Management of Hyperglycemia in COVID-19 and Post-COVID-19 Syndrome - Proposed Guidelines for India.

SARS-CoV-2 virus spread rapidly all over the globe in 2020 and the second wave has taken our nation, India by storm. The pandemic has posed unique challenges in people with metabolic disorders, including diabetes, hypertension, obesity, pulmonary, cardiovascular, kidney and non-alcoholic fatty liver disease. Uncontrolled diabetes, in conjunction with endocrine, inflammatory and metabolic effects of the infection itself has made management of hyperglycemia in COVID-19 infection particularly challenging. Furthermore, the post-COVID-19 syndrome has also emerged as a sequela in COVID-19 survivors, increasing the risk of death, complications and adding further burden on the health care system. With more than a year of experience, we have gained substantial insight; and now provide practical recommendations on the management of hyperglycemia in COVID-19 as well as post COVID-19 syndrome.

New and Unique Clusters of Type 2 Diabetes Identified in Indians.

Type 2 diabetes (T2D), the most common form of diabetes, is recognized as being a heterogenous disorder, and presents a universal threat to health. In T2D, the pathophysiology and phenotype differ significantly by ethnicity, particularly among Asian Indians, who are known to have the 'Asian Indian phenotype', which makes them more susceptible to develop T2D than white Caucasians. The recent subclassification of T2D into different subtypes or clusters, which behave differently with respect to clinical presentation and risk of developing complications is a remarkable development. Five unique "clusters" of individuals with diabetes were described in the Scandinavian population [Severe Autoimmune Diabetes (SAID), Severe Insulin Deficient Diabetes (SIDD), Severe Insulin Resistant Diabetes (SIRD), Mild Obesity-related Diabetes (MOD) and Mild Age-Related Diabetes (MARD)]. For the first time in India, identification of clusters of diabetes was done on 19,084 individuals with T2D, using 8 clinically relevant variables (age at diagnosis, BMI, waist circumference, HbA1c, triglycerides, HDL cholesterol and fasting and stimulated C-peptide). Four replicable clusters were identified [SIDD, MARD, IROD (Insulin Resistant Obese Diabetes) and CIRDD (Combined Insulin Resistant and Deficient Diabetes)], two of which were unique to the Indian population (IROD and CIRDD). Clustering of T2D helps i) to accurately subclassify diabetes into different subtypes, ii) plan therapies based on the pathophysiology, iii) predict prognosis and prevent diabetic complications and iv) helps in our approach to precision diabetes. Further studies would help us to refine the usefulness of these clusters of T2D particularly in the Indian population, with respect to selection of appropriate therapies and hopefully in the prevention of complications of diabetes.