Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg.
Diabetologia. 2024 Aug;67(8):1567-1581. doi: 10.1007/s00125-024-06179-4. Epub 2024 May 23.
AIMS/HYPOTHESIS: Our study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes.
In the Study of the French-speaking Society of Type 1 Diabetes (SFDT1), we characterised glycaemic heterogeneity thanks to a set of complementary metrics: HbA, time in range (TIR), time below range (TBR), CV, Gold score and glycaemia risk index (GRI). Applying the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm, we created a phenotypic tree, i.e. a 2D visual mapping. We also carried out a clustering analysis for comparison.
We included 618 participants with type 1 diabetes (52.9% men, mean age 40.6 years [SD 14.1]). Our phenotypic tree identified seven glycaemic phenotypes. The 2D phenotypic tree comprised a main branch in the proximal region and glycaemic phenotypes in the distal areas. Dimension 1, the horizontal dimension, was positively associated with GRI (coefficient [95% CI]) (0.54 [0.52, 0.57]), HbA (0.39 [0.35, 0.42]), CV (0.24 [0.19, 0.28]) and TBR (0.11 [0.06, 0.15]), and negatively with TIR (-0.52 [-0.54, -0.49]). The vertical dimension was positively associated with TBR (0.41 [0.38, 0.44]), CV (0.40 [0.37, 0.43]), TIR (0.16 [0.12, 0.20]), Gold score (0.10 [0.06, 0.15]) and GRI (0.06 [0.02, 0.11]), and negatively with HbA (-0.21 [-0.25, -0.17]). Notably, socioeconomic factors, cardiovascular risk indicators, retinopathy and treatment strategy were significant determinants of glycaemic phenotype diversity. The phenotypic tree enabled more granularity than traditional clustering in revealing clinically relevant subgroups of people with type 1 diabetes.
CONCLUSIONS/INTERPRETATION: Our study advances the current understanding of the complex glycaemic profile in people with type 1 diabetes and suggests that strategies based on isolated glycaemic metrics might not capture the complexity of the glycaemic phenotypes in real life. Relying on these phenotypes could improve patient stratification in type 1 diabetes care and personalise disease management.
目的/假设：本研究旨在揭示 1 型糖尿病患者的血糖表型异质性。
在法国 1 型糖尿病学会（SFDT1）的研究中，我们通过一系列互补指标来描述血糖异质性：HbA、血糖控制目标范围内时间（TIR）、血糖控制目标范围外时间（TBR）、变异系数（CV）、Gold 评分和血糖风险指数（GRI）。应用判别维度缩减树（DDRTree）算法，我们创建了一个表型树，即二维可视化映射。我们还进行了聚类分析以作比较。
我们纳入了 618 名 1 型糖尿病患者（52.9%为男性，平均年龄 40.6 岁[SD 14.1]）。我们的表型树确定了七种血糖表型。二维表型树由近端区域的主分支和远端区域的血糖表型组成。第一维，水平维度，与 GRI（系数[95%CI]）呈正相关（0.54[0.52，0.57]），与 HbA（0.39[0.35，0.42]）、CV（0.24[0.19，0.28]）和 TBR（0.11[0.06，0.15]）呈正相关，与 TIR（-0.52[-0.54，-0.49]）呈负相关。垂直维度与 TBR（0.41[0.38，0.44]）、CV（0.40[0.37，0.43]）、TIR（0.16[0.12，0.20]）、Gold 评分（0.10[0.06，0.15]）和 GRI（0.06[0.02，0.11]）呈正相关，与 HbA（-0.21[-0.25，-0.17]）呈负相关。值得注意的是，社会经济因素、心血管风险指标、视网膜病变和治疗策略是血糖表型多样性的重要决定因素。表型树比传统聚类更能揭示 1 型糖尿病患者的临床相关亚组。
结论/解释：本研究加深了我们对 1 型糖尿病患者复杂血糖谱的理解，并表明基于孤立血糖指标的策略可能无法捕捉到现实生活中血糖表型的复杂性。依赖这些表型可以改善 1 型糖尿病护理中的患者分层，并使疾病管理个体化。
