# Deconstructing major depressive episodes across unipolar and bipolar depression by severity and duration: a cross-diagnostic cluster analysis on a large, international, observational study

Filippo Corponi<sup>1,2</sup>, Gerard Anmella<sup>2</sup>, Isabella Pacchiarotti<sup>2,3,4</sup>, Ludovic Samalin<sup>2</sup>, Norma Verdolini<sup>2,3,4</sup>, Dina Popovic<sup>5</sup>, Jean-Michel Azorin<sup>6</sup>, Jules Angst<sup>7</sup>, Charles L. Bowden<sup>8</sup>, Sergey Mosolov<sup>9</sup>, Allan H. Young<sup>10</sup>, Giulio Perugi<sup>11</sup>, Eduard Vieta<sup>12,3,4</sup> and Andrea Murru<sup>12,3,4</sup>

## Abstract

A cross-diagnostic, post-hoc analysis of the BRIDGE-II-MIX study was performed to investigate how unipolar and bipolar patients suffering from an acute major depressive episode (MDE) cluster according to severity and duration. Duration of index episode, Clinical Global Impression-Bipolar Version-Depression (CGI-BP-D) and Global Assessment of Functioning (GAF) were used as clustering variables. MANOVA and post-hoc ANOVAs examined between-group differences in clustering variables. A stepwise backward regression model explored the relationship with the 56 clinical-demographic variables available. Agglomerative hierarchical clustering with two clusters was shown as the best fit and separated the study population (n = 2314) into 65.73% (Cluster 1 (C1)) and 34.26% (Cluster 2 (C2)). MANOVA showed a significant main effect for cluster group (*p* < 0.001) but ANOVA revealed that significant between-group differences were restricted to CGI-BP-D (*p* < 0.001) and GAF (*p* < 0.001), showing greater severity in C2. Psychotic features and a minimum of three DSM-5 criteria for mixed features (DSM-5-3C) had the strongest association with C2, that with greater disease burden, while non-mixed depression in bipolar disorder (BD) type II had negative association. Mixed affect defined as DSM-5-3C associates with greater acute severity and overall impairment, independently of the diagnosis of bipolar or unipolar depression. In this study a pure, non-mixed depression in BD type II significantly associates with lesser burden of clinical and functional severity. The lack of association for less restrictive, researched-based definitions of mixed features underlines DSM-5-3C specificity. If confirmed in further prospective studies, these findings would warrant major revisions of treatment algorithms for both unipolar and bipolar depression.

## Introduction

*The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) defines a major depressive episode (MDE) as a syndromic category wherein five (or*

more) symptoms are present and result in a clear-cut worsening of previous functioning during a minimum of 2 weeks<fnref n="1" />. DSM-5 construes MDE as unidimensional, aiming at an unachieved diagnostic reliability<fnref n="2" />, along with providing a number of specifiers defined as discrete entities. Thus, MDE is a transdiagnostic nosographic construct straddling major depressive disorder (MDD) and bipolar disorder (BD). The commonly used definitions of unipolar and bipolar depression represent an attempt to overcome this pitfall. Unipolar and bipolar depressions

Correspondence: Eduard Vieta (evieta@clinic.cat)

<figure><img src="image_1.png" /></figure>

<footnote n="1">¹Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy</footnote>

<footnote n="2">²Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
Full list of author information is available at the end of the article</footnote>
both impose a large burden for healthcare systems³. While
as of today no clinical hallmark or biomarker can definitely
differentiate a MDE as belonging to either MDD or BD⁴,
depressions in MDD and BD probably do represent dis-
tinct conditions, with only partially overlapping genetic
underpinnings⁵ and different therapeutic management⁶⁻⁸.
Despite (hypo)mania being the earmark of BD, depression
is generally the predominant mood state, representing the
most prevalent polarity at illness onset and greatest con-
tribution to psychosocial disability⁹.

