International Journal of Diabetes in Developing Countries最新文献

Background

The incidence of type II diabetes mellitus (T2DM) has quadruplicated in the recent decades and Prevention of T2DM cases is possible by changing lifestyle practices. The process of diagnosis of diabetes is a tedious one. The advent and advancement in (AI) techniques presents a probable solution to this critical problem.

Objective

The study aims to assess the diverse attributes of the test sample population across Assam and enhance the early prediction of Type II Diabetes Mellitus by employing artificial neural networks.

Methods

The aim of this study is to design a suitable AI model that prognosticates the likelihood of diabetes in individuals with maximum accuracy based on the levels of liver enzymes. This work also analyzes the effect of fast food intake, sleeping patterns, and consumption of alcohol on healthy controls and contemplates their susceptibility to contract T2DM.

Results

The AI model accurately predicted T2DM likelihood and revealed significant links between unhealthy behaviors and increased T2DM risk among healthy individuals.

Conclusions

The study underscores lifestyle modifications for T2DM prevention, highlighting AI’s potential in diagnosis and the impact of unhealthy habits on T2DM susceptibility.

Background

Novel topical esmolol is shown to significantly improve wound healing than standard of care. Certain patient-related factors especially anemia and poor glycemic control may impede wound healing.

Objective

To study whether novel topical esmolol may circumvent patient-related factors to improve wound healing in diabetic foot ulcer (DFU).

Methods

The present study is a double-blind, vehicle (placebo)-controlled, randomized clinical trial in subjects with non-infected DFU of University of Texas grade 1A and 1C. Participants were randomized to receive either topical esmolol gel (Galnobax) with standard of care (SoC), SoC only, and vehicle (Placebo) with SoC in 3:3:1 proportion. The hematologic and biochemistry parameters were evaluated at the screening visit and at every 4 weeks after randomization during treatment phase and at the end of the study (EOS). Outcome was the proportion of complete ulcer closure with reference to baseline hemoglobin, albumin, and HbA1c during the 12-week treatment phase.

Results

A total of 176 subjects were included. Ninety-four out of 140 participants (67.1%) had anemia at baseline. Among anemic participants, 57.4% ulcers closed in the Galnobax group whereas 42.6% closed in the SoC only group during the 12-week treatment phase (p = 0.148, OR = 1.823, 95% CI = 0.80–4.13). One-third participants reported albumin < 4.0 g/dL. Among participants having albumin < 4.0 g/dL (one-third of participants), the proportion of ulcer closure was 60.9% in the Galnobax group compared to 42.1% in the SoC only group (p = 0.225, OR = 2.139, 95% CI = 0.62–7.37). The proportion of ulcer closure in Galnobax with the SoC group was higher (72.5%) compared to the SoC only group (43.5%) (p = 0.0067, OR = 3.427, 95% CI = 1.38–8.48) in participants with poor glycemic control (HbA1c > 8%).

Conclusions

Novel topical esmolol with SoC treatment exhibited significant DFU healing independent of patient-related factors including anemia, hypoalbuminemia, and poor glycemic control.

Background

Several studies have reported that increased pentraxin-3 (PTX3) levels are associated with impaired renal function in chronic kidney disease (CKD). PTX3 levels increase progressively with diabetic nephropathy (DN) and may be a biomarker for early diagnosis of DN.

Objective

The study evaluates serum PTX3 levels and their association with the development of DN. We also aim to find out whether serum PTX3 is a better marker than high-sensitive CRP (hs-CRP) for DN.

Methods

In this study, we evaluated serum PTX3 levels in 150 patients which were distributed into three groups that are 50 patients with DN, 50 patients with diabetes mellitus (DM) without DN and 50 controls (not any evidence of DM). DN patients were subdivided: 32 patients with microalbuminuria and 18 patients with macroalbuminuria. Serum PTX3 levels were evaluated using an enzyme-linked immunosorbent assay (ELISA) kit.

Results

DN group patients had a higher value of PTX3 (p < 0.001) as compared to DM without DN and control groups. hs-CRP levels were higher in DM without DN patients compared to DN patients and controls. PTX3 (p = 0.33) and hs-CRP (p = 0.10) levels among microalbuminuria and macroalbuminuria patients were statistically not significant.

