Simplifying prediction of disease progression in pre-symptomatic type 1 diabetes using a single blood sample,Diabetologia

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Simplifying prediction of disease progression in pre-symptomatic type 1 diabetes using a single blood sample
Diabetologia ( IF 8.2 ) Pub Date : 2021-08-02 , DOI: 10.1007/s00125-021-05523-2
Naiara G Bediaga _{1,

2} , Connie S N Li-Wai-Suen _{2,

3} , Michael J Haller ₄ , Stephen E Gitelman ₅ , Carmella Evans-Molina ₆ , Peter A Gottlieb ₇ , Markus Hippich ₈ , Anette-Gabriele Ziegler _{8,

9} , Ake Lernmark ₁₀ , Linda A DiMeglio _{6,

11} , Diane K Wherrett ₁₂ , Peter G Colman ₁₃ , Leonard C Harrison _{1,

2} , John M Wentworth _{1,

2,

13}

Affiliation

Department of Population Health and Immunity, Walter and Eliza Hall Institute, Parkville, VIC, Australia.
Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia.
Department of Bioinformatics, Walter and Eliza Hall Institute, Parkville, VIC, Australia.
University of Florida Diabetes Institute, Gainesville, FL, USA.
Department of Pediatrics and Diabetes Center, University of California at San Francisco, San Francisco, CA, USA.
Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA.
Barbara Davis Center, University of Colorado School of Medicine, Aurora, CO, USA.
Helmholtz Zentrum München, Institute of Diabetes Research, German Research Center for Environmental Health, Munich-Neuherberg, Germany.
Forschergruppe Diabetes, Technical University Munich at Klinikum rechts der Isar, Munich, Germany.
Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden.
Division of Pediatric Endocrinology, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.
Division of Endocrinology, Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Parkville, VIC, Australia.

Aims/hypothesis

Accurate prediction of disease progression in individuals with pre-symptomatic type 1 diabetes has potential to prevent ketoacidosis and accelerate development of disease-modifying therapies. Current tools for predicting risk require multiple blood samples taken during an OGTT. Our aim was to develop and validate a simpler tool based on a single blood draw.

Methods

Models to predict disease progression using a single OGTT time point (0, 30, 60, 90 or 120 min) were developed using TrialNet data collected from relatives with type 1 diabetes and validated in independent populations at high genetic risk of type 1 diabetes (TrialNet, Diabetes Prevention Trial–Type 1, The Environmental Determinants of Diabetes in the Young [1]) and in a general population of Bavarian children who participated in Fr1da.

Results

Cox proportional hazards models combining plasma glucose, C-peptide, sex, age, BMI, HbA_1c and insulinoma antigen-2 autoantibody status predicted disease progression in all populations. In TrialNet, the AUC for receiver operating characteristic curves for models named M₆₀, M₉₀ and M₁₂₀, based on sampling at 60, 90 and 120 min, was 0.760, 0.761 and 0.745, respectively. These were not significantly different from the AUC of 0.760 for the gold standard Diabetes Prevention Trial Risk Score, which requires five OGTT blood samples. In TEDDY, where only 120 min blood sampling had been performed, the M₁₂₀ AUC was 0.865. In Fr1da, the M₁₂₀ AUC of 0.742 was significantly greater than the M₆₀ AUC of 0.615.

Conclusions/interpretation

Prediction models based on a single OGTT blood draw accurately predict disease progression from stage 1 or 2 to stage 3 type 1 diabetes. The operational simplicity of M₁₂₀, its validity across different at-risk populations and the requirement for 120 min sampling to stage type 1 diabetes suggest M₁₂₀ could be readily applied to decrease the cost and complexity of risk stratification.

Graphical abstract

中文翻译：

使用单一血液样本简化症状前 1 型糖尿病疾病进展的预测

目标/假设

准确预测症状前 1 型糖尿病患者的疾病进展有可能预防酮症酸中毒并加速疾病缓解疗法的开发。当前用于预测风险的工具需要在 OGTT 期间采集多个血液样本。我们的目标是开发和验证基于单次抽血的更简单的工具。

方法

使用单个 OGTT 时间点（0、30、60、90 或 120 分钟）预测疾病进展的模型是使用从 1 型糖尿病亲属收集的 TrialNet 数据开发的，并在具有 1 型糖尿病高遗传风险的独立人群中验证（TrialNet , 糖尿病预防试验 - 1 型，年轻人糖尿病的环境决定因素 [1]) 和参加 Fr1da 的巴伐利亚儿童的一般人群。

结果

结合血浆葡萄糖、C 肽、性别、年龄、BMI、HbA _1c和胰岛素瘤抗原 2 自身抗体状态的 Cox 比例风险模型可预测所有人群的疾病进展。在 TrialNet 中，基于 60、90 和 120 分钟采样的名为 M ₆₀、M ₉₀和M ₁₂₀的模型的受试者工作特征曲线的 AUC分别为 0.760、0.761 和 0.745。这些与金标准糖尿病预防试验风险评分的 AUC 0.760 没有显着差异，后者需要五个 OGTT 血液样本。在 TEDDY 中，仅进行了 120 分钟的血液采样，M ₁₂₀ AUC 为 0.865。在 Fr1da 中，M ₁₂₀ AUC 的 0.742 明显大于 M₆₀ AUC 为 0.615。