Evaluation of Risk Factors for Diabetic Peripheral Neuropathy Among Saudi Type 2 Diabetic Patients with Longer Duration of Diabetes

Correlations between distal sensorimotor polyneuropathy and cardiovascular complications in diabetic patients in the North-Eastern region of Hungary

Distal sensorimotor polyneuropathy (DSPN) is the earliest detectable and the most frequent microvascular complication in diabetes mellitus. Several studies have previously demonstrated correlations between cardiovascular risk factors in diabetic patients and independent risk factors for diabetic neuropathy. Our objective was to retrospectively analyze data from diabetic patients in the North-East region of Hungary who underwent neuropathy screening at the Diabetic Neuropathy Center, University of Debrecen, between 2017 and 2021. We aimed to investigate the correlations between cardiovascular risk factors and microvascular complications among patients with DSPN. The median age of the patients was 67 years, 59,6% were female, and 91,1% had type 2 diabetes. The prevalence of DSPN among the study subjects was 71.7%. A significantly longer duration of diabetes (p<0.01) was noted in patients with DSPN. Those with DSPN demonstrated a significantly higher HbA1c level (p<0.001) and a greater frequency of insulin use (p = 0.001). We observed a significantly elevated albumin/creatinine ratio (p<0.001) and a significantly lower eGFR (p<0.001) in patients with DSPN. Diabetic retinopathy exhibited a significantly higher prevalence in patients with DSPN (p<0.001). A higher prevalence of myocardial infarction (p<0.05), ischemic heart disease (p<0.001), peripheral arterial disease (p<0.05) and a history of atherosclerosis (p<0.05) was observed in patients with DSPN. In a multivariate logistic regression analysis, the following factors were independently associated with the presence of DSPN: higher HbA1c (OR:2.58, 95% CI:1.89-3.52, p<0.001), age (OR:1.03, 95% CI:1.01-1.05, p = 0.006), albumin/creatinine ratio above 3 mg/mmol (OR:1.23, 95% CI:1.06-1.45, p = 0.008), retinopathy (OR:6.06, 95% CI:1.33-27.53, p = 0.02), and composite cardiovascular endpoint (OR:1.95, 95% CI:1.19-3.19, p = 0.008). Our study revealed that age, elevated HbA1c levels, significant albuminuria, retinopathy, and cardiovascular complications may increase the risk of DSPN. Further investigation of these associations is necessary to understand the impact of patient characteristics during the treatment of diabetic neuropathy.

Recent Advances in Biomolecular Patho-Mechanistic Pathways behind the Development and Progression of Diabetic Neuropathy

Diabetic neuropathy (DN) is a neurodegenerative disorder that is primarily characterized by distal sensory loss, reduced mobility, and foot ulcers that may potentially lead to amputation. The multifaceted etiology of DN is linked to a range of inflammatory, vascular, metabolic, and other neurodegenerative factors. Chronic inflammation, endothelial dysfunction, and oxidative stress are the three basic biological changes that contribute to the development of DN. Although our understanding of the intricacies of DN has advanced significantly over the past decade, the distinctive mechanisms underlying the condition are still poorly understood, which may be the reason behind the lack of an effective treatment and cure for DN. The present study delivers a comprehensive understanding and highlights the potential role of the several pathways and molecular mechanisms underlying the etiopathogenesis of DN. Moreover, Schwann cells and satellite glial cells, as integral factors in the pathogenesis of DN, have been enlightened. This work will motivate allied research disciplines to gain a better understanding and analysis of the current state of the biomolecular mechanisms behind the pathogenesis of DN, which will be essential to effectively address every facet of DN, from prevention to treatment.

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.

Assessment of Neuropathy by Temperature Threshold Testing in Type 2 Diabetes Mellitus

Introduction

Diagnosing diabetic neuropathy is a challenge at times as it is asymptomatic. Diagnosing diabetic neuropathy involves the use of quantitative sensory testing, nerve conduction study, and autonomic testing. Tempearture threshold testing (TTT) can aid in diagnosing small fiber neuropathy at early stages. This study aimed to assess the small fiber neuropathy using TTT in diabetes mellitus (DM) and correlate with age, duration of diabetes, and lipid profile.

Materials and Methods

The study was commenced after obtaining ethics approval from the institute ethics committee. The study participants included 100 patients with type 2 DM of both genders between the ages of 40 and 65 years. The glycemic status and lipid profile were noted along with physical examination. Neuropathy assessment was done using Michigan Neuropathy Screening Instrument (MNSI) and TTT.

