Diabetic Neuropathy among Type 2 Diabetes Mellitus Patients at Amplas Primary Health Care in Medan City

Evaluation of Factors Affecting Neuropathy in Patients With Type 2 Diabetes Using Artificial Neural Networks

INTRODUCTION

Neuropathy is a common and debilitating complication in type 2 diabetes, affecting quality of life and increasing healthcare costs. Identifying risk factors is essential for early intervention and management. This study aims to evaluate the factors influencing the occurrence of neuropathy in patients with type 2 diabetes using artificial neural networks.

METHODS

In this cohort study, data from 371 patients with type 2 diabetes from Fereydunshahr, Iran, were analyzed over a 12-year follow-up period. Participants were selected based on diabetes screenings conducted in 2008 and 2009. Artificial neural networks with varying architectures were trained and validated, and their performance was compared to logistic regression models using receiver operating characteristic (ROC) curve analysis.

RESULTS

The prevalence of neuropathy in this cohort study was 31.2%. The best-fitted artificial neural network and logistic regression model had area under the curve (AUC) values of 0.903 and 0.803, respectively. Significant risk factors identified included gender, race, family history of diabetes, type of diabetes treatment, cholesterol levels, triglyceride levels, high-density lipoprotein (HDL) levels, and duration of diabetes. Notably, women, patients with a family history of diabetes, and those using injectable or combined injectable and oral medications were at higher risk of developing neuropathy.

CONCLUSION

These findings highlight the importance of vigilant monitoring and proactive management of neuropathy risk factors, especially in women, patients with a family history of diabetes, and those using injectable or combined diabetic medications.

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.

Validity and Reliability of a Vibration-Based Cell Phone in Detecting Peripheral Neuropathy among Patients with a Risk of Diabetic Foot Ulcer

The aim of this study was to determine the validity and reliability of a vibration-based cell phone in detecting peripheral neuropathy among individuals with a risk of diabetic foot ulcer (DFU). The current study consisted of 3 phases: a pilot study, concurrent validity study, and interreliability study. A 128 Hz tuning fork and vibration-based cell phone using the Vibrations-Test app was compared in the primary care setting. The pilot study confirmed that the vibration-based cell phone has an adequate reliability (r > 0.70, P < .001). The concurrent validity test involved 96 participants (aged 55.78 ± 8.32 years). The Pearson correlation test found the highest correlation in the fifth metatarsal heads (r = 0.741, right feet; 0.772, left feet; P < .001). In the 4 primary care settings, the interrater reliability between nurses of the vibration-based cell phone were k = 1.000, 1.000, 1.000, and 0.720. The concurrent validity test found moderate to strong correlation results and that the interrater reliability had a strong agreement that was almost perfect. Thus, vibration-based cell phone applications can be used as screening tools for detecting neuropathy among individuals with a risk of DFU.

Alterations in epidermal function in type 2 diabetes: Implications for the management of this disease

Epidermal function is regulated by numerous exogenous and endogenous factors, including age, psychological stress, certain skin disorders, ultraviolet irradiation and pollution, and epidermal function itself can regulate cutaneous and extracutaneous functions. The biophysical properties of the stratum corneum reflect the status of both epidermal function and systemic conditions. Type 2 diabetes in both murine models and humans displays alterations in epidermal functions, including reduced levels of stratum corneum hydration and increased epidermal permeability as well as delayed permeability barrier recovery, which can all provoke and exacerbate cutaneous inflammation. Because inflammation plays a pathogenic role in type 2 diabetes, a therapy that improves epidermal functions could be an alternative approach to mitigating type 2 diabetes and its associated cutaneous disorders.

Diabetic Neuropathy: a Critical, Narrative Review of Published Data from 2019

PURPOSE OF REVIEW

This manuscript is a systematic, narrative review that compiles and describes all data available from 2019 related to epidemiologic, diagnostic, and therapeutic advances in diabetic neuropathy (DN).

RECENT FINDINGS

Epidemiology of DN is discussed. Diagnostic modalities include predictive models, electrodiagnostics, imaging, and biomarkers. A majority of studies on the treatment of diabetic peripheral neuropathy (DPN) involve pharmacotherapy, but complementary and alternative medicine, exercise, modalities, psychological, interventional, and surgical options are also explored. DN is a highly prevalent and debilitating consequence of diabetes that can present challenges to the clinician as the assessment is largely subjective with different phenotypic presentations among patients. Treatment of DN is largely symptomatic as the pathogenesis of DN is not fully understood and is likely multifactorial. It is evident from the broad range of treatments that too often provide unsatisfactory relief that there is no consensus about a single most effective treatment for DN, and monotherapy rarely proves to be successful.