英国埃克塞特大学医学院Andrew T Hattersley课题组报道了使用常规临床特征优化2型糖尿病处方的五类药物模型。相关论文于2025年2月25日发表于国际顶尖学术期刊《柳叶刀》杂志上。

背景:支持2型糖尿病患者个性化选择最佳降糖治疗的数据很少。该课题组的目的是确定常规可用的临床特征是否可以用来预测五种降糖药物的相对降糖效果。

方法:该课题组研究人员开发并验证了一个五种药物类别模型，以12个月绝对糖化血红蛋白（HbA1c）来预测启动二肽基肽酶-4抑制剂、胰高血糖素样肽-1受体激动剂、钠-葡萄糖共转运蛋白-2抑制剂、磺脲类药物和噻唑烷二酮类药物的相对降糖效果。该模型使用了药物开始时2型糖尿病患者的9个常规临床特征作为预测因素（年龄、糖尿病病程、性别、基线HbA1c、BMI、肾小球滤过率、高密度脂蛋白胆固醇、总胆固醇和丙氨酸转氨酶）。该模型是根据英国临床实践研究数据链[CPRD] Aurum)的观察数据开发和验证的，研究对象是18-79岁的2型糖尿病患者，他们在2004年1月1日至2020年10月14日期间服用了五种药物中的一种，并根据地理区域和日历周期进行了保留验证。该模型在三个已发表的2型糖尿病随机药物试验（TriMaster三药交叉试验和两个平行组试验[NCT00622284和NCT01167881]）的个体水平数据中得到进一步验证。为了在CPRD中进行验证，研究组评估了匹配（1:1）的一致组和不一致组之间观察到的降糖效果的差异，这些组接受的治疗与模型预测的最佳治疗一致或不一致，最佳治疗被定义为具有最高预测降糖效果的药物类别（即最低预测的12个月HbA1c）。进一步的验证包括对所有数据集的药物类别进行两两比较。小组还评估了CPRD模型一致组和模型不一致组与长期结局的相关性，评估了5年血糖衰竭（确诊HbA1c≥69 mmol/mol）、全因死亡率、主要不良心血管事件或心力衰竭（mce - hf）结局、肾脏进展和微血管并发症的风险，使用Cox比例风险回归对相关人口统计学和临床协变量进行了调整。

发现:5种药物分类模型是从100在珠江三角洲地区有107种药物首次使用。在整个CPRD队列（联合开发和验证队列）中，32305 (15.2%) / 212166个药物起始点属于模型预测的最佳治疗。在模型一致性组中，CPRD地理验证队列（n=24）平均观察到的12个月HbA1c获益为5.3 mmol/mol （95% CI 4.9 - 5.7）。746例起始用药，n=12与匹配模型不一致组相比，CPRD时间验证队列（n=9682个药物起始点，n=4841个配对对）中，CPRD时间验证队列（n=9682个药物起始点，n=4841个配对对）的差异为5.0 mmol/mol（4.3 - 6）。预测的HbA1c差异与三个临床试验中观察到的HbA1c差异在药物类别的两两比较中以及在CPRD的五种药物类别的两两比较中得到了很好的校准。CPRD模型一致组的5年血糖衰竭风险低于模型不一致组（校正风险比[aHR] 0.62）。对于长期非血糖结局，模型一致组与模型不一致组的5年全因死亡率风险相似（aHR为0.95 [0.83 - 0.09]），MACE-HF结局（aHR为0.85[0.76 - 0.95]）、肾脏进展（aHR为0.71[0.64 - 0.79]）和微血管并发症（aHR为0.86[0.78 - 0.96]）的风险较低。

研究结果表明，研究小组已经开发了一个五种药物类别模型，该模型使用常规临床数据来确定2型糖尿病患者的最佳降糖疗法。接受模型预测的最佳治疗的患者12个月HbA1c较低，不太可能需要额外的降糖治疗，并且与非最佳治疗的患者相比，糖尿病并发症的风险较低。通过设置特定的优化，使用常规收集的参数意味着该模型很容易在世界上大多数国家引入临床护理。

附：英文原文

Title: A five-drug class model using routinely available clinical features to optimise prescribing in type 2 diabetes: a prediction model development and validation study

Author: John M Dennis, Katherine G Young, Pedro Cardoso, Laura M Güdemann, Andrew P McGovern, Andrew Farmer, Rury R Holman, Naveed Sattar, Trevelyan J McKinley, Ewan R Pearson, Angus G Jones, Beverley M Shields, Andrew T Hattersley

Issue&Volume: 2025-02-25

Abstract: Background

Data to support individualised choice of optimal glucose-lowering therapy are scarce for people with type 2 diabetes. We aimed to establish whether routinely available clinical features can be used to predict the relative glycaemic effectiveness of five glucose-lowering drug classes.

