Evidence map›Paper›PMID 30374195›Full record

ArticleScientific reports2018

Importance of family history of diabetes in computing a diabetes risk score in Korean prediabetic population.

Morena Ustulin, Sang Youl Rhee, Suk Chon, Kyu Keung Ahn, Ji Eun Lim, Bermseok Oh, Sung-Hoon Kim, Sei Hyun Baik, Yongsoo Park, Moon Suk Nam and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. 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.

Morena UstulinDepartment of Medicine, Graduate School, Kyung Hee University, Seoul, Korea.
Sang Youl RheeDepartment of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea.ORCID http://orcid.org/0000-0003-0119-5818
Suk ChonDepartment of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea.
Kyu Keung AhnDepartment of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea.
Ji Eun LimDepartment of Biochemistry and Molecular Biology, Kyung Hee University School of Medicine, Seoul, Korea.
Bermseok OhDepartment of Biochemistry and Molecular Biology, Kyung Hee University School of Medicine, Seoul, Korea.
Sung-Hoon KimDivision of Endocrinology and Metabolism, Department of Medicine, Cheil General Hospital and Women's Healthcare Center, College of Medicine, Dankook University, Seoul, Korea.
Sei Hyun BaikDivision of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, Korea University, Seoul, Korea.
Yongsoo ParkDepartment of Internal Medicine, College of Medicine, Hanyang University, Guri, Korea.
Moon Suk NamDepartment of Internal Medicine, College of Medicine, Inha University, Incheon, Korea.
Kwan Woo LeeDepartment of Endocrinology and Metabolism, College of Medicine, Ajou University, Suwon, Korea.
Young Seol KimDepartment of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea.
Jeong-Taek WooDepartment of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea. jtwoomd@khmc.or.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prediabetic subjects represent a vulnerable population, requiring special care to reduce the risk of diabetes onset. We developed and validated a diabetes risk score for prediabetic subjects using the Korea National Diabetes Program (KNDP) cohort. Subjects included in the multicenter and prospective cohort (n = 1162) had high diabetes risk at baseline (2005) and were followed until 2012. Survival analysis was performed to analyze the prospective cohort over time, and the bootstrap method was used to validate our model. We confirmed our findings in an external cohort. A diabetes risk score was calculated and the cut-off defined using a receiver operating characteristic curve. Age, body mass index, total cholesterol, and family history of diabetes were associated with diabetes. The model performed well after correction for optimism (C

Indexed as

AlgorithmsArea Under CurveCohort StudiesDiabetes Mellitus, Type 2FemaleHumansMaleMedical History TakingMiddle AgedPrediabetic StateProportional Hazards ModelsProspective StudiesRepublic of KoreaRisk FactorsROC Curve

Identifiers

PMID30374195
PMCPMC6206127

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.