Results

The prevalence of small fiber neuropathy based on TTT was 63%. The lipid profile was similar in both the groups. The MNSI B scale had significantly higher scores in the neuropathy group. In the neuropathy group, the thresholds for hot were significantly greater in all four limbs and cold were significantly lower. Age and years of DM were positively correlated with the neuropathy. Hot threshold in the lower limb had shown a strong positive correlation.

Conclusion

The age and duration of diabetes are independent risk factors for diabetic peripheral neuropathy. Small fiber neuropathy is a prequel to the motor neuropathy. Hot threshold testing in the lower limb is more sensitive than cold threshold testing for diagnosing small fiber neuropathy.

Development of risk prediction scores for diabetic peripheral neuropathy patients

Objectives

Risk prediction scores are important for early diagnosis and treatment of diseases. Diabetic peripheral neuropathy (DPN) is a common complication of type 2 diabetes, but the early diagnosis is challenging. This study developed a risk prediction model for DPN based on modifiable risk factors.

Materials and Methods

The study included 315 type 2 diabetes patients with and without DPN. Demographic, biochemical, and diagnostic data were collected. Multinomial logistic regression analysis was used to identify independent risk factors for DPN.

Results

Hemoglobin% and total red blood cells were identified as independent risk factors for DPN, used to develop a risk prediction score.

Conclusion

The risk prediction score developed in this study can be used by physicians to quickly assess a patient's risk of DPN and select appropriate therapeutic options. Routine monitoring of modifiable risk factors can improve DPN prognosis. Patients stratified by risk scores can better understand their risk and seek appropriate care.

The prevalence of peripheral neuropathy among the patients with diabetes in Pakistan: a systematic review and meta-analysis

The most frequent complication of diabetes is peripheral neuropathy. The estimated prevalence of peripheral neuropathy in people with diabetes varies substantially between published studies in Pakistan. We conducted this meta-analysis to summarize the prevalence of peripheral neuropathy in people with diabetes. Different electronic databases were systematically searched using keywords and MeSH terms. Random-effects meta-analysis was conducted to pool the prevalence of peripheral neuropathy in people with diabetes in Pakistan. Heterogeneity was investigated by random-effects meta-regression and stratification. Two independent authors reviewed studies, extracted data, and conducted the risk of bias analysis. Nineteen studies with a total of 8487 diabetic patients were included. The overall pooled prevalence of diabetic peripheral neuropathy was 43.16% (95% CI 32.93-53.69%), with significant heterogeneity between estimates. The prevalence of peripheral neuropathy among those newly diagnosed with diabetes was 26.52% (95% CI 14.97-39.96%, n = 5). According to the subgroup meta-analysis, the pooled prevalence of diabetic peripheral neuropathy was highest in Khyber Pakhtunkhwa (55.29%; 95% CI 23.91-84.50%), followed by Sindh (40.04%; 95% CI 24.00-57.25%), and the lowest was found in Punjab (34.90%; 95% CI 15.05-57.95%). A significant association was found between the pooled prevalence estimate and the duration of diabetes. The results of this meta-analysis indicate a relatively high prevalence of peripheral neuropathy in people with diabetes in Pakistan. The study protocol has been registered in the PROSPERO, with the registration number CRD42022371617.

The Effect of Postinduction Blood Glucose on Intraoperative Hypothermia

Background and Objectives: Hypothermia frequently occurs in patients undergoing surgery and is associated with adverse complications. Therefore, this study aimed to investigate the postinduction blood glucose and occurrence of intraoperative hypothermia in patients undergoing laparoscopic surgery. Materials and Methods: This retrospective observational study included 334 patients aged ≥20 years who had undergone elective laparoscopic surgery. The primary outcome of this study was the incidence of intraoperative hypothermia. Stratified analysis revealed differences between patients with and without diabetes. Results: Hypothermia occurred in 200 (59.9%) patients. In multivariate analysis, out-of-range postinduction glucose was independently associated with hypothermia (>150 mg/dL: odds ratio 2.17, 95% confidence interval (1.02, 4.61), p = 0.045; <110 mg/dL: odds ratio 2.02, 95% confidence interval (1.15, 3.55), p = 0.015), whereas preoperative HbA1c >6% was not significantly associated with hypothermia (odds ratio 1.02, 95% confidence interval (0.56, 1.84), p = 0.961). Considering only patients with diabetes, the incidence of hypothermia was lower (p = 0.002), the duration of hypothermia was shorter (p = 0.007), and the minimum temperature was higher (p = 0.006) in those with a postinduction glucose level of 110-150 mg/dL. Conclusions: The postinduction glucose level is independently associated with intraoperative hypothermia. Out-of-range postinduction glucose appeared to have an impact on the development of hypothermia in patients with diabetes, especially those with a postinduction glucose level <110 mg/dL.