COMPREHENSIVE REVIEW OF FACTORS IMPLICATED IN THE HETEROGENEITY OF RESPONSE IN DEPRESSION: Review: Heterogeneity in Depression
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BACKGROUND: Heterogeneity in overall response and outcomes to pharmacological treatment has been reported in several depression studies but with few sources that integrate these results. The goal of this study was to review the literature and attempt to identify nongenetic factors potentially predictive of overall response to depression treatments. METHODS: A comprehensive review of the literature from the last 10 years was performed using three key databases (PubMed, EMBASE, and Cochrane). All relevant studies that met the inclusion criteria were selected and scored for their levels of evidence using the NICE scoring method. A subjective assessment of the strength of evidence for each factor was performed using predefined criteria. RESULTS: Our broad search yielded 76 articles relevant to treatment heterogeneity. Sociodemographic factors, disease characteristics, and comorbidities were the most heavily researched areas. Some of the factors associated with more favorable overall response include being married, other social support, and low levels of baseline depressive symptoms. Evidence relating to baseline disease severity as a factor predictive of antidepressant response was particularly convincing among the factors reviewed. The presence of comorbid anxiety and pain contributed to worse antidepressant treatment outcomes. CONCLUSIONS: Several factors either predictive of or associated with overall response to antidepressant treatment have been identified. Inclusion of factors predictive of response in the design of future trials may help tailor treatments to depression patients presenting to the average clinical practice, resulting in improved outcomes.
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Depression is not a homogeneous disorder; depression severity, symptom patterns and age at onset vary considerably between individuals.
“depression is not a homogeneous disorder; instead, depression severity, symptom patterns and age at onset vary considerably between individuals. 3-5”
From Childhood maltreatment and characteristics of adult depression: Meta-analysis (2017) · cites this paper as [5]
The Baseline Severity and Onset Age of Depression sections discuss differences in depression severity and age at onset among patients. The Introduction also describes diverse patients, but the passages reviewed do not establish the claim about variation in symptom patterns; the paper’s focus is heterogeneity of treatment response. Page numbers were not supplied in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPoor social support at baseline reduces the effect of treatment for depression.
“Poor social support at baseline reduces the effect of treatment for depression,24,25 and treatment responders for depression are more likely to have better social support at baseline than nonresponders.26,27”
Most prior studies have examined social support as a moderator or outcome of treatment.
“Most prior studies have examined social support as a moderator or outcome of treatment.22,25”
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A permutation test for assessing the presence of individual differences in treatment effects
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Dennison, J. B., Tepfer, L. J., & Smith, D. V. (2023) · Human Brain Mapping · doi:10.1002/hbm.26254 · Cited by 8 · see citing works
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Luo, Z., Hu, Z., Qiu, X., Li, W., Wang, C., Lan, X., Mai, S., Chen, Y., Liu, G., Zhang, F., et al. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.01.025 · Cited by 7 · see citing works
Ratzsch, J., Richter, M. F., Blitz, R., Čolić, L., Gutfleisch, L., Goltermann, J., Gruber, M., Straube, B., Alexander, N., Jamalabadi, H., et al. (2025) · Psychological Medicine · doi:10.1017/s0033291725000984 · Cited by 7 · see citing works
Hack, L. M. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118 · Cited by 7 · see citing works
Inferring Depression and Its Semantic Underpinnings from Simple Lexical Choices
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Animal Models to Investigate Social Support Induced Anxiety Reductions