As opposed to the plethora of specifiers included in the DSM-5, a model mapping gradations of severity and duration might capture much of the heterogeneity in depressive burden<sup>10,11</sup>. The inclusion of specifiers in the DSM-5 tried to bridge the need to assess severity and duration whilst keeping a categorical framework<sup>12</sup>. Illness severity can be conceptualized in terms of symptoms' intensity and global functioning<sup>13</sup>. However, defining depression severity as a direct result of symptoms presence and intensity alone may be unjustified, due to the intrinsic heterogeneity of the depressive symptom as a clinical construct, and their different impact on the overall impairment that a patient may experience<sup>14</sup>. Along with the intensity of symptoms, their duration is a major element, which must be considered to perform a diagnosis of MDE<sup>1</sup>. Past studies link longer duration of index episode with greater severity<sup>15-19</sup>, higher comorbidities' burden (i.e., dysthymia and anxiety), suicidal behavior<sup>19</sup>, and lower probability of recovery,<sup>20</sup> furthermore, duration might not be related to the index episode being a recurrent or first-onset episode<sup>19</sup>.

The BDs: Improving Diagnosis, Guidance and Education-mixed features (BRIDGE-II-MIX) study was a large, multinational, cross-sectional study, which enrolled 2811 adults aged 18 years and older with MDE according to *Diagnostic and Statistical Manual of Mental Disorders*, Fourth Edition Text Revision (DSM-4-TR) diagnostic criteria at the time of the consultation and applied a descriptive, bottom-up approach with the primary aim of detecting mixed symptoms among such patients<sup>21</sup>. Several subanalyses have investigated the effects of comorbidity<sup>22-24</sup>, recurrence<sup>25,26</sup>, specific symptoms<sup>27-31</sup>, and treatment response<sup>32,33</sup> in patients with MDE from this data set, but none looked into the impact of severity and duration.

Cluster analysis is a statistical technique that identifies
subgroups in wider multidimensional or heterogeneous
data, which application to multifaceted diseases, such as
major depression, could help dissect disease hetero-
geneity, advancing diagnostic criteria, and improving
treatment plans³⁴⁻³⁶.

Thus, the aim of this post-hoc study was to determine
cross-diagnostic clinical clusters based on depressive
burden, i.e., index episode's clinical severity, and duration,

within an acutely depressed population of unipolar and
bipolar patients.

Method

Sample and assessment

The BRIDGE-II-MIX study was a multicenter, interna-
tional, non-interventional, cross-sectional study conducted
between June 2009 and July 2010 and involving 239 hos-
pital based or community psychiatrists from eight coun-
tries across three continents, i.e. Bulgaria, Egypt, Morocco,
the Netherlands, Portugal, Russia, Spain, and Turkey. The
study enrolled 2811 adults (aged 18 years or older),
experiencing an acute MDE according to DSM-IV-TR
criteria (APA, 2000). In a single consultation, psychiatrist
completed a structured case report for each patients,
including inclusion criteria, sociodemographic variables,
psychiatric medical history and features of the depressive
episode, including DSM criteria for BD, previous response
to antidepressants, psychiatric comorbidity and current
treatment<sup>21,27</sup> According to DSM-IV-TR, 735 patients
(26.15%) fulfilled criteria for BD, of whom 400 patients
(14.23%) met criteria for bipolar I disorder and 335
patients (11.92%) for bipolar-II disorder. On the other
hand, 2076 patients (73.85%) met a DSM-IV-TR diagnosis
of MDD, of whom 691 (24.58%) were at their first MDE.

The primary objective of the BRIDGE-II-MIX study was
to establish the frequency of depressive mixed states by
analyzing all the relevant symptoms of either pole. After
the publication of DSM-5, this was post-hoc defined as (1)
the proportion of patients fulfilling DSM-5 criteria for
MDE with mixed features (American Psychiatric Asso-
ciation, 2013), and (2) research-based diagnostic criteria
for mixed states (RBDC). DSM-5 criteria require the
presence for at least a week of an MDE and at least 3 of
the following (nonoverlapping) hypomanic symptoms: (1)
elevated, expansive mood, (2) inflated self-esteem or
grandiosity, (3) more talkative than usual or pressure to
keep talking, (4) flight of ideas or subjective experience
that thoughts are racing, (5) increase in energy or goal-
directed activity, (6) increased or excessive involvement in
activities that have a high potential for painful con-
sequences, and (7) decreased need for sleep. The
BRIDGE-II-MIX study also adopted the definition of
DSM-5 subthreshold criteria for MDE with mixed fea-
tures, for which the presence of an MDE plus 2 non-
overlapping hypomanic symptoms are required. RBDC
are defined by the presence of MDE plus 3 out of the
following 14 hypomanic symptoms for at least a week:
irritable mood, affective lability, distractibility, psycho-
motor agitation, impulsivity, aggression (verbal or physi-
cal), racing thoughts, more talkative/pressure to keep
talking, hyperactivity, increased energy, risky behavior,
grandiosity, elation, and hyper-sexuality. The proportion
of patients fulfilling criteria for BD according to the
DSM-4-TR and bipolarity specifier proposed by Angst et al.<sup>37-39</sup> was also identified. The bipolarity specifier attributes a diagnosis of BD to patients who experienced an episode of elevated mood or irritable mood or increased activity with at least three of the symptoms listed under Criterion B of the DSM-4-TR, associated with at least one of the three following consequences: (1) unequivocal and observable change in functioning uncharacteristic of the person's usual behavior, (2) marked impairment in social or occupational functioning observable by others or (3) requiring hospitalization or outpatient treatment. No minimum duration was required, and no exclusion criteria were applied.

