Ferrat Lauric A, Templeman Erin L, Steck Andrea K, Parikh Hemang M, You Lu, Onengut-Gumuscu Suna, Gottlieb Peter A, Triolo Taylor M, Rich Stephen S, Krischer Jeffrey, McQueen R Brett, Oram Richard A, Redondo Maria J
Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland.
Diabetologia. 2025 May 10. doi: 10.1007/s00125-025-06434-2.
AIMS/HYPOTHESIS: Efficient prediction of clinical type 1 diabetes is important for risk stratification and monitoring of autoantibody-positive individuals. In this study, we compared type 1 diabetes predictive models for predictive performance, cost and participant time needed for testing.
We developed 1943 predictive models using a Cox model based on a type 1 diabetes genetic risk score (GRS2), autoantibody count and types, BMI, age, self-reported gender and OGTT-derived glucose and C-peptide measures. We trained and validated the models using halves of a dataset comprising autoantibody-positive first-degree relatives of individuals with type 1 diabetes (n=3967, 49% female, 14.9 ± 12.1 years of age) from the TrialNet Pathway to Prevention study. The median duration of follow-up was 4.7 years (IQR 2.0-8.1), and 1311 participants developed clinical type 1 diabetes. Models were compared for predictive performances, estimated cost and participant time.
Models that included metabolic measures had best performance, with most exhibiting small performance differences (less than 3% and p>0.05). However, the cost and participant time associated with measuring metabolic variables ranged between US$56 and US$293 and 10-165 min, respectively. The predictive model performance had temporal variability, with the highest GRS2 influence and discriminative power being exhibited in the earliest preclinical stages. OGTT-derived metabolic measures had a similar performance to HbA- or Index-derived models, with an important difference in cost and participant time.
CONCLUSIONS/INTERPRETATION: Cost-performance model analyses identified trade-offs between cost and performance models, and identified cost-minimising options to tailor risk-screening strategies.
目的/假设：对临床1型糖尿病进行有效预测对于自身抗体阳性个体的风险分层和监测至关重要。在本研究中，我们比较了1型糖尿病预测模型的预测性能、成本以及检测所需的受试者时间。
我们基于1型糖尿病遗传风险评分（GRS2）、自身抗体计数及类型、体重指数、年龄、自我报告的性别以及口服葡萄糖耐量试验衍生的葡萄糖和C肽测量值，使用Cox模型开发了1943个预测模型。我们使用来自预防试验网途径研究的1型糖尿病患者自身抗体阳性的一级亲属数据集（n = 3967，49%为女性，年龄14.9±12.1岁）的一半进行模型训练和验证。随访的中位持续时间为4.7年（四分位间距2.0 - 8.1），1311名受试者发生了临床1型糖尿病。对模型的预测性能、估计成本和受试者时间进行了比较。
包含代谢指标的模型性能最佳，大多数模型的性能差异较小（小于3%且p>0.05）。然而，测量代谢变量的成本和受试者时间分别在56美元至293美元以及10至165分钟之间。预测模型性能存在时间变异性，在临床前期最早阶段GRS2的影响和鉴别能力最高。口服葡萄糖耐量试验衍生的代谢指标与糖化血红蛋白或指数衍生模型的性能相似，但在成本和受试者时间方面存在重要差异。
结论/解读：成本 - 性能模型分析确定了成本和性能模型之间的权衡，并确定了可定制风险筛查策略的成本最小化方案。
