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Published September 30, 2021 | Version v1
Journal article Open

Optimal waist circumference cut-off points and prediabetic state as predictors of metabolic syndrome prevalence among Saudi adults in Jeddah city

  • 1. Public Health Administration, Ministry of Health, Madinah, Saudi Arabia.
  • 2. Department of Community, Medicine, College of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.
  • 3. Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.
  • 4. Public Health Administration, Ministry of Health, Jazan, Saudi Arabia.

Description

With increasing prevalence of metabolic syndrome and absence of local waist circumference cut-off point, it is important to determine the local cut-off point of waist circumference to predict metabolic syndrome.

The aim of this study is to estimate the prevalence of metabolic syndrome among Saudi adults who attend primary health care centers (PHC) in Jeddah city in 2017, and to determine the appropriate waist circumference cut-off value for identifying a person at risk for the metabolic syndrome.

A cross-sectional analytic study was conducted. Results shows; mean age of participants was 30.94±9.70 and waist circumference was 96.87±17.40 in males and 86.51±15.30 in females. The prevalence (CI: 95%) of pre-diabetes was 16.3% (12.9%; 20.1%), while the prevalence of metabolic syndrome was {(95% CI = 10.6%) (7.80%; 13.8%)}. Abdominal obesity was the most frequent component of MetS, detected in 60.6%, and followed by high blood pressure in 25.0%. In males, a waist circumference≥93.5 cm is likely to indicate MetS with 100% sensitivity and 47.2% specificity; whereas, in females, optimal waist circumference cut-off was determined as ≥83.5 cm, showing 92.3% sensitivity and 46.4% specificity. In multivariate logistic regression, extended waist circumference was the strongest predictor of MetS {OR (95%CI) =3.75 (1.30; 10.81); p=0.014}, followed by the presence pre-diabetes {OR (95%CI) =2.31 (1.06; 5.04); p=0.035}. Further, high educational level was a significant predictor for MetS {OR (95%CI) = 0.34 (0.12; 0.99); p=0.047}; while age and marital status were not significant predictors.

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