Integrated analysis of long non‑coding RNA megacluster, microRNA‑132 and microRNA‑133a and their implications for...

Inefficient control of elevated blood sugar levels can lead to certain health complications such as diabetic nephropathy (DN) and cardiovascular disease (CVD). The identification of effective biomarkers for monitoring diabetes was performed in the present study. The present study aimed to investigate the implications of long non-coding RNA megacluster (lnc-MGC), microRNA (miR)-132 and miR-133a, and their correlation with lactate dehydrogenase (LDH) activity and glycated hemoglobin (HbA1C) levels to identify biomarkers for the early diagnosis of diabetes mellitus, induced DN and CVD. The present study included a total of 200 patients with type 2 diabetes, as well as 40 healthy subjects as controls. The diabetic patients were classified into six groups based on their estimated HbA1c level, glomerular filtration rate and LDH activity, while the healthy controls constituted the seventh group. Diabetic patients exhibited significant increases in parameters related to diabetes as fasting blood sugar, HbA1c levels, cardiac injury and kidney failure. Furthermore, the expression levels of TNF-α were significantly increased in the diabetic groups compared with healthy controls. Diabetic patients with cardiovascular dysfunction showed significantly increased expression levels of miR-132, miR-133a and lnc-MGC, compared with the healthy group. The expression of circulating miR-132 in blood was low in the groups of diabetic patients compared with the healthy controls, and demonstrated a negative correlation with LDH and HbA1C levels. Expression levels of miR-132, miR-133a and lnc-MGC, along with their correlations with LDH and HbA1C levels, could be used to distinguish diabetic patients with reduced CVD from those at early stage diabetes, which indicated their potential as biomarkers for CV complications associated with diabetes mellitus in the future.