Evidence map›Paper›PMID 27221651›Full record

Trial reportScientific reports2016

Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study.

Anxin Wang, Guojuan Chen, Zhaoping Su, Xiaoxue Liu, Xiangtong Liu, Haibin Li, Yanxia Luo, Lixin Tao, Jin Guo, Long Liu and 3 more

Abstract readClinical TrialMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. 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

13 authors.

Anxin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Guojuan ChenDepartment of Neurology, Tangshan Gongren Hospital, North China University of Science and Technology, Tangshan, China.
Zhaoping SuDepartment of Epidemiology and Health Statistics, Academy of Public Health and Management, Weifang Medical University, Weifang, China.
Xiaoxue LiuDepartment of Cardiology, Tangshan People's Hospital, Tangshan, China.
Xiangtong LiuDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Haibin LiDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Yanxia LuoDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Lixin TaoDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Jin GuoDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Long LiuDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Shuohua ChenDepartment of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China.
Shouling WuDepartment of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China.
Xiuhua GuoDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Few risk scores have been specifically developed to identify individuals at high risk of type 2 diabetes in China. In the present study, we aimed to develop such risk scores, based on simple clinical variables. We studied a population-based cohort of 73,987 adults, aged 18 years and over. After 5.35 ± 1.59 years of follow-up, 4,726 participants (9.58%) in the exploration cohort developed type 2 diabetes and 2,327 participants (9.44%) in the validation cohort developed type 2 diabetes. Age, gender, body mass index, family history of diabetes, education, blood pressure, and resting heart rate were selected to form the concise score with an area under the receiver operating characteristic curve (AUC) of 0.67. The variables in the concise score combined with fasting plasma glucose (FPG), and triglyceride (TG) or use of lipid-lowering drugs constituted the accurate score with an AUC value of 0.77. The utility of the two scores was confirmed in the validation cohort with AUCs of 0.66 and 0.77, respectively. In summary, the concise score, based on non-laboratory variables, could be used to identify individuals at high risk of developing diabetes within Chinese population; the accurate score, which also uses FPG and TG data, is better at identifying such individuals.

Indexed as

AdolescentAdultAgedAged, 80 and overAsian PeopleChinaDiabetes Mellitus, Type 2FemaleHumansIncidenceMaleMiddle AgedProspective StudiesRisk Factors

Identifiers

PMID27221651
PMCPMC4879553

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.