Diabetic Peripheral Neuropathy Associated with Cardiovascular Risk Factors and Glucagon-Like Peptide-1 Concentrations Among Newly Diagnosed Patients with Type 2 Diabetes Mellitus

Survival, incidence, and predictors of diabetic neuropathy among type 2 diabetic patients in hospitals of Addis Ababa

Background

Diabetic neuropathy is a very common complication of diabetes mellitus. Thus, measuring the incidence of diabetic neuropathy is a key element in tracking the progress of epidemics of diabetes mellitus and an indication of early accessibility for healthcare in terms of type 2 diabetic patients.

Objective

To assess survival, incidence, and predictors of diabetic neuropathy among type 2 diabetic patients in hospitals of Addis Ababa from June 25 to August 25, 2023.

Methods

An institutional-based retrospective follow-up study design was used among newly diagnosed type 2 diabetic patients at hospitals of Addis Ababa. A chart review tool that contains socio-demographic, clinical, and comorbidity characteristics, biochemical characteristics, and the status of type 2 patients was used. A cleaned data was exported from Epi-data manager 4.6 version to SPSS version 25 for analysis. Bivariate Cox regression analysis was done to identify predictors of diabetic neuropathy at a 95% confidence level.

Result

A total of 414 type 2 diabetic patients were included in the study. Of these, 97 (23.4%) developed diabetic neuropathy. Variables like having hypertension (AHR 11.25, 95% CI 3.73-33.93), anemia (AHR 4.18, 95% CI 1.78-9.82), high-density lipoprotein < 40 mg/dl (AHR 5.07, 95% CI 1.38-18.67), high creatinine level (AHR 14.67, 95% CI 4.27-50.40), diabetic retinopathy (AHR 4.32, 95% CI 1.32-14.18), and diabetic nephropathy (AHR 2.50, 95% CI 1.09-6.57) were associated with the incidence of diabetic neuropathy. The mean time to develop diabetic neuropathy was 4.94 years, CI (4.50-5.38), and the mean survival time was 6.61 years.

Conclusion

The incidence of diabetic neuropathy was high relative to other studies. Variables like having hypertension, anemia, high-density lipoprotein, high creatinine level, diabetic retinopathy, and diabetic nephropathy were predictors of diabetic neuropathy. The mean time to develop diabetic neuropathy was 5 years, with a survival mean time of 7 years.

Diabetic Sensorimotor Polyneuropathy: An Overview on Epidemiology, Risk Factors, Classification, Diagnosis, and Treatment

Diabetic distal symmetric sensorimotor polyneuropathy (DSPN) is a common complication of diabetes with devastating consequences. Hyperglycaemia is the major aetiological factor, while emerging data demonstrate that cardiometabolic risk factors also contribute to its development. Diagnosis of DSPN involves interview of medical and neurological history, foot inspection, and sensory and motor function examination with specific tests such as temperature and pinprick perception for small nerve fibers, and vibration and light touch assessments for large nerve fibers. Management includes optimised glycaemic control, treatment of cardiovascular risk factors, and symptomatic treatment aiming at improving life quality. This article provides an overview on epidemiology, risk factors, classification, diagnosis and current treatment of DSPN.

Diabetic peripheral neuropathy among adult type 2 diabetes patients in Adama, Ethiopia: health facility-based study

Diabetic peripheral neuropathy is the most prominent microvascular complication of diabetes mellitus and the leading cause of ulceration, amputation, and extended hospitalization. Evidence regarding the magnitude and factors associated with diabetic peripheral neuropathy is not well documented in Ethiopia, particularly in the study area. A facility-based cross-sectional study was conducted among 293 adult type 2 diabetic patients who were on treatment and follow-up from May to June 31, 2023. To select participants in the study, a systematic random sampling method was utilized. Data were collected using semi-structured questionnaires and medical record reviews. The Michigan Neuropathy Screening Instrument (MNSI) was employed to assess diabetic peripheral neuropathy. To model the association between diabetic peripheral neuropathy and independent variables, binary logistic regression model was used. An adjusted odds ratio with a 95% confidence interval was used to estimate the association and statistical significance was proclaimed at a p-value < 0.05. The magnitude of diabetic peripheral neuropathy was 14.3% (95% CI 10.4-18.0). It was 13.4% (95% CI 8.4-19.1) among males and 15.4% (95% CI 10.1-22.2) among females. Age above 60 years (AOR = 5.06, 95% CI 1.60-15.96), being rural resident (AOR = 2.41; 95% CI 1.15-5.06), duration of diabetes above 5 years (AOR = 2.48, 95% CI 1.16-5.27) and having comorbid hypertension (AOR = 2.56, 95% CI 1.24-5.28) were independently associated with diabetic peripheral neuropathy. One in seven adult type 2 diabetes patients in the study area had diabetic peripheral neuropathy. Factors such as age, place of residence, duration of diabetes, and comorbid hypertension showed positive associations with diabetic peripheral neuropathy. Thus, it is imperative to give special consideration to diabetic patients who are elderly, living in rural areas, experiencing a prolonged duration of diabetes, or dealing with comorbid hypertension.

