Mean corpuscular volume (
) is a routinely measured hematological parameter that influences blood viscosity by altering red blood cell volume and packing density. Although
is physiologically linked to hemorheological behavior, to the authors’ knowledge, its direct
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Mean corpuscular volume (
) is a routinely measured hematological parameter that influences blood viscosity by altering red blood cell volume and packing density. Although
is physiologically linked to hemorheological behavior, to the authors’ knowledge, its direct role in modulating large-artery hemodynamics has not been systematically quantified. This study introduces an
-driven effective Newtonian viscosity mode to evaluate the first-order impact of
variation on carotid bifurcation flow. Rather than employing shear-dependent constitutive laws, blood viscosity was scaled through an
-based formulation, yielding three Newtonian fluids corresponding to clinically relevant
levels of 70, 90, and 110 fL. Pulsatile CFD simulations were performed in four idealized carotid bifurcation geometries (40°, 50°, 65°, and 100°) to assess the combined influence of vascular geometry and
-dependent viscosity variation. Hemodynamic indices including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT) were quantified, and a two-way analysis of variance (ANOVA) was employed to distinguish the relative contributions of geometric configuration and
. Across the investigated
range, increasing
produced a geometry-dependent modulation of shear-based indices, with TAWSS increasing by up to approximately 11%, while OSI and RRT decreased by about 20–25% and 10%, respectively, particularly in geometries exhibiting pronounced flow separation. Although vascular geometry remained the dominant determinant of overall hemodynamic patterns,
-induced viscosity scaling significantly modulated low-shear and recirculation regions. These findings suggest that
-dependent viscosity scaling can complement patient-specific hemodynamic assessments and provide a rational baseline for future shear-dependent and personalized rheological modeling frameworks.
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