Literature DB >> 23435184

A risk score for identifying overweight adolescents with dysglycemia in primary care settings.

Joyce M Lee1, Achamyeleh Gebremariam, Susan J Woolford, Beth A Tarini, Melissa A Valerio, Surair Bashir, Ashley J Eason, Preciosa Y Choi, James G Gurney.   

Abstract

OBJECTIVE: To develop a clinical risk scoring system for identifying adolescents with dysglycemia (prediabetes or diabetes) who need further confirmatory testing and to determine whether the addition of non-fasting tests would improve the prediction of dysglycemia. STUDY
DESIGN: A sample of 176 overweight and obese adolescents (10-17 years) had a history/physical exam, a 2-h oral glucose tolerance test, and non-fasting tests [hemoglobin A1c, 1-h glucose challenge test (GCT), and random glucose test] performed. Given the low number of children with diabetes, we created several risk scoring systems combining the clinical characteristics with non-fasting tests for identifying adolescents with dysglycemia and compared the test performance.
RESULTS: Sixty percent of participants were white and 32% were black; 39.2% had prediabetes and 1.1% had diabetes. A basic model including demographics, body mass index percentile, family history of diabetes, and acanthosis nigricans had reasonable test performance [area under the curve (AUC), 0.75; 95% confidence interval (95% CI), 0.68-0.82]. The addition of random glucose (AUC, 0.81; 95% CI, 0.75-0.87) or 1-h GCT (AUC, 0.82; 95% CI, 0.75-0.88) to the basic model significantly improved the predictive capacity, but the addition of hemoglobin A1c did not (AUC, 0.76; 95% CI, 0.68-0.83). The clinical score thresholds to consider for the basic plus random glucose model are total score cutoffs of 60 or 65 (sensitivity 86% and 65% and specificity 60% and 78%, respectively) and for the basic plus 1-h GCT model are total score cutoffs of 50 or 55 (sensitivity 87% and 73% and specificity 59% and 76%, respectively).
CONCLUSIONS: Pending a validation in additional populations, a risk score combining the clinical characteristics with non-fasting test results may be a useful tool for identifying children with dysglycemia in the primary care setting.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 23435184      PMCID: PMC3837697          DOI: 10.1515/jpem-2012-0259

Source DB:  PubMed          Journal:  J Pediatr Endocrinol Metab        ISSN: 0334-018X            Impact factor:   1.634


  29 in total

1.  Risk scores for type 2 diabetes can be applied in some populations but not all.

Authors:  Charlotte Glümer; Dorte Vistisen; Knut Borch-Johnsen; Stephen Colagiuri
Journal:  Diabetes Care       Date:  2006-02       Impact factor: 19.112

2.  Obesity at the onset of diabetes in an ethnically diverse population of children: what does it mean for epidemiologists and clinicians?

Authors:  Rebecca B Lipton; Melinda Drum; Deborah Burnet; Barry Rich; Andrew Cooper; Elizabeth Baumann; William Hagopian
Journal:  Pediatrics       Date:  2005-05       Impact factor: 7.124

3.  Risk of cardiovascular and all-cause mortality in individuals with diabetes mellitus, impaired fasting glucose, and impaired glucose tolerance: the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab).

Authors:  Elizabeth L M Barr; Paul Z Zimmet; Timothy A Welborn; Damien Jolley; Dianna J Magliano; David W Dunstan; Adrian J Cameron; Terry Dwyer; Hugh R Taylor; Andrew M Tonkin; Tien Y Wong; John McNeil; Jonathan E Shaw
Journal:  Circulation       Date:  2007-06-18       Impact factor: 29.690

4.  Screening for type 2 diabetes mellitus in children and adolescents: attitudes, barriers, and practices among pediatric clinicians.

Authors:  Erinn T Rhodes; Jonathan A Finkelstein; Richard Marshall; Carole Allen; Matthew W Gillman; David S Ludwig
Journal:  Ambul Pediatr       Date:  2006 Mar-Apr

5.  Increased incidence of non-insulin-dependent diabetes mellitus among adolescents.

Authors:  O Pinhas-Hamiel; L M Dolan; S R Daniels; D Standiford; P R Khoury; P Zeitler
Journal:  J Pediatr       Date:  1996-05       Impact factor: 4.406

6.  Criteria for oral glucose tolerance testing of obese minority youth.

Authors:  Mala Puri; Katherine Freeman; Mireya Garcia; Hadassa Nussbaum; Joan R Dimartino-Nardi
Journal:  J Pediatr Endocrinol Metab       Date:  2007-06       Impact factor: 1.634

Review 7.  Why young adults hold the key to assessing the obesity epidemic in children.

Authors:  Joyce M Lee
Journal:  Arch Pediatr Adolesc Med       Date:  2008-07

8.  Reproducibility of the oral glucose tolerance test in overweight children.

Authors:  I M Libman; E Barinas-Mitchell; A Bartucci; R Robertson; S Arslanian
Journal:  J Clin Endocrinol Metab       Date:  2008-08-19       Impact factor: 5.958

9.  Racial/ethnic differences in body fatness among children and adolescents.

Authors:  David S Freedman; Jack Wang; John C Thornton; Zuguo Mei; Richard N Pierson; William H Dietz; Mary Horlick
Journal:  Obesity (Silver Spring)       Date:  2008-02-28       Impact factor: 5.002

10.  Prevalence of pre-diabetes and its association with clustering of cardiometabolic risk factors and hyperinsulinemia among U.S. adolescents: National Health and Nutrition Examination Survey 2005-2006.

Authors:  Chaoyang Li; Earl S Ford; Guixiang Zhao; Ali H Mokdad
Journal:  Diabetes Care       Date:  2008-10-28       Impact factor: 17.152

View more
  2 in total

Review 1.  Addressing prediabetes in childhood obesity treatment programs: support from research and current practice.

Authors:  Matthew A Haemer; H Mollie Grow; Cristina Fernandez; Gloria J Lukasiewicz; Erinn T Rhodes; Laura A Shaffer; Brooke Sweeney; Susan J Woolford; Elizabeth Estrada
Journal:  Child Obes       Date:  2014-07-23       Impact factor: 2.992

2.  Predicting youth diabetes risk using NHANES data and machine learning.

Authors:  Nita Vangeepuram; Bian Liu; Po-Hsiang Chiu; Linhua Wang; Gaurav Pandey
Journal:  Sci Rep       Date:  2021-05-27       Impact factor: 4.379

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.