Metabolic syndrome and body shape predict differences in health parameters in farm working women

Mentoor, Ilze ; Kruger, Maritza ; Nell, Theo (2018-04-04)

CITATION: Mentoor, I., Kruger, M. & Nell, T. 2018. Metabolic syndrome and body shape predict differences in health parameters in farm working women. BMC Public Health, 18:453, doi:10.1186/s12889-018-5378-9.

The original publication is available at http://bmcpublichealth.biomedcentral.com

Publication of this article was funded by the Stellenbosch University Open Access Fund.

Article

Background: Sufficient evidence associate body shape to detrimental lifestyle diseases including the metabolic syndrome (MetS). The prevalence of the MetS, as well as effects of the MetS and body shape on body composition, insulin-like growth factor-1 (IGF-1), C-reactive protein (CRP) and sex hormone parameters were investigated in a female farm worker population in the Western Cape. Methods: Women between the ages of 20–60 years were classified according to the International Diabetes Federation’s definition of the MetS. Assessments included body shape (android/gynoid), blood pressure, anthropometric, bioelectrical impedance analyses and blood analyses for fasting glucose and insulin, lipid profile, IGF-1, CRP, and sex hormone parameters. Results: The prevalence of the MetS was 52%, with abdominal obesity 68.8%, hypertension 66.4% and low high density lipoprotein-cholesterol (HDL-c) levels (64.1%) being the more prevalent MetS risk factors. The MetS, irrespective of body shape, was found to be associated with body mass index (p < 0.01), fat mass (%) (p < 0.01), waist circumference (p < 0.001), HDL-c (p < 0.001), systolic blood pressure (p < 0.05) and diastolic blood pressure (p < 0.01). No significant differences were observed for IGF-1, CRP and sex hormone parameters. Conclusion: The prevalence of the MetS and its individual risk factors were found to be significantly high in this female farm worker population. Additionally, the study showed that the MetS, body shape and/or both could predict differences in body composition, physiological and biochemical parameters in women.

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