The study was conducted according to the Declaration of Helsinki (Hong Kong Amendment; http://www.wma.net) and the Good Epidemiology Practice and the International Epidemiologic Association (IEA) European Federation (http://iea.web.org). Good Epidemiologic Practice (GEP)—IEA Guidelines were followed for proper conduct of epidemiologic research, as well as pertinent national, legal, and regulatory requirements. Written informed consent was obtained from each patient. In each country, the protocol was approved by the local ethics committee.

## Measures of depressive severity and duration

We took a complete cases approach, including only patients without missing observations. If missing data meets the Missing Completely at Random (MCAR) assumption, removal of observations with missing data is expected to produce unbiased estimates of means, variances, and regression weights (Allison, 2002). We used the Hawkins test of multivariate normality and heteroscedasticity, as implemented in the 'MissMech' R package<sup>40,41</sup>, to address this question. A nonsignificant *p* value from this test would indicate a lack of sufficient evidence to reject the null hypothesis that data are MCAR. For this post-hoc analysis, we defined our clustering variables as: (1) duration of index episode: Collected retrospectively and measured in days. (2) Depression severity: Assessed at the time of consultation using the item for depression of the CGI-BP (CGI-BP-D)<sup>42</sup> which evaluates how severely ill on a scale from 1 (normal, not ill) to 7 (very severely ill) a patient is. (3) Functional impairment: estimated at the time of consultation using the Global Assessment of Functioning (GAF)<sup>43</sup>, which measures how much a person's symptoms affect his or her day-to-day life on a scale of 0-100. In line with previous recommendations, we checked that the sample size to include in the cluster analysis was no <5 × 2<sup>k</sup> (k = number of clustering variables)<sup>42</sup>.

## Statistical analysis

### Data normalization and exploratory analyses

Each measure was z-transformed prior to being entered as independent variable in the cluster analysis. Clustering

tendency, i.e., inherent grouping structure, was assessed using the Hopkins statistic which examines whether objects in a data set differ significantly from the assumption that they are uniformly distributed in the multi-dimensional space. A value close to 1 indicates highly clustered data, random data results in values around 0.5 while uniformly distributed data yield values close to 0<sup>44,45</sup>.

## Cluster analysis

Optimal clustering algorithm and number of clusters were determined using the 'clValid' R package<sup>46</sup>, which allows for simultaneous comparison of multiple clustering algorithms and numbers of clusters in terms of validation measures. We tested for the presence of two to six clusters and we implemented three clustering methods: (1) k-means (2) k-medoids or partitioning around medoids (PAM) and (3) agglomerative hierarchical clustering<sup>47,48</sup>. The validation measures used to compare different clustering solutions comprised internal and stability criteria. Internal criteria were calculated as connectivity, silhouette width and Dunn index. Stability criteria upon removal of each variable were: APN (average proportion of non-overlap); AD (average distance); ADM (average distance between means between cluster centers); FOM (figure of merit).

