Is my depression the same as your depression?

Depression is frequent (Otte et al., 2016; World Health Organisation), accounting for the largest proportion of disability-adjusted life years (DALYs) amongst psychological well being diagnoses (GBD 2019 Mental Disorders Collaborators, 2022).

There are a number of methods to outline and measure depression, all of which depend on the evaluation of signs. For instance, based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013), a person struggling with depression will present at the least 5 of 9 pre-defined signs inside a two-week interval, considered one of which should be low temper or anhedonia (the lack of curiosity in or enjoyment of actions).

However, individuals with depression range vastly in the quantity and mixture of signs they expertise. In reality, quite a few mixtures of signs fulfill the DSM-5 standards for depression, resulting in big variability in scientific profiles. For occasion, a whole lot of distinctive patterns of signs had been recognized in a single massive pattern of adults with depression (Fried & Nesse, 2015). Research specializing in particular person signs has strengthened this conclusion, and additional means that particular signs are differentially related to psychosocial impairment (Fried & Nesse, 2014). Importantly, signs may additionally exist in dynamic relationships (Borsboom, 2017): that’s, particular person signs can have an effect on each other. For instance, insomnia might decrease focus ranges which in flip might trigger emotions of low self-worth. Importantly, two people with the same recognised general severity of depression and/or comparable symptom profiles may present very completely different relationships between signs. However, analysis has hitherto devoted little time to exploring particular person variations in these ‘symptom dynamics’.

This research by Omid V. Ebrahimi and colleagues (2024) examined depression symptom dynamics by combining ecological momentary evaluation (EMA) and community evaluation. In EMA, contributors’ temper and behavior are repeatedly sampled of their on a regular basis surroundings, in actual time all through the day. In community evaluation (an item-level statistical framework for psychological variables) every symptom is represented by a node, and relationships between signs are represented as edges between nodes, permitting symptom dynamics to be quantified over time.

People with depression range considerably in the quantity and mixture of signs they expertise main to large variability in scientific profiles.

Methods

Ebrahimi and colleagues used knowledge from the ZELF-i randomised managed trial (Bastiaansen et al., 2018), which investigated the results of self-monitoring depression utilizing EMA. Eligible contributors (n=74) had been aged between 18-65 years and had been recognized with depression by a clinician. Depression severity was assessed with the self-reported Inventory of Depressive Symptomatology (IDS-SR). Participants had been prompted to report their temper 5 occasions per day over 28 days, throughout 3-hour time home windows. EMA temper gadgets had been matched to depression signs and had been scored on a visible analogue scale (ranging 1-100).

To analyse the knowledge, dynamic community evaluation was used to estimate individual-specific networks utilizing a method referred to as the “graphical vector autoregressive model” (GVAR). This mannequin yields two networks for every particular person:

1. The “temporal” community, which represents the impression of every symptom on different signs at a later time level (on this case, three hours later).
2. The “contemporaneous” community, which represents associations between signs after accounting for temporal relationships, occurring inside the same 3-hour time window.

Once these networks had been estimated for every particular person, the authors in contrast networks from completely different people with equivalent general severity scores to evaluate the prevalence of variations in community dynamics. To do that, they used a statistical approach referred to as the “individual network invariance test” (INIT). This take a look at includes both setting the edges in networks to be equal throughout people or permitting them to range, after which assessing the proof for every mannequin. Additionally, intensive simulations had been carried out to analyze attainable biases in community comparisons as a consequence of pattern dimension, lacking knowledge, and response charges.

This research used community evaluation and ecological momentary evaluation to discover relationships between signs of depression over time, evaluating contributors with the same general depression severity to at least one one other.

Results

A complete of 74 contributors between 18 and 64 years previous had been included in the research (on common round 34 years previous), and simply over half of the pattern (56.16%) recognized as feminine. Overall, the most continuously reported stage of depression severity was ‘severe’ (i.e., contributors most continuously scored greater than 31 out of a attainable 84 on the IDS-SR). Twenty-three completely different depression severity ranges had been recognized. Each of those ranges included at the least two contributors, with a most of six contributors in every stage.

