Correlates of Leisure-Time Physical Activity Participation Among Latino Children and Adolescents with Acanthosis Nigricans

pre-printChildhood obesity has become a serious public health concern in the United States. The prevalence rates of childhood obesity largely increased in the 1980s and 1990s and remained persistently high between 1999-2000 and 2007-2008 in the United States [20]. The highest extreme obesity prevalence rate, defined at the 97th percentile or higher of the age-gender-specific growth chart [13, 22], was found in Mexican American youth ages 6-11, closely followed by other Latino youth of the same age group. Childhood obesity has negative consequences on many health outcomes [19]. For example, weight-related type-2 diabetes mellitus (T2DM), previously only observed among adults, is now more and more being diagnosed in youth [1, 15]. Public health measures are urgently needed to improve early identification of at-risk youth and implement interventions effective in delaying or even preventing the development of metabolic abnormalities or other morbidities associated with obesity and insulin resistance [5, 6, 30]

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. Objectives: 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. Conclusion: 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