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

Hu, Huanhuan; Nakagawa, Tohru; Yamamoto, S.; Honda, Takeshi; Okazaki, Hiroko; Uehara, Akihiko; Yamamoto, Makoto; Miyamoto, Toshiaki; Kochi, Takeshi; Eguchi, M.; Murakami, Taizo; Shimizu, Makiko; Tomita, Kentaro; Nagahama, Satsue; Imai, Toshio; Nishihara, Akiko; Sasaki, Nozomu; Ogasawara, Takayuki; Hori, Akihiro; Nanri, Akiko; Akter, Shamima; Kuwahara, Keisuke; Kashino, Ikuko; Kabe, Isamu; Mizoue, Tetsuya; Sone, Toshio; Dohi, Seitaro

doi:10.1111/jdi.12809

Cited by 11 publications

(14 citation statements)

References 17 publications

(41 reference statements)

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“…Our findings align with previous studies, indicating that the risk prediction models including both HbA1c and FPG can predict the risk for developing diabetes with satisfactory accuracy 4 16 17. Our results extend these findings by demonstrating that further adding HOMA-IR and HOMA-β to the model does not materially improve the prediction performance.…”

Section: Discussionsupporting

confidence: 91%

“…Person-time was calculated from the date of the baseline examination to the date of first heath examination when diabetes was identified or to the date of the last health checkup, whichever occurred first. The Cox proportional hazards regression analysis was used to develop the risk models for diabetes, as in our previous study 4. We initially conducted a baseline model (model 1) including age, sex, BMI, smoking, dyslipidemia, hypertension, and HbA1c.…”

Section: Methodsmentioning

confidence: 99%

“…Numerous risk prediction models for diabetes based on different sets of predictors have been developed worldwide 3. We previously developed a highly accurate prediction model including both HbA1c and fasting plasma glucose (FPG) 4. A recent study of US community-dwelling adults reported that the predictive performance for diabetes significantly improved with the addition of HOMA-IR, an insulin resistance (IR) marker, to the model including HbA1c 5.…”

Section: Introductionmentioning

confidence: 99%

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Should insulin resistance (HOMA-IR), insulin secretion (HOMA-β), and visceral fat area be considered for improving the performance of diabetes risk prediction models

Hu,

Nakagawa,

Honda

et al. 2024

BMJ Open Diab Res Care

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IntroductionInsulin resistance and defects in pancreatic beta cells are the two major pathophysiologic abnormalities that underlie type 2 diabetes. In addition, visceral fat area (VFA) is reported to be a stronger predictor for diabetes than body mass index (BMI). Here, we tested whether the performance of diabetes prediction models could be improved by adding HOMA-IR and HOMA-β and replacing BMI with VFA.Research design and methodsWe developed five prediction models using data from a cohort study (5578 individuals, of whom 94.7% were male, and 943 had incident diabetes). We conducted a baseline model (model 1) including age, sex, BMI, smoking, dyslipidemia, hypertension, and HbA1c. Subsequently, we developed another four models: model 2, predictors in model 1 plus fasting plasma glucose (FPG); model 3, predictors in model 1 plus HOMA-IR and HOMA-β; model 4, predictors in model 1 plus FPG, HOMA-IR, and HOMA-β; model 5, replaced BMI with VFA in model 2. We assessed model discrimination and calibration for the first 10 years of follow-up.ResultsThe addition of FPG to model 1 obviously increased the value of the area under the receiver operating characteristic curve from 0.79 (95% CI 0.78, 0.81) to 0.84 (0.83, 0.85). Compared with model 1, model 2 also significantly improved the risk reclassification and discrimination, with a continuous net reclassification improvement index of 0.61 (0.56, 0.70) and an integrated discrimination improvement index of 0.09 (0.08, 0.10). Adding HOMA-IR and HOMA-β (models 3 and 4) or replacing BMI with VFA (model 5) did not further materially improve the performance.ConclusionsThis cohort study, primarily composed of male workers, suggests that a model with BMI, FPG, and HbA1c effectively identifies those at high diabetes risk. However, adding HOMA-IR, HOMA-β, or replacing BMI with VFA does not significantly improve the model. Further studies are needed to confirm our findings.

