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How well do anthropometric indices correlate with cardiovascular risk factors? A cross-sectional study in Croatia

BACKGROUND: Usefulness of anthropometric indices (AI) as predictors of CV risk is unclear and remains controversial. MATERIAL/METHODS: To evaluate the correlation between AI and CV risk factors in the Croatian adult population and to observe possible differences between coastal and inland regions an...

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Detalles Bibliográficos
Autores principales: Vrdoljak, Davorka, Marković, Biserka Bergman, Kranjčević, Ksenija, Lalić, Dragica Ivezić, Vučak, Jasna, Katić, Milica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3560586/
https://www.ncbi.nlm.nih.gov/pubmed/22293890
http://dx.doi.org/10.12659/MSM.882451
Descripción
Sumario:BACKGROUND: Usefulness of anthropometric indices (AI) as predictors of CV risk is unclear and remains controversial. MATERIAL/METHODS: To evaluate the correlation between AI and CV risk factors in the Croatian adult population and to observe possible differences between coastal and inland regions and urban and rural settlements. CRISIC-fm (ISRCTN31857696) is a prospective, randomized cohort study conducted in GP (general practitioner) practices in Croatia. Between May and July 2008, 59 GPs each recruited 55 participants aged ≥40 years, who visited a practice for any reason. Height, weight, waist and hip circumference and blood pressure were measured. Blood samples were analyzed in accredited laboratories. RESULTS: Out of 2467 participants (61.9% women, 38.1% men), 36.3% were obese, with fewer in coastal than inland areas. More obese people were in rural areas. Logistic regression showed BMI was the most important predictor of hypertension, diabetes and dyslipidemia in both regions (except for diabetes in the coastal area), and for urban and rural settlements (except for diabetes in rural areas). WtHR was a significant predictor for hypertension and dyslipidemia in the coastal (but only for hypertension in the inland area), and in urban settlements (in rural only for hypertension). None of the AI showed significant correlation with total CV risk, but WC and BMI did with stroke risk. Receiver operating curve (ROC) analyses showed that WtHR was a better predictor than all other AI for hypertension and dyslipidemia. CONCLUSIONS: Results encourage the use of BMI and WtHR as important tools in predicting CV risk in GP’s practice.