Aims/hypothesis

A precision medicine approach in type 2 diabetes could enhance targeting specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to use the recently developed Bayesian causal forest (BCF) method to develop and validate an individualised treatment selection algorithm for two major type 2 diabetes drug classes, sodium–glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA).

Methods

We designed a predictive algorithm using BCF to estimate individual-level conditional average treatment effects for 12-month glycaemic outcome (HbA_1c) between SGLT2i and GLP1-RA, based on routine clinical features of 46,394 people with type 2 diabetes in primary care in England (Clinical Practice Research Datalink; 27,319 for model development, 19,075 for hold-out validation), with additional external validation in 2252 people with type 2 diabetes from Scotland (SCI-Diabetes [Tayside & Fife]). Differences in glycaemic outcome with GLP1-RA by sex seen in clinical data were replicated in clinical trial data (HARMONY programme: liraglutide [n=389] and albiglutide [n=1682]). As secondary outcomes, we evaluated the impacts of targeting therapy based on glycaemic response on weight change, tolerability and longer-term risk of new-onset microvascular complications, macrovascular complications and adverse kidney events.

Results

Model development identified marked heterogeneity in glycaemic response, with 4787 (17.5%) of the development cohort having a predicted HbA_1c benefit >3 mmol/mol (>0.3%) with SGLT2i over GLP1-RA and 5551 (20.3%) having a predicted HbA_1c benefit >3 mmol/mol with GLP1-RA over SGLT2i. Calibration was good in hold-back validation, and external validation in an independent Scottish dataset identified clear differences in glycaemic outcomes between those predicted to benefit from each therapy. Sex, with women markedly more responsive to GLP1-RA, was identified as a major treatment effect modifier in both the UK observational datasets and in clinical trial data: HARMONY-7 liraglutide (GLP1-RA): 4.4 mmol/mol (95% credible interval [95% CrI] 2.2, 6.3) (0.4% [95% CrI 0.2, 0.6]) greater response in women than men. Targeting the two therapies based on predicted glycaemic response was also associated with improvements in short-term tolerability and long-term risk of new-onset microvascular complications.

Conclusions/interpretation

Precision medicine approaches can facilitate effective individualised treatment choice between SGLT2i and GLP1-RA therapies, and the use of routinely collected clinical features for treatment selection could support low-cost deployment in many countries.

Graphical Abstract

中文翻译：

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基于表型的SGLT2抑制剂和GLP-1受体激动剂靶向治疗2型糖尿病

目标/假设

2 型糖尿病的精准医疗方法可以增强针对最有可能受益的个体患者的特定降糖疗法。我们的目标是使用最近开发的贝叶斯因果森林 (BCF) 方法来开发和验证两种主要 2 型糖尿病药物类别：钠-葡萄糖协同转运蛋白 2 抑制剂 (SGLT2i) 和胰高血糖素样肽-1 受体激动剂的个体化治疗选择算法(GLP1-RA)。

方法

我们根据英国初级保健中 46,394 名 2 型糖尿病患者的常规临床特征，设计了一种使用 BCF 的预测算法来估计 SGLT2i 和 GLP1-RA 之间 12 个月血糖结果 (HbA _1c ) 的个体水平条件平均治疗效果（临床实践研究数据链；27,319 个用于模型开发，19,075 个用于保留验证），并对来自苏格兰的 2252 名 2 型糖尿病患者进行了额外的外部验证（SCI-糖尿病 [Tayside & Fife]）。临床数据中观察到的 GLP1-RA 血糖结果的性别差异在临床试验数据中得到了重复（HARMONY 计划：利拉鲁肽 [ n = 389] 和阿必鲁肽 [ n = 1682]）。作为次要结局，我们评估了基于血糖反应的靶向治疗对体重变化、耐受性以及新发微血管并发症、大血管并发症和肾脏不良事件的长期风险的影响。

结果

模型开发发现了血糖反应的显着异质性，其中 4787 名（17.5%）开发队列中 SGLT2i 相对于 GLP1-RA 的预测 HbA _1c益处 >3 mmol/mol（>0.3%），5551 名（20.3%）的预测 HbA 1c 益处超过 GLP1-RA与 SGLT2i 相比，GLP1-RA 的HbA _{1c益处 >3 mmol/mol。}抑制验证中的校准效果良好，独立苏格兰数据集中的外部验证发现，预计从每种治疗中受益的患者之间的血糖结果存在明显差异。在英国观察数据集和临床试验数据中，性别被确定为主要的治疗效果调节剂，其中女性对 GLP1-RA 的反应明显更大：HARMONY-7 利拉鲁肽 (GLP1-RA)：4.4 mmol/mol（95% 可信）区间 [95% CrI] 2.2, 6.3) (0.4% [95% CrI 0.2, 0.6]) 女性的反应高于男性。基于预测的血糖反应来针对这两种疗法也与短期耐受性和新发微血管并发症的长期风险的改善相关。

结论/解释

精准医学方法可以促进 SGLT2i 和 GLP1-RA 疗法之间有效的个体化治疗选择，并且使用常规收集的临床特征进行治疗选择可以支持在许多国家的低成本部署。

图形概要