Methods

We developed and validated a five-drug class model to predict the relative glycaemic effectiveness, in terms of absolute 12-month glycated haemoglobin (HbA1c), for initiating dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, sodium–glucose co-transporter-2 inhibitors, sulfonylureas, and thiazolidinediones. The model used nine routinely available clinical features of people with type 2 diabetes at drug initiation as predictive factors (age, duration of diabetes, sex, and baseline HbA1c, BMI, estimated glomerular filtration rate, HDL cholesterol, total cholesterol, and alanine aminotransferase). The model was developed and validated with observational data from England (Clinical Practice Research Datalink [CPRD] Aurum), in people with type 2 diabetes aged 18–79 years initiating one of the five drug classes between Jan 1, 2004, and Oct 14, 2020, with holdback validation according to geographical region and calendar period. The model was further validated in individual-level data from three published randomised drug trials in type 2 diabetes (TriMaster three-drug crossover trial and two parallel-arm trials [NCT00622284 and NCT01167881]). For validation in CPRD, we assessed differences in observed glycaemic effectiveness between matched (1:1) concordant and discordant groups receiving therapy that was either concordant or discordant with model-predicted optimal therapy, with optimal therapy defined as the drug class with the highest predicted glycaemic effectiveness (ie, lowest predicted 12-month HbA1c). Further validation involved pairwise drug class comparisons in all datasets. We also evaluated associations with long-term outcomes in model-concordant and model-discordant groups in CPRD, assessing 5-year risks of glycaemic failure (confirmed HbA1c ≥69 mmol/mol), all-cause mortality, major adverse cardiovascular events or heart failure (MACE-HF) outcomes, renal progression, and microvascular complications using Cox proportional hazards regression adjusting for relevant demographic and clinical covariates.

Findings

The five-drug class model was developed from 100107 drug initiations in CPRD. In the overall CPRD cohort (combined development and validation cohorts), 32305 (15·2%) of 212166 drug initiations were of the model-predicted optimal therapy. In model-concordant groups, mean observed 12-month HbA1c benefit was 5·3 mmol/mol (95% CI 4·9–5·7) in the CPRD geographical validation cohort (n=24746 drug initiations, n=12373 matched pairs) and 5·0 mmol/mol (4·3–5·6) in the CPRD temporal validation cohort (n=9682 drug initiations, n=4841 matched pairs) compared with matched model-discordant groups. Predicted HbA1c differences were well calibrated with observed HbA1c differences in the three clinical trials in pairwise drug class comparisons, and in pairwise comparisons of the five drug classes in CPRD. 5-year risk of glycaemic failure was lower in model-concordant versus model-discordant groups in CPRD (adjusted hazard ratio [aHR] 0·62 [95% CI 0·59–0·64]). For long-term non-glycaemic outcomes, model-concordant versus model-discordant groups had a similar 5-year risk of all-cause mortality (aHR 0·95 [0·83–1·09]) and lower risks of MACE-HF outcomes (aHR 0·85 [0·76–0·95]), renal progression (aHR 0·71 [0·64–0·79]), and microvascular complications (aHR 0·86 [0·78–0·96]).

Interpretation

We have developed a five-drug class model that uses routine clinical data to identify optimal glucose-lowering therapies for people with type 2 diabetes. Individuals on model-predicted optimal therapy had lower 12-month HbA1c, were less likely to need additional glucose-lowering therapy, and had a lower risk of diabetes complications than individuals on non-optimal therapy. With setting-specific optimisation, the use of routinely collected parameters means that the model is easy to introduce to clinical care in most countries worldwide.

DOI: 10.1016/S0140-6736(24)02617-5

Source: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)02617-5/abstract