Evidence map›Paper›PMID 27371780›Full record

ArticleDiabetes research and clinical practice2016

Comparison of screening scores for diabetes and prediabetes.

Eduard Poltavskiy, Dae Jung Kim, Heejung Bang

Abstract readComparative Study
In one paragraph

Article in Diabetes research and clinical practice, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
4.7field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.

  1. Pooled it
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  15. Implementation of a Prediabetes Risk Test for an Underserved Population in a Federally Qualified Health Center.Clinical diabetes : a publication of the American Diabetes Association · 2022
    Article
  16. Article
  17. Prediabetes/diabetes screening strategy at the periodontal clinic.Clinical and experimental dental research · 2021
    Article
  18. Article
  19. A combined strategy of feature selection and machine learning to identify predictors of prediabetes.Journal of the American Medical Informatics Association : JAMIA · 2020
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Eduard PoltavskiyGraduate Group in Epidemiology, University of California, Davis, USA.
Dae Jung KimDepartment of Endocrinology and Metabolism, Ajou University School of Medicine, Suwon, South Korea.
Heejung BangGraduate Group in Epidemiology, University of California, Davis, USA; Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, USA. Electronic address: hbang@ucdavis.edu.
University of California, Davis · USAjou University · KR

Funding

UC Davis Clinical and Translational Science CenterUL1TR000002 · NCATS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI BERGLUND, LARS F · 2012 to 2015
$12.2M
NCATS NIH HHS UL1 TR000002
6 · The paper itself

Abstract

aimsThere are numerous risk or screening scores for the prediction of type-2 diabetes mellitus (DM). In contrast, few scores are available for preDM. In this paper, we compare the two screening scores from the American Diabetes Association (ADA) and Centers for Disease Control and Prevention (CDC) that can be used for DM as well as preDM.

methodsAdult participants (N=9391) without known DM from the National Health and Nutrition Examination Surveys 2009-12 were included. We fitted the factors/items in the ADA and CDC scores in logistic regression with the outcomes of undiagnosed DM, preDM, and combination, and assessed the association and discrimination accuracy. We also evaluated the suggested cutpoints that define high risk individuals. We mimicked the original models/settings but also tested various deviations/modifications often encountered in practice.

resultsBoth scores performed well and robustly, while the ADA score performed somewhat better (e.g., AUC=0.77 for ADA and 0.73-0.74 for CDC for DM; 0.72-0.74 and 0.70-0.71 for preDM). The same predictors and scoring rules seem to be reasonably justified with different cutpoints for DM and preDM, which can make usage easier and consistent. Some factors such as race and HDL/LDL cholesterols may be useful additions to health education.

conclusionsCurrent DM education and screening focus on the prevention and management of DM. The ADA and CDC scores could further help when we identify individuals at high risk for preDM, and teach the importance of preDM during which lifestyle intervention can be effective and urgently needed.

Indexed as

AdultArea Under CurveBlood GlucoseDiabetes Mellitus, Type 2FemaleGlycated HemoglobinHumansLogistic ModelsMaleMass ScreeningMiddle AgedNutrition SurveysPrediabetic StateRisk FactorsROC CurveUnited StatesBlood GlucoseGlycated Hemoglobinhemoglobin A1c protein, humanADACDCPrediabetesRisk assessmentRisk scoreScreening

Identifiers

PMID27371780
PMCPMC4972666
OpenAlexW2439183545

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.