Defining metabolic syndrome and factors associated with metabolic syndrome in a poly-pharmaceutical population. Issue 8 (16th November 2016)

• Record Type: Journal Article
• Authors: McStea, Megan; McGeechan, Kevin; Kamaruzzaman, Shahrul Bahyah; Rajasuriar, Reena; Tan, Maw Pin
• Is Part Of: Postgraduate medicine. Volume 128:Issue 8(2016)
• Page Start: 797
• Page End: 804
• Publication Date: 2016-11-16
• Subjects: Metabolic syndrome -- aged -- polypharmacy -- generalised Poisson model -- count variable -- cerebrovascular disease -- hypertension; Medicine -- Periodicals; Medicine -- Periodicals; Medicine; Periodicals; 610.5
• DOI: 10.1080/00325481.2016.1229103
• Languages: English
• ISSNs: 0032-5481

Abstract

ABSTRACT: Objectives: Metabolic Syndrome (METs) definitions vary and diagnosis takes into account consumption of medications commonly prescribed for conditions defining METs. This paper evaluates the potential differences in population characteristics using two different methods of defining METs, with and without the adjustment of the effects of pharmacotherapy on biochemical and blood pressure (BP) measurements Methods: This was a cross-sectional study utilizing the Malaysian Elders Longitudinal Research (MELoR) cohort comprising urban community-dwellers aged ≥55 years. Participants were interviewed using a structured questionnaire during home visits where medications were reviewed. Health impacts assessed included heart disease, stroke, body mass index (BMI), peptic ulcers, arthritis, and number of medications and comorbidities. Risk factors and health impacts associated with METs were determined by Poisson multivariate regression models using a binary and count dependent variables. Results: A total of 891 participants with a mean (SD) age of 68.6 (7.3) years were included. The prevalence of METs vary from 52.7% to 35.1% depending upon the definition used. The risk factors associated with METs were increasing age, ethnicity, lower education levels, BMI, stroke and medication use. Male gender was considered a risk factor following modification for medication usage using a count model. The drug-modified model removed marginal candidates prescribed medications used for specific conditions which defined METs who did not meet the criteria once their BP or biochemical parameters were modified for the effects of medication-use. Conclusion: The IDF definition for METs that makes allowance for treatment for each specific condition can lead to an overestimation in the prevalence of METs in population studies. Not including those medicated with normal results conversely underestimates the prevalence of METs. We have therefore proposed adjustments to BP and lipid measurements based on pooled mean effects from published systematic reviews to mitigate bias in future research on prevalence of METs.