Conclusion

This study concludes that serum PTX3 can be used as a diagnostic marker for DN before the development of apparent chronic kidney disease and PTX3 was found to be a better marker than hs-CRP for diagnosing DN.

Abstract

Abstract

Background

Diabetic retinopathy (DR) is an eye disease developed due to long-term diabetes mellitus, which affects retinal damage. The treatment at the right time supports people in retaining vision, and the early detection of DR is the only solution to prevent blindness.

Objective

The development of DR shows few symptoms in the early stage of progression; it is difficult to identify the disease to give treatment from the beginning. Manual diagnosis of DR on fundus images is time-consuming, costly, and liable to be misdiagnosed when compared to computer-aided diagnosis systems.

Methods

In this work, we proposed a deep convolutional neural network for the recognition and classification of diabetic retinopathy lesions to identify the severity of the disease. The performance evaluation of the proposed model was tested with other machine learning classifiers such as K-nearest neighbor (KNN), Naïve Bayes (NB), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF).

Results

Our proposed model achieves 98.5% accuracy for the recognition and classification of the severity level of DR stages such as no DR, mild DR, moderate DR, severe DR, and proliferative DR.

Conclusion

The training and testing of our model are carried out on images from the Kaggle APTOS dataset, and this work can act as a base for the autonomous screening of DR.

Abstract

Background

Diabetes self-care has been described as an evolutionary, sustainable process of knowledge development in chronic medical conditions. It has been discovered that there is a positive correlation between glycemic control and improvement in quality of life.

Objective

This study aims to evaluate the knowledge of self-care activities in type 2 diabetes mellitus(T2DM) and the effect of pharmacist-led intervention in glycemic control using Diabetes Self-Care Management Questionnaire-Revised (DSMQ-R).

Methods

The interventional study was conducted among diabetics in a tertiary care hospital for over 6 months. Baseline and follow-up data were analysed using t-tests and linear regression to assess changes in knowledge and their relationship with fasting blood sugar (FBS).

Results

This study recruited 179 participants, predominantly in the 56–65 age group and mostly male (53%). Importantly 85% with prior T2DM diagnoses, addressing a specific population. The study successfully investigated the link between self-care knowledge and blood sugar control in T2DM patients. While the initial correlation between questionnaire data and fasting blood sugar was moderate (r = 0.027, p = 0.358), a meaningful 30.46% improvement in participants’ knowledge was observed (p < 0.05). Notably, half achieved good glycemic control, highlighting the positive impact of education on self-care practices. This confirms the potential of such interventions to empower individuals with T2DM management tools.

Conclusion

By demonstrating a significant improvement in knowledge and observing good glycemic control in a substantial portion of participants, the study highlights its potential to empower individuals with T2DM.

Background

India is the second major epicenter of diabetes mellitus (DM) prevalence after China and predicted to overtake China by 2045. DM is associated with multimorbidity which has tremendous impact on mental health. However, little is known about patterns of morbidities and their associations with depression.

Objective

The purpose of this article is to ascertain multimorbidity in the DM population and to assess the relationship between multimorbidity and depressive symptoms.

Methods

A cross-sectional analysis was conducted using data from the “Longitudinal Ageing Study in India (LASI),” a national representative survey. Regression analysis was used to investigate the relationship between multimorbidity and depressive symptoms. The mean difference of depressive symptoms with multimorbidity was calculated using one-way ANOVA.

Results

The total population included in the study was 8855. Around 72.8% had multimorbidity, out of which 43%, 20.4%, and 9.4% fall under single, double, and triple or more than triple morbidity category, respectively. The odds of having depression in single morbidity (AOR, 1.24), double morbidity (AOR, 1.34), and triple or more morbidity (AOR, 1.51), poor self-rated health (AOR, 1.45), unenrolled in health insurance policy (AOR, 1.21), hospitalized in the past 12 months (AOR, 1.12), and taking insulin injections (AOR, 1.24) were significantly higher. There were significant mean differences in depression with different categories of multimorbidity conditions (F = 19.63, p = 0.000).

Conclusion

Higher rate of multimorbidity among DM patients was recorded. Multimorbidity substantially increases the risk of acquiring depression. Timely identification and appropriate management multimorbidity of diabetes are important for reducing to incidence of depression and better quality of life and functionality.