Why too soon? Predictors of time to diabetic peripheral neuropathy among newly diagnosed diabetes mellitus patients: a multicenter follow-up study at health-care setting of Ethiopia

BACKGROUND

Due to the rising number of diabetic patients, the burden of diabetic peripheral neuropathy (DPN) is clearly posing a major challenge to the long-term viability of the health-care system. Despite this, most DPN epidemiological research in eastern Africa, including Ethiopia, has so far been limited to survey studies. Thus, we determined the incidence of DPN and its predictors among diabetic patients in tertiary health-care setting of southwest Ethiopia.

METHODS

A multicenter retrospective follow-up study was carried out on 567 randomly selected diabetic patients. Data were entered using Epi-Data v4.6 and analyzed using R v4.0.4. The survival curves were estimated using the Kaplan-Meier, and compared using Log-rank test between groups of categorical variables. The PHA were evaluated using the Schoenfeld residuals test. Multivariable Gompertz proportional hazard model was used to examine the predictors of DPN at 5% level of significance.

RESULTS

Overall, of 567 DM patients 119 developed DPN with an incidence rate of 3.75, 95%CI [3.13, 4.49] per 100 PY. About 15.13% and 69% of DPN cases occurred within 2 and 5 years of DM diagnosis, respectively. In the multivariable Gompertz PH model, being female [AHR = 1.47; 95% CI (1.01, 2.15)], T2DM [AHR = 3.49 95% CI (1.82, 6.71)], having diabetic retinopathy [AHR = 1.9 95% CI (1.25, 2.91)], positive proteinuria [AHR = 2.22 95% CI (1.35, 3.65)], being obese [AHR = 3.94 95% CI (1.2, 12.89)] and overweight [AHR = 3.34 95% CI (1.09, 10.25)] significantly predicts the future risk of DPN.

CONCLUSION

Nearly, 7 in 10 of DPN cases occurred within short period of time (5 year) of DM diagnosis. Being female, T2DM, DR, positive proteinuria, obese and overweight significantly predicts the risk of DPN. Therefore, we recommend screening and early diagnosis of diabetes with its complication. While doing so, attention should be given for DM patients with DR and positive proteinuria at baseline.

Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project

AIMS

As cardiovascular disease (CVD) is a leading cause of death for patients with diabetes mellitus (DM), we aimed to find important factors that predict cardiovascular (CV) risk using a machine learning (ML) approach.

METHODS AND RESULTS

We performed a single center, observational study in a cohort of 238 DM patients (mean age ± SD 52.15 ± 17.27 years, 54% female) as a part of the Silesia Diabetes-Heart Project. Having gathered patients' medical history, demographic data, laboratory test results, results from the Michigan Neuropathy Screening Instrument (assessing diabetic peripheral neuropathy) and Ewing's battery examination (determining the presence of cardiovascular autonomic neuropathy), we managed use a ML approach to predict the occurrence of overt CVD on the basis of five most discriminative predictors with the area under the receiver operating characteristic curve of 0.86 (95% CI 0.80-0.91). Those features included the presence of past or current foot ulceration, age, the treatment with beta-blocker (BB) and angiotensin converting enzyme inhibitor (ACEi). On the basis of the aforementioned parameters, unsupervised clustering identified different CV risk groups. The highest CV risk was determined for the eldest patients treated in large extent with ACEi but not BB and having current foot ulceration, and for slightly younger individuals treated extensively with both above-mentioned drugs, with relatively small percentage of diabetic ulceration.

CONCLUSIONS

Using a ML approach in a prospective cohort of patients with DM, we identified important factors that predicted CV risk. If a patient was treated with ACEi or BB, is older and has/had a foot ulcer, this strongly predicts that he/she is at high risk of having overt CVD.

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.