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Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Should insulin resistance (HOMA-IR), insulin secretion (HOMA-β), and visceral fat area be considered for improving the performance of diabetes risk prediction models

Hu,

Nakagawa,

Honda

et al. 2024

BMJ Open Diab Res Care

Self Cite

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show abstract

“…The risk models that included HbA1c showed better predictive ability, with an AUC of 0.786, and the predictive model performed best when both FPG and HbA1c levels were included (AUC = 0.845), consistent with previous studies. 18 – 21 Although the AUC values decreased after optimism correction, all remained reliable, as also observed in the internal-external cross-validation and external validation cohort. The AUC values were higher in the J-ECOH Study than in the JPHC Diabetes Study, indicating that the developed models were generally good at discrimination.…”

Section: Discussionsupporting

confidence: 72%

Development and Validation of Prediction Models for the 5-year Risk of Type 2 Diabetes in a Japanese Population: Japan Public Health Center-based Prospective (JPHC) Diabetes Study

Goto

Konishi

et al. 2024

Journal of Epidemiology

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Background This study aimed to develop models to predict the 5-year incidence of type 2 diabetes mellitus (T2DM) in a Japanese population and validate them externally in an independent Japanese population. Methods Data from 10,986 participants (aged 46–75 years) in the development cohort of the Japan Public Health Center-based Prospective Diabetes Study and 11,345 participants (aged 46–75 years) in the validation cohort of the Japan Epidemiology Collaboration on Occupational Health Study were used to develop and validate the risk scores in logistic regression models. Results We considered non-invasive (sex, body mass index, family history of diabetes mellitus, and diastolic blood pressure) and invasive (glycated hemoglobin [HbA1c] and fasting plasma glucose [FPG]) predictors to predict the 5-year probability of incident diabetes. The area under the receiver operating characteristic curve was 0.643 for the non-invasive risk model, 0.786 for the invasive risk model with HbA1c but not FPG, and 0.845 for the invasive risk model with HbA1c and FPG. The optimism for the performance of all models was small by internal validation. In the internal-external cross-validation, these models tended to show similar discriminative ability across different areas. The discriminative ability of each model was confirmed using external validation datasets. The invasive risk model with only HbA1c was well-calibrated in the validation cohort. Conclusion Our invasive risk models are expected to discriminate between high- and low-risk individuals with T2DM in a Japanese population.

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“…Many studies have noted the value of minimally-invasive diabetes risk scores in clinical care settings, but only one has noted the potential value in work settings as a tool for health promotion [ 16 , 17 ]. We tested the score in two “non-clinical” settings in which the ability to assess risk for diabetes is important, i.e.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a diabetes risk score among two populations

et al. 2021

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The purpose of this study was to assess the validity of a practical diabetes risk score amongst two heterogenous populations, a working population and a non-working population. Study population 1 (n = 2,089) participated in a large-scale screening program offered to retired workers to discover previously undetected/incipient chronic illness. Study population 2 (n = 3,293) was part of a Colorado worksite wellness program health risk assessment. We assessed the relationship between a continuous diabetes risk score at baseline and development of diabetes in the future using logistic regression. Receiver operating curves and sensitivity/specificity of the models were calculated. Across both study populations, we observed that participants with diabetes at follow-up had higher diabetes risk scores at baseline than participants who did not have diabetes at follow-up. On average, the odds ratio of developing diabetes in the future was 1.38 (95% CI: 1.26–1.50, p < 0.0001) for study population 1 and 1.68 (95% CI: 1.45–1.95, p-value < 0.0001) for study population 2. These findings indicate that the diabetes risk score may be generalizable to diverse individuals, and thus potentially a population level diabetes screening tool. Minimally-invasive diabetes risk scores can aid in the identification of sub-populations of individuals at risk for diabetes.

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

Cited by 11 publications

References 17 publications

Should insulin resistance (HOMA-IR), insulin secretion (HOMA-β), and visceral fat area be considered for improving the performance of diabetes risk prediction models

Should insulin resistance (HOMA-IR), insulin secretion (HOMA-β), and visceral fat area be considered for improving the performance of diabetes risk prediction models

Development and Validation of Prediction Models for the 5-year Risk of Type 2 Diabetes in a Japanese Population: Japan Public Health Center-based Prospective (JPHC) Diabetes Study

Development and validation of a diabetes risk score among two populations

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