Atypical depression: current perspectives

Łojko, Dorota; Rybakowski, Janusz

doi:10.2147/ndt.s147317

Cited by 75 publications

(69 citation statements)

References 87 publications

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“…Finally, our study has also a translational relevance, since inflammatory markers-and especially CRP levels-have often been associated with atypical depression forms, which are more likely to be resistant to treatment with first-line antidepressants, like selective serotonin reuptake inhibitors (SSRI;Chamberlain et al, 2019;Hickman et al, 2014;Köhler-Forsberg et al, 2017;Mora et al, 2018;Strawbridge et al, 2017). Conversely, other classes of antidepressants like monoamine oxidase inhibitors (MAOI) have been reported to be more effective in this sense (Cleare, Fekadu, & Rane, 2012;Łojko & Rybakowski, 2017), and have also been associated with a reduction of oxidative stress and chronic inflammation levels (Sturza et al, 2019). While in the current study, we could not identify the type of antidepressants used by participants, access to regional drug prescription registries will possibly allow us to investigate separately treatment-resistant and -responsive depression forms in the future.…”

Section: Discussionmentioning

confidence: 86%

Associations between systemic inflammation and somatic depressive symptoms: Findings from the Moli‐sani study

Gialluisi

Castelnuovo

Bracone

et al. 2020

Depression and Anxiety

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Background The link between systemic inflammation and depression has been deeply investigated, but relatively few studies explored symptom‐specific associations, mostly focusing on common inflammatory biomarkers like C‐reactive protein (CRP) levels. Methods We investigated associations of low‐grade inflammation with depressive symptoms assessed through a reduced version of Patient Health Questionnaire 9 (PHQ‐9) in a large population‐based cohort of adult Italians (N = 13 301). We built logistic regressions between each depressive symptom and composite index of systemic inflammation based on four circulating biomarkers, namely CRP, Plt, WBC, and GLR (INFLA)‐score, a composite blood‐based inflammation index, and with its component biomarkers, namely CRP, platelets count (Plt), white blood cells count (WBC), and granulocyte‐to‐lymphocyte ratio (GLR). Results We observed a strong association of the altered appetite/eating symptom with standardized INFLA‐score (OR [95% CI] = 1.19 [1.12‐1.26]; corrected p = 3.0 × 10−7), CRP (1.28 [1.20‐1.36]; p = 1.9 × 10−13), and WBC (1.13 [1.06‐1.20]; p = 2.3 × 10−3), and of tiredness/low energy with GLR (1.11 [1.05‐1.17]; p = 9.4 × 10−3). These associations remained stable within nondepressed participants (PHQ‐9 < 10), and after adjustment for the use of antidepressants, main chronic conditions, and lifestyle factors; while they were notably attenuated within depressed participants (PHQ‐9 ≥ 10) and—for altered appetite only—by adjustment for obesity. Conclusions This study provides a robust replication of the association previously reported between CRP and altered appetite in a large US population cohort, and supports a link between systemic inflammation, altered appetite, and tiredness. Moreover, it extends this evidence to inflammatory markers other than CRP and suggests new targets for the treatment of atypical depression.

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Section: Discussionmentioning

confidence: 86%

Associations between systemic inflammation and somatic depressive symptoms: Findings from the Moli‐sani study

Gialluisi

Castelnuovo

Bracone

et al. 2020

Depression and Anxiety

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“…However, MAOIs are no longer widely used, and evidence that patients with atypical depression respond better to MAOIs than to the newer generation medications has been lacking 73 . Moreover, a recent meta-analysis found that atypical depression did not predict or moderate the response to either cognitive behavioral therapy or antidepressant medication 53 .…”

Section: Clinical Subtypesmentioning

confidence: 99%

The clinical characterization of the adult patient with depression aimed at personalization of management

et al. 2020

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Depression is widely acknowledged to be a heterogeneous entity, and the need to further characterize the individual patient who has received this diagnosis in order to personalize the management plan has been repeatedly emphasized. However, the research evidence that should guide this personalization is at present fragmentary, and the selection of treatment is usually based on the clinician's and/or the patient's preference and on safety issues, in a trial‐and‐error fashion, paying little attention to the particular features of the specific case. This may be one of the reasons why the majority of patients with a diagnosis of depression do not achieve remission with the first treatment they receive. The predominant pessimism about the actual feasibility of the personalization of treatment of depression in routine clinical practice has recently been tempered by some secondary analyses of databases from clinical trials, using approaches such as individual patient data meta‐analysis and machine learning, which indicate that some variables may indeed contribute to the identification of patients who are likely to respond differently to various antidepressant drugs or to antidepressant medication vs. specific psychotherapies. The need to develop decision support tools guiding the personalization of treatment of depression has been recently reaffirmed, and the point made that these tools should be developed through large observational studies using a comprehensive battery of self‐report and clinical measures. The present paper aims to describe systematically the salient domains that should be considered in this effort to personalize depression treatment. For each domain, the available research evidence is summarized, and the relevant assessment instruments are reviewed, with special attention to their suitability for use in routine clinical practice, also in view of their possible inclusion in the above‐mentioned comprehensive battery of measures. The main unmet needs that research should address in this area are emphasized. Where the available evidence allows providing the clinician with specific advice that can already be used today to make the management of depression more personalized, this advice is highlighted. Indeed, some sections of the paper, such as those on neurocognition and on physical comorbidities, indicate that the modern management of depression is becoming increasingly complex, with several components other than simply the choice of an antidepressant and/or a psychotherapy, some of which can already be reliably personalized.

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“…99 In conclusion, beyond the older findings of the superiority of MAOIs compared to TCAs in treating MDD-AF, so far, no specific indications are available to help clinicians in selection among newer antidepressants. 113 However, as associations between atypical depression and bipolarity were found, which can have therapeutic implications, 114 careful information about possible risk factors for bipolarity should be collected in clinical practice before starting treatment when atypical features are present. Likewise, some associations found between atypical depression, overweight/obesity, and resistance to leptine 114,115 suggest that clinicians should consider antidepressants with limited effects on appetite and/body weight when treating patients with MDD-AF.…”

Section: Mdd With Atypical Featuresmentioning

confidence: 99%

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Perna

Alciati²,

Daccò

et al. 2020

Psychiatry Investig

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Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient’s unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.

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Atypical depression: current perspectives

Cited by 75 publications

References 87 publications

Associations between systemic inflammation and somatic depressive symptoms: Findings from the Moli‐sani study

Associations between systemic inflammation and somatic depressive symptoms: Findings from the Moli‐sani study

The clinical characterization of the adult patient with depression aimed at personalization of management

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

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