Blood-based biomarkers predicting response to antidepressants
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Updated: Feb 15, 2026

Prediction of Red Blood Cell Antibody Significance Using the Monocyte-Macrophage Assay
Published on: February 7, 2025
Yasmin Busch1, Andreas Menke2,3
1Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Wuerzburg, Margarete-Hoeppel-Platz 1, 97080, Würzburg, Germany.
Abstract:
Major depressive disorder is a common, serious and in some cases, life-threatening condition and affects approximately 350 million people globally. Although there is effective treatment available for it, more than 50% of the patients fail to respond to the first antidepressant they receive. The selection of a distinct treatment is still exclusively based on clinical judgment without incorporating lab-derived objective measures. However, there is growing evidence of biomarkers that it helps to improve diagnostic processes and treatment algorithms. Here genetic markers and blood-based biomarkers of the monoamine pathways, inflammatory pathways and the hypothalamic-pituitary-adrenal (HPA) axis are reviewed. Promising findings arise from studies investigating inflammatory pathways and immune markers that may identify patients suitable for anti-inflammatory based treatment regimes. Next, an early normalization of a disturbed HPA axis or depleted neurotrophic factors may predict stable treatment response. Genetic markers within the serotonergic system may identify patients who are vulnerable because of stressful life events, but evidence for guiding treatment regimes still is inconsistent. Therefore, there is still a great need for studies investigating and validating biomarkers for the prediction of treatment response to facilitate the treatment selection and shorten the time to remission and thus provide personalized medicine in psychiatry.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

