Corponi Filippo, Lefrere Antoine, Leboyer Marion, Bellivier Frank, Godin Ophelia, Loftus Josephine, Courtet Philippe, Dubertret Caroline, Haffen Emmanuel, Llorca Pierre Michel, Roux Paul, Polosan Mircea, Schwan Raymund, Samalin Ludovic, Olié Emilie, Etain Bruno, Seriès Peggy, Belzeaux Raoul
School of Informatics, University of Edinburgh, Edinburgh, UK.
Assistance Publique Hôpitaux de Marseille, Pôle de Psychiatrie, Marseille, France.
Psychol Med. 2023 Oct;53(14):6724-6732. doi: 10.1017/S003329172300020X. Epub 2023 Feb 28.
Converging evidence suggests that a subgroup of bipolar disorder (BD) with an early age at onset (AAO) may develop from aberrant neurodevelopment. However, the definition of early AAO remains unprecise. We thus tested which age cut-off for early AAO best corresponds to distinguishable neurodevelopmental pathways.
We analyzed data from the FondaMental Advanced Center of Expertise-Bipolar Disorder cohort, a naturalistic sample of 4421 patients. First, a supervised learning framework was applied in binary classification experiments using neurodevelopmental history to predict early AAO, defined either with Gaussian mixture models (GMM) clustering or with each of the different cut-offs in the range 14 to 25 years. Second, an unsupervised learning approach was used to find clusters based on neurodevelopmental factors and to examine the overlap between such data-driven groups and definitions of early AAO used for supervised learning.
A young cut-off, i.e. 14 up to 16 years, induced higher separability [mean nested cross-validation test AUROC = 0.7327 (± 0.0169) for ⩽16 years]. Predictive performance deteriorated increasing the cut-off or setting early AAO with GMM. Similarly, defining early AAO below 17 years was associated with a higher degree of overlap with data-driven clusters (Normalized Mutual Information = 0.41 for ⩽17 years) relatively to other definitions.
Early AAO best captures distinctive neurodevelopmental patterns when defined as ⩽17 years. GMM-based definition of early AAO falls short of mapping to highly distinguishable neurodevelopmental pathways. These results should be used to improve patients' stratification in future studies of BD pathophysiology and biomarkers.
越来越多的证据表明，早发型双相情感障碍（BD）的一个亚组可能源于异常的神经发育。然而，早发型的定义仍不精确。因此，我们测试了早发型的哪个年龄界限最能对应可区分的神经发育途径。
我们分析了来自双相情感障碍专科高级中心队列的4421例患者的自然样本数据。首先，在二元分类实验中应用监督学习框架，使用神经发育史来预测早发型，早发型的定义采用高斯混合模型（GMM）聚类或14至25岁范围内的每个不同界限。其次，使用无监督学习方法基于神经发育因素寻找聚类，并检查这些数据驱动的组与用于监督学习的早发型定义之间的重叠情况。
较低的界限，即14至16岁，诱导出更高的可分离性[≤16岁时平均嵌套交叉验证测试AUROC = 0.7327（±0.0169）]。随着界限的增加或使用GMM设定早发型，预测性能会下降。同样，将早发型定义为低于17岁与相对于其他定义的数据驱动聚类有更高程度的重叠（≤17岁时标准化互信息 = 0.41）。
当早发型定义为≤17岁时，最能捕捉独特的神经发育模式。基于GMM的早发型定义未能映射到高度可区分的神经发育途径。这些结果应用于改善未来双相情感障碍病理生理学和生物标志物研究中的患者分层。
