ArticleBMJ open2025
Development of risk prediction equations for 5-year diabetes incidence using Japanese health check-up data: a retrospective cohort study.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Development of a Type 2 Diabetes Prediction Model Using Specific Health Checkup Data and Extraction of Predictive Factors.Bioengineering (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to develop risk prediction equations for the 5-year incidence of diabetes among the Japanese population using health check-up data. We hypothesised that demographic and laboratory data from health check-ups could predict diabetes onset with high accuracy.
designRetrospective cohort study.
settingData from a health examination in Japan between 2008 and 2016.
participantsData were analysed from 31 084 participants aged 30-69 years. The presence of baseline diabetes and endocrine disease was included in the exclusion criteria, as were participants with missing data for the analysis. The study population was randomly divided into derivation and validation cohorts in a 1:1 ratio. PRIMARY OUTCOME MEASURES: The primary outcome was the incidence of diabetes at the 5-year follow-up, defined as a fasting blood glucose level ≥126 mg/dL, glycosylated haemoglobin A1c (National Glycohemoglobin Standardization Program (NGSP)) ≥6.5%, or initiation of diabetes treatment. Predictor variables included age, sex, body mass index, blood pressure, underlying diseases, lifestyle factors and laboratory measurements. The primary measure was the area under the receiver operating characteristic curve (AUC) for the predictive equations.
resultsIn the derivation cohort, diabetes incidence was 5.0%. The prediction equation incorporating age, sex, body mass index, fasting blood glucose and glycosylated haemoglobin A1c showed good discriminatory ability with an AUC of 0.89, sensitivity of 0.81 and specificity of 0.81 in the validation cohort.
conclusionsThe equation with laboratory measures effectively predicted the 5-year diabetes risk in the general Japanese population. It has potential clinical utility for identifying individuals at high risk of diabetes and guiding preventive interventions.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.