Depression presents differently in different people. There are over 200 different combinations of symptoms that will meet the criteria for a diagnosis of major depressive disorder. Individuals also unsurprisingly respond differently to different antidepressant medications, and some may not benefit from available treatments at all. This makes depression a diverse and challenging condition to research and treat, leading researchers to identify and characterise depression subtypes. One such subtype is ‘atypical depression’.
The definition of atypical depression has gone through many iterations since it was first conceptualised at the end of the 1950s. It was first defined to characterise patients with depression who responded preferentially to monoamine oxidase inhibitors (a class of antidepressants), but more recent definitions focus on the presence of specific symptoms (Łojko and Rybakowski, 2017). These include:
- Mood reactivity (mood brightening in response to positive events)
- Weight gain or appetite increase
- Hypersomnia (excessive daytime sleepiness or sleeping for long periods)
- Leaden paralysis (heavy feeling in arms or legs)
- Oversensitivity to social rejection
Atypical depression is present in around 15% – 29% of patients with major depressive disorder (Thase, 2007). Although the relevance of some of the above symptoms has been questioned (Thase, 2009), studies have shown that atypical depression may have distinct clinical, biological and genetic underpinnings (Milaneschi et al., 2020).
In their recent paper, Shin et al. (2026) aimed to explore associations between atypical depression and clinical characteristics, genetic profiles, and antidepressant responses.
Comparing cases of depression can feel like comparing apples to oranges, given the wide variation in symptoms and treatment response, prompting attempts to identify meaningful subtypes such as atypical depression.
Methods
Shin et al. used data from 14,897 participants of the Australian Genetics of Depression Study (AGDS), which recruited ~21,000 Australian individuals with depression (75% female).
Atypical depression was defined as co-occurring weight gain and hypersomnia during the individual’s worst depressive episode and was compared to all other cases of depression.
To investigate whether atypical depression has unique clinical, genetic and treatment response characteristics, participant responses were assessed using questionnaires on:
- Depression and other mental health symptoms
- Substance use
- Stressful events
- Chronotype (whether someone is more alert in the morning or in the evening)
- Antidepressant response and side effects.
Genetic data was also used to calculate polygenic scores for psychiatric disorders and traits related to sleep, metabolism and inflammation. These scores quantify an individual’s genetic predisposition to a certain disorder/trait.
Results
Clinical characteristics
Shin et al. found several differences between those with atypical depression (21%) and those with ‘any other depression’. The atypical group was more often female (79% vs 73%) and reported more lifetime stressful events and a greater number of depressive episodes. They were also more likely to meet criteria for:
- Major depressive disorder (99.6% vs 84.8%)
- Generalised anxiety disorder (53% vs 48%)
- Nicotine use disorder (14% vs 11%).
Those with atypical depression scored higher on measures of mania, suicidality and psychosis and were less likely to self-identify as ‘morning people’ (13% vs 20%), with lower daylight exposure and greater seasonal variation in their depressive symptoms.
Metabolic and physical health
The atypical depression group had a higher BMI (an average of 32 kg/m² vs 27 kg/m²), consistent with its definition, alongside higher rates of hypertension (18% vs 14%) and diabetes or hyperglycaemia (8% vs 5%). There was little evidence of differences in cardiovascular outcomes (heart attack, heart disease, or stroke), likely reflecting their low prevalence in this relatively young sample (mean age ~44 years).
Genetic associations
There were no clear differences in family history of mental illness between the groups. However, atypical depression was associated with higher polygenic scores for several psychiatric traits, including major depression, attention-deficit hyperactivity disorder, bipolar disorder and neuroticism, corresponding to a 7% – 10% increased odds of atypical depression.
Atypical depression was also linked to a higher genetic predisposition to higher BMI, type 2 diabetes, some inflammatory markers, and insulin resistance, and a lower genetic predisposition to HDL cholesterol and being a ‘morning person’. There was no evidence of differences for autism spectrum disorder, Alzheimer’s disease or schizophrenia.
