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BMI Standard Deviation Score (bmi + standard_deviation_score)

Distribution by Scientific Domains

Medical Sciences	100%

Selected Abstracts

Childhood growth and age at diagnosis with Type 1 diabetes in Colorado young people

DIABETIC MEDICINE, Issue 10 2009
K. Vehik
Abstract Objective, Studies have suggested that the age at diagnosis of Type 1 diabetes (T1D) is decreasing over time. The overload hypothesis postulates that risk factors, such as accelerated growth, may be responsible for this decrease. We assessed changes in age, body mass index (BMI), weight and height at diagnosis with T1D in non-Hispanic white (NHW) and Hispanic (HISP) young people from Colorado, using data from the IDDM Registry and SEARCH Study. Methods, In three time periods, 656 (1978,1983), 562 (1984,1988) and 712 (2002,2004) young people aged 2,17 years were newly diagnosed with T1D. Age, weight, height and presence of diabetic ketoacidosis (DKA) at diagnosis with T1D were obtained from medical records. Trends over the three time periods were assessed with regression analyses. Results, Age at diagnosis decreased by 9.6 months over time (P = 0.0002). Mean BMI standard deviation score (SDS), weight SDS and height SDS increased over time (P < 0.0001), while prevalence of DKA decreased (P < 0.0001). Increasing height over time accounted for 15% (P = 0.04) of the decreasing age at diagnosis with T1D. Conclusions, Our study provides evidence that increased linear growth, but not increased BMI or weight over time, may account, at least in part, for the younger age at diagnosis of T1D in Colorado children. This finding supports the hypothesis that increasing environmental pressure resulting from changes in potentially preventable risk factors may accelerate the onset of T1D in children. [source]

Anthropometric indices as predictors of the metabolic syndrome and its components in adolescents

PEDIATRICS INTERNATIONAL, Issue 3 2010
Christian Jung
Abstract Background:, Overweight and related health problems are becoming increasingly recognized, especially in children and adolescents. For early screening, different anthropometrical measurements of obesity have been proposed to identify individuals at risk. We compared body mass index (BMI), BMI standard deviation score, waist circumference, waist-to-hip ratio (WHR), and waist/height ratio with respect to their power to predict the metabolic syndrome, its components and low-grade inflammation. Methods:, A total of 79 male Caucasian German adolescents (13,17 years) were studied. All anthropometrical measurements of obesity were recorded and blood samples drawn. Predictive power was estimated using receiver operating characteristic curves, by comparing the area under the curve (AUC). Results:, Except for WHR, all tested anthropometrical measurements of obesity showed comparably good AUC values for correct prediction, with the highest AUC for BMI (P < 0.001, AUC = 0.885 ± 0.039). Superior prediction power was not observed for BMI standard deviation score, waist circumference, WHR or waist/height ratio. Furthermore, BMI was the best predictor of elevated C-reactive protein levels as a marker for low-grade inflammation (P < 0.001, AUC = 0.786 ± 0.064). Conclusions:, In this cross-sectional study the well-established parameter BMI was shown to have the best predictive power to identify metabolic syndrome, its components and markers for low-grade inflammation. Newly developed parameters did not provide superior values. Future longitudinal studies are needed to compare these anthropometrical markers in larger cohorts, incorporating different age groups and ethnic backgrounds. [source]

Trends in body mass in Swedish adolescents between 2001 and 2007

ACTA PAEDIATRICA, Issue 3 2009
Örjan B Ekblom
Abstract Aim: (1) Compare BMI standard deviation scores (BMIsds) in 16-year olds in 2001 and 2007 to assess trends. (2) Describe tracking of BMIsds between ages 10 and 16 years, in a longitudinal 6-year follow-up. (3) Identify possible predictors in 2001 for high BMIsds in 2007 and increase in BMIsds between 2001 and 2007. Methods: A six-year follow-up study on 296 subjects, aged 10 years at baseline and a panel study among 16-year olds. BMIsds was used as the main outcome. Results. No difference in BMIsds in 16-year-old adolescents was found between 2001 and 2007. Strong tracking (r = 0.80, 95% CI: 0.75,0.84) was found for BMIsds between ages 10 and 16 years. Low aerobic capacity and high BMIsds at age 10 years predicted overweight at age 16 years. High BMIsds in 2001 predicted a decline in BMIsds (OR: 0.58, 95% CI: 0.43,0.76) and high level of self-reported moderate-to-vigorous physical activity predicted an increased BMIsds (OR: 1.38, 95% CI: 1.13,1.67). Conclusion: There was no difference in prevalence of overweight plus obesity between the 2001 and 2007 samples. Normal weight and good aerobic fitness in 10-year-old children seems to decrease the risk of elevated relative BMI in 16-year olds. [source]

Reference values for change in body mass index from birth to 18 years of age

ACTA PAEDIATRICA, Issue 6 2003
J Karlberg
Body mass index (BMI) has become the measure of choice for determination of nutritional status during the paediatric years, as in adults. Recently, several cross-sectional BMI childhood reference values standards have been published. In order precisely to evaluate childhood nutritional interventions, reference values allowing for the evaluation of changes in BMI values are also needed. For the first time, such reference values can be presented based on 3650 longitudinally followed healthy Swedish children born full term. The reference values for the change in BMI are given as the change in BMI standard deviation scores. The reference values are given as means of mathematical functions adjusting for gender, age of the child and the length of the interval between two measurements for interval lengths of 0.25 to 1.0 y before 2y of age and of 1 to 5 y between birth and 18 y. The usefulness of the reference values is proved by a graph that forms a part of a clinical computer program; the -2 to +2 standard deviation range of the predicted change in BMI can be computed for an individual child and drawn in the graph as an extended support for clinical decision-making. Conclusion: For the first time this communication gives access to BMI growth rate values that can be used both in research and in the clinic to evaluate various interventions, be they nutritional, surgical or therapeutic. [source]