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Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
5-Lipoxygenase (5-LO) is the key enzyme in the formation of pro-inflammatory leukotrienes (LT) which play an important role in a number of inflammatory diseases. Accordingly, 5-LO inhibitors are frequently used to study the role of 5-LO and LT in models of inflammation and cancer. Interestingly, the therapeutic efficacy of these inhibitors is highly variable. Here we show that the frequently used 5-LO inhibitors AA-861, BWA4C, C06, CJ-13,610 and the FDA approved compound zileuton as well as the pan-LO inhibitor nordihydroguaiaretic acid interfere with prostaglandin E2 (PGE2) release into the supernatants of cytokine-stimulated (TNFα/IL-1β) HeLa cervix carcinoma, A549 lung cancer as well as HCA-7 colon carcinoma cells with similar potencies compared to their LT inhibitory activities (IC50 values ranging from 0.1–9.1 µM). In addition, AA-861, BWA4C, CJ-13,610 and zileuton concentration-dependently inhibited bacterial lipopolysaccharide triggered prostaglandin (PG) release into human whole blood. Western Blot analysis revealed that inhibition of expression of enzymes involved in PG synthesis was not part of the underlying mechanism. Also, liberation of arachidonic acid which is the substrate for PG synthesis as well as PGH2 and PGE2 formation were not impaired by the compounds. However, accumulation of intracellular PGE2 was found in the inhibitor treated HeLa cells suggesting inhibition of PG export as major mechanism. Further, experiments showed that the PG exporter ATP-binding cassette transporter multidrug resistance protein 4 (MRP-4) is targeted by the inhibitors and may be involved in the 5-LO inhibitor-mediated PGE2 inhibition. In conclusion, the pharmacological effects of a number of 5-LO inhibitors are compound-specific and involve the potent inhibition of PGE2 export. Results from experimental models on the role of 5-LO in inflammation and pain using 5-LO inhibitors may be misleading and their use as pharmacological tools in experimental models has to be revisited. In addition, 5-LO inhibitors may serve as new scaffolds for the development of potent prostaglandin export inhibitors.