Journal of King Saud University: Science (Sep 2024)

Correlation between platelet metrics and cardiovascular risk in prediabetes with coronary artery disease: A two-year cross-sectional study

  • Sunil Kumar,
  • Harshitha Reddy,
  • Sourya Acharya,
  • Avinash Parepalli,
  • Dhruv Tawar,
  • Samyank Jain kumar,
  • Meraj Khan,
  • Mohammad Athar,
  • Esam Ibraheem Azhar,
  • Sayed Sartaj Sohrab

Journal volume & issue
Vol. 36, no. 8
p. 103337

Abstract

Read online

Objectives: Prediabetes is associated with coronary artery disease (CAD), as even a 1% increase in glycated hemoglobin (HbA1c) may increase CAD severity and associated mortality over ten years. A definite association exists between platelet indices, CAD, and diabetes mellitus. Although research has demonstrated an association between CAD and prediabetes as well as platelet indices, there have been no attempts to assess the association of platelet indices in prediabetic patients who are at risk of CAD. Methods: A cross-sectional study took place between 2019 and 2020 in the medical department of a rural medical college located in Central Maharashtra, India. A total of 180 patients with prediabetes and documented CAD on coronary angiography were enrolled in this study. For all participants, platelet indices, blood sugar levels, glycosylated hemoglobin (HbA1c) levels, lipid profiles, and anthropometric measurements were recorded, and then statistical analysis was conducted. Results: Mean platelet volume had a substantial positive correlation with HbA1c, fasting blood sugar, postprandial blood sugar, systolic blood pressure, diastolic blood pressure, body mass index, waist circumference, and waist/hip ratio, with correlation coefficients of 0.2, 0.173, 0.219, 0.218, 0.234, 0.165, 0.182, and 0.164, respectively. A significant negative correlation was found between platelet distribution width and high-density lipoprotein (HDL) level, with a correlation coefficient of −0.373. Conclusion: Platelet indices, which are routinely available through standard clinical investigations, can effectively predict the risk of CAD in prediabetic patients. Their strong association with multiple risk factors allows for enhanced prognosis and facilitates early intervention planning for CAD in this high-risk group.

Keywords