Martin Walter
Sebastian Olbrich2
Nils Opel
Maria Strauss
Bin Zhang
Lena Vera Danyeli- 1Department of Psychiatry, University Hospital Jena, Jena, Germany
- 2Department of Psychiatry, Psychiatric University Hospital Zurich, Zürich, Switzerland
- 3Department of Psychiatry, University Hospital Leipzig, Leipzig, Saxony, Germany
- 4Tianjin Anding Hospital, Tianjin, China
Editorial on the Research Topic
Recent advances on the multimodal search for markers of treatment response in affective disorders: from bench to bedside, volume II
Affective disorders and their comorbidities concern the largest group of psychiatric patients due to 1) the early and continuous lifelong high incidence of the disorders and 2) their chronic nature, which is often due to inadequate or delayed treatment responses (1–3). This contrasts with the comparably high level of treatment success during controlled treatment escalation, with response rates of 60-80% to ECT even in difficult-to-treat patients. Although the application of stratified treatment regimens—starting with relatively well-tolerated drugs or psycho(mono-)therapy—has been shown to be more effective compared to free treatment regimens (4), the majority of algorithms still begin with first-line drug suggestions that offer only moderate expected response rates. For these first-line treatments, remission rates are as low as 33% (STAR-D), decreasing further at subsequent treatment levels. Consequently, poor or significantly delayed remission rates add to a complex situation that recently resulted in as few as 7% of patients (across several diagnoses) achieving timely and efficient treatment on an international level (5). Not surprisingly, the lengthy search for the right individual treatment, alongside the side effects accompanying ineffective treatments, which may even worsen clinical status, leads to a higher number of patients who, disappointed, drop out of treatment courses that might eventually have led to a significant response. The search for (bio-)markers predicting individual treatment responses has spanned the careers of many clinicians and scientists and has led to some candidate markers, that, however, have relatively low predictive values or replicability (6). While factors predicting general treatment resistance have been reported, these, however, seem to have little value in supporting a shift towards more targeted interventions.
Given the fact that there is a very large group of patients who would, in principle, respond or remit once efficient treatments are identified and applied, and given the long course of ineffective treatment, during which patients lose adherence, identifying and implementing new, additional predictors promises substantial clinical and health economic benefits. These benefits are maximized when these markers not only allow early transition to escalated treatments but also identify subsystems that characterize biological patient subgroups with specific therapeutic targets that differ from those addressed by first- or second-line treatments. Earlier collections of such candidates have particularly demonstrated potential ways to incorporate additional knowledge into treatment prediction (7).
Over the past five years, evidence has emerged regarding 1) new mechanisms and candidates and 2) new treatment opportunities made possible by large-scale reimbursement options e.g. TMS or NMDA antagonists. These new opportunities and insights have fueled research into treatment rationales that go beyond monoaminergic approaches, identifying exciting new candidate markers related to glutamatergic modulation. New therapeutically driven routes towards glutamatergic mechanisms have emerged alongside growing insights into biological phenotypes underlying long-debated clinical subgroups of depression, mainly melancholic and atypical subtypes (8). Importantly, these subtypes, with distinct symptom and response profiles, have been increasingly characterized by multidimensional biological markers (9) that link regional brain imaging evidence, patient history and the molecular convergence of several biological systems. Ongoing evidence on patients who are best characterized as “immunometabolic” (10), suggests a convergence of a metabolic/adipose load with accompanying (low-grade) inflammatory signatures in these patients indicating the need for new and adapted treatment strategies. The emergence of neuropsychoimmunological patient characteristics has interestingly converged with the introduction of new treatments at the level of glutamatergic signaling, where activation of IDO is related to downstream changes in kynurenic metabolites with NMDA-ergic properties.
This development is reflected in the contributions to our second edition: Consequently, Reininghaus et al. expanded upon their previous characterization of kynurenic pathway abnormalities in depression (11) to explore their predictive potential for individualized treatment outcomes. They showed that statically altered kynurenic acid (KYN) can not only help identify non-responders, but that KYN increases over the course of treatment signifying treatment response.
Importantly, such metabolic signatures have recently become a focus of non-invasive imaging studies (12). In this Research Topic, Moreau et al. reviewed evidence from 20 different studies on imaging treatment predictors in OCD. As a result, they concluded that structural assessments failed to provide consistent markers of treatment response. Their findings suggest that relevant processes may be more effectively captured by dynamic i.e. metabolic or neurophysiological investigations. Such metabolic investigations have become possible through the introduction of feasible MR spectroscopy sequences (MRS), which can capture a wide range of metabolic markers in certain brain regions. Watling et al. applied this technique to an emerging candidate of metabolic abnormality, namely glutathione (GSH), the brain’s most abundant antioxidant. While they failed to find differential GSH levels in a group of PTSD patients, they reported initial evidence of altered metalloproteinase (MMP)-9 and myeloperoxidase (MPO) levels, which are also related to disease progression.
Previous accounts of the application of quantitative EEG in treatment prediction (13) have been followed up by the final contribution to this second volume: Kim et al. demonstrated that a QEEG allowed for successful identification of depression patients with an accuracy of 92.31% and a 10-fold cross-validation loss of 0.13%. While these numbers are already considerably higher than those reported for MRI-based imaging signals (14), the true power of electrophysiological signals, however, might be found in the correct labeling of treatment responders for various treatment options in depression (15, 16).
In conclusion, the most recent advances concern both new targets and techniques with an expanding set of potential biomarkers across multiple modalities, which, however, may depict different aspects of similar mechanisms or patient characteristics. Therefore, the search for suitable treatment markers to guide specific interventions remains an open quest with high potential pains and gains.
