Smyrnis Alexandros, Theleritis Christos, Ferentinos Panagiotis, Smyrnis Nikolaos
Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece.
2ndPsychiatry Department, National and Kapodistrian University of Athens, Medical School, University General Hospital "ATTIKON", Athens, Greece.
Front Psychiatry. 2024 Dec 3;15:1463974. doi: 10.3389/fpsyt.2024.1463974. eCollection 2024.
Associating temporal variation of biomarkers with the onset of psychotic relapse could help demystify the pathogenesis of psychosis as a pathological brain state, while allowing for timely intervention, thus ameliorating clinical outcome. In this systematic review, we evaluated the predictive accuracy of a broad spectrum of biomarkers for psychotic relapse. We also underline methodological concerns, focusing on the value of prospective studies for relapse onset estimation.
Following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, a list of search strings related to biomarkers and relapse was assimilated and run against the PubMed and Scopus databases, yielding a total of 808 unique records. After exclusion of studies related to the distinction of patients from controls or treatment effects, the 42 remaining studies were divided into 5 groups, based on the type of biomarker used as a predictor: the genetic biomarker subgroup (n = 4, or 9%), the blood-based biomarker subgroup (n = 15, or 36%), the neuroimaging biomarker subgroup (n = 10, or 24%), the cognitive-behavioral biomarker subgroup (n = 5, or 12%) and the wearables biomarker subgroup (n = 8, or 19%).
In the first 4 groups, several factors were found to correlate with the state of relapse, such as the genetic risk profile, Interleukin-6, Vitamin D or panels consisting of multiple markers (blood-based), ventricular volume, grey matter volume in the right hippocampus, various functional connectivity metrics (neuroimaging), working memory and executive function (cognition). In the wearables group, machine learning models were trained based on features such as heart rate, acceleration, and geolocation, which were measured continuously. While the achieved predictive accuracy differed compared to chance, its power was moderate (max reported AUC = 0.77).
The first 4 groups revealed risk factors, but cross-sectional designs or sparse sampling in prospective studies did not allow for relapse onset estimations. Studies involving wearables provide more concrete predictions of relapse but utilized markers such as geolocation do not advance pathophysiological understanding. A combination of the two approaches is warranted to fully understand and predict relapse.
将生物标志物的时间变化与精神病性复发的发作相关联，有助于揭开精神病作为一种病理性脑状态的发病机制之谜，同时实现及时干预，从而改善临床结局。在本系统评价中，我们评估了多种生物标志物对精神病性复发的预测准确性。我们还强调了方法学方面的问题，重点关注前瞻性研究对复发发作估计的价值。
遵循PRISMA（系统评价和Meta分析的首选报告项目）指南，整理了一份与生物标志物和复发相关的检索词列表，并在PubMed和Scopus数据库中进行检索，共得到808条独特记录。在排除与区分患者和对照或治疗效果相关的研究后，根据用作预测指标的生物标志物类型，将其余42项研究分为5组：遗传生物标志物亚组（n = 4，或9%）、血液生物标志物亚组（n = 15，或36%）、神经影像学生物标志物亚组（n = 10，或24%）、认知行为生物标志物亚组（n = 5，或12%）和可穿戴设备生物标志物亚组（n = 8，或19%）。
在前4组中，发现了几个与复发状态相关的因素，如遗传风险概况、白细胞介素-6、维生素D或由多种标志物组成的指标（血液相关）、脑室体积、右侧海马灰质体积、各种功能连接指标（神经影像学）、工作记忆和执行功能（认知）。在可穿戴设备组中，基于连续测量的心率、加速度和地理位置等特征训练了机器学习模型。虽然所达到的预测准确性与随机情况相比有所不同，但其效力中等（报告的最大AUC = 0.77）。
前4组揭示了风险因素，但前瞻性研究中的横断面设计或稀疏采样无法进行复发发作估计。涉及可穿戴设备的研究提供了更具体的复发预测，但所使用的地理位置等标志物并未推进对病理生理学的理解。有必要将这两种方法结合起来，以全面理解和预测复发。
