INTRODUCTION:
Various hematological parameters are affected by thyroid hormone metabolism; however, the patterns of these effects and how it can be used in diagnosis have not been well established. This research was conducted to assess and compare hematological parameters in patients with thyroid disorder as opposed to healthy controls.
MATERIALS AND METHODS:
A cross-sectional, descriptive, and comparative study was done on two groups (
n
= 55) with thyroid dysfunction and healthy controls respectively (
n
= 32) was collected from October 20, 2024 to July 28, 2025. Demographics, clinical characteristics, and thyroid data were recorded for all patients. The hematology parameters of focus were as follows, the white blood cell (WBC) count, lymphocyte (LYM) count, lymphocyte percentage (LYM%), red blood cell (RBC) count, platelet count (PLT), red cell distribution width (RDW), packed cell volume (PCV) Hematocrit (HCT%), and the ratio of LYMs to monocytes. Receiver operating characteristic (ROC) curve to assess the diagnostic performance of these parameters.
RESULTS:
In patients with thyroid disease and were significant with WBC count compared to controls (median: 7.50 vs. 7.70,
P
< 0.001) similarly LYM count (median: 2.30 vs. 2.25 × 10
3
/L,
P
= 0.009). Furthermore, patients had significantly higher RBC counts (median: 4.80, 4.69,
P
< 0.001), hemoglobin counts (median: 13.05, 12.55 g/dL,
P
= 0.003), and PLTs (median: 285.00 vs. 171.50 × 10
1
/L,
P
< 0.001). Patients had significantly decreased packed cell volume (PCV/HCT%) and red cell distribution width (RDW%) (
P
= 0.03 for both). Platelet–large cell ratio showed no variation (
P
= 0.356). ROC analysis revealed that LYM% (area under the curve [AUC] =0.766,
P
= 0.010), RDW% (AUC = 0.736,
P
= 0.004), PLT (AUC = 0.773,
P
= 0.006) and lymphocyt e-to-monocyte ratio (LMR) (AUC = 0.756,
P
= 0.15) might act as useful biomarkers to distinguish patients from controls.
CONCLUSION:
Hematological indices, in particular LYM%, RDW%, PLT, and LMR, have significant changes in thyroid diseases patients and may be the low-cost biomarkers for their screening and differentiation. To validate these findings, more extensive research is required.