The headline results of the paper was that 63% of contributors that matched on general symptom severity confirmed completely different symptom networks, as assessed by INIT. For instance, two contributors had a depression severity rating of 31 (out of a attainable 84), and had been matched on age (23-24), gender (feminine) and academic attainment (had at the least accomplished a high-school training). The temporal networks for these two contributors confirmed that whereas in a single participant the symptom of lethargy preceded the symptom of anhedonia, in the second participant anhedonia preceded lethargy. Similarly, the symptom of restlessness preceded depressed temper in the first participant, whereas the reverse was the case for the second participant.

Interestingly, two core signs of depression, anhedonia and depressed temper, affected one another in a mutually reinforcing cycle (a ‘vicious cycle’), with every symptom growing the stage of the different over time. However, this was solely true in a few of the contributors with the same general depression severity, and was absent in different contributors. This exemplifies the proof that even when contributors had been matched on general severity, there have been variations in the underlying relationships between signs. In different phrases, though contributors might have been comparable in demographic traits (like age, gender and training), and depression severity (extreme depression), specializing in particular person signs of depression, and notably the associations between them over time, revealed probably essential variations in symptom dynamics.

63% of contributors who had the same general depression severity, confirmed differing symptom networks (relationships between particular person signs).

Conclusions

This paper gives clear proof that the relationships between depressive signs range between people with depression who’re matched on general depression severity. This gives distinctive perception into an essential supply of scientific heterogeneity in depression. The authors counsel that taking into account the relationship between particular person signs over time may be an essential manner of characterising depression in people, and could also be key to the growth and tailoring of personalised interventions.

This research gives proof that there are substantial particular person variations in how particular person signs of depression work together with one another over time.

Strengths and limitations

This paper was descriptive in design, offering a proof of precept of the existence of particular person variations in symptom dynamics between individuals with depression. The dataset for within-person analyses is substantial, complemented by an intensive and rigorous investigation of symptom dynamics, sensitivity analyses with simulations, and open entry to all code and supplies. As the authors notice:

The proportion of particular person variations in symptom dynamics is prone to have been underestimated, given the methodology’s conservativeness

… that means the precise variations are seemingly a lot bigger than these introduced on this paper. The pattern dimension is average for between-person analyses, and solely 23 (out of a attainable 84) depression severity ranges had been recognized.

As in all community analyses, the exact sample of outcomes will depend upon the selection of nodes. Importantly, some key signs of depression had been unavailable on this dataset (e.g., focus and sleep issues, emotions of worthlessness, and suicidal ideas). In explicit, focus issues are recognized to contribute considerably to purposeful impairment (Fried & Nesse, 2014), and sleep issues are related to antidepressant remedy (Boschloo et al., 2019). It can be essential to incorporate these signs in future investigations to characterise depression dynamics extra utterly.

Participants had been matched on general symptom severity, assessed by whole rating on the IDS-SR. However, signs of depression are heterogeneous, and abstract scores typically neglect this essential supply of variability. Matching contributors on their symptom profiles (both precisely or with comparable symptom mixtures) is a possible various strategy that would offer a extra convincing demonstration of the worth of community dynamics over and above present measures. However, this might require a lot bigger pattern sizes than at present obtainable in most EMA research.

The authors conclude that there are substantial particular person variations in how depression signs work together with one another over time. In different phrases, by specializing in particular person signs, the research finds nice variability in associations between signs over time throughout people, revealing a probably essential supply of heterogeneity. Disentangling this heterogeneity may assist to extra precisely describe a person’s expertise of depression. However, it stays to be seen whether or not symptom dynamics are essential in relation to predicting both one’s evolution of depression (e.g., remitting, relapsing or continual) or response to remedy.

