Characterizing Red Blood Cell Rheological Parameters Using Automated Syllectometry: Conventional and Exploratory Multivariate Analyses

INTRODUCTION: Red blood cell (RBC) biomechanics are key determinants of blood rheology, microcirculatory flow, and tissue oxygen delivery, yet remain underutilized in clinical practice because of methodological variability and limited standardization. This study aimed to define preliminary reference distributions for RBC rheological parameters using the automated MIZAR syllectometry platform and to characterize inter-parameter relationships using exploratory multivariate analyses. METHODS: Whole blood samples from 70 healthy adults were analyzed to generate 13 MIZAR parameters reflecting RBC deformability, aggregation, and elasticity. Conventional statistical methods were used to define reference distributions and assess demographic influences. Principal component analysis (PCA), hierarchical clustering, and correlation analysis were applied to explore covariance structure and statistical interrelationships within the dataset. RESULTS: Within-run precision was high for several MIZAR parameters, particularly at 800 nm, with mean coefficients of variation < 2% for AI, Int(a), AMP, T1/2, OTF, OT, and ED. Most parameters demonstrated minimal age-related variation. Several parameters differed by sex, particularly those related to aggregation amplitude and optical measurements. PCA showed that the first two components explained 60.9% of total variance, while hierarchical clustering identified five exploratory parameter groupings broadly consistent with their measured rheological characteristics. Correlation analysis further characterized inter-parameter associations. CONCLUSION: These findings provide the first preliminary reference dataset for MIZAR-derived RBC parameters and identify patterns of covariance and interrelationships within a healthy cohort. Integrating conventional and exploratory multivariate analyses provides a framework for characterizing interrelated hemorheological measurements. Validation in independent and clinically diverse populations is required to determine the reproducibility and clinical utility of these findings.