V Módis László, Matuz András, Aradi Zsófia, Horváth Ildikó Fanny, Szántó Antónia, Bugán Antal
Department of Behavioural Sciences, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
Szabolcs-Szatmár-Bereg County Teaching Hospital, Nagykálló Sántha Kálmán Member Hospital, Nagykálló, Hungary.
Front Psychiatry. 2025 Jun 3;16:1549756. doi: 10.3389/fpsyt.2025.1549756. eCollection 2025.
Besides primary Sjögren's syndrome (pSS) is generally assessed through biological markers, growing evidence suggests that psychological and social factors-such as anxiety, depression, personality traits, and social support-may also play a role in disease burden. Relative contribution of these biopsychosocial dimensions to disease activity in pSS, however, has not been quantitatively compared. This study aimed to evaluate the predictive weight of different factors in determining both objective and subjective disease burden using machine learning (ML) models.
117 pSS patients, whose biological (blood cell counts, complement activity, IgG, RF, SSA, SSB), psychological (personality traits, depression, anxiety, basic self-esteem assessed via self-reported questionnaires), and social (socioeconomic status and social support) measures were collected in a composite database. Outcome variables were SSA/SSB autoantibodies and EULAR Sjögren Syndrome Patient Reported Index (ESSPRI), as indicators of biological and perceived disease burden, respectively. Three machine learning algorithms were trained to predict outcome variables, first by each measure category, then on the entire set of predictor variables. Permutation feature importance was used to assess the importance of the predictors. The five most important predictors were selected for all target outcomes.
Concerning autoantibodies, the model performed best with biological input only, in the case of ESSPRI, the complete dataset gave the best performance. Trait anxiety was selected as important negative predictor of both autoantibodies. Besides, biological measures (IgG, RF, platelet count) and age were among the five most important features. State anxiety and temperament trait 'Fatigability' were important positive predictors of ESSPRI, while character trait 'Pure-hearted conscience', IgG and RF were important negative predictors.
Unexpected psychobiological correlations, like trait anxiety and IgG/RF as negative predictors of autoantibodies and ESSPRI, respectively, suggest different (immunobiological and psychosomatic) disease mechanisms and symptom burden. Importance of psychological factors in estimating disease burden may pave the way toward novel, more sensitive diagnostic tools and therapeutic methods and better understanding of pathomechanisms of pSS.
除了通过生物标志物对原发性干燥综合征（pSS）进行常规评估外，越来越多的证据表明，心理和社会因素，如焦虑、抑郁、人格特质和社会支持，也可能在疾病负担中发挥作用。然而，这些生物心理社会维度对pSS疾病活动的相对贡献尚未进行定量比较。本研究旨在使用机器学习（ML）模型评估不同因素在确定客观和主观疾病负担方面的预测权重。
117例pSS患者的生物（血细胞计数、补体活性、IgG、RF、SSA、SSB）、心理（人格特质、抑郁、焦虑、通过自我报告问卷评估的基本自尊）和社会（社会经济地位和社会支持）测量数据被收集到一个综合数据库中。结果变量分别为SSA/SSB自身抗体和欧洲抗风湿病联盟干燥综合征患者报告指数（ESSPRI），作为生物性和感知到的疾病负担指标。训练了三种机器学习算法来预测结果变量，首先按每个测量类别进行，然后在整个预测变量集上进行。排列特征重要性用于评估预测因子的重要性。为所有目标结果选择了五个最重要的预测因子。
关于自身抗体，仅生物输入时模型表现最佳；对于ESSPRI，完整数据集表现最佳。特质焦虑被选为自身抗体的重要负向预测因子。此外，生物测量指标（IgG、RF、血小板计数）和年龄是五个最重要的特征之一。状态焦虑和气质特质“易疲劳性”是ESSPRI的重要正向预测因子，而性格特质“心地纯洁”、IgG和RF是重要负向预测因子。
意外的心理生物学相关性，如特质焦虑以及IgG/RF分别作为自身抗体和ESSPRI的负向预测因子，提示了不同的（免疫生物学和身心）疾病机制和症状负担。心理因素在评估疾病负担中的重要性可能为新型、更敏感的诊断工具和治疗方法以及更好地理解pSS的发病机制铺平道路。
