Metabolic syndrome: an all or none or a continuum load of risk?
OBJECTIVE
The aim of this study was to determine if an increasing number of traits of metabolic syndrome was associated with an increased severity of each of the traits.


METHODS
A cohort of otherwise healthy 387 Latin-American subjects was evaluated for traits of metabolic syndrome according to National Cholesterol Education Program Adult Treatment Panel III (NECP ATP III) guidelines. Waist cricumference and triglyceride, high-density lipoprotein cholesterol (HDL-C), and blood pressure (BP) levels were measured. Glucose and insulin levels were obtained after 75 g of oral glucose.


RESULTS
The prevalence of subjects with no traits and 1, 2, 3, and 4-5 traits was 10.1%, 27.1%, 36.7%, 20.9%, and 5.1%, respectively. Low HDL-C accounted for 55.2% and larger waist circumference for 30.5% of all cases with one trait. High BP and high glucose contributed mainly as the 4th or 5th trait. Higher values for obesity, abdominal obesity, dyslipidemia, BP, hyperglycemia, and hyperinsulinemia were observed as the number of traits increased from 0 to 4-5 traits. More traits meant more severe traits, even after adjusting by age. Subjects with metabolic syndrome but with 4-5 traits had a much higher risk load than those with 3 traits, due to more traits and more severe traits.


CONCLUSIONS
We found that with an increasing number of traits of the metabolic syndrome that the severity of each trait increased. A gradual increase in risk load defined by trait clustering and severity was observed when moving from no traits to fully blown metabolic syndrome. Such a continuum of risk was also observed among subjects with metabolic syndrome, implying that subjects diagnosed with the syndrome may be at quite different risk load.
Division of Metabolic and Cardiovascular Research, College of Pharmacy, HPD, Nova Southeastern University, Fort Lauderdale, Florida, USA. lcubeddu@nova.edu
Luigi X
LX
Cubeddu
Irene S
IS
Hoffmann
D000328:Adult; D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism; D001794:Blood Pressure; D002318:Cardiovascular Diseases / Q000097:blood / Q000453:epidemiology* / Q000503:physiopathology; D016009:Chi-Square Distribution; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D050171:Dyslipidemias / Q000175:diagnosis / Q000453:epidemiology; D005260:Female; D044882:Glucose Metabolism Disorders / Q000175:diagnosis / Q000453:epidemiology; D005951:Glucose Tolerance Test; D006801:Humans; D006973:Hypertension / Q000175:diagnosis / Q000453:epidemiology; D007328:Insulin / Q000097:blood; D008055:Lipids / Q000097:blood; D016015:Logistic Models; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology* / Q000503:physiopathology; D008875:Middle Aged; D009765:Obesity / Q000175:diagnosis / Q000453:epidemiology; D011237:Predictive Value of Tests; D011379:Prognosis; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D014687:Venezuela / Q000453:epidemiology; D055105:Waist Circumference
D015415:Biomarkers; D001786:Blood Glucose; D007328:Insulin; D008055:Lipids