歯科チームが新しいケア・パスウェイで100万人以上の未診断の糖尿病を発見する可能性(Dental teams could detect undiagnosed diabetes in more than one million people with new care pathway)

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2024-09-12 バーミンガム大学

英国の歯科チームが、定期的な歯科検診中に2型糖尿病の早期発見を目指す研究が進行中です。バーミンガム大学の研究者たちは、歯科診療所でのプレ糖尿病・糖尿病のスクリーニング方法「DDS」を開発。初期試験では、受診者の約15%が糖尿病リスクの基準を超えていることが判明しました。現在、HaleonとNIHRの支援を受け、さらに50の歯科診療所で1万人以上を対象に検証が進められ、早期発見・治療の新たなケアパスが模索されています。

<関連情報>

非糖尿病性高血糖と未診断2型糖尿病を特定するための多変量予測モデルの開発と外部検証 歯科における糖尿病リスク評価スコア(DDS) Development and External Validation of a Multivariable Prediction Model to Identify Nondiabetic Hyperglycemia and Undiagnosed Type 2 Diabetes: Diabetes Risk Assessment in Dentistry Score (DDS)

Z. Yonel, T. Kocher, […], and B. Holtfreter
Journal of Dental Research  Published:October 17, 2022
DOI:https://doi.org/10.1177/00220345221129807

Abstract

The aim of this study was to develop and externally validate a score for use in dental settings to identify those at risk of undiagnosed nondiabetic hyperglycemia (NDH) or type 2 diabetes (T2D). The Studies of Health in Pomerania (SHIP) project comprises 2 representative population-based cohort studies conducted in northeast Germany. SHIP-TREND-0, 2008 to 2012 (the development data set) had 3,339 eligible participants, with 329 having undiagnosed NDH or T2D. Missing data were replaced using multiple imputation. Potential covariates were selected for inclusion in the model using backward elimination. Heuristic shrinkage was used to reduce overfitting, and the final model was adjusted for optimism. We report the full model and a simplified paper-based point-score system. External validation of the model and score employed an independent data set comprising 2,359 participants with 357 events. Predictive performance, discrimination, calibration, and clinical utility were assessed. The final model included age, sex, body mass index, smoking status, first-degree relative with diabetes, presence of a dental prosthesis, presence of mobile teeth, history of periodontal treatment, and probing pocket depths ≥5 mm as well as prespecified interaction terms. In SHIP-TREND-0, the model area under the curve (AUC) was 0.72 (95% confidence interval [CI] 0.69, 0.75), calibration in the large was -0.025. The point score AUC was 0.69 (95% CI 0.65, 0.72), with sensitivity of 77.0 (95% CI 76.8, 77.2), specificity of 51.5 (95% CI 51.4, 51.7), negative predictive value of 94.5 (95% CI 94.5, 94.6), and positive predictive value of 17.0 (95% CI 17.0, 17.1). External validation of the point score gave an AUC of 0.69 (95% CI 0.66, 0.71), sensitivity of 79.2 (95% CI 79.0, 79.4), specificity of 49.9 (95% CI 49.8, 50.00), negative predictive value 91.5 (95% CI 91.5, 91.6), and positive predictive value of 25.9 (95% CI 25.8, 26.0). A validated prediction model involving dental variables can identify NDH or undiagnosed T2DM. Further studies are required to validate the model for different European populations.

医療・健康
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