Insulin resistance, endothelial and adipose dysfunction and atherosclerosis

Diabetes mellitus is a multisystem group of disorders associated with insulin resistance, metabolic stress, endothelial and adipose dysfunction and accelerated atherosclerosis. As the different players leading to atherosclerosis are known, the pathogenesis and eventually targets for treatment can be identified. The purpose of this review is to update the newer aspects of pathogenic factors as well as newer putative biomarkers in atherosclerosis. Hyperglycemia causes beta cell dysfunction, which results in impaired insulin secretion, endoplasmic reticulum stress and overproduction of reactive oxygen species. Lipotoxicity, or the accumulation of increased amounts of lipids in non-adipose tissue is found in the insulin producing pancreatic beta cells impairing their function. Gluco-lipotoxicity leads to the production of inflammatory cytokines, which damage the vasculature. Endothelial dysfunction, which occurs due to all these insults can be studied by biochemical alterations and by non-invasive imaging techniques. In addition, epigenetic changes have been identified in the pathogenesis. More recent biomediators were identified to be involved in the process of atherogenesis including adiponectin, leptin, resistin, adropin, visfatin, hepatokines, bone morphogenetic protein, nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), families of micro RNAs, extracellular vesicles (exosomes, ectosomes) and a variety of environmental factors. In view of managing conventional risk factors has not prevented atherosclerotic complications, the better understanding the role of pro- and anti- atherogenic factors may allow the development of novel drugs to modify them

Quality of care: assessment

To translate science into clinical practice we must first assess the quality of care that is being delivered. The resulting information about qualitative and quantitative parameters can then be assessed. Ultimately insights can be obtained into improving the quality of care in diabetes mellitus. The Diabetes Quality Improvement Programme in USA has shown such an exercise is feasible. A similar exercise in India is necessary to improve the quality of diabetes care

The impact of a collaborative care model on health trajectories among patients with co–morbid depression and diabetes: The INDEPENDENT study

Context: Collaborative care models for depression have been successful in a variety of settings, but their success may differ by patient engagement. We conducted a post-hoc analysis of the INDEPENDENT trial to investigate the role of differential engagement of participants on health outcomes over 3 years. Settings and Design: INDEPENDENT study was a parallel, single-blinded, randomised clinical trial conducted at four socio-economically diverse clinics in India. Participants were randomised to receive either active collaborative care or usual care for 12 months and followed up for 24 months. Method: We grouped intervention participants by engagement, defined as moderate (≤7 visits) or high, (8 or more visits) and compared them with usual care participants. Improvements in composite measure (depressive symptoms and at least one of three cardio-metabolic) were the primary outcome. Statistical Analysis: Mean levels of depression and cardio-metabolic measures were analysed over time using computer package IBM SPSS Statistics 25. Results: The composite outcome was sustained the highest in the moderate engagers [27.5%, 95% confidence interval (CI): 19.5, 36.7] and the lowest in high engagers (15.8%, 95% CI: 8.1, 26.8). This pattern was observed for individual parameters – depressive symptoms and glycosylated haemoglobin. Progressive reductions in mean depressive symptom scores were observed for moderate engagers and usual care group from baseline to 36 months. However, in high engagers of collaborative care, mean depressive symptoms were higher at 36 months compared to 12 months. Conclusion: Sustained benefits of collaborative care were larger in participants with moderate engagement compared with high engagement, although a majority of participants relapsed on one or more outcome measures by 36 months. High engagers of collaborative care for co-morbid depression and diabetes may need light touch interventions for longer periods to maintain health and reduce depressive symptoms

Proteomic Analysis in Diabetic Cardiomyopathy using Bioinformatics Approach

Diabetic cardiomyopathy is a distinct clinical entity that produces asymptomatic heart failure in diabetic patients without evidence of coronary artery disease and hypertension. Abnormalities in diabetic cardiomyopathy include: myocardial hypertrophy, impairment of contractile proteins, accumulation of extracellular matrix proteins, formation of advanced glycation end products, and decreased left ventricular compliance. These abnormalities lead to the most common clinical presentation of diabetic cardiomyopathy in the form of diastolic dysfunction. We evaluated the role of various proteins that are likely to be involved in diabetic cardiomyopathy by employing multiple sequence alignment using ClustalW tool and constructed a Phylogenetic tree using functional protein sequences extracted from NCBI. Phylogenetic tree was constructed using Neighbour—Joining Algorithm in bioinformatics approach. These results suggest a causal relationship between altered calcium homeostasis and diabetic cardiomyopathy that implies that efforts directed to normalize calcium homeostasis could form a novel therapeutic approach

Proteomic Analysis in Diabetic Cardiomyopathy using Bioinformatics Approach

Diabetic cardiomyopathy is a distinct clinical entity that produces asymptomatic heart failure in diabetic patients without evidence of coronary artery disease and hypertension. Abnormalities in diabetic cardiomyopathy include: myocardial hypertrophy, impairment of contractile proteins, accumulation of extracellular matrix proteins, formation of advanced glycation end products, and decreased left ventricular compliance. These abnormalities lead to the most common clinical presentation of diabetic cardiomyopathy in the form of diastolic dysfunction. We evaluated the role of various proteins that are likely to be involved in diabetic cardiomyopathy by employing multiple sequence alignment using ClustalW tool and constructed a Phylogenetic tree using functional protein sequences extracted from NCBI. Phylogenetic tree was constructed using Neighbour—Joining Algorithm in bioinformatics approach. These results suggest a causal relationship between altered calcium homeostasis and diabetic cardiomyopathy that implies that efforts directed to normalize calcium homeostasis could form a novel therapeutic approach