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Voelker, J., Joshi, K., Daly, E., Papademetriou, E., Rotter, D., Sheehan, J. J., Kuvadia, H., Liu, X., Dasgupta, A., & Potluri, R. C. (2021) · Brain and Behavior · doi:10.1002/brb3.2000 · Cited by 5 · see citing works
Brancati, G. E., Dominicis, F. D., Petrucci, A., Pallucchini, A., Carli, M., Medda, P., Schiavi, E., Rossi, P. D., Vicari, S., & Perugi, G. (2023) · The World Journal of Biological Psychiatry · doi:10.1080/15622975.2023.2168750 · Cited by 5 · see citing works
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Zeng, Q., Wang, W., Fang, Y., Mellor, D. J., McCabe, M. P., Byrne, L. K., Zuo, S., & Xu, Y. (2016) · Psychiatry Research · doi:10.1016/j.psychres.2016.04.111 · Cited by 5 · see citing works
Evaluation of Medical Technologies with Uncertain Benefits
Lakdawalla, D. N., & Phelps, C. (2019) · National Bureau of Economic Research · doi:10.3386/w26058 · Cited by 5 · see citing works
Pfeiffer, P. N., Zivin, K., Hosanagar, A., Panaite, V., Ganoczy, D. A., Kim, H. M., Hofer, T. P., & Piette, J. D. (2022) · The Journal of Behavioral Health Services & Research · doi:10.1007/s11414-022-09813-4 · Cited by 4 · see citing works
Chapter 12. Toward Precision Cognitive-Behavioral Therapy Via Reinforcement Learning Theory
Niv, Y., Hitchcock, P., Berwian, I. M., & Schoen, G. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg12 · Cited by 3 · see citing works
A Permutation Test for Assessing the Presence of Individual Differences in Treatment EffectsPreprint
Chang, C., Jaki, T., Sadiq, M. S., Kuhlemeier, A., Feaster, D. J., Cole, N., Lamont, A. E., Oberski, D. L., Desai, Y., & Van Horn, M. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.07248 · Cited by 3 · see citing works
Silverstein, S. M., Demmin, D. L., & Fradkin, S. I. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg05 · Cited by 2 · see citing works
This and that in depression: Cross-linguistic semantic effects
Kruse, L., Rocca, R., Todisco, E., Vesper, C., Waade, P. T., & Wallentin, M. (2025) · PLOS mental health. · doi:10.1371/journal.pmen.0000438 · Cited by 2 · see citing works
Munkholm, K., Homan, S., & Homan, P. (2019) · doi:10.31219/osf.io/srzx5 · Cited by 2 · see citing works
Poplin, T., Ironside, M., Kuplicki, R., Aupperle, R. L., Guinjoan, S. M., Khalsa, S. S., Stewart, J. L., Victor, T. A., Paulus, M. P., & Kirlić, N. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.17.562565 · Cited by 2 · see citing works
Trajectories of perinatal depression among women living with HIV in Uganda
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Chapter 9. Translational Neurobiological Approaches to Precision Medicine for Fear and PTSD
Harnett, N. G., Seligowski, A. V., Pine, D. S., & Ressler, K. J. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg09 · Cited by 1 · see citing works
Lilian Salvo, G., Saldivia, S., Parra, C., Rodríguez, R., Cifuentes, M., Acevedo, P., Díaz, M., Ormazabal, M., Guerra, I., Navarrete, N., et al. (2017) · Revista médica de Chile · doi:10.4067/s0034-98872017000300007 · Cited by 1 · see citing works
Trösken, A., & Renneberg, B. (2019) · Verhaltenstherapie · doi:10.1159/000489670 · Cited by 1 · see citing works
Nkyi, A. K., & Ninnoni, J. P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0299391 · Cited by 1 · see citing works
Clinical associations with treatment resistance in depression: An electronic health record study
Coombes, B. J., Sánchez-Ruiz, J. A., Fennessy, B., Pazdernik, V. K., Adekkanattu, P. M., Núñez, N. A., Lepow, L., Beaupre, L. M., Ryu, E., Talati, A., et al. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.116203 · Cited by 1 · see citing works
Chapter 2. The Future Of Precision Transcranial Magnetic Stimulation In Psychiatry
Philip, N. S., Zandvakili, A., & Berlow, Y. A. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg02 · Cited by 1 · see citing works
Connor, M., Adrian Wells, A., & Fisher, P. (2015) · doi:10.1002/9781119114482.ch1 · Cited by 1 · see citing works
Phenotype-by-phenome-wide association study of treatment resistant depressionPreprint
Coombes, B. J., Sánchez-Ruiz, J. A., Fennessy, B., Pazdernik, V., Adekkanattu, P. M., Núñez, N. A., Lepow, L., Ryu, E., Talati, A., Jenkins, G. D., et al. (2022) · medRxiv · doi:10.1101/2022.08.23.22279074 · Cited by 1 · see citing works
CONSUMO DE ANTIDEPRESSIVOS ENTRE IDOSOS: EVIDÊNCIAS DO PROJETO BAMBUÍ.
