Impact of sequential adjustment on the association between metabolic syndrome and stroke: A population-based study in Korea
Chae, S. H.; Moon, I. Y.; Yi, C.
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Background: Stroke is among the foremost contributors to mortality and lasting disability and continues to place a heavy clinical and societal burden worldwide. Although metabolic syndrome (MetS) is recognized as a contributor to stroke risk, insufficient attention has been paid to how this relationship behaves when potential confounders are entered into the model in a stepwise manner. Objectives: This study aimed to characterize the relationship between MetS and stroke prevalence using a series of sequentially adjusted models built from the Korea National Health and Nutrition Examination Survey (KNHANES). We explored whether self-rated health is a useful functional indicator for stratifying stroke risk. Methods: Of the 22,559 KNHANES VIII respondents, 12,536 participants aged [≥]19 years and with information required to classify physician-diagnosed stroke (DI3_dg) or define MetS were included in the final analysis. Associations were estimated using complex-sample logistic regression under a sequential adjustment scheme: Model 1 (unadjusted), Model 2 (adjusted for age and sex), Model 3 (further adjusted for educational level and family history of stroke), and Model 4 (additionally incorporating economic activity status and self-rated health). Results: MetS was associated with an increased risk of stroke in all models: Model 1 (odds ratio [OR] 3.372, 95% confidence interval [CI] 2.443-4.654), Model 2 (OR 1.956, 95% CI 1.396-2.740), Model 3 (OR 1.813, 95% CI 1.292-2.546), and Model 4 (OR 1.636, 95% CI 1.153-2.321). A graded pattern was noted concerning self-rated health, with progressively poorer perceived health corresponding to higher odds of stroke, and the "very poor" category showed substantially elevated odds (OR 9.836 in Model 4). Conclusions: MetS was independently associated with stroke prevalence even after sequential adjustment. Self-rated health appears to capture both metabolic burden and broader functional health aspects.
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