Antidepressant response
Those reporting benefit from SNRIs or SSRIs were 12% – 15% less likely to have atypical depression, with no difference for tricyclic antidepressants. Atypical depression was also linked to more reported treatment side -ffects, including drowsiness, fatigue, headaches, and suicidal thoughts.
Other analyses
Findings were broadly similar when restricted to participants with major depressive disorder (88%) or females (74%), although results were less precise in male-only analyses due to the smaller sample size. Individuals with only one atypical symptom (weight gain or hypersomnia) showed weaker but similar effect sizes compared to those meeting full criteria.
Lastly, adjusting statistical models for BMI weakened most genetic associations, except for being a ‘morning person’ and strengthened associations with antidepressant non-response.
Atypical depression was linked to worse clinical burden, higher metabolic risk, distinct genetics, and different antidepressant response compared with other depression.
Conclusions
This study concluded that atypical depression, defined by co-occurring weight gain and hypersomnia, is a clinically meaningful subtype of depression.
The authors highlight that the association with polygenic scores for being a ‘morning person’ may point to circadian disruption (alterations to the body’s natural, internal 24-hour cycles that help regulate bodily functions) in this subgroup.
They also note that the weakening of other genetic associations after controlling for BMI suggests that body mass may partly explain the relationship between genetic predisposition and atypical depression.
Atypical depression may represent a distinct subtype linked to circadian disruption and metabolic factors, with BMI potentially partly explaining its genetic associations.
Strengths and limitations
The key strengths of this study include its large sample size (i.e., good statistical power), and the range of characteristics studied: from clinical characteristics, comorbidities, and genetic predisposition to antidepressant treatment response and side-effects. Given the large number of statistical tests conducted, the authors applied ‘multiple testing’ corrections to reduce any ‘false positive’ findings, though the reliance on a ‘statistical significance’ thresholds has long been argued against (Sterne and Davey Smith, 2001). Rather, results of medical research should be interpreted in the context of the estimated effect size and strengths and limitations of the study design.
There are also some important limitations to consider. The study defined atypical depression using two retrospectively reported criteria. This means that classification was limited and reliant on memory, introducing recall bias, especially if factors influenced symptom reporting (e.g., current weight or chronotype may affect participant’s memory of their weight gain or sleep during their worst depressive episode). We also don’t know how long ago the episode occurred or if symptoms persisted.
The cross-sectional, retrospective design prevents the authors from drawing any conclusions about the order of events. For example, were antidepressant responses or side-effects due to the atypical symptoms or a cause of them? Similarly, we remain unsure where BMI fits in this story. Although BMI was adjusted for, we can’t tell whether BMI explains the link between genetic predisposition and atypical depression, if it is a consequence of atypical depression itself (Lasserre et al., 2014), or is simply part of the atypical symptom cluster. To answer these questions, we’d need to conduct formal statistical testing (e.g., mediation analyses) in samples with prospective, longitudinal data.
Another limitation highlighted by the authors is that BMI measures were missing for nearly a third of the sample. For polygenic score analyses, the authors compared the effects sizes in the sample after adjusting for BMI (sample size = 8,251) to effect sizes from the main analyses (sample size = 12,001) which means they did not compare effects in the same sample. As such, the reduction of effect size in the BMI-adjusted results may actually be due to the smaller sample size (lower statistical power) or the fact that they are comparing analyses in a subsample of people who reported their BMI to analyses in the full sample (selection bias).
Lastly, and importantly, this study was limited to individuals with genetically inferred European ancestry meaning that results are not generalisable to other populations.
Large sample and broad analyses strengthen findings, but retrospective symptom reporting, limited atypical depression definition, missing data and restricted ancestry limit causal interpretation and generalisability.
Implications for practice
This interesting study further supports the existence of atypical depression as a symptom subtype in depression, and that it may arise from different underlying mechanisms, including genetic, inflammatory and metabolomic pathways.
