Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables
Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables
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DOI：
10.1016/s2213-8587(18)30051-2
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发表时间：
2018-05-01
影响因子：
44.5
通讯作者：
Groop, Leif
中科院分区：
文献类型：
--
作者：
Ahlqvist, Emma;Storm, Petter;Groop, Leif
背景糖尿病目前分为两种主要形式，1型和2型糖尿病，但2型糖尿病尤其是高度异质性的。一个完善的分类可以提供一个强大的工具，个性化的治疗方案，并确定个人在diagnosis.Methods并发症的风险增加，我们做了数据驱动的聚类分析（k-均值和层次聚类），在新诊断的糖尿病患者（n=8980）从瑞典所有新的糖尿病在斯堪尼亚队列。聚类基于6个变量（谷氨酸脱羧酶抗体、诊断时的年龄、BMI、HbA（1c）和同型平衡模型评估2 β细胞功能和胰岛素抵抗的估计值），并与来自患者记录的并发症发展和药物处方的前瞻性数据相关。在三个独立的队列中进行复制：斯堪尼亚糖尿病登记处（n=1466），乌普萨拉所有新发糖尿病患者（n=844）和糖尿病登记处瓦萨（n=3485）。考克斯回归和Logistic回归被用来比较时间，药物治疗，时间达到治疗目标，糖尿病并发症和遗传associations.Findings的风险，我们确定了5个可复制集群的糖尿病患者，有显着不同的患者特征和糖尿病并发症的风险。特别是，第3组的个体（对胰岛素抵抗最强）患糖尿病肾病的风险显著高于第4组和第5组的个体，但他们接受了类似的糖尿病治疗。第2组（胰岛素缺乏）有最高的视网膜病变风险。为了支持聚类，在集群中的遗传关联不同于传统的2型diabetes.Interpretation中看到的，我们将患者分为五个亚组，具有不同的疾病进展和糖尿病并发症的风险。这种新的子分层可能最终有助于为受益最大的患者量身定制和靶向早期治疗，从而代表了糖尿病精准医学的第一步。
Background 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.Methods 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(1c), and homoeostatic model assessment 2 estimates of beta-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.Findings 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.Interpretation 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.