Aims/hypothesis

This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist’s novel diabetes subgroups and previously analysed by Slieker et al.

Methods

We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA_1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed.

Results

Subgroups’ risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%.

Conclusions/interpretation

Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.

Graphical Abstract

中文翻译：

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数据驱动的 2 型糖尿病亚组的临床特征、并发症和治疗选择的轨迹

目标/假设

本研究旨在探索两个初级保健登记处（荷兰和苏格兰）的k均值聚类对 2 型糖尿病患者进行分类的亚组的附加价值，其灵感来自于 Ahlqvist 的新型糖尿病亚组，并由 Slieker 等人之前进行了分析。

方法

我们使用了两个荷兰和苏格兰糖尿病队列（N = 3054 和 6145；中位随访时间分别为 11.2 和 12.3 年），并通过k均值聚类定义了五个亚组，其中包括基线年龄、BMI、HbA _1c、HDL-胆固醇和C肽。我们通过风险因素值轨迹（随机截距模型）、糖尿病相关并发症发生时间（对数秩检验和 Cox 模型）和用药模式（多项逻辑模型）研究了亚组之间的差异。我们还直接使用聚类指标作为进展预测因子与k均值离散子组进行比较。评估了随访期间的聚类一致性。

结果

亚组的危险因素存在显着差异，并且这些差异在随访过程中基本保持一致。在所有亚组中，与轻度胰岛素抵抗个体相比，严重胰岛素抵抗个体在之前（HR 1.65；95% CI 1.40，1.94）和调整年龄效应后（HR 1.72；95% CI 1.46，2.02）面临显着更高的心肌梗死风险。患有高 HDL 胆固醇的糖尿病。严重胰岛素缺乏型糖尿病患者接受的治疗最为集中，超过 25% 的患者在诊断后 10 年时服用了胰岛素。对于相对于轻度糖尿病的严重胰岛素缺乏糖尿病，在其他因素不变的情况下，使用胰岛素相对于不使用普通治疗的相对风险预计会增加 3.07 倍（95% CI 2.73, 3.44）。相对于亚组，聚类指标可以更好地预测进展变异，但将两者结合后预测准确性可能会提高。聚类在 8 年内保持一致，准确度范围为 59% 至 72%。

结论/解释

数据驱动的亚组分配在随访过程中总体上保持一致，并捕捉到了危险因素轨迹、用药模式和并发症风险的显着差异。子组更好地作为补充，而不是作为压缩聚类指标的基础。

图形概要