Atypical symptoms in hospitalised patients with major depressive episode: frequency, clinical characteristics, and internal validity

A factor analytic comparison of three commonly used depression scales (HAMD, MADRS, BDI) in a large sample of depressed inpatients

BACKGROUND

Quantifying depression mainly relies on the use of depression scales, and understanding their factor structure is crucial for evaluating their validity.

METHODS

This post-hoc analysis utilized prospectively collected data from a naturalistic study of 1014 inpatients with major depression. Confirmatory and exploratory factor analyses were performed to test the psychometric abilities of the Hamilton Depression Rating Scale, the Montgomery Asberg Depression Rating Scale, and the self-rated Beck Depression Inventory. A combined factor analysis was also conducted including all items of all scales.

RESULTS

All three scales showed good to very good internal consistency. The HAMD-17 had four factors: an "anxiety" factor, a "depression" factor, an "insomnia" factor, and a "somatic" factor. The MADRS also had four factors: a "sadness" factor, a neurovegetative factor, a "detachment" factor and a "negative thoughts" factor, while the BDI had three factors: a "negative attitude towards self" factor, a "performance impairment" factor, and a "somatic" factor. The combined factor analysis suggested that self-ratings might reflect a distinct illness dimension within major depression.

CONCLUSIONS

The factors obtained in this study are comparable to those found in previous research. Self and clinician ratings are complementary and not redundant, highlighting the importance of using multiple measures to quantify depression.

Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.

Major depressive disorder subtypes and depression symptoms in multiple sclerosis: What is different compared to the general population?

OBJECTIVE

To compare and characterize major depressive disorder (MDD) subtypes (i.e., pure atypical, pure melancholic and mixed atypical-melancholic) and depression symptoms in persons with multiple sclerosis (PwMS) with persons without MS (Pw/oMS) fulfilling the DSM-5 criteria for a past 12-month MDD.

METHODS

MDD in PwMS (n = 92) from the Swiss Multiple Sclerosis Registry was compared with Pw/oMS (n = 277) from a Swiss community-based study. Epidemiological MDD diagnoses were based on the Mini-SPIKE (shortened form of the Structured Psychopathological Interview and Rating of the Social Consequences for Epidemiology). Logistic and multinomial regression analyses (adjusted for sex, age, civil status, depression and severity) were computed for comparisons and characterization. Latent class analysis (LCA) was conducted to empirically identify depression subtypes in PwMS.

RESULTS

PwMS had a higher risk for the mixed atypical-melancholic MDD subtype (OR = 2.22, 95% CI = 1.03-4.80) compared to Pw/oMS. MDD in PwMS was specifically characterized by a higher risk of the two somatic atypical depression symptoms 'weight gain' (OR = 6.91, 95% CI = 2.20-21.70) and 'leaden paralysis' (OR = 3.03, 95% CI = 1.35-6.82) and the symptom 'irritable/angry' (OR = 3.18, 95% CI = 1.08-9.39).

CONCLUSIONS

MDD in PwMS was characterized by a higher risk for specific somatic atypical depression symptoms and the mixed atypical-melancholic MDD subtype. The pure atypical MDD subtype, however, did not differentiate between PwMS and Pw/oMS. Given the high phenomenological overlap with MS symptoms, the mixed atypical-melancholic MDD subtype represents a particular diagnostic challenge.

Combining machine learning algorithms for prediction of antidepressant treatment response

OBJECTIVES

Predictors for unfavorable treatment outcome in major depressive disorder (MDD) applicable for treatment selection are still lacking. The database of a longitudinal multicenter study on 1079 acutely depressed patients, performed by the German research network on depression (GRND), allows supervised and unsupervised learning to further elucidate the interplay of clinical and psycho-sociodemographic variables and their predictive impact on treatment outcome phenotypes.

EXPERIMENTAL PROCEDURES

Treatment response was defined by a change of HAM-D 17-item baseline score ≥50% and remission by the established threshold of ≤7, respectively, after up to eight weeks of inpatient treatment. After hierarchical symptom clustering and stratification by treatment subtypes (serotonin reuptake inhibitors, tricyclic antidepressants, antipsychotic, and lithium augmentation), prediction models for different outcome phenotypes were computed with random forest in a cross-center validation design. In total, 88 predictors were implemented.

