Metabolic risk-factor clustering estimation in children: to draw a line across pediatric metabolic syndrome.
BACKGROUND
The diagnostic criteria of the metabolic syndrome (MS) have been applied in studies of obese adults to estimate the metabolic risk-associated with obesity, even though no general consensus exists concerning its definition and clinical value. We reviewed the current literature on the MS, focusing on those studies that used the MS diagnostic criteria to analyze children, and we observed extreme heterogeneity for the sets of variables and cutoff values chosen.


OBJECTIVE
To discuss concerns regarding the use of the existing definition of the MS (as defined in adults) in children and adolescents, analyzing the scientific evidence needed to detect a clustering of cardiovascular risk-factors. Finally, we propose a new methodological approach for estimating metabolic risk-factor clustering in children and adolescents.


RESULTS
Major concerns were the lack of information on the background derived from a child's family and personal history; the lack of consensus on insulin levels, lipid parameters, markers of inflammation or steato-hepatitis; the lack of an additive relevant effect of the MS definition to obesity per se. We propose the adoption of 10 evidence-based items from which to quantify metabolic risk-factor clustering, collected in a multilevel Metabolic Individual Risk-factor And CLustering Estimation (MIRACLE) approach, and thus avoiding the use of the current MS term in children.


CONCLUSIONS
Pediatricians should consider a novel and specific approach to assessing children/adolescents and should not simply derive or adapt definitions from adults. Evaluation of insulin and lipid levels should be included only when specific references for the relation of age, gender, pubertal status and ethnic origin to health risk become available. This new approach could be useful for improving the overall quality of patient evaluation and for optimizing the use of the limited resources available facing to the obesity epidemic.
Brambilla
P
P
ASL Provincia di Milano 2, Milano, Italy.
Lissau
I
I
Flodmark
C-E
CE
Moreno
L A
LA
Widhalm
K
K
Wabitsch
M
M
Pietrobelli
A
A
D000293:Adolescent; D015415:Biomarkers / Q000097:blood; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000150:complications / Q000378:metabolism; D002648:Child; D016000:Cluster Analysis; D019317:Evidence-Based Medicine; D005947:Glucose / Q000378:metabolism; D006801:Humans; D007328:Insulin / Q000378:metabolism; D007333:Insulin Resistance; D008055:Lipids / Q000097:blood; D024821:Metabolic Syndrome / Q000150:complications / Q000175:diagnosis* / Q000378:metabolism; D009765:Obesity / Q000150:complications* / Q000378:metabolism; D012307:Risk Factors
D015415:Biomarkers; D007328:Insulin; D008055:Lipids; D005947:Glucose