Prevalence and interrelationships between cardio-metabolic risk factors in abdominally obese individuals.
OBJECTIVE
The aim of this study was to examine the prevalence of metabolic syndrome and identify relationships between clustering and severity of cardio-metabolic risk factors in abdominally obese adults.


METHODS
Cardio-metabolic risk factors were assessed in a sample of 300 abdominally obese volunteers (233 females, 67 males, mean age 43.7 years) who were not being treated for diabetes, hypertension or dyslipidemia. Waist circumference (WC), blood pressure, fasting lipids, and glucose were measured and prevalence of metabolic syndrome was determined according to International Diabetes Federation (IDF) criteria. Correlation analysis and Poisson regression were used to examine associations between the presence of a particular risk factor and the propensity for clustering and derangement of other risk factors, using continuous data for risk factors and categorical data for number of metabolic syndrome components.


RESULTS
In all, 53% had metabolic syndrome and only 16% were free of cardio-metabolic abnormalities. In order of importance, diastolic blood pressure (DBP), high-density lipoprotein cholesterol (HDL), and triglycerides (TGs) were most strongly associated with greater clustering of risk factors, with a one standard deviation difference being associated with a respective difference of 9.65, 1.23, and 0.12 in the number of risk factors present. A greater number of risk factors was associated with an increased derangement for any given risk factor, with this effect being greatest for dyslipidemia, as represented by the TG:HDL ratio.


CONCLUSIONS
In abdominally obese individuals, DBP was strongly associated with metabolic syndrome component clustering, which may reflect the pathogenic progression of metabolic syndrome, as DBP is likely to be elevated following establishment of other risk factors. Also, dyslipidemia was strongly related to the magnitude of derangement of cardio-metabolic risk factors which may indicate that increases in dyslipidemia may drive the pathogenic progression of metabolic syndrome once acquired.
Pettman
Tahna L
TL
ATN Centre for Metabolic Fitness and Nutritional Physiology Research Centre, Sansom Institute for Health Research, University of South Australia, Adelaide, Australia.
Buckley
Jonathan D
JD
Coates
Alison M
AM
Misan
Gary M H
GM
Petkov
John
J
Howe
Peter R C
PR
D000328:Adult; D001786:Blood Glucose / Q000378:metabolism; D001794:Blood Pressure; D016000:Cluster Analysis; D050171:Dyslipidemias / Q000453:epidemiology; D005260:Female; D006801:Humans; D006943:Hyperglycemia / Q000453:epidemiology; D006973:Hypertension / Q000453:epidemiology; D008055:Lipids / Q000097:blood; D016015:Logistic Models; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000453:epidemiology* / Q000503:physiopathology; D056128:Obesity, Abdominal / Q000097:blood / Q000453:epidemiology* / Q000503:physiopathology; D016017:Odds Ratio; D016012:Poisson Distribution; D015995:Prevalence; D018570:Risk Assessment; D012307:Risk Factors; D013021:South Australia / Q000453:epidemiology; D055105:Waist Circumference
D001786:Blood Glucose; D008055:Lipids