in the condition. Materials and Methods: This study aimed to explore the subtle correlation between the Ewing test, Sudoscan-cardiovascular autonomic neuropathy score, and cardiovascular risk calculated using SCORE 2 Diabetes in individuals with T2DM. The methodology involved detailed assessments including Sudoscan tests to evaluate sudomotor function and various cardiovascular reflex tests (CART). The cohort consisted of 211 patients diagnosed with T2DM with overweight or obesity without established ASCVD, aged between 40 to 69 years. Results: The prevalence of CAN in our group was 67.2%. In the study group, according SCORE2-Diabetes, four patients (1.9%) were classified with moderate cardiovascular risk, thirty-five (16.6%) with high risk, and one hundred seventy-two (81.5%) with very high cardiovascular risk. Conclusions: On multiple linear regression, the SCORE2-Diabetes algorithm remained significantly associated with Sudoscan CAN-score and Sudoscan Nephro-score and Ewing test score. Testing for the diagnosis of CAN in very high-risk patients should be performed because approximately 70% of them associate CAN. Increased cardiovascular risk is associated with sudomotor damage and that Sudoscan is an effective and non-invasive measure of identifying such risk.

Early detection of diabetic neuropathy based on health belief model: a scoping review.

Introduction: Uncontrolled blood sugar levels may result in complications, namely diabetic neuropathy. Diabetic neuropathy is a nerve disorder that causes symptoms of numbness, foot deformity, dry skin, and thickening of the feet. The severity of diabetic neuropathy carries the risk of developing diabetic ulcers and amputation. Early detection of diabetic neuropathy can prevent the risk of diabetic ulcers. The purpose: to identify early detection of diabetic neuropathy based on the health belief model. Method: This research searched for articles in 6 databases via Scopus, Ebsco, Pubmed, Sage journal, Science Direct, and SpringerLink with the keywords "screening Neuropathy" AND "Detection Neuropathy" AND "Scoring Neuropathy" AND "Diabetic" published in 2019-2023. In this study, articles were identified based on PICO analysis. Researchers used rayyan.AI in the literature selection process and PRISMA Flow-Chart 2020 to record the article filtering process. To identify the risk of bias, researchers used the JBI checklist for diagnostic test accuracy. Results: This research identified articles through PRISMA Flow-Chart 2020, obtaining 20 articles that discussed early detection of diabetic neuropathy. Conclusion: This review reports on the importance of early detection of neuropathy for diagnosing neuropathy and determining appropriate management. Neuropathy patients who receive appropriate treatment can prevent the occurrence of diabetic ulcers. The most frequently used neuropathy instruments are the vibration perception threshold (VPT) and questionnaire Michigan Neuropathy Screening Instrument (MNSI). Health workers can combine neuropathy instruments to accurately diagnose neuropathy.

Development and validation of a risk nomogram model for predicting peripheral neuropathy in patients with type 2 diabetes mellitus.

Objective: Diabetic peripheral neuropathy frequently occurs and presents severely in individuals suffering from type 2 diabetes mellitus, representing a significant complication. The objective of this research was to develop a risk nomogram for DPN, ensuring its internal validity and evaluating its capacity to predict the condition. Methods: In this retrospective analysis, Suqian First Hospital's cohort from January 2021 to June 2022 encompassed 397 individuals diagnosed with T2DM. A random number table method was utilized to allocate these patients into two groups for training and validation, following a 7:3 ratio. By applying univariate and multivariable logistic regression, predictive factors were refined to construct the nomogram. The model's prediction accuracy was assessed through metrics like the ROC area, HL test, and an analysis of the calibration curve. DCA further appraised the clinical applicability of the model. Emphasis was also placed on internal validation to confirm the model's dependability and consistency. Results: Out of 36 evaluated clinicopathological characteristics, a set of four, duration, TBIL, TG, and DPVD, were identified as key variables for constructing the predictive nomogram. The model exhibited robust discriminatory power, evidenced by an AUC of 0.771 (95% CI: 0.714-0.828) in the training cohort and an AUC of 0.754 (95% CI: 0.663-0.845) in the validation group. The congruence of the model's predictions with actual findings was corroborated by the calibration curve. Furthermore, DCA affirmed the clinical value of the model in predicting DPN. Conclusion: This research introduces an innovative risk nomogram designed for the prediction of diabetic peripheral neuropathy in individuals suffering from type 2 diabetes mellitus. It offers a valuable resource for healthcare professionals to pinpoint those at elevated risk of developing this complication. As a functional instrument, it stands as a viable option for the prognostication of DPN in clinical settings.

Screening for diabetic peripheral neuropathy at community pharmacies in Slovakia.