Issues and challenges in diabetic neuropathy management: A narrative review

Diabetic neuropathy (DN) is a devastating disorder with an increasing prevalence globally. This epidemic can pose a critical burden on individuals and com-munities, subsequently affecting the productivity and economic output of a country. With more people living a sedentary lifestyle, the incidence of DN is escalating worldwide. Many researchers have relentlessly worked on ways to combat this devastating disease. Their efforts have given rise to a number of commercially available therapies that can alleviate the symptoms of DN. Unfortunately, most of these therapies are only partially effective. Worse still, some are associated with unfavorable side effects. This narrative review aims to highlight current issues and challenges in the management of DN, especially from the perspective of molecular mechanisms that lead to its progression, with the hope of providing future direction in the management of DN. To improve the approaches to diabetic management, the suggested resolutions in the literature are also discussed in this review. This review will provide an in-depth understanding of the causative mechanisms of DN, apart from the insights to improve the quality and strategic approaches to DN management.

A Review of Recent Pharmacological Advances in the Management of Diabetes-Associated Peripheral Neuropathy

Diabetic peripheral neuropathy is a common complication of longstanding diabetes mellitus. These neuropathies can present in various forms, and with the increasing prevalence of diabetes mellitus, a subsequent increase in peripheral neuropathy cases has been noted. Peripheral neuropathy has a significant societal and economic burden, with patients requiring concomitant medication and often experiencing a decline in their quality of life. There is currently a wide variety of pharmacological interventions, including serotonin norepinephrine reuptake inhibitors, gapentanoids, sodium channel blockers, and tricyclic antidepressants. These medications will be discussed, as well as their respective efficacies. Recent advances in the treatment of diabetes mellitus with incretin system-modulating drugs, specifically glucagon-like peptide-1 agonists, have been promising, and their potential implication in the treatment of peripheral diabetic neuropathy is discussed in this review.

MACHINE LEARNING ANALYSIS OF THE UK BIOBANK REVEALS PROGNOSTIC AND DIAGNOSTIC IMMUNE BIOMARKERS FOR POLYNEUROPATHY AND NEUROPATHIC PAIN IN DIABETES

AIMS

We assessed the health data of 11,047 people with diabetes in the UK Biobank to rank 329 risk factors for diabetic polyneuropathy (DPN) and DPN with chronic neuropathic pain without a priori assumption.

METHODS

The Integrated Disease Explanation and Risk Scoring (IDEARS) platform applies machine learning algorithms to multimodal data to determine individual disease risk, and rank risk factor importance using mean SHapley Additive exPlanations (SHAP) score.

RESULTS

IDEARS models showed discriminative performances with AUC > 0.64. Lower socioeconomic status, being overweight, poor overall health, cystatin C, HbA1C, and immune activation marker, C-reactive protein (CRP), predict DPN risk. Neutrophils and monocytes were higher in males and lymphocytes lower in females with diabetes that develop DPN. Neutrophil-to-Lymphocyte Ratio (NLR) was increased and IGF-1 levels decreased in people with type 2 diabetes that later develop DPN. CRP was significantly elevated in those with DPN and chronic neuropathic pain compared to DPN without pain.

CONCLUSIONS

Lifestyle factors and blood biomarkers predict the later development of DPN and may relate to DPN pathomechanisms. Our results are consistent with DPN as a disease involving systemic inflammation. We advocate for the use of these biomarkers clinically to predict future DPN risk and improve early diagnosis.

Correlation between health literacy and health‐related quality of life in patients with diabetic peripheral neuropathy: The mediating role of self‐management

AIM

The aims of the study were to analyse the current situation of health literacy (HL), self-management and health-related quality of life (HRQOL) in patients with diabetic peripheral neuropathy (DPN), to explore the correlation between the three and to verify the mediating role of self-management in HL and HRQOL.

DESIGN

A cross-sectional survey.

METHODS

The convenience sampling method was used to select 401 DPN patients attending the First Hospital of Jinzhou Medical University in Liaoning Province, China, from December 2020 to December 2021 as the study population. The research instrument included socio-demographic characteristics questionnaire, Health Literacy Management Scale (HeLMS), Summary of Diabetes Self-Care Activities (SDSCA) and Short-Form 12-item Health Survey (SF-12). SPSS 25.0 was applied to the data for descriptive analysis, Pearson correlation analysis and stratified multiple regression analysis. Mediating effects were tested using SPSS PROCESS macro 4.0 software.

RESULTS

HL and self-management of DPN patients correlated positively with HRQOL. The mediation role of self-management was significant in the relationship between HL and physical and mental HRQOL (physical component summary: β = 0.26, P < 0.01; mental component summary: β = 0.18, P < 0.01), with mediating effects accounting for 35.62% and 34.62% of the total effect.

CONCLUSIONS

There was a positive correlation between HL, self-management and HRQOL in patients with DPN. Self-management plays a partially mediating role in the relationship between HL and HRQOL in DPN patients. It means that HRQOL in this population can be improved by increasing HL and thus self-management in DPN patients.

PATIENT OR PUBLIC CONTRIBUTION

None.