da Saúde, M. (2015)
Wu, P.-C., Tsai, S., Chen, T.-J., & Chen, M. (2023) · Taiwanese Journal of Psychiatry · doi:10.4103/tpsy.tpsy_14_23
GYDYMUI ATSPARI DEPRESIJA: RIZIKOS VEIKSNIAI IR GYDYMAS
Miglinaitė, M. (2024) · Health Sciences · doi:10.35988/sm-hs.2024.124
Siaba, S., Casal, B., & Rivera, B. (2024) · Revista Hacienda Pública Española · doi:10.7866/hpe-rpe.24.2.1
Chapter 10. Latent Variable–Based Predictive And Explanatory Disease Models
Paulus, M. P. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg10
(2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.bm01
Da, C. M., Ribeiro, F., Felger, J. C., Treadway, M. T., Goldsmith, D. R., Haroon, E., & Miller, A. H. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg06
(2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.fm01
Chapter 7. Pharmacogenetic Testing: A Tool for Precision Prescribing in Psychiatry
Bousman, C., Forbes, M., & Dunlop, B. W. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg07
Chapter 8. Treatment Prediction Biomarkers For Major Depressive Disorder
Sacchet, M. D., Webb, C. A., & Pizzagalli, D. A. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg08
Chapter 3. Neural Mechanisms Of Bipolar Disorder: Toward Personalized Markers of Future Illness Risk
Phillips, M. L. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg03
Chapter 11. Computational Cognitive Methods For Precision Psychiatry
Huys, Q. J. M. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg11
Vinogradov, S., Mishra, J., Nahum, M., & Ramsay, I. S. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg04
(2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.index
Chapter 13. Moving From Precision to Personalized Psychiatry: Clinical Perspectives on the New Era
Perna, G., & Nemeroff, C. B. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg13
- Authors
- Carter, G. C., Cantrell, R. A., Zarotsky, V., Haynes, V. S., Phillips, G., Alatorre, C. I., Goetz, I., Paczkowski, R., & Marangell, L. B.
- Affiliations
- Eli Lilly (United States) (US)
- Optum (United States) (US)
- Corresponding author
- Victoria Zarotsky
- ORCID
- Venue
- Depression and Anxiety
- Publisher
- Wiley
- Published
- 2012-04
- Volume
- 29
- Issue
- 4
- Pages
- 340-354
- ISSN
- 1091-4269 (ISSN-L)
- OpenAlex
- W1781867922
- Cited by
- 109
- References
- 82
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 9 · 2023: 9 · 2022: 5 · 2021: 27 · 2020: 3 · 2019: 12 · 2018: 7 · 2017: 2 · 2016: 5 · 2015: 5 · 2014: 6 · 2013: 7 · 2012: 2
- FWCI
- 0.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Depression and Anxiety (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Eli Lilly and Company
- Topics
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Keywords
- depression, antidepressant response, treatment response heterogeneity, predictors of treatment response, baseline disease severity, anxiety comorbidity, comorbid pain, social support, marital status, sociodemographic factors
- MeSH terms
- Depressive Disorder, Humans, Treatment Outcome
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