AIM: To identify diabetic patients with a potential risk of developing diabetic peripheral neuropathy (DPN) in community pharmacies in Slovakia using a modified Michigan Neuropathy Screening Instrument questionnaire (MNSIq-12). METHODS: This cross-sectional study enrolled 703 patients with type 1 and type 2 diabetes mellitus who had not been diagnosed with DPN. The study took place in selected community pharmacies across Slovakia in October 2019. The MNSIq-12 was administered by pharmacy students, and a Michigan score <1.5 was considered risky. The groups divided based on the Michigan score were compared in terms of duration of diabetes, age, body mass index (BMI), sex, weekly physical activity, level of education, and smoking. RESULTS: The risk of developing DPN was detected in 6.6% of respondents with type 1 diabetes and 13.4% with type 2 diabetes. Patients with both types of diabetes (38.2%; 67.0%) reported fatigue and heaviness in the legs as the most common clinical symptoms that may indicate the development of DPN. Those with a Michigan score <1.5 were older (P<0.0001), had a higher BMI (P<0.0001), a lower level of education (P=0.0020), and were less physically active (P<0.0001). Conclusion Approximately one-eighth of patients with diabetes who visited community pharmacies were potentially at risk for developing DPN. The modified MNSIq-12 was shown to be a simple, time-effective, and non-invasive indicative screening tool that can be applied in the environment of community pharmacies.

Painful forms of diabetic neuropathy.

Diabetic neuropathy is a frequent and severe degenerative complication of diabetes. The diagnosis is easily performed in painful symptomatic patients. Sensitivity disorders responsible for numbness, tingling, and loss of feeling are part and parcel of diabetic foot syndrome and require investigation in view of preventing trophic ulcers. To date, there exists no specific treatment for diabetic neuropathy possibly preventable by careful control of metabolic disorder. Effective management of diabetic patients would make it possible to limit the dramatic consequences of diabetic neuropathy while at the same time acting on other complications.

Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes.

AIM: To develop and employ machine learning (ML) algorithms to analyse electrocardiograms (ECGs) for the diagnosis of cardiac autonomic neuropathy (CAN). MATERIALS AND METHODS: We used motif and discord extraction techniques, alongside long short-term memory networks, to analyse 12-lead, 10-s ECG tracings to detect CAN in patients with diabetes. The performance of these methods with the support vector machine classification model was evaluated using 10-fold cross validation with the following metrics: accuracy, precision, recall, F1 score, and area under the receiver-operating characteristic curve (AUC). RESULTS: Among 205 patients (mean age 54 ± 17 years, 54% female), 100 were diagnosed with CAN, including 38 with definite or severe CAN (dsCAN) and 62 with early CAN (eCAN). The best model performance for dsCAN classification was achieved using both motifs and discords, with an accuracy of 0.92, an F1 score of 0.92, a recall at 0.94, a precision of 0.91, and an excellent AUC of 0.93 (95% confidence interval [CI] 0.91-0.94). For the detection of any stage of CAN, the approach combining motifs and discords yielded the best results, with an accuracy of 0.65, F1 score of 0.68, a recall of 0.75, a precision of 0.68, and an AUC of 0.68 (95% CI 0.54-0.81). CONCLUSION: Our study highlights the potential of using ML techniques, particularly motifs and discords, to effectively detect dsCAN in patients with diabetes. This approach could be applied in large-scale screening of CAN, particularly to identify definite/severe CAN where cardiovascular risk factor modification may be initiated.

A review regarding the article 'Diabetic peripheral neuropathy is associated with diabetic kidney disease and cardiovascular disease: The silesia diabetes-heart project'.

One of the most common micro vascular complications of diabetes is diabetic peripheral neuropathy (DPN). The well-recognized risk factors for DPN are hyperglycemia, dyslipidemia, and hypertension. DPN is associated with a high mortality rate and poor prognosis. Its pathogenesis is not fully understood, and clinical treatment is focused on relieving its clinical symptoms, as well as improving blood sugar control and cardiovascular risk factors. DPN and its clinically effective treatments need to be studied. Microvascular complications of diabetes present a significant challenge due to their diverse presentations, significant morbidity, and as strong predictors of cardiovascular disease. Prevention and management strategies should focus on lifestyle modification, education and awareness, systematic screening for early complications, and intensive management of modifiable risk factors. There was an association between DPN and DKD as well as CVD, BMI and age demonstrated. These may indicate that in case of having one diabetes complication diagnosed, it is important to screen for others, including macrovascular ones, as they may be undiagnosed due to their "silent" nature. Further studies are expected to strengthen basic research on the subject, reveal modern medical mechanisms, and provide fresh ideas and innovative methods for the treatment of DPN.

Assessment of the severity of diabetic polyneuropathy aids in predicting the risk of developing diabetic complications in patients with untreated diabetes.

This study aimed to determine the efficacy of assessing the severity of diabetic polyneuropathy (DPN) in patients with untreated diabetes. Seventy-two patients with untreated type 2 diabetes who were hospitalized for glycemic control were enrolled and divided into the following two groups: patients who had no prior diagnosis and patients who were unattended or had discontinued treatment. Electrophysiological criteria consistent with Baba's classification were used to diagnose and assess the severity of DPN. The patients were divided into three subgroups: no DPN (stage 0), mild DPN (stage 1), and moderate or more-severe DPN (stages 2-4). Intergroup comparisons were performed for the clinical characteristics and the results of the nerve conduction studies. Twenty-two (30%), 25 (35%), and 25 (35%) patients were categorized into the no DPN, mild DPN, and moderate or more-severe DPN subgroups, respectively. The number of patients who were unattended or had discontinued treatment in the moderate or more-severe DPN subgroup was significantly higher than that in the no DPN subgroup. The patients in the moderate or more-severe DPN subgroup had an increased risk of developing diabetic retinopathy and nephropathy, with odds ratios of 19.5 and 11.0 for advanced stages of retinopathy and nephropathy, respectively. Thus, the assessment of the severity of DPN could aid in the prediction of the risk of developing diabetic complications in patients with untreated diabetes.

