VOLUME 5 , ISSUE 2 ( April-June, 2019 ) > List of Articles
Bhargavi K Nagabhushan, JP Geetha
Keywords : Diabetes mellitus, Neutrophil lymphocyte ratio, Platelet lymphocyte ratio
DOI: 10.5005/jp-journals-10045-00114
License: CC BY-NC 4.0
Published Online: 01-12-2019
Copyright Statement: Copyright © 2019; The Author(s).
Introduction: Diabetes mellitus (DM) is a chronic metabolic disorder with high morbidity and mortality. Neutrophil–lymphocyte ratio (NLR) and platelet lymphocyte ratio (PLR) have emerged as novel indicators of subclinical inflammation and can be used as potential indicators of vascular complications and poorer outcome in patients with DM. This study was conducted to evaluate the role of NLR and PLR as inflammatory biomarkers of type 2 DM. Aim: The aim of this study is to assess NLR and PLR as predictive inflammatory markers in DM. Materials and methods: This was a cross-sectional study carried out in Sri Siddhartha Medical College, Tumkur, from September 2018 to November 2018. The source of data included patients attending medicine outpatient department (OPD) with type 2 DM aged between 18 years and 60 years without other comorbidities. The values of glucose parameters and HbA1c were obtained from the case files. Complete blood count (CBC) was measured using the Sysmex XN 330 automatic hematology analyzer. Results: The study was carried out on 150 diabetics and 50 subjects were used as controls. Out of 150 cases, 110 patients had well-controlled DM (HbA1c7%). Diabetic patients had a significantly higher NLR and PLR as compared to the controls (p = 0.003 and p = 0.008, respectively). Patients with poorly controlled DM had a significantly higher NLR and PLR as compared to subjects with well-controlled DM (p = 0.02 and 0.007). Conclusion: Increased levels of NLR and PLR are associated with poor glycemic control. It can be used as a disease monitoring tool during the follow-up of the diabetic patients. Clinical significance: NLR and PLR parameters are widely available, reliable, and inexpensive and are used in the prediction of diabetes-related complications in the future so that effective measures can be taken to prevent complications.