RESULTS

Clustering revealed four distinct HAM-D subscores related to emotional, anxious, sleep, and appetite symptoms, respectively. After feature selection, classification models reached moderate to high accuracies up to 0.85. Highest accuracies were observed for the SSRI and TCA subgroups and for sleep and appetite symptoms, while anxious symptoms showed poor predictability.

CONCLUSION

Our results support a decisive role for machine learning in the management of antidepressant treatment. Treatment- and symptom-specific algorithms may increase accuracies by reducing heterogeneity. Especially, predictors related to duration of illness, baseline depression severity, anxiety and somatic symptoms, and personality traits moderate treatment success. However, prospectives application of machine learning models will be necessary to prove their value for the clinic.

Position statement of the European Psychiatric Association (EPA) on the value of antidepressants in the treatment of unipolar depression

This position statement will address in an evidence-based approach some of the important issues and controversies of current drug treatment of depression such as the efficacy of antidepressants, their effect on suicidality and their place in a complex psychiatric treatment strategy including psychotherapy. The efficacy of antidepressants is clinically relevant. The highest effect size was demonstrated for severe depression. Based on responder rates and based on double-blind placebo-controlled studies, the number needed to treat (NNT) is 5–7 for acute treatment and four for maintenance treatment. Monotherapy with one drug is often not sufficient and has to be followed by other antidepressants or by comedication/augmentation therapy approaches. Generally, antidepressants reduce suicidality, but under special conditions like young age or personality disorder, they can also increase suicidality. However, under the conditions of good clinical practice, the risk–benefit relationship of treatment with antidepressants can be judged as favourable also in this respect. The capacity of psychiatrists to individualise and optimise treatment decisions in terms of ‘the right drug/treatment for the right patient’ is still restricted since currently there are no sufficient powerful clinical or biological predictors which could help to achieve this goal. There is hope that in future pharmacogenetics will contribute significantly to a personalised treatment. With regard to plasma concentration, therapeutic drug monitoring (TDM) is a useful tool to optimize plasma levels therapeutic outcome. The ideal that all steps of clinical decision-making can be based on the strict rules of evidence-based medicine is far away from reality. Clinical experience so far still has a great impact.

What are depressive symptoms in acutely ill patients with schizophrenia spectrum disorder?

Background:

Aim was to examine depressive symptoms in acutely ill schizophrenia patients on a single symptom basis and to evaluate their relationship with positive, negative and general psychopathological symptoms.

Methods:

Two hundred and seventy-eight patients suffering from a schizophrenia spectrum disorder were analysed within a naturalistic study by the German Research Network on Schizophrenia. Using the Calgary Depression Scale for Schizophrenia (CDSS) depressive symptoms were examined and the Positive and Negative Syndrome Scale (PANSS) was applied to assess positive, negative and general symptoms. Correlation and factor analyses were calculated to detect the underlying structure and relationship of the patient’s symptoms.

Results:

The most prevalent depressive symptoms identified were depressed mood (80%), observed depression (62%) and hopelessness (54%). Thirty-nine percent of the patients suffered from depressive symptoms when applying the recommended cut-off of a CDSS total score of > 6 points at admission. Negligible correlations were found between depressive and positive symptoms as well as most PANSS negative and global symptoms despite items on depression, guilt and social withdrawal. The factor analysis revealed that the factor loading with the PANSS negative items accounted for most of the data variance followed by a factor with positive symptoms and three depression-associated factors.

Limitations:

The naturalistic study design does not allow a sufficient control of study results for the effect of different pharmacological treatments possibly influencing the appearance of depressive symptoms.

Conclusion:

Results suggest that depressive symptoms measured with the CDSS are a discrete symptom domain with only partial overlap with positive or negative symptoms.

Prevalence and clinical features of atypical depression among patients with major depressive disorder in China

BACKGROUND

Little is known about the demographic and clinical features of the atypical subtype of major depressive disorder (MDD) patients in China. This study set out to investigate the prevalence of atypical depression in MDD patients in China, and identify its demographic and clinical features.

METHODS

The study was conducted in 13 major psychiatric hospitals or in the psychiatric units of general hospitals in China, and recruited a sample of 1172 patients diagnosed with MDD. The patients' demographic and clinical features and prescriptions of psychotropic drugs were collected using a standardized questionnaire designed for the study.