Implications for observe

This research described a brand new manner of characterising fluctuations in particular person signs of depression, and utilized a novel statistical process to wealthy, time-intensive knowledge. This symptom-level strategy remains to be in its early levels, which precludes drawing clear scientific implications from the authors’ findings.

However, the research does open up probably promising avenues for future analysis, which may enhance the precision of psychological evaluation and subsequent choice of remedy. For occasion, monitoring the growth of signs of depression, and the extent to which signs of depression have an effect on one another, may assist establish individuals who would profit from fast, time-sensitive interventions, maybe focused at explicit signs. This research additionally stresses the significance of recognising the heterogeneity between particular person experiences of depression and the potential impact of this on affected person responses to remedy.

In abstract, characterising the relationships between signs has the potential to assist us additional our understanding of essential dynamics in the course of depression, and will assist us higher characterise how depression manifests in a given particular person. Monitoring the temporal fluctuations of signs might present helpful info on maladaptive associations between signs, for each clinicians and people experiencing depression.

Understanding how signs work together over time might assist sufferers and clinicians to higher describe and monitor the episode of depression being skilled.

Statement of pursuits

Giulia Piazza and Jonathan Roiser have beforehand co-authored a community research with Sacha Epskamp, a co-author of the paper mentioned on this weblog.

Links

Primary paper

Ebrahimi, O. V., Borsboom, D., Hoekstra, R. H. A., Epskamp, S., Ostinelli, E. G., Bastiaansen, J. A., & Cipriani, A. (2024). Towards precision in the diagnostic profiling of sufferers: Leveraging symptom dynamics as a scientific characterisation dimension in the evaluation of main depressive dysfunction. The British Journal of Psychiatry, 224(5), 157–163.

Other references

American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (fifth ed.).

Bastiaansen, J. A., Meurs, M., Stelwagen, R., Wunderink, L., Schoevers, R. A., Wichers, M., & Oldehinkel, A. J. (2018). Self-monitoring and personalised suggestions based mostly on the experiencing sampling methodology as a device to spice up depression remedy: A protocol of a realistic randomized managed trial (ZELF-i). BMC Psychiatry, 18(1), 276.

Borsboom, D. (2017). A community principle of psychological problems. World Psychiatry, 16(1), 5–13.

Boschloo, L., Bekhuis, E., Weitz, E. S., Reijnders, M., DeRubeis, R. J., Dimidjian, S., Dunner, D. L., Dunlop, B. W., Hegerl, U., Hollon, S. D., Jarrett, R. B., Kennedy, S. H., Miranda, J., Mohr, D. C., Simons, A. D., Parker, G., Petrak, F., Herpertz, S., Quilty, L. C., … Cuijpers, P. (2019). The symptom-specific efficacy of antidepressant treatment vs. cognitive behavioral remedy in the remedy of depression: Results from a person affected person knowledge meta-analysis. World Psychiatry, 18(2), 183–191.

Fried, E. I., & Nesse, R. M. (2014). The impression of particular person depressive signs on impairment of psychosocial functioning. PloS One, 9(2), e90311.

Fried, E. I., & Nesse, R. M. (2015). Depression shouldn’t be a constant syndrome: An investigation of distinctive symptom patterns in the STAR*D research. Journal of Affective Disorders, 172, 96–102.

GBD 2019 Mental Disorders Collaborators. (2022). Global, regional, and nationwide burden of 12 psychological problems in 204 international locations and territories, 1990–2019: A scientific evaluation for the Global Burden of Disease Study 2019. The Lancet Psychiatry, 9(2), 137–150.

Otte, C., Gold, S. M., Penninx, B. W., Pariante, C. M., Etkin, A., Fava, M., Mohr, D. C., & Schatzberg, A. F. (2016). Major depressive dysfunction. Nature Reviews Disease Primers, 2(1), Article 1.

World Health Organisation. Depressive dysfunction (depression). Retrieved 22 November 2023.

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