Literature DB >> 18310308

Age, BMI, and race are less important than random plasma glucose in identifying risk of glucose intolerance: the Screening for Impaired Glucose Tolerance Study (SIGT 5).

David C Ziemer1, Paul Kolm, William S Weintraub, Viola Vaccarino, Mary K Rhee, Jane M Caudle, Jade M Irving, David D Koch, K M Venkat Narayan, Lawrence S Phillips.   

Abstract

OBJECTIVE: Age, BMI, and race/ethnicity are used in National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and American Diabetes Association (ADA) guidelines to prompt screening for pre-diabetes and diabetes, but cutoffs have not been evaluated rigorously. RESEARCH DESIGN AND METHODS: Random plasma glucose (RPG) was measured and 75-g oral glucose tolerance tests were performed in 1,139 individuals without known diabetes. Screening performance was assessed by logistic regression and area under the receiver operating characteristic curve (AROC).
RESULTS: NIDDK/ADA indicators age >45 years and BMI >25 kg/m(2) provided significant detection of both diabetes and dysglycemia (both AROCs 0.63), but screening was better with continuous-variable models of age, BMI, and race and better still with models of age, BMI, race, sex, and family history (AROC 0.78 and 0.72). However, screening was even better with RPG alone (AROCs 0.81 and 0.72). RPG >125 mg/dl could be used to prompt further evaluation with an OGTT.
CONCLUSIONS: Use of age, BMI, and race/ethnicity in guidelines for screening to detect diabetes and pre-diabetes may be less important than evaluation of RPG. RPG should be investigated further as a convenient, inexpensive screen with good predictive utility.

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Year:  2008        PMID: 18310308      PMCID: PMC3685424          DOI: 10.2337/dc07-2282

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


  10 in total

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2.  The performance of a risk score in predicting undiagnosed hyperglycemia.

Authors:  P J Park; S J Griffin; L Sargeant; N J Wareham
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3.  Predicting impaired glucose tolerance using common clinical information: data from the Third National Health and Nutrition Examination Survey.

Authors:  Karin M Nelson; Edward J Boyko
Journal:  Diabetes Care       Date:  2003-07       Impact factor: 19.112

4.  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
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Review 5.  The relation of fasting and 2-h postchallenge plasma glucose concentrations to mortality: data from the Baltimore Longitudinal Study of Aging with a critical review of the literature.

Authors:  John D Sorkin; Denis C Muller; Jerome L Fleg; Reubin Andres
Journal:  Diabetes Care       Date:  2005-11       Impact factor: 19.112

6.  Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.

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7.  The diabetes risk score: a practical tool to predict type 2 diabetes risk.

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8.  The cost-effectiveness of screening for type 2 diabetes. CDC Diabetes Cost-Effectiveness Study Group, Centers for Disease Control and Prevention.

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Journal:  JAMA       Date:  1998-11-25       Impact factor: 56.272

9.  Opportunistic screening for diabetes in routine clinical practice.

Authors:  Mark W Ealovega; Bahman P Tabaei; Michael Brandle; Ray Burke; William H Herman
Journal:  Diabetes Care       Date:  2004-01       Impact factor: 19.112

10.  The performance of a risk score as a screening test for undiagnosed hyperglycemia in ethnic minority groups: data from the 1999 health survey for England.

Authors:  Annemieke M W Spijkerman; Matthew F Yuyun; Simon J Griffin; Jacqueline M Dekker; Giel Nijpels; Nicholas J Wareham
Journal:  Diabetes Care       Date:  2004-01       Impact factor: 19.112

  10 in total
  6 in total

1.  Performance of a Random Glucose Case-Finding Strategy to Detect Undiagnosed Diabetes.

Authors:  Michael E Bowen; Lei Xuan; Ildiko Lingvay; Ethan A Halm
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2.  Random blood glucose: a robust risk factor for type 2 diabetes.

Authors:  Michael E Bowen; Lei Xuan; Ildiko Lingvay; Ethan A Halm
Journal:  J Clin Endocrinol Metab       Date:  2015-02-04       Impact factor: 5.958

3.  Distribution of hyperglycaemia and related cardiovascular disease risk factors in low-income countries: a cross-sectional population-based survey in rural Uganda.

Authors:  Dermot Maher; Laban Waswa; Kathy Baisley; Alex Karabarinde; Nigel Unwin; Heiner Grosskurth
Journal:  Int J Epidemiol       Date:  2010-10-05       Impact factor: 7.196

4.  Pre-diagnosis blood glucose and prognosis in women with breast cancer.

Authors:  Behjatolah Monzavi-Karbassi; Rhonda Gentry; Varinder Kaur; Eric R Siegel; Fariba Jousheghany; Srikanth Medarametla; Barbara J Fuhrman; A Mazin Safar; Laura F Hutchins; Thomas Kieber-Emmons
Journal:  Cancer Metab       Date:  2016-04-06

5.  Development and validation of prediabetes risk score for predicting prediabetes among Indonesian adults in primary care: Cross-sectional diagnostic study.

Authors:  Isti Ilmiati Fujiati; Harun Alrasyid Damanik; Adang Bachtiar; Andi Armyn Nurdin; Paul Ward
Journal:  Interv Med Appl Sci       Date:  2017-06

6.  Patient, Provider, and System Factors Associated With Failure to Follow-Up Elevated Glucose Results in Patients Without Diagnosed Diabetes.

Authors:  Michael E Bowen; Zahra Merchant; Kazeen Abdullah; Deepa Bhat; Jason Fish; Ethan A Halm
Journal:  Health Serv Res Manag Epidemiol       Date:  2017-08-29
  6 in total

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