Corrigendum: Genomic surveillance and phylodynamic analyses reveal the emergence of novel mutations and co-mutation patterns within SARS-CoV-2 variants prevalent in India.

[This corrects the article DOI: 10.3389/fmicb.2021.703933.].

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 ß cell dysfunction. RESULTS: Three major clusters were identified. The first cluster characterized by recent onset T2DM had moderately preserved ß 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 ß 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.

Genomic Surveillance and Phylodynamic Analyses Reveal the Emergence of Novel Mutations and Co-mutation Patterns Within SARS-CoV-2 Variants Prevalent in India.

Identification of the genomic diversity and the phylodynamic profiles of prevalent variants is critical to understand the evolution and spread of SARS-CoV-2 variants. We performed whole-genome sequencing of 54 SARS-CoV-2 variants collected from COVID-19 patients in Kolkata, West Bengal during August-October 2020. Phylogeographic and phylodynamic analyses were performed using these 54 and other sequences from India and abroad that are available in the GISAID database. We estimated the clade dynamics of the Indian variants and compared the clade-specific mutations and the co-mutation patterns across states and union territories of India over the time course. Frequent mutations and co-mutations observed within the major clades across time periods do not show much overlap, indicating the emergence of newer mutations in the viral population prevailing in the country. Furthermore, we explored the possible association of specific mutations and co-mutations with the infection outcomes manifested in Indian patients.

Subcutaneous amyloidoma models for screening potential anti-fibrillating agents in vivo.

Here, we describe a robust protocol using mouse models to screen potential insulin-stabilizers and insulin moieties. We have generated a mouse model of amyloidoma, found in diabetic patients undergoing insulin therapy. This model can be used to screen potential insulin stabilizers and insulin moieties to prevent amyloidoma formation. This protocol can further be used for the preclinical validation of therapeutically relevant insulin stabilizers and formulations. The protocol highlights all the critical steps for generating amyloidoma in a preclinical model. For complete details on the use and execution of this profile, please refer to Mukherjee et al. (2021).

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

AIMS: Obesity associated prolonged hyperinsulinemia followed by ß-cell failure is well established as the pathology behind type 2 diabetes mellitus (T2DM). However, studies on nonobese T2DM have reported it to be a distinct clinical entity with predominant insulin secretory defect. We, therefore, hypothesized that compensatory hyperinsulinemia in response to weight gain is impaired in nonobese subjects. METHODS: This was a cross-sectional study from a community-based metabolic health screening program. Adiposity parameters including body mass index (BMI), waist circumference (WC), body fat percentage, plasma leptin concentration and metabolic parameters namely fasting insulin, glucose, total cholesterol, and triglycerides were measured in 650 individuals (73% healthy, 62% nonobese with a BMI <25). RESULTS: In contrast to obese T2DM, nonobese T2DM patients did not exhibit significant hyperinsulinemia compared with the nonobese healthy group. Age, sex, and fasting glucose adjusted insulin levels, homeostatic model assessment of insulin resistance (HOMA-IR) and HOMA-beta cell function (HOMA-B) were increased in obese T2DM compared with nonobese T2DM. Although adiposity parameters showed strong correlation with fasting insulin in obese healthy (r = 0.38, 0.38, and 0.42, respectively; all p values < 0.001) and T2DM (r = 0.54, 0.54, and 0.66, respectively; all p < 0.001), only BMI and leptin showed a weak correlation with insulin in the nonobese healthy group (0.13 and 0.13, respectively; all p < 0.05) which were completely lost in the nonobese T2DM. CONCLUSIONS: Compensatory hyperinsulinemia in response to weight gain is impaired in the nonobese population making insulin secretory defect rather than IR the major pathology behind nonobese T2DM.