RESULTS

The prevalence of atypical depression was 15.3%. In multiple logistic regression analyses, compared to the non-atypical depression patients, the atypical depression patients were more likely to have depressive episodes with suicide ideation and attempts (OR = 1.49, 95% CI = 1.06, 2.10, P = 0.023), depressive episodes with psychotic features (OR = 2.15, 95% CI = 1.43, 3.22, P < 0.001), seasonal depressive episodes (OR = 1.77, 95% CI = 1.12, 2.78, P = 0.014), an earlier age of onset (OR = 0.98, 95% CI = 0.96, 0.99, P = 0.001), and lifetime depressive episodes (OR = 1.07, 95% CI = 1.01, 1.13, P = 0.020).

LIMITATIONS

The assessment of atypical features was not based on a validated rating scale.

CONCLUSION

Our results indicate that atypical depression is common in Chinese patients with MDD. MDD with atypical features may be more severe and debilitating than patients with non-atypical features.

Subtypes of depression and their overlap in a naturalistic inpatient sample of major depressive disorder

Subtyping depression is important in order to further delineate biological causes of depressive syndromes. The aim of this study was to evaluate clinical and outcome characteristics of distinct subtypes of depression and to assess proportion and features of patients fulfilling criteria for more than one subtype. Melancholic, atypical and anxious subtypes of depression were assessed in a naturalistic sample of 833 inpatients using DSM-IV specifiers based on operationalized criteria. Baseline characteristics and outcome criteria at discharge were compared between distinct subtypes and their overlap. A substantial proportion of patients (16%) were classified with more than one subtype of depression, 28% were of the distinct anxious, 7% of the distinct atypical and 5% of the distinct melancholic subtype. Distinct melancholic patients had shortest duration of episode, highest baseline depression severity, but were more often early improvers; distinct anxious patients had higher NEO-Five Factor Inventory (NEO-FFI) neuroticism scores compared with patients with unspecific subtype. Melancholic patients with overlap of anxious features had worse treatment outcome compared to distinct melancholic and distinct anxious subtype. Distinct subtypes differed in only few variables and patients with overlap of depression subtypes may have independent clinical and outcome characteristics. Studies investigating biological causes of subtypes of depression should take influence of features of other subtypes into account.

The relationship between atypical depression and insülin resistance in patients with polycystic ovary syndrome and major depression

In this study, we aimed to examine the relationship between atypical depression and insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS) and major depression. A total of 176 subjects (69 patients with PCOS, 58 patients with depression, and 49 healthy controls) were included in the study. The Beck Depression Inventory (BDI), the Beck Anxiety Inventory (BAI), the Beck Hopelessness Scale (BHS), and the Scale for Suicide Ideation (SSI) were administered. Data concerning their height, weight, fasting a.m. serum levels of insulin, glucose level, and total testosterone level were collected from all participants. The body mass index (BMI) and the Homeostasis Model Assessment Insulin Resistance index (HOMA-IR) were both calculated. 34 (49.3%) of the PCOS patients met the criteria for depression. 26 (76.5%) of them had atypical depression, 8 (23.5%) had non-atypical depression. 27 (46.6%) of the 58 depressed patients had atypical depression. Insulin resistance was higher in the PCOS patients than in the control subjects and the depression patients. There was no association between atypical depression and IR in patients with PCOS and depression. We concluded that there is no relationship between IR and atypical depression.

Depression with melancholic features is associated with higher long-term risk for dementia

BACKGROUND

Depression has been reported to increase the risk of subsequently developing dementia, but the nature of this relation remains to be elucidated. Depression can be a prodrome/manifestation of dementia or an early risk factor, and the effect may differ according to depression subtypes. Our aim was to study the association between early-onset depression and different depression subtypes, and the later occurrence of dementia.

METHODS

We conducted a cohort study including 322 subjects with depression, recruited between 1977 and 1984. A comparison cohort (non-exposed) was recruited retrospectively, to include 322 subjects admitted at the same hospital for routine surgery (appendicectomy or cholecystectomy), at the same period as the depressed cohort. Subjects were contacted again between 2009 and 2014, to assess their dementia status. We computed the risk for dementia in subjects with early onset depression and quantified the association between different depression subtypes (namely melancholic, anxious, and psychotic) and dementia.

