Impaired compensatory hyperinsulinemia among nonobese type 2 diabetes patients: a cross-sectional study

The effect of vitamin D supplementation on glycemic status and C-reactive protein levels in type 2 diabetic patients with ischemic heart disease: A protocol for systematic review and meta-analysis

BACKGROUND

Vitamin D might be beneficial in diabetic patients with ischemic heart disease through its favorable effect on metabolic profiles and biomarkers of inflammation and oxidative stress. We performed a protocol for systematic review and meta-analysis to assess whether vitamin D supplementation could improve glucose control and inflammation in type 2 diabetic patients with ischemic heart disease.

METHODS

The proposed systematic review and meta-analysis will conform to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols. Seven electronic databases including Web of Science, Embase, PubMed, Wanfang Data, Scopus, Science Direct, Cochrane Library were searched in October 2022 by 2 independent reviewers. The risk of bias assessment of the included studies was assessed using the tool recommended in the Cochrane Handbook for Systematic Reviews of Interventions (version 5.1.0). Data analysis was performed with Review Manager Software (RevMan Version 5.4, The Cochrane Collaboration, Copenhagen, Denmark).

RESULTS

This study will provide a high-quality synthesis to assess the effectiveness and safety of vitamin D supplementation on type 2 diabetic patients with ischemic heart disease.

CONCLUSION

This systematic review may lead to several recommendations, for both patients and researchers, as which is the best therapy for type 2 diabetic patients with ischemic heart disease and how future studies need to be designed, considering what is available now and what is the reality of the patient.

Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach

Background

Studies on Type-2 Diabetes Mellitus (T2DM) have revealed heterogeneous sub-populations in terms of underlying pathologies. However, the identification of sub-populations in epidemiological datasets remains unexplored. We here focus on the detection of T2DM clusters in epidemiological data, specifically analysing the National Family Health Survey-4 (NFHS-4) dataset from India containing a wide spectrum of features, including medical history, dietary and addiction habits, socio-economic and lifestyle patterns of 10,125 T2DM patients.

Methods

Epidemiological data provide challenges for analysis due to the diverse types of features in it. In this case, applying the state-of-the-art dimension reduction tool UMAP conventionally was found to be ineffective for the NFHS-4 dataset, which contains diverse feature types. We implemented a distributed clustering workflow combining different similarity measure settings of UMAP, for clustering continuous, ordinal and nominal features separately. We integrated the reduced dimensions from each feature-type-distributed clustering to obtain interpretable and unbiased clustering of the data.

Results

Our analysis reveals four significant clusters, with two of them comprising mainly of non-obese T2DM patients. These non-obese clusters have lower mean age and majorly comprises of rural residents. Surprisingly, one of the obese clusters had 90% of the T2DM patients practising a non-vegetarian diet though they did not show an increased intake of plant-based protein-rich foods.

Conclusions

From a methodological perspective, we show that for diverse data types, frequent in epidemiological datasets, feature-type-distributed clustering using UMAP is effective as opposed to the conventional use of the UMAP algorithm. The application of UMAP-based clustering workflow for this type of dataset is novel in itself. Our findings demonstrate the presence of heterogeneity among Indian T2DM patients with regard to socio-demography and dietary patterns. From our analysis, we conclude that the existence of significant non-obese T2DM sub-populations characterized by younger age groups and economic disadvantage raises the need for different screening criteria for T2DM among rural Indian residents.

Distinct pathoclinical clusters among patients with uncontrolled type 2 diabetes: results from a prospective study in rural India

INTRODUCTION

Inadequate glycemic control among patients with type 2 diabetes mellitus (T2DM) poses an enormous challenge. Whether this uncontrolled T2DM population is a heterogenous mix of disease subtypes remains unknown. Identification of these subtypes would result in a customized T2DM management protocol thereby paving the way toward personalized therapy.

RESEARCH DESIGN AND METHODS

Electronic health records of 339 patients with uncontrolled T2DM patients followed up for a median period of 14 months were analyzed using Uniform Manifold Approximation and Projection followed by density-based spatial clustering of applications with noise. Baseline clinical features and final diagnoses with drug combinations were selected in the analysis. A 30 min oral glucose tolerance test was next performed for assessing the underlying insulin resistance and β cell dysfunction.

RESULTS

Three major clusters were identified. The first cluster characterized by recent onset T2DM had moderately preserved β cell function. The second cluster with a longer duration of T2DM and associated hypertension showed the best glycemic control with dual antidiabetic therapy. The third cluster with the longest history of T2DM and no history of hypertension had the worst glycemic control in spite of the highest percentage of patients on triple therapy (34.58%) and quadruple therapy (8.41%).

CONCLUSIONS

Uncontrolled T2DM comprises a heterogeneous population with respect to disease duration, presence of co-morbidities and β cell function without significant difference in insulin resistance. Stratifying them on the basis of pathoclinical features is the first step toward a personalized management in T2DM.