Cluster group means for all measures of depressive burden, i.e., index episode duration, CGI-BP-D, and GAF, were compared using MANOVA, each measure of depressive burden was then examined separately using ANOVA to assess individual contribution. Subsequently, a stepwise backward logistic regression model was used to identify the predictive value on cluster membership of the 56 relevant clinical-demographic features available from the BRIDGE-II-MIX study. The Akaike information criterion (AIC) was used for stepwise backward variable selection. AIC performs feature selection striking a balance between goodness of fit and overfitting. Stepwise backward logistic regression was carried out using the 'MASS' R package<sup>49</sup>. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for the observed associations.

All statistical analyses were performed using R Statistical Software (Foundation for Statistical Computing, Vienna, Austria).

## Results

### Cluster analysis

Out of the 2811 patients considered, after screening for missing values 2314 complete cases were included in the analysis. Missing data met the MCAR assumption, thus supporting listwise deletion as an appropriate approach. Hopkins index of 0.15 suggested inherent grouping structure. All internal validation measures and 2 out of 4 stability validation criteria favored agglomerative
hierarchical clustering with 2 clusters as optimal clustering fit to the data set (Supplementary Fig. 1). Dendrogram produced from Ward's clustering (Supplementary Fig. 2) was cut to produce two clusters and subjects were assigned cluster membership accordingly.

C1 and C2 comprised 65.73% (n = 1521) and 34.26% (n = 793) of the study population respectively. Using Pil-lai's trace, MANOVA showed a significant main effect for cluster group (V = 0.35, F<sub>3,2312</sub> = 432, p < 0.001) but, as revealed by ANOVA, significant between-group dif-ferences were restricted to CGI-BP-D (F<sub>1,2312</sub> = 190, p < 0.001) and GAF (F<sub>1,2312</sub> = 1297, p < 0.001), both indi-cating greater disease severity in C2. Duration of index episode did not significantly separate the two clusters (F<sub>1,2312</sub> = 0.34, p = 0.56).

### Stepwise AIC backward logistic regression

Starting with the full model including all 56 clinical-demographic independent variables (Table 1), stepwise backward selection of independent variables reached AIC optimization at 19 variables (Table 2): 34 variables were thus removed from the full model. The final 19 variables model showed significantly better fit than the null model (χ<sup>2</sup> = 148, df = 19, p < 0.001). In the final model, 7 out of 19 of the variables retained were significantly associated with cluster membership. Relative ORs with 95% CIs are shown in Fig. 1. Psychotic features (OR = 2.12, 95% CI = 1.54; 2.93) was the variable most strongly associated with C2 membership, followed by a minimum of three DSM-5 criteria for mixed features (OR = 1.69, 95% CI = 1.13; 2.54) and history of antidepressant resistance (OR = 1.63, 95% CI = 1.33; 2.00). Years of illness (OR = 1.02, 95% CI = 1.01–1.02) and onset age of depressive symptoms (OR = 1.02, 95% CI = 1.01–1.02) had only a tiny positive association. On the contrary, BD type II was negatively associated with C2 membership (OR = 0.69, 95% CI = 0.49; 0.97).

### Discussion

This post-hoc analysis of the BRIDGE-II-MIX study aimed at clustering a sample of acutely depressed patients according to measures of depressive burden, i.e., symptom intensity, functional impairment, and duration. Our analysis identified two clusters, showing little (C1) and great (C2) severity. The great severity cluster C2 included ~1 out of 3 patients displaying a significantly greater depressive burden as defined by CGI-BP-D and GAF, whereas the duration of the index episode did not significantly contribute to clusters' separation. Among other possible explanations, such as differences in latency to access to mental health-related services, the cross-sectional design of the study may account for the lack of clusters' separation by longer illness duration, since records for index episode's duration did not cover the