RESULTS

The odds of dementia were increased by 2.90 times (95% C.I. 1.61-5.21; p<0.0001) for the depressed cohort when compared to the surgical cohort. When the analysis was restricted to patients younger than 45 years old at baseline, the odds for dementia in the depressed cohort were also significantly higher when compared to the surgical cohort (8.53; 95% C.I. 2.40-30.16). In the multivariate Cox analysis, subjects having depression with melancholic features had an increased risk for developing dementia compared to those without melancholic features (HR=3.64; 95% C.I. 1.78-11.26; p=0.025).

LIMITATIONS

About 59% of the participants with depression and 53% of those non-exposed were lost during follow up. The inclusion of biological biomarkers would strengthen the results. The sample included a low number of bipolar patients.

CONCLUSIONS

These results support depression as an early risk factor for dementia. Depression with melancholic features was found as an important risk factor for dementia, playing a main role in the relation between these disorders.

Evidence for Broadening Criteria for Atypical Depression Which May Define a Reactive Depressive Disorder

Objective. Arguing that additional symptoms should be added to the criteria for atypical depression. Method. Published research articles on atypical depression are reviewed. Results. (1) The original studies upon which the criteria for atypical depression were based cited fatigue, insomnia, pain, and loss of weight as characteristic symptoms. (2) Several studies of DSM depressive criteria found patients with atypical depression to exhibit high levels of insomnia, fatigue, and loss of appetite/weight. (3) Several studies have found atypical depression to be comorbid with headaches, bulimia, and body image issues. (4) Most probands who report atypical depression meet criteria for "somatic depression," defined as depression associated with several of disordered eating, poor body image, headaches, fatigue, and insomnia. The gender difference in prevalence of atypical depression results from its overlap with somatic depression. Somatic depression is associated with psychosocial measures related to gender, linking it with the descriptions of atypical depression as "reactive" appearing in the studies upon which the original criteria for atypical depression were based. Conclusion. Insomnia, disordered eating, poor body image, and aches/pains should be added as criteria for atypical depression matching criteria for somatic depression defining a reactive depressive disorder possibly distinct from endogenous melancholic depression.

Beyond "somatization" and "psychologization": symptom-level variation in depressed Han Chinese and Euro-Canadian outpatients

The finding that people of Chinese heritage tend to emphasize somatic rather than psychological symptoms of depression has frequently been discussed in the culture and mental health literature since the 1970s. Recent studies have confirmed that Chinese samples report more somatic and fewer psychological depression symptoms compared to "Western" samples. The question remains, however, as to whether or not these effects are attributable to variation in all the constituent symptoms or to a subset. If the latter, there is the additional possibility that some symptoms might show a divergent pattern. Such findings would have implications for how cultural variations in symptom presentation are interpreted, and would also inform the cultural study of affective experiences more broadly. The current study addressed these issues in Chinese (n = 175) and Euro-Canadian (n = 107) psychiatric outpatients originally described by Ryder et al. (2008). Differential item functioning (DIF) was used to examine whether specific somatic and psychological symptoms diverged from the overall patterns of cultural variation. Chi-square analyses were used to examine atypical somatic symptoms (e.g., hypersomnia), previously neglected in this literature. No DIF was observed for the typical somatic symptoms, but Euro-Canadians reported greater levels of atypical somatic symptoms, and showed higher rates of atypical depression. DIF was observed for psychological symptoms-the Chinese reported high levels of "suppressed emotions" and "depressed mood," relative to their overall psychological symptom reporting. Chinese outpatients also spontaneously reported "depressed mood" at similar levels as the Euro-Canadians, contrary to prevailing ideas about Chinese unwillingness to discuss depression. Overall, the findings provide a more nuanced picture of how culture shapes symptom presentation and point toward future studies designed to unpack cultural variation in narrower subsets of depressive symptoms.

A predictive model for diagnosing bipolar disorder based on the clinical characteristics of major depressive episodes in Chinese population

BACKGROUND

A correct timely diagnosis of bipolar depression remains a big challenge for clinicians. This study aimed to develop a clinical characteristic based model to predict the diagnosis of bipolar disorder among patients with current major depressive episodes.

METHODS

A prospective study was carried out on 344 patients with current major depressive episodes, with 268 completing 1-year follow-up. Data were collected through structured interviews. Univariate binary logistic regression was conducted to select potential predictive variables among 19 initial variables, and then multivariate binary logistic regression was performed to analyze the combination of risk factors and build a predictive model. Receiver operating characteristic (ROC) curve was plotted.

