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.

中文翻译：

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使用干血斑蛋白质组学数据和数字心理健康评估开发诊断预测模型，以识别情绪低落个体的重度抑郁症

在初级保健机构中，只有不到一半的重度抑郁症 (MDD) 患者得到正确诊断，因此临床需要开发一种客观且易于获得的测试，以实现更早、更准确的诊断。本研究的目的是开发诊断预测模型，根据来自干血斑 (DBS) 蛋白质组学（代表 115 种蛋白质的 194 种肽）的数据和新的数字心理健康评估（102社会人口学、临床和人格特征）。为此，我们调查了 130 名情绪低落的控制者，53 名患有现有 MDD 诊断的当前抑郁症患者（已确定的当前 MDD），40 名患有新 MDD 诊断的当前抑郁症患者（新的当前 MDD），72 名目前没有患有 MDD 诊断的抑郁症患者（已确定的非当前 MDD）。使用重复的嵌套交叉验证方法来评估模型选择的变化并确保模型的可重复性。经过训练以区分已建立的当前 MDD 患者和低情绪控制 (AUC = 0.94 ± 0.01) 的预测模型在外推以区分新的当前 MDD 患者和低情绪控制 (AUC = 0.80 ± 0.01) 时表现出良好的预测性能，如以及已建立的非当前 MDD 患者和情绪低落控制之间 (AUC = 0.79 ± 0.01)。重要的是，我们确定 DBS 蛋白 A1AG1、A2GL、AL1A1、APOE 和 CFAH 作为 MDD 的重要预测因子，表明免疫系统失调；以及自我评价的心理健康状况不佳，BMI，减少积极情绪和温柔的日常体验。尽管需要进一步验证，但我们的初步发现证明了此类预测模型在临床实践中用作检测 MDD 的诊断辅助工具的潜力。