<table><caption>**Table 1** The 56 clinical-demographic variables available from the BRIDGE-II-MIX study and explored in the stepwise backward logistic regression model.</caption><thead><tr><th></th><th colspan="2">Cluster 1<br>(n = 1521)</th><th colspan="2">Cluster 2<br>(n = 793)</th></tr><tr><th></th><th>n</th><th>%</th><th>n</th><th>%</th></tr></thead><tbody><tr><td colspan="5"><strong>Categorical variables (YES)</strong></td></tr><tr><td>Female gender</td><td>1057</td><td>69.49</td><td>536</td><td>67.59</td></tr><tr><td>Inpatient status</td><td>366</td><td>24.06</td><td>244</td><td>30.76</td></tr><tr><td>Family history of bipolar disorder<br>among first degree relatives</td><td>230</td><td>15.12</td><td>120</td><td>15.13</td></tr><tr><td colspan="5"><strong>Diagnosis</strong></td></tr><tr><td>Bipolar disorder type I</td><td>200</td><td>13.21</td><td>142</td><td>17.9</td></tr><tr><td>Bipolar disorder type II</td><td>190</td><td>12.42</td><td>95</td><td>11.97</td></tr><tr><td>First major depressive episode</td><td>350</td><td>23.01</td><td>168</td><td>21.12</td></tr><tr><td>Major depressive disorder</td><td>781</td><td>51.34</td><td>388</td><td>48.92</td></tr><tr><td colspan="5"><strong>Concurrent symptoms experienced most of the time during the<br>past week</strong></td></tr><tr><td>Anxiety</td><td>1211</td><td>79.61</td><td>623</td><td>78.56</td></tr><tr><td>Hyperphagia</td><td>218</td><td>14.33</td><td>114</td><td>14.37</td></tr><tr><td>Hypersomnia</td><td>244</td><td>16.04</td><td>120</td><td>15.13</td></tr><tr><td>Hypo-sexuality</td><td>935</td><td>61.47</td><td>533</td><td>67.21</td></tr><tr><td>Insomnia</td><td>1084</td><td>71.26</td><td>580</td><td>73.13</td></tr><tr><td>Leaden paralysis</td><td>364</td><td>23.93</td><td>222</td><td>27.99</td></tr><tr><td>Memory problems</td><td>935</td><td>60.81</td><td>486</td><td>61.28</td></tr><tr><td>Mood reactivity</td><td>646</td><td>42.47</td><td>317</td><td>39.97</td></tr><tr><td>Panic attacks</td><td>225</td><td>14.79</td><td>133</td><td>16.77</td></tr><tr><td>Psychotic features</td><td>85</td><td>5.58</td><td>99</td><td>12.48</td></tr><tr><td>Reduced appetite</td><td>905</td><td>59.50</td><td>514</td><td>64.82</td></tr><tr><td colspan="5"><strong>Research-Based Diagnostic Criteria (RBDC) for mixed states</strong></td></tr><tr><td>Distractibility</td><td>371</td><td>24.39</td><td>214</td><td>26.98</td></tr><tr><td>Elation</td><td>65</td><td>4.27</td><td>47</td><td>5.92</td></tr><tr><td>Grandiosity</td><td>46</td><td>3.02</td><td>49</td><td>6.17</td></tr><tr><td>Hyperactivity</td><td>110</td><td>7.23</td><td>82</td><td>10.34</td></tr><tr><td>Hyper-sexuality</td><td>36</td><td>2.36</td><td>27</td><td>3.40</td></tr><tr><td>Impulsivity</td><td>214</td><td>14.06</td><td>118</td><td>14.88</td></tr><tr><td>Increased energy</td><td>90</td><td>5.91</td><td>74</td><td>9.31</td></tr><tr><td>Irritable mood</td><td>489</td><td>32.14</td><td>259</td><td>32.66</td></tr><tr><td>Mood lability</td><td>464</td><td>30.50</td><td>239</td><td>30.14</td></tr><tr><td>More talkative/Pressure to talk</td><td>168</td><td>11.04</td><td>106</td><td>13.36</td></tr><tr><td>Psychomotor agitation</td><td>224</td><td>14.72</td><td>141</td><td>17.78</td></tr><tr><td>Racing thoughts</td><td>160</td><td>10.51</td><td>110</td><td>13.87</td></tr></tbody></table>
