Delli Colli Claudia, Viglione Aurelia, Poggini Silvia, Cirulli Francesca, Chiarotti Flavia, Giuliani Alessandro, Branchi Igor
Center for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Rome, Italy.
Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy.
Transl Psychiatry. 2025 Jan 28;15(1):32. doi: 10.1038/s41398-025-03246-1.
Predicting disease trajectories in patients with major depressive disorder (MDD) can allow designing personalized therapeutic strategies. In this study, we aimed to show that measuring patients' plasticity - that is the susceptibility to modify the mental state - identifies at baseline who will recover, anticipating the time to transition to wellbeing. We conducted a secondary analysis in two randomized clinical trials, STAR*D and CO-MED. Symptom severity was assessed using the Quick Inventory of Depressive Symptomatology while the context was measured at enrollment with the Quality-of-Life Enjoyment and Satisfaction Questionnaire. Patients were retrospectively grouped based on both their time to response or remission and their plasticity levels at baseline assessed through a network-based mathematical approach that operationalizes plasticity as the inverse of the symptom network connectivity strength. The results show that plasticity levels at baseline anticipate time to response and time to remission. Connectivity strength among symptoms is significantly lower - and thus plasticity higher - in patients experiencing a fast recovery. When the interplay between plasticity and context is considered, plasticity levels are predictive of disease trajectories only in subjects experiencing a favorable context, confirming that plasticity magnifies the influence of the context on mood. In conclusion, the assessment of plasticity levels at baseline holds promise for predicting MDD trajectories, potentially informing the design of personalized treatments and interventions. The combination of high plasticity and the experience of a favorable context emerges as critical to achieve recovery.
预测重度抑郁症（MDD）患者的疾病轨迹有助于制定个性化治疗策略。在本研究中，我们旨在表明，测量患者的可塑性——即改变心理状态的易感性——能够在基线时识别出哪些患者会康复，并预测向康复状态转变的时间。我们在两项随机临床试验STAR*D和CO-MED中进行了二次分析。使用抑郁症状快速量表评估症状严重程度，同时在入组时用生活质量享受与满意度问卷测量背景情况。通过一种基于网络的数学方法，将可塑性定义为症状网络连接强度的倒数，据此对患者进行回顾性分组，该方法用于评估患者在基线时的可塑性水平以及达到缓解或康复的时间。结果表明，基线时的可塑性水平可预测达到缓解和康复的时间。快速康复的患者症状之间的连接强度显著较低，因此可塑性较高。当考虑可塑性与背景情况之间的相互作用时，可塑性水平仅在背景情况良好的受试者中能够预测疾病轨迹，这证实了可塑性会放大背景情况对情绪的影响。总之，评估基线时的可塑性水平有望预测MDD轨迹，可能为个性化治疗和干预措施的设计提供依据。高可塑性与良好背景情况的结合对于实现康复至关重要。
