Evidence map›Paper›PMID 29380553›Full record

ArticleJournal of diabetes investigation2018

Development and validation of risk models to predict the 7-year risk of type 2 diabetes: The Japan Epidemiology Collaboration on Occupational Health Study.

Huanhuan Hu, Tohru Nakagawa, Shuichiro Yamamoto, Toru Honda, Hiroko Okazaki, Akihiko Uehara, Makoto Yamamoto, Toshiaki Miyamoto, Takeshi Kochi, Masafumi Eguchi and 18 more

Abstract readValidation Study
In one paragraph

Article in Journal of diabetes investigation, 2018. 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
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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

28 authors.

Huanhuan HuDepartment of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.ORCID http://orcid.org/0000-0001-8558-2895
Tohru NakagawaHitachi, Ltd., Ibaraki, Japan.
Shuichiro YamamotoHitachi, Ltd., Ibaraki, Japan.
Toru HondaHitachi, Ltd., Ibaraki, Japan.
Hiroko OkazakiMitsui Chemicals, Inc., Tokyo, Japan.
Akihiko UeharaSeijinkai Shizunai Hospital, Hokkaido, Japan.
Makoto YamamotoYamaha Corporation, Shizuoka, Japan.
Toshiaki MiyamotoNippon Steel & Sumitomo Metal Corporation Kimitsu Works, Chiba, Japan.
Takeshi KochiFurukawa Electric Co., Ltd., Tokyo, Japan.
Masafumi EguchiFurukawa Electric Co., Ltd., Tokyo, Japan.
Taizo MurakamiMizue Medical Clinic, Keihin Occupational Health Center, Kanagawa, Japan.
Makiko ShimizuMizue Medical Clinic, Keihin Occupational Health Center, Kanagawa, Japan.
Kentaro TomitaMitsubishi Plastics, Inc., Tokyo, Japan.
Satsue NagahamaAll Japan Labor Welfare Foundation, Tokyo, Japan.
Teppei ImaiAzbil Corporation, Tokyo, Japan.
Akiko NishiharaAzbil Corporation, Tokyo, Japan.
Naoko SasakiMitsubishi Fuso Truck and Bus Corporation, Kanagawa, Japan.
Takayuki OgasawaraMitsubishi Fuso Truck and Bus Corporation, Kanagawa, Japan.
Ai HoriDepartment of Global Public Health, University of Tsukuba, Ibaraki, Japan.
Akiko NanriDepartment of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.ORCID http://orcid.org/0000-0002-7487-8778
Shamima AkterDepartment of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.
Keisuke KuwaharaDepartment of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.
Ikuko KashinoDepartment of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.
Isamu KabeFurukawa Electric Co., Ltd., Tokyo, Japan.
Tetsuya MizoueDepartment of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.
Tomofumi SoneNational Institute of Public Health, Saitama, Japan.
Seitaro DohiMitsui Chemicals, Inc., Tokyo, Japan.
Japan Epidemiology Collaboration on Occupational Health Study Group

Funding

Industrial Disease Clinical Research Grants 140202-01Industrial Disease Clinical Research Grants 150903-01Industrial Disease Clinical Research Grants 170301-01Japan Society for the Promotion of Science KAKENHI 16H05251National Center for Global Health and Medicine 28-Shi-1206
6 · The paper itself

Abstract

AIMS/

introductionWe previously developed a 3-year diabetes risk score in the working population. The objective of the present study was to develop and validate flexible risk models that can predict the risk of diabetes for any arbitrary time-point during 7 years. MATERIALS AND

methodsThe participants were 46,198 Japanese employees aged 30-59 years, without diabetes at baseline and with a maximum follow-up period of 8 years. Incident diabetes was defined according to the American Diabetes Association criteria. With routine health checkup data (age, sex, abdominal obesity, body mass index, smoking status, hypertension status, dyslipidemia, glycated hemoglobin and fasting plasma glucose), we developed non-invasive and invasive risk models based on the Cox proportional hazards regression model among a random two-thirds of the participants, and used another one-third for validation.

resultsThe range of the area under the receiver operating characteristic curve increased from 0.73 (95% confidence interval 0.72-0.74) for the non-invasive prediction model to 0.89 (95% confidence interval 0.89-0.90) for the invasive prediction model containing dyslipidemia, glycated hemoglobin and fasting plasma glucose. The invasive models showed improved integrated discrimination and reclassification performance, as compared with the non-invasive model. Calibration appeared good between the predicted and observed risks. These models performed well in the validation cohort.

conclusionsThe present non-invasive and invasive models for the prediction of diabetes risk up to 7 years showed fair and excellent performance, respectively. The invasive models can be used to identify high-risk individuals, who would benefit greatly from lifestyle modification for the prevention or delay of diabetes.

Indexed as

Occupational HealthAdultBiomarkersCohort StudiesDiabetes Mellitus, Type 2FemaleFollow-Up StudiesHumansIncidenceJapanMaleMiddle AgedPrognosisProportional Hazards ModelsRisk FactorsROC CurveBiomarkersJapaneseRisk modelType 2 diabetes

Identifiers

PMID29380553
PMCPMC6123034

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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.