<table><caption>**Table 1 continued**</caption><thead><tr><th rowspan="2"></th><th colspan="2">Cluster 1<br>(n = 1521)</th><th colspan="2">Cluster 2<br>(n = 793)</th></tr><tr><th>n</th><th>%</th><th>n</th><th>%</th></tr></thead><tbody><tr><td>Risky behavior</td><td>94</td><td>6.18</td><td>71</td><td>8.95</td></tr><tr><td>Verbal or physical aggressivity</td><td>194</td><td>12.75</td><td>122</td><td>15.38</td></tr><tr><td>Past response to antidepressant treatment</td><td></td><td></td><td></td><td></td></tr><tr><td>History of (hypo)manic switches ensuing antidepressant treatment</td><td>260</td><td>17.09</td><td>163</td><td>20.55</td></tr><tr><td>History of resistance to antidepressant treatment</td><td>359</td><td>23.60</td><td>278</td><td>35.05</td></tr><tr><td>History of mood lability ensuing antidepressant treatment</td><td>440</td><td>28.92</td><td>269</td><td>33.92</td></tr><tr><td>History of irritability ensuing antidepressant treatment</td><td>374</td><td>24.59</td><td>239</td><td>30.13</td></tr><tr><td>Mixed affect definitions</td><td></td><td></td><td></td><td></td></tr><tr><td>A minimum of 2 DSM-5 criteria for mixed features</td><td>217</td><td>14.26</td><td>147</td><td>18.53</td></tr><tr><td>A minimum of 3 DSM-5 criteria for mixed features</td><td>98</td><td>6.44</td><td>93</td><td>11.73</td></tr><tr><td>A minimum of 2 intradepression (hypo)manic symptoms</td><td>588</td><td>38.65</td><td>326</td><td>41.1</td></tr><tr><td>A minimum of 3 intradepression (hypo)manic symptoms</td><td>420</td><td>27.61</td><td>247</td><td>31.14</td></tr><tr><td>Comorbidities</td><td></td><td></td><td></td><td></td></tr><tr><td>Alcohol abuse</td><td>97</td><td>6.37</td><td>53</td><td>6.68</td></tr><tr><td>Borderline personality disorder</td><td>90</td><td>5.91</td><td>67</td><td>8.44</td></tr><tr><td>Generalized anxiety disorder</td><td>264</td><td>17.35</td><td>141</td><td>17.78</td></tr><tr><td>Obsessive-compulsive disorder</td><td>73</td><td>4.79</td><td>43</td><td>5.42</td></tr><tr><td>Panic disorder</td><td>148</td><td>9.73</td><td>89</td><td>11.22</td></tr><tr><td>Social phobia</td><td>106</td><td>6.96</td><td>70</td><td>8.82</td></tr><tr><td>Substance abuse</td><td>39</td><td>2.56</td><td>26</td><td>3.27</td></tr><tr><td></td><td>Mean</td><td>SD</td><td>Mean</td><td>SD</td></tr><tr><td>Numeric variables</td><td></td><td></td><td></td><td></td></tr><tr><td>Days, if any, with (hypo)mania in the last year</td><td>12.1</td><td>31.9</td><td>14.7</td><td>33</td></tr><tr><td>Days with depression in the last year</td><td>85.7</td><td>65.5</td><td>89</td><td>58.3</td></tr><tr><td>Number of mood (depressive and/or (hypo)manic) episodes in the last year</td><td>2.17</td><td>4.35</td><td>2.70</td><td>5.62</td></tr><tr><td>Number of previous depressive episodes</td><td>4.58</td><td>5.7</td><td>5.47</td><td>6.89</td></tr><tr><td>Number of previous hospitalizations</td><td>1.59</td><td>3.26</td><td>2.31</td><td>5</td></tr><tr><td>Number of previous suicide attempts</td><td>0.32</td><td>0.91</td><td>0.65</td><td>2.3</td></tr></tbody></table>

