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April 1, 2026Medicina0 citationsOpen Access

Red Cell Distribution Width-Standard Deviation Is Associated with Cumulative Metabolic Burden but Not Independently with Metabolic Syndrome

KLKemal Ozan Lule NLNezihe Otay Lule MSMert Deniz Savcilioglu

Key Points

This study aims to investigate whether RDW-SD is an independent determinant of metabolic syndrome or a reflection of cumulative metabolic burden.
Conducted a single-center observational study with 222 adults evaluated for metabolic syndrome.
Excluded participants based on anemia, extreme mean corpuscular volume, or acute inflammation.
Used hierarchical multivariable logistic regression to assess associations between RDW-SD and metabolic syndrome.
Examined the relationship between RDW-SD and the number of metabolic syndrome components using multivariable linear regression.
Evaluated discriminative performance through ROC curve analysis.
68.0% of participants had metabolic syndrome.
RDW-SD levels were significantly higher in those with metabolic syndrome and increased across quartiles.
RDW-SD was independently associated with the number of metabolic syndrome components (standardized β = 0.226, p < 0.001).
No independent association was found between RDW-SD and metabolic syndrome presence (OR = 1.07, p = 0.198).
ROC analysis indicated poor discriminatory ability for RDW-SD (AUC = 0.611).

Abstract

Background and Objectives: Red cell distribution width (RDW) has been associated with adverse cardiometabolic outcomes; however, whether RDW—particularly RDW standard deviation (RDW-SD)—represents an independent determinant of metabolic syndrome (MetS) or reflects cumulative metabolic burden remains unclear. This study evaluated the association between RDW-SD and MetS presence and examined its relationship with the quantitative accumulation of MetS components. Materials and Methods: In this single-center observational study, 222 adults undergoing evaluation for MetS were consecutively recruited. Participants with overt anemia, extreme mean corpuscular volume values, or acute inflammation were excluded. MetS was defined according to revised NCEP ATP-III criteria. Associations between RDW-SD and MetS were assessed using hierarchical multivariable logistic regression models. The relationship between RDW-SD and the number of MetS components was examined using multivariable linear regression. Discriminative performance was evaluated by receiver operating characteristic (ROC) curve analysis. Results: MetS was present in 68.0% of participants. RDW-SD levels were significantly higher in individuals with MetS and increased progressively across quartiles. RDW-SD was independently associated with the number of MetS components (standardized β = 0.226, p < 0.001). However, RDW-SD was not independently associated with MetS presence in fully adjusted logistic models (OR = 1.07, 95% CI: 0.97–1.18, p = 0.198). The addition of RDW-SD provided minimal incremental explanatory value (Nagelkerke R2 increase from 0.348 to 0.356). ROC analysis demonstrated poor discriminatory ability (area under the curve AUC = 0.611, 95% CI: 0.535–0.687), supporting limited standalone diagnostic utility. Conclusions: RDW-SD was independently associated with cumulative metabolic burden but not with the independent presence of MetS after adjustment for established cardiometabolic factors. Given the cross-sectional design, these findings should be interpreted as associative rather than causal.

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