Predictive Value of Combined HbA1c and Neutrophil-to-Lymphocyte Ratio for Diabetic Peripheral Neuropathy in Type 2 Diabetes.

BACKGROUND Diabetic peripheral neuropathy (DPN) is a prevalent complication affecting over 60% of type 2 diabetes patients. Early diagnosis is challenging, leading to irreversible impacts on quality of life. This study explores the predictive value of combining HbA1c and Neutrophil-to-Lymphocyte Ratio (NLR) for early DPN detection. MATERIAL AND METHODS An observational study was conducted at the First People's Hospital of Linping District, Hangzhou spanning from May 2019 to July 2020. Data on sex, age, biochemical measurements were collected from electronic medical records and analyzed. Employing multivariate logistic regression analysis, we sought to comprehend the factors influencing the development of DPN. To assess the predictive value of individual and combined testing for DPN, a receiver operating characteristic (ROC) curve was plotted. The data analysis was executed using R software (Version: 4.1.0). RESULTS The univariate and multivariate logistic regression analysis identified the level of glycated hemoglobin (HbA1C) (OR=1.94, 95% CI: 1.27-3.14) and neutrophil-to-lymphocyte ratio (NLR) (OR=4.60, 95% CI: 1.15-22.62, P=0.04) as significant risk factors for the development of DPN. Receiver operating characteristic (ROC) curve analysis demonstrated that HbA1c, NLR, and their combined detection exhibited high sensitivity in predicting the development of DPN (71.60%, 90.00%, and 97.2%, respectively), with moderate specificity (63.8%, 45.00%, and 50.00%, respectively). The area under the curve (AUC) for these predictors was 0.703, 0.661, and 0.733, respectively. CONCLUSIONS HbA1c and NLR emerge as noteworthy risk indicators associated with the manifestation of DPN in patients with type 2 diabetes. The combined detection of HbA1c and NLR exhibits a heightened predictive value for the development of DPN.

Multi-feature, Chinese-Western medicine-integrated prediction model for diabetic peripheral neuropathy based on machine learning and SHAP.

BACKGROUND: Clinical studies have shown that diabetic peripheral neuropathy (DPN) has been on the rise, with most patients presenting with severe and progressive symptoms. Currently, most of the available prediction models for DPN are derived from general clinical information and laboratory indicators. Several Traditional Chinese medicine (TCM) indicators have been utilised to construct prediction models. In this study, we established a novel machine learning-based multi-featured Chinese-Western medicine-integrated prediction model for DPN using clinical features of TCM. MATERIALS AND METHODS: The clinical data of 1581 patients with Type 2 diabetes mellitus (T2DM) treated at the Department of Endocrinology of the First Affiliated Hospital of Anhui University of Chinese Medicine were collected. The data (including general information, laboratory parameters and TCM features) of 1142 patients with T2DM were selected after data cleaning. After baseline description analysis of the variables, the data were divided into training and validation sets. Four prediction models were established and their performance was evaluated using validation sets. Meanwhile, the accuracy, precision, recall, F1 score and area under the curve (AUC) of ROC were calculated using ten-fold cross-validation to further assess the performance of the models. An explanatory analysis of the results of the DPN prediction model was carried out using the SHAP framework based on machine learning-based prediction models. RESULTS: Of the 1142 patients with T2DM, 681 had a comorbidity of DPN, while 461 did not. There was a significant difference between the two groups in terms of age, cause of disease, systolic pressure, HbA1c, ALT, RBC, Cr, BUN, red blood cells in the urine, glucose in the urine, and protein in the urine (p < 0.05). T2DM patients with a comorbidity of DPN exhibited diverse TCM symptoms, including limb numbness, limb pain, hypodynamia, thirst with desire for drinks, dry mouth and throat, blurred vision, gloomy complexion, and unsmooth pulse, with statistically significant differences (p < 0.05). Our results showed that the proposed multi-featured Chinese-Western medicine-integrated prediction model was superior to conventional models without characteristic TCM indicators. The model showed the best performance (accuracy = 0.8109, precision = 0.8029, recall = 0.9060, F1 score = 0.8511, and AUC = 0.9002). SHAP analysis revealed that the dominant risk factors that caused DPN were TCM symptoms (limb numbness, thirst with desire for drinks, blurred vision), age, cause of disease, and glycosylated haemoglobin. These risk factors were exerted positive effects on the DPN prediction models. CONCLUSIONS: A multi-feature, Chinese-Western medicine-integrated prediction model for DPN was established and validated. The model improves early-stage identification of high-risk groups for DPN in the diagnosis and treatment of T2DM, while also providing informative support for the intelligent management of chronic conditions such as diabetes.