Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden.
Department of Primary Health Care, Vaasa Central Hospital, Vaasa, Finland; Diabetes Center, Vaasa Health Care Center, Vaasa, Finland.
Lancet Diabetes Endocrinol. 2018 May;6(5):361-369. doi: 10.1016/S2213-8587(18)30051-2. Epub 2018 Mar 5.
Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis.
We did data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA, and homoeostatic model assessment 2 estimates of β-cell function and insulin resistance), and were related to prospective data from patient records on development of complications and prescription of medication. Replication was done in three independent cohorts: the Scania Diabetes Registry (n=1466), All New Diabetics in Uppsala (n=844), and Diabetes Registry Vaasa (n=3485). Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, and risk of diabetic complications and genetic associations.
We identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes.
We stratified patients into five subgroups with differing disease progression and risk of diabetic complications. This new substratification might eventually help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.
Swedish Research Council, European Research Council, Vinnova, Academy of Finland, Novo Nordisk Foundation, Scania University Hospital, Sigrid Juselius Foundation, Innovative Medicines Initiative 2 Joint Undertaking, Vasa Hospital district, Jakobstadsnejden Heart Foundation, Folkhälsan Research Foundation, Ollqvist Foundation, and Swedish Foundation for Strategic Research.
糖尿病目前分为 1 型和 2 型两种主要形式，但 2 型糖尿病尤其具有高度异质性。更精细的分类可以为个体化治疗方案提供有力工具，并在诊断时识别出并发症风险增加的个体。
我们对来自瑞典斯卡纳新诊断糖尿病患者（n=8980）进行了数据驱动的聚类分析（k-均值和层次聚类）。聚类基于六个变量（谷氨酸脱羧酶抗体、诊断时的年龄、BMI、HbA、稳态模型评估 2 估计的β细胞功能和胰岛素抵抗），并与患者记录中关于并发症发展和药物处方的前瞻性数据相关。在三个独立队列中进行了复制：斯卡纳糖尿病登记处（n=1466）、乌普萨拉新诊断糖尿病患者（n=844）和瓦萨糖尿病登记处（n=3485）。使用 Cox 回归和逻辑回归比较药物使用时间、达到治疗目标的时间以及糖尿病并发症和遗传关联的风险。
我们确定了五种可复制的糖尿病患者聚类，这些聚类具有显著不同的患者特征和糖尿病并发症风险。特别是，聚类 3（对胰岛素最具抵抗力）的个体发生糖尿病肾病的风险明显高于聚类 4 和 5 的个体，但接受了相似的糖尿病治疗。聚类 2（胰岛素缺乏）发生视网膜病变的风险最高。支持聚类，聚类中的遗传关联与传统 2 型糖尿病中的遗传关联不同。
我们将患者分为五个具有不同疾病进展和糖尿病并发症风险的亚组。这种新的细分最终可能有助于针对最受益的患者定制和靶向早期治疗，从而代表糖尿病精准医学的第一步。
瑞典研究委员会、欧洲研究委员会、Vinnova、芬兰科学院、诺和诺德基金会、斯科讷大学医院、西格斯乔德基金会、创新药物倡议 2 联合企业、瓦萨医院区、雅各布斯塔德森心基金会、福斯卡延健康基金会、奥洛夫松基金会和瑞典战略研究基金会。