Author contributions
MW: Writing – original draft. SO: Writing – review & editing. NO: Writing – review & editing. MS: Writing – review & editing. BZ: Writing – review & editing. LD: Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1. Nandi A, Beard JR, and Galea S. Epidemiologic heterogeneity of common mood and anxiety disorders over the lifecourse in the general population: a systematic review. BMC Psychiatry. (2009) 9:31. doi: 10.1186/1471-244X-9-31
2. Martín Calvo MJ, Fernández Mayo L, García del Castillo I, et al. Comorbidity of affective disorders. Eur Psychiatry. (2011) 26:1714–4. doi: 10.1016/S0924-9338(11)73418-6
3. Wang PS, Angermeyer M, Borges G, Bruffaerts R, Tat Chiu W, DE Girolamo G, et al. Delay and failure in treatment seeking after first onset of mental disorders in the World Health Organization’s World Mental Health Survey Initiative. World Psychiatry. (2007) 6:177–85.
4. Adli M, Berghöfer A, Linden M, Helmchen H, Müller-Oerlinghausen B, Mackert A, et al. Effectiveness and feasibility of a standardized stepwise drug treatment regimen algorithm for inpatients with depressive disorders: results of a 2-year observational algorithm study. J Clin Psychiatry. (2002) 63:782–90. doi: 10.4088/jcp.v63n0906
5. Vigo DV, Stein DJ, Harris MG, Kazdin AE, Viana MC, Munthali R, et al. Effective treatment for mental and substance use disorders in 21 countries. JAMA Psychiatry. (2025):e244378. doi: 10.1001/jamapsychiatry.2024.4378
6. Bartova L, Dold M, Kautzky A, Fabbri C, Spies M, Serretti A, et al. Results of the European Group for the Study of Resistant Depression (GSRD) - basis for further research and clinical practice. World J Biol Psychiatry. (2019) 20:427–48. doi: 10.1080/15622975.2019.1635270
7. Schmidt FM, Sander C, and Walter M. Editorial: recent advances on the multimodal search for markers of treatment response in affective disorders: from bench to bedside? Front Psychiatry. (2019) 10:790. doi: 10.3389/fpsyt.2019.00790
8. Lorenzo-Luaces L, Buss JF, and Fried EI. Heterogeneity in major depression and its melancholic and atypical specifiers: a secondary analysis of STAR*D. BMC Psychiatry. (2021) 21:454. doi: 10.1186/s12888-021-03444-3
9. Woelfer M, Kasties V, Kahlfuss S, and Walter M. The role of depressive subtypes within the neuroinflammation hypothesis of major depressive disorder. Neuroscience. (2019) 403:93–110. doi: 10.1016/j.neuroscience.2018.03.034
10. Milaneschi Y, Lamers F, Berk M, and Penninx BWJH. Depression heterogeneity and its biological underpinnings: toward immunometabolic depression. Biol Psychiatry. (2020) 88:369–80. doi: 10.1016/j.biopsych.2020.01.014
11. Reininghaus EZ, Dalkner N, Riedrich K, Fuchs D, Gostner JM, and Reininghaus B. Sex specific changes in tryptophan breakdown over a 6 week treatment period. Front Psychiatry. (2019) 10:74. doi: 10.3389/fpsyt.2019.00074
12. Sen ZD, Danyeli LV, Woelfer M, Lamers F, Wagner G, Sobanski T, et al. Linking atypical depression and insulin resistance-related disorders via low-grade chronic inflammation: Integrating the phenotypic, molecular and neuroanatomical dimensions. Brain behavior Immun. (2021) 93:335–52. doi: 10.1016/j.bbi.2020.12.020
13. Schiller MJ. Quantitative electroencephalography in guiding treatment of major depression. Front Psychiatry. (2019) 9:779. doi: 10.3389/fpsyt.2018.00779
14. Winter NR, Blanke J, Leenings R, Ernsting J, Fisch L, Sarink K, et al. A systematic evaluation of machine learning-based biomarkers for major depressive disorder. JAMA Psychiatry. (2024) 81:386–95. doi: 10.1001/jamapsychiatry.2023.5083
15. Arns M, Olbrich S, and Sack AT. Biomarker-driven stratified psychiatry: from stepped-care to matched-care in mental health. Nat Ment Health. (2023) 1:917–9. doi: 10.1038/s44220-023-00156-3
16. Ip CT, Olbrich S, Ganz M, Ozenne B, Köhler-Forsberg K, Dam VH, et al. Pretreatment qEEG biomarkers for predicting pharmacological treatment outcome in major depressive disorder: Independent validation from the NeuroPharm study. Eur Neuropsychopharmacol. (2021) 49:101–12. doi: 10.1016/j.euroneuro.2021.03.024
Keywords: depression, biomarker, subtypes of depression, treatment resistance, imaging
Citation: Walter M, Olbrich S, Opel N, Strauss M, Zhang B and Danyeli LV (2025) Editorial: Recent advances on the multimodal search for markers of treatment response in affective disorders: from bench to bedside, volume II. Front. Psychiatry 16:1597565. doi: 10.3389/fpsyt.2025.1597565
Received: 21 March 2025; Accepted: 27 June 2025;
Published: 28 August 2025.
Edited and Reviewed by:
Raquel Romay-Tallon, Rush University, United StatesCopyright © 2025 Walter, Olbrich, Opel, Strauss, Zhang and Danyeli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Martin Walter, bWFydGluLndhbHRlckBtZWQudW5pLWplbmEuZGU=