Evidence map›Paper›PMID 24819157›Full record

ArticlePloS one2014

Development of a new risk score for incident type 2 diabetes using updated diagnostic criteria in middle-aged and older chinese.

Xingwang Ye, Geng Zong, Xin Liu, Gang Liu, Wei Gan, Jingwen Zhu, Ling Lu, Liang Sun, Huaixing Li, Frank B Hu and 1 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 3 pooled it
1.9field-weighted citation impact, top 14% 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

12 citing papers in PubMed, 3 syntheses or guidelines pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Differing Contributions of Classical Risk Factors to Type 2 Diabetes in Multi-Ethnic Malaysian Populations.International journal of environmental research and public health · 2018
    Article
  7. Article
  8. Risk Score for Detecting Dysglycemia: A Cross-Sectional Study of a Working-Age Population in an Oil Field in China.Medical science monitor : international medical journal of experimental and clinical research · 2017
    Article
  9. Article
  10. Observational
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  12. Article
4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

11 authors at 4 institutions in 2 countries.

Xingwang YeKey Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China; SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
Geng ZongKey Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China.
Xin LiuKey Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China.
Gang LiuKey Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China.
Wei GanKey Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China.
Jingwen ZhuKey Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China.
Ling LuKey Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China.
Liang SunKey Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China; SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
Huaixing LiKey Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China.
Frank B HuDepartments of Nutrition and Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.
Xu LinKey Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China.
Shanghai Institutes for Biological Sciences · CNUniversity of Chinese Academy of Sciences · CNChinese Academy of Sciences · CNHarvard University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Type 2 diabetes mellitus (T2DM) reaches an epidemic proportion among adults in China. However, no simple score has been created for the prediction of T2DM incidence diagnosed by updated criteria with hemoglobin A1c (HbA1c) ≥ 6.5% included in Chinese. In a 6-year follow-up cohort in Beijing and Shanghai, China, we recruited a total of 2529 adults aged 50-70 years in 2005 and followed them up in 2011. Fasting plasma glucose (FPG), HbA1c, and C-reactive protein (CRP) were measured and incident diabetes was identified by the recently updated criteria. Of the 1912 participants without T2DM at baseline, 924 were identified as having T2DM at follow-up, and most of them (72.4%) were diagnosed using the HbA1c criterion. Baseline body mass index, FPG, HbA1c, CRP, hypertension, and female gender were all significantly associated with incident T2DM. Based upon these risk factors, a simple score was developed with an estimated area under the receiver operating characteristic curve of 0.714 (95% confidence interval: 0.691, 0.737), which performed better than most of existing risk score models developed for eastern Asian populations. This simple, newly constructed score of six parameters may be useful in predicting T2DM in middle-aged and older Chinese.

Indexed as

AgedBlood GlucoseChinaC-Reactive ProteinDiabetes Mellitus, Type 2FastingFemaleFollow-Up StudiesHumansMaleMiddle AgedModels, StatisticalRisk AssessmentRisk FactorsSex DistributionBlood GlucoseC-Reactive Protein

Identifiers

PMID24819157
PMCPMC4018395
OpenAlexW2105260085

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.