RESULTS

Of 19 initial variables, 13 variables were preliminarily selected, and then forward stepwise exercise produced a final model consisting of 6 variables: age at first onset, maximum duration of depressive episodes, somatalgia, hypersomnia, diurnal variation of mood, irritability. The correct prediction rate of this model was 78% (95%CI: 75%-86%) and the area under the ROC curve was 0.85 (95%CI: 0.80-0.90). The cut-off point for age at first onset was 28.5 years old, while the cut-off point for maximum duration of depressive episode was 7.5 months.

LIMITATIONS

The limitations of this study include small sample size, relatively short follow-up period and lack of treatment information.

CONCLUSION

Our predictive models based on six clinical characteristics of major depressive episodes prove to be robust and can help differentiate bipolar depression from unipolar depression.

[On the differential diagnostics of depersonalization experiences]

Depersonalization represents an unspecific symptom which is to be found across the entire spectrum of psychiatric nosology. Delineating the historical lines of development of the depersonalization concept and reviewing existing psychopathological experiential knowledge reveals that depersonalization is underpinned by highly diverse modes of experience. In terms of differential diagnostics at the symptom level, a distinction can be made between depersonalization as a neurotic phenomenon on the one hand and a psychotic form occurring in schizophrenia and melancholia on the other. The reference points defined here extend beyond current descriptive classifications and open up the diagnostic process to allow an inclusion of etiological and therapeutic aspects.

Type-D personality and depersonalization are associated with suicidal ideation in the German general population aged 35-74: results from the Gutenberg Heart Study

BACKGROUND

Suicidal ideation (SID) is a major risk factor for suicide attempts. Mental disorders are among the strongest correlates of suicide, with depression and anxiety disorders playing a major role. The present study aims to investigate the contribution of under researched factors contributing to SID such as depersonalization, Type-D personality and cardiovascular risk factors.

METHODS

Factors associated with SID were investigated in a sample of N=5000 participants (aged 35-74 years) of the community-based survey "Gutenberg Heart Study". The factors were assessed by self-report instruments, computer-assisted interviews and medical examination.

RESULTS

7.5% of the sample reported SID over the last 2 weeks. In the univariate analysis SID was significantly associated with female sex, living without a partner, low socioeconomic status, diagnosis of coronary heart disease, family history of myocardial infarction, smoking and mental distress. In the full adjusted model significant associations remained with age (in years) OR 1.02 (95%CI 1.01-1.04, p=0.002), self-reported depression OR 3.21 (95%CI 2.23-4.62, p<0.0001), panic disorder OR 1.56 (95%CI 1.03-2.36, p=0.036), depersonalization OR 2.45 (95%CI 1.78-3.38, p<0.0001), Type-D personality OR 1.98 (95%CI 1.49-2.63, p<0.0001) and impairment by mental distress OR 2.15 (95%CI 1.74-2.67, p<0.0001).

LIMITATIONS

Main limitations are the reliance on self-report measures of SID and of mental distress.

CONCLUSIONS

For the first time it has been shown that in the general population depersonalization and Type-D personality are uniquely associated with SID. These associations need further elucidation.

Efficacy evaluation for depression with somatic symptoms treated by electroacupuncture combined with Fluoxetine

OBJECTIVE

This study is to investigate the clinical therapeutic effects and safety of treating mild or moderate depression with somatic symptoms with electroacupuncture combined with Fluoxetine.

METHODS

95 cases of mild or moderate depression with somatic symptoms were randomly divided into a Fluoxetine group, and an electroacupuncture plus Fluoxetine group. Hamilton Depression Scale (HAMD) was used for the assessment of clinical therapeutic effects and Treatment Emergent Symptom Scale (TESS) was used for assessment of adverse reactions.

RESULTS

The total effective rate was 77.27% in the Fluoxetine group and 78.26% in the electroacupuncture plus Fluoxetine group, showing no statistically significant difference between these two groups (P > 0.05). However, the treatment took effect after two weeks in the electroacupuncture plus Fluoxetine group but after four weeks in Fluoxetine group. During this time, a better therapeutic effect on depression with mild or moderate somatic symptoms was found in the electroacupuncture plus Fluoxetine group, which also had fewer adverse reactions than the Fluoxetine group.

CONCLUSION

Electroacupuncture combined with Fluoxetine takes effect faster for relieving the somatic symptoms with fewer adverse reactions. It is worth popularizing clinically.