Given the number of potential symptom combinations, it is not surprising that depression subtypes exist. It has long been argued that we need to move away from categorising mental health problems into discrete diagnoses in research as these don’t capture the heterogeneity in symptoms, risk factors, or treatment responses (Morris et al., 2022). The heterogeneous nature of depression, as well as other psychiatric disorders, is probably one reason why researchers have struggled to identify consistent biological markers or universally effective treatments.
The reduced response to SSRIs and SNRIs in atypical depression implies that different subtypes may require different treatment strategies. Such strategies could potentially focus on core symptoms and behaviours (such as weight gain and nicotine use) that, in turn, increase risk of other adverse outcomes such as heart disease. In fact, the observed links with metabolic risk highlight the importance of integrating physical health screening (e.g. diabetes, hypertension risk) into mental health care.
Further characterisation of depression subtypes is needed to help clinicians provide prognoses and tailor treatments more effectively to patient needs. By expanding our understanding of the mechanisms underlying specific symptom clusters (such as weight gain and hypersomnia), we could even move towards treatment of specific clinical features that cut across diagnostic boundaries. However, to achieve this, we need consensus on what these symptom clusters are and how to best measure them. We will also need to take into account personal experience as it’s not only presence of symptoms but the way they are experienced in the wider context that inevitably impacts someone’s quality of life.
As such, I think we are currently a long way from the ‘case-by-case’ ideal of precision psychiatry, but by conducting studies like Shin et al.’s we are moving ever so slightly closer to making this a reality. However, I’m not a clinician and remain interested to hear how this could realistically fit within real-world clinical practice.
Atypical depression may require different treatment approaches, with implications for both mental and physical healthcare.
Statement of interests
Hannah Jones has no personal or professional links to this study, and no conflicts of interest to declare.
Editor
Edited by Éimear Foley. AI tools assisted with language refinement and formatting during the editorial phase.
Links
Primary paper
Mirim Shin, Jacob Crouse, Tian Lin, Enda Byrne, Brittany Mitchell, Penelope Lind, Richard Parker, Sarah Mckenna, Emiliana Tonini, Joanne Carpenter, Kathleen Merikangas, Naomi Wray, Sarah Medland, Nicholas Martin & Ian Hickie (2026). ‘Atypical depression is associated with a distinct clinical, neurobiological, treatment response, and polygenic risk profile’, Biol Psychiatry. https://doi.org/10.1016/j.biopsych.2026.01.003.
Other references
Lasserre, A. M., et al. (2014). ‘Depression with atypical features and increase in obesity, body mass index, waist circumference, and fat mass: a prospective, population-based study’, JAMA Psychiatry, 71 (8), pp. 880-8. https://doi.org/10.1001/jamapsychiatry.2014.411.
Łojko, D. and Rybakowski, J. K. (2017). ‘Atypical depression: current perspectives’, Neuropsychiatr Dis Treat, 13 pp. 2447-2456. https://doi.org/10.2147/NDT.S147317.
Milaneschi, Y., et al. (2020). ‘Depression Heterogeneity and Its Biological Underpinnings: Toward Immunometabolic Depression’, Biol Psychiatry, 88 (5), pp. 369-380. https://doi.org/10.1016/j.biopsych.2020.01.014.
Morris, S. E., et al. (2022). ‘Revisiting the seven pillars of RDoC’, BMC Med, 20 (1), p. 220. https://doi.org/10.1186/s12916-022-02414-0.
Sterne, J. A. and Davey Smith, G. (2001). ‘Sifting the evidence-what’s wrong with significance tests?’, BMJ, 322 (7280), pp. 226-31. https://doi.org/10.1136/bmj.322.7280.226.
Thase, M. E. (2007). ‘Recognition and diagnosis of atypical depression’, J Clin Psychiatry, 68 Suppl 8 pp. 11-6. Available at: https://www.ncbi.nlm.nih.gov/pubmed/17640153.
Thase, M. E. (2009). ‘Atypical depression: useful concept, but it’s time to revise the DSM-IV criteria’, Neuropsychopharmacology, 34 (13), pp. 2633-41. https://doi.org/10.1038/npp.2009.100.
