Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
Psyomics Ltd., Cambridge, UK.
Brain Behav Immun. 2020 Nov;90:184-195. doi: 10.1016/j.bbi.2020.08.011. Epub 2020 Aug 27.
With less than half of patients with major depressive disorder (MDD) correctly diagnosed within the primary care setting, there is a clinical need to develop an objective and readily accessible test to enable earlier and more accurate diagnosis. The aim of this study was to develop diagnostic prediction models to identify MDD patients among individuals presenting with subclinical low mood, based on data from dried blood spot (DBS) proteomics (194 peptides representing 115 proteins) and a novel digital mental health assessment (102 sociodemographic, clinical and personality characteristics). To this end, we investigated 130 low mood controls, 53 currently depressed individuals with an existing MDD diagnosis (established current MDD), 40 currently depressed individuals with a new MDD diagnosis (new current MDD), and 72 currently not depressed individuals with an existing MDD diagnosis (established non-current MDD). A repeated nested cross-validation approach was used to evaluate variation in model selection and ensure model reproducibility. Prediction models that were trained to differentiate between established current MDD patients and low mood controls (AUC = 0.94 ± 0.01) demonstrated a good predictive performance when extrapolated to differentiate between new current MDD patients and low mood controls (AUC = 0.80 ± 0.01), as well as between established non-current MDD patients and low mood controls (AUC = 0.79 ± 0.01). Importantly, we identified DBS proteins A1AG1, A2GL, AL1A1, APOE and CFAH as important predictors of MDD, indicative of immune system dysregulation; as well as poor self-rated mental health, BMI, reduced daily experiences of positive emotions, and tender-mindedness. Despite the need for further validation, our preliminary findings demonstrate the potential of such prediction models to be used as a diagnostic aid for detecting MDD in clinical practice.
在初级保健环境中，只有不到一半的重度抑郁症 (MDD) 患者得到正确诊断，因此需要开发一种客观且易于获得的测试方法，以便更早、更准确地进行诊断。本研究旨在基于来自干血斑 (DBS) 蛋白质组学（代表 115 种蛋白质的 194 种肽）和新型数字心理健康评估（102 种社会人口学、临床和人格特征）的数据，开发用于识别有亚临床情绪低落表现的 MDD 患者的诊断预测模型。为此，我们调查了 130 名情绪低落的对照组、53 名目前患有 MDD 诊断的现患抑郁症患者（现患 MDD）、40 名新诊断为 MDD 的现患抑郁症患者（新发现患 MDD）和 72 名目前未患有 MDD 诊断的现患抑郁症患者（现患非 MDD）。我们使用重复嵌套交叉验证方法来评估模型选择中的变异性并确保模型的可重复性。当用于区分新现患 MDD 患者和情绪低落对照组时，训练用于区分现患 MDD 患者和情绪低落对照组的预测模型（AUC=0.94±0.01）表现出良好的预测性能（AUC=0.80±0.01），以及区分现患非 MDD 患者和情绪低落对照组（AUC=0.79±0.01）。重要的是，我们确定 DBS 蛋白 A1AG1、A2GL、AL1A1、APOE 和 CFAH 是 MDD 的重要预测因子，表明免疫系统失调；以及自我报告的心理健康状况较差、BMI、减少每天体验积极情绪和易感性。尽管需要进一步验证，但我们的初步发现表明，此类预测模型有可能作为临床实践中检测 MDD 的诊断辅助工具。
