Predictive Factors for Diabetes Mellitus: Insights from Complete Blood Count Analysis; [Faktor Ramalan untuk Diabetes Melitus: Suatu Pandangan daripada Analisis Kiraan Darah Lengkap]

• Published in: Sains Malaysiana
• Main Author: Kenyang A.A.; Juhan N.; Zubairi Y.Z.; Azizan N.; Mun H.C.
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208472522&doi=10.17576%2fjsm-2024-5310-25&partnerID=40&md5=ed6ec9dc15a9ed08770960a1ef281c68

The 10th edition of the International Diabetes Federation reports that 537 million people worldwide had diabetes in 2021. In Southeast Asia, countries like Malaysia are facing a growing burden of diabetes. This highlights the urgent need for innovative and resourceful approaches to diabetes management. As the prevalence of diabetes continues to rise in these countries, tailored strategies are necessary. To identify and evaluate the potential prognostic indicators for diabetes mellitus, this study involved a dataset consisting of 500 entries, comprising demographic information and selected blood cells from the Complete Blood Count (CBC) test results obtained from the Clinical Laboratory Faculty of Medicine & Health Sciences, Universiti Malaysia Sabah. Using univariate and multivariate logistic regression analysis, the prognostic predictors for diabetes mellitus were identified. In the univariate analysis, all variables are statistically significance at 5% level of significance. However, at multivariate analysis, only age, mean corpuscular hemoglobin concentration (MCHC), white blood cells (WBC) and hematocrit (HCT) emerged as significant predictors of diabetes mellitus. Notably, the abnormal level in WBC exhibited the greatest association with diabetes mellitus, reflecting a 114.7% increased risk compared to a normal WBC level. The statistic value obtained from Hosmer-Lemeshow was 0.944 indicating a well-fitting model. Additionally, the receiver operator characteristic (ROC) curve has a value of 0.7, indicating a strong performance of the model. In conclusion, CBC parameters can be accurate markers and useful in assisting clinical decision-making when properly applied and interpreted. © 2024 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.