Voluntary Wheel Running Augments Vascular Function in Rats with Chronic Kidney Disease

Please view abstract in the attached PDF file

Diurnal cortisol and obesity in adolescents with and without Down syndrome

BackgroundThe prevalence of obesity in adolescents with Down syndrome (DS) far exceeds that in the general population. Cortisol, an adrenal hormone, can be obesogenic when dysregulated. However, the diurnal patterns of this hormone have not been examined among individuals with DS. Variations in adiposity may also mediate cortisol regulation. This study sought to examine diurnal cortisol patterns in adolescents with DS as well as associations between cortisol function and obesity.MethodA total of 32 adolescents, including 16 with DS and 16 controls with typical development (TD) of similar sex, age and Tanner pubertal stage (P > 0.05), participated in this preliminary study. Participants completed a dual‐energy X‐ray absorptiometry scan to measure body composition and collected saliva samples for cortisol measurements in the morning, afternoon and night. Linear mixed models with random intercepts and repeated measures were used to examine the daily trajectory of log‐transformed cortisol concentrations between adolescents with and without DS. A second model examined the interaction between DS and presence of elevated body fatness.ResultsAdolescents with DS had higher morning cortisol concentrations (intercept = 0.37 μg/dL), but this was not significantly different than in TD (0.35 μg/dL, P = 0.16). Cortisol significantly declined across hours (b = −0.026 μg/dL/h, P  0.05; d = 0.30).ConclusionsThis study is the first to examine diurnal cortisol in DS but is limited in sample size. These preliminary findings suggest that diurnal cortisol patterns are not significantly different between adolescents with DS and TD and that cortisol levels are not associated with adiposity in this population. Despite these non‐significant differences, youth with DS continue to be an ‘at‐risk’ population for paediatric obesity in need of clinical intervention.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151976/1/jir12682_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151976/2/jir12682.pd

Association of Intrauterine Exposure to Maternal Diabetes and Obesity With Type 2 Diabetes in Youth: The SEARCH Case-Control Study

OBJECTIVE—Limited data exist on the association between in utero exposure to maternal diabetes and obesity and type 2 diabetes in diverse youth. These associations were explored in African-American, Hispanic, and non-Hispanic white youth participating in the SEARCH Case-Control Study

Recruitment into diabetes prevention programs : what is the impact of errors in self-reported measures of obesity?

BackgroundError in self-reported measures of obesity has been frequently described, but the effect of self-reported error on recruitment into diabetes prevention programs is not well established. The aim of this study was to examine the effect of using self-reported obesity data from the Finnish diabetes risk score (FINDRISC) on recruitment into the Greater Green Triangle Diabetes Prevention Project (GGT DPP).MethodsThe GGT DPP was a structured group-based lifestyle modification program delivered in primary health care settings in South-Eastern Australia. Between 2004&ndash;05, 850 FINDRISC forms were collected during recruitment for the GGT DPP. Eligible individuals, at moderate to high risk of developing diabetes, were invited to undertake baseline tests, including anthropometric measurements performed by specially trained nurses. In addition to errors in calculating total risk scores, accuracy of self-reported data (height, weight, waist circumference (WC) and Body Mass Index (BMI)) from FINDRISCs was compared with baseline data, with impact on participation eligibility presented.ResultsOverall, calculation errors impacted on eligibility in 18 cases (2.1%). Of n&thinsp;=&thinsp;279 GGT DPP participants with measured data, errors (total score calculation, BMI or WC) in self-report were found in n&thinsp;=&thinsp;90 (32.3%). These errors were equally likely to result in under- or over-reported risk. Under-reporting was more common in those reporting lower risk scores (Spearman-rho&thinsp;=&thinsp;&minus;0.226, p-value&thinsp;&lt;&thinsp;0.001). However, underestimation resulted in only 6% of individuals at high risk of diabetes being incorrectly categorised as moderate or low risk of diabetes.ConclusionsOverall FINDRISC was found to be an effective tool to screen and recruit participants at moderate to high risk of diabetes, accurately categorising levels of overweight and obesity using self-report data. The results could be generalisable to other diabetes prevention programs using screening tools which include self-reported levels of obesity.<br /

A new scoring system in Cystic Fibrosis: statistical tools for database analysis – a preliminary report

<p>Abstract</p> <p>Background</p> <p>Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21^stcentury. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system.</p> <p>Methods</p> <p>The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets.</p> <p>Results</p> <p>(1) Feature selection: CAP has a more effective "modelling" focus than DA.</p> <p>(2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males.</p> <p>Conclusion</p> <p>Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset.</p

Etiological Approach to Characterization of Diabetes Type: The SEARCH for Diabetes in Youth Study

OBJECTIVE To describe an etiologic approach to classification of diabetes types in youth based on the 1997 American Diabetes Association (ADA) framework, using data from the SEARCH for Diabetes in Youth Study

A new growth chart for preterm babies: Babson and Benda's chart updated with recent data and a new format

BACKGROUND: The Babson and Benda 1976 "fetal-infant growth graph" for preterm infants is commonly used in neonatal intensive care. Its limits include the small sample size which provides low confidence in the extremes of the data, the 26 weeks start and the 500 gram graph increments. The purpose of this study was to develop an updated growth chart beginning at 22 weeks based on a meta-analysis of published reference studies. METHODS: The literature was searched from 1980 to 2002 for more recent data to complete the pre and post term sections of the chart. Data were selected from population studies with large sample sizes. Comparisons were made between the new chart and the Babson and Benda graph. To validate the growth chart the growth results from the National Institute of Child Health and Human Development Neonatal Research Network (NICHD) were superimposed on the new chart. RESULTS: The new data produced curves that generally followed patterns similar to the old growth graph. Mean differences between the curves of the two charts reached statistical significance after term. Babson's 10(th )percentiles fell between the new data percentiles: the 5th to 17th for weight, the 5th and 15th for head circumference, and the 6th and 16th for length. The growth patterns of the NICHD infants deviated away from the curves of the chart in the first weeks after birth. When the infants reached an average weight of 2 kilograms, those with a birthweight in the range of 700 to 1000 grams had achieved greater than the 10(th )percentile on average for head growth, but remained below the 3(rd )percentile for weight and length. CONCLUSION: The updated growth chart allows a comparison of an infant's growth first with the fetus as early as 22 weeks and then with the term infant to 10 weeks. Comparison of the size of the NICHD infants at a weight of 2 kilograms provides evidence that on average preterm infants are growth retarded with respect to weight and length while their head size has caught up to birth percentiles. As with all meta-analyses, the validity of this growth chart is limited by the heterogeneity of the data sources. Further validation is needed to illustrate the growth patterns of preterm infants to older ages