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Diagnostic prediction model development using data from dried blood spot proteomics and a digital mental health assessment to identify major depressive disorder among individuals presenting with low mood

Sung Yeon Sarah Han, Jakub Tomasik, Nitin Rustogi, Santiago G. Lago, Giles Barton-Owen, Pawel Eljasz, Jason D. Cooper, Sureyya Ozcan, Tony Olmert, Lynn P. Farrag, Lauren V. Friend, Emily Bell (+4 others)
2020 Brain, behavior, and immunity  

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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
more » ... igital 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.
doi:10.1016/j.bbi.2020.08.011 pmid:32861718 fatcat:pm2yqa4yqrajrinvrxxpkyizgq
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Citation

Sung Yeon Sarah Han, Jakub Tomasik, Nitin Rustogi, Santiago G. Lago, Giles Barton-Owen, Pawel Eljasz, Jason D. Cooper, Sureyya Ozcan, Tony Olmert, Lynn P. Farrag, Lauren V. Friend, Emily Bell, Dan Cowell, Grégoire Thomas, Robin Tuytten, Sabine Bahn. "Diagnostic prediction model development using data from dried blood spot proteomics and a digital mental health assessment to identify major depressive disorder among individuals presenting with low mood." Brain, behavior, and immunity (2020)