Clinical correlates of atypical depression and validation of the French version of the Scale for Atypical Symptoms (SAS)

BACKGROUND

The 8-item "Scale for Atypical Symptoms" (SAS) and its structured interview, the SIGH-SAD, have been developed to assess atypical symptoms of depression in winter depression. Although they are commonly used, no validation study has yet been conducted.

METHODS

270 consecutive depressed inpatients were assessed prospectively. Pearson's correlation coefficients between fulfilment of Liebowitz criteria for atypical depression and both the SAS score and the atypical balance [ratio of the AS score to the total score on the Hamilton Depression Rating Scale 29-item (HDRS-29)] were calculated. The SAS was evaluated against Liebowitz criteria using binary logistic regression. A ROC curve was performed with the atypical balance against the fulfilment of Liebowitz criteria.

RESULTS

18.5% of patients met the criteria for atypical depression. The presence of an atypical depression was significantly correlated with both the atypical score (r=0.42) and the atypical balance (r=0.51). The logistic regression showed that a higher score on the SAS, the absence of a somatic syndrome (ICD-10) and a lower HDRS-21 score were independent predictors of an atypical depression while age, gender and bipolarity were not. The ROC curve showed that an atypical balance of 29% was the optimal threshold for the diagnosis of atypical depression (sensitivity=0.86, specificity=0.79).

LIMITATION

Patients with bipolar I and II were not distinguished.

CONCLUSION

Atypical depression is relatively frequent in hospitalised patients. The concurrent validity of the French version of the SAS and its structured interview, the SIGH-SAD is satisfactory.

Mirtazapine versus venlafaxine for the treatment of somatic symptoms associated with major depressive disorder: a randomized, open-labeled trial

Somatic symptoms are often important in the treatment of major depressive disorder (MDD). The aim of this open-labeled trial was to examine the efficacy of mirtazapine for the treatment of MDD with clinically significant somatic symptoms, as compared with venlafaxine. A total of 126 patients with MDD (score >/=18 on the Hamilton Rating Scale for Depression-17) were included in both the intent-to-treat (n=73 in the mirtazapine group and n=53 in the venlafaxine group) and completer analysis (n=51 and n=37, respectively). After treatment, both treatment groups showed similar improvements in depressive symptoms. Repeated measures analysis of variance for the intent-to-treat population revealed that there were no significant differences in mean change of the Symptom Check List-90-Revised (SCL-90-R) somatization subscores between the two groups. For completers, there was a significant timextreatment interaction in the SCL-90-R somatization subscores, but the differences between the two groups at endpoint did not reach statistical significance in post-hoc analysis. In conclusion, this study suggests that overall efficacies of mirtazapine and venlafaxine are similar for the treatment of overall symptoms in MDD, and both drugs may be useful for the treatment of somatic symptoms in MDD patients.

Epidemiologic heterogeneity of common mood and anxiety disorders over the lifecourse in the general population: a systematic review

BACKGROUND

Clinical evidence has long suggested there may be heterogeneity in the patterns and predictors of common mood and anxiety disorders; however, epidemiologic studies have generally treated these outcomes as homogenous entities. The objective of this study was to systematically review the epidemiologic evidence for potential patterns of heterogeneity of common mood and anxiety disorders over the lifecourse in the general population.

METHODS

We reviewed epidemiologic studies examining heterogeneity in either the nature of symptoms experienced ("symptom syndromes") or in patterns of symptoms over time ("symptom trajectories"). To be included, studies of syndromes were required to identify distinct symptom subtypes, and studies of trajectories were required to identify distinct longitudinal patterns of symptoms in at least three waves of follow-up. Studies based on clinical or patient populations were excluded.

RESULTS

While research in this field is in its infancy, we found growing evidence that, not only can mood and anxiety disorders be differentiated by symptom syndromes and trajectories, but that the factors associated with these disorders may vary between these subtypes. Whether this reflects a causal pathway, where genetic or environmental factors influence the nature of the symptom or trajectory subtype experienced by an individual, or whether individuals with different subtypes differed in their susceptibility to different environmental factors, could not be determined. Few studies addressed issues of comorbidity or transitions in symptoms between common disorders.

CONCLUSION

Understanding the diversity of these conditions may help us identify preventable factors that are only associated with some subtypes of these common disorders.