<table><caption>**Table 1 continued**</caption><thead><tr><th rowspan="2"></th><th colspan="2">Cluster 1<br>(n = 1521)</th><th colspan="2">Cluster 2<br>(n = 793)</th></tr><tr><th>n</th><th>%</th><th>n</th><th>%</th></tr></thead><tbody><tr><td>Onset age of depressive symptoms</td><td>34.9</td><td>12.5</td><td>36.1</td><td>12.7</td></tr><tr><td>Total number of Research-Based<br>Diagnostic Criteria (RBDC) for<br>mixed states</td><td>1.79</td><td>2.57</td><td>2.09</td><td>2.99</td></tr><tr><td>Years of illness</td><td>7.79</td><td>9.38</td><td>9.44</td><td>10.1</td></tr></tbody></table>

Values across Cluster 1 and Cluster 2 are shown.

<table><caption>**Table 2 The 19 variables selected in the final logistic regression model.**</caption><thead><tr><th>Variable</th><th>Coefficient</th><th>SE</th><th>z value</th><th>p value</th></tr></thead><tbody><tr><td>A minimum of 3 DSM-5 criteria for mixed features</td><td>0.53</td><td>0.21</td><td>2.54</td><td>0.01*</td></tr><tr><td>Bipolar disorder type II</td><td>-0.36</td><td>0.17</td><td>-2.08</td><td>0.04*</td></tr><tr><td>First major depressive episode</td><td>0.04</td><td>0.17</td><td>0.25</td><td>0.80</td></tr><tr><td>Grandiosity</td><td>0.40</td><td>0.26</td><td>1.54</td><td>0.12</td></tr><tr><td>History of resistance to antidepressant treatment</td><td>0.49</td><td>0.10</td><td>4.69</td><td>&lt;0.001*</td></tr><tr><td>Hypo-sexuality</td><td>0.19</td><td>0.10</td><td>1.90</td><td>0.06</td></tr><tr><td>Impulsivity</td><td>-0.28</td><td>0.16</td><td>-1.72</td><td>0.09</td></tr><tr><td>Major depressive disorder</td><td>-0.23</td><td>0.14</td><td>-1.72</td><td>0.09</td></tr><tr><td>Memory problems</td><td>-0.18</td><td>0.10</td><td>-1.84</td><td>0.07</td></tr><tr><td>Mood lability</td><td>-0.23</td><td>0.12</td><td>-1.93</td><td>0.05</td></tr><tr><td>Mood reactivity</td><td>-0.15</td><td>0.10</td><td>-1.54</td><td>0.12</td></tr><tr><td>Number of mood (depressive and/or (hypo)manic) episodes in the last year</td><td>0.02</td><td>0.01</td><td>1.62</td><td>0.10</td></tr><tr><td>Number of previous suicide attempts</td><td>0.16</td><td>0.04</td><td>3.57</td><td>&lt;0.001*</td></tr><tr><td>Onset age of depressive symptoms</td><td>0.02</td><td>0.00</td><td>4.02</td><td>&lt;0.001*</td></tr><tr><td>Psychotic features</td><td>0.75</td><td>0.16</td><td>4.60</td><td>&lt;0.001*</td></tr><tr><td>Racing thoughts</td><td>0.23</td><td>0.16</td><td>1.43</td><td>0.15</td></tr><tr><td>Reduced appetite</td><td>0.19</td><td>0.10</td><td>1.93</td><td>0.05</td></tr><tr><td>Verbal or physical aggressivity</td><td>0.26</td><td>0.15</td><td>1.72</td><td>0.09</td></tr><tr><td>Years of illness from onset</td><td>0.02</td><td>0.01</td><td>3.42</td><td>&lt;0.001*</td></tr></tbody></table>

The Akaike information criterion (AIC) was used for stepwise backward variable selection, starting from the full model with 56 variables. Cluster membership (the dependent variable) was coded as 1 for Cluster 2 (positive case) and 0 for Cluster 1 (negative case).
*p values < 0.05.
entire course of the episode. Interestingly, the cluster
including the most severe patients has BD less repre-
sented (proportionally) as diagnosis subgroup in com-
parison to the other cluster.

The regression model revealed that psychotic features
had the largest OR for positive association with C2
membership (Fig. 1). The link between psychosis and
depression severity is indeed long-established<sup>50,51</sup>, since
the presence of psychotic features underpins cognitive
and processing impairments across multiple severe men-
tal disorders, irrespective of the clinical diagnosis<sup>52</sup>.

A key finding of our study is that the clinical pre-
sentation with mixed features according to DSM-5
criteria represents the second strongest association
with the C2. Strengthening this result, pure, non-mixed
presentation in acute bipolar-II depression represents the
unique significant negative association with illness sever-
ity. Thus, the absence of mixed features seems a protec-
tive factor for illness severity and interestingly this was
not influenced by possible treatment differences between
BD types I and II. To the best of our knowledge, this
finding has no precedent in scientific literature. Despite
the limitations further disclosed, should it be confirmed
by future, prospective evidence, this finding could have an
impact on the methodology of studies designs requested
by regulatory agencies. In fact, these results go against the
common depiction of a severity progression spanning
from unipolar depression, to BD-II and BD-I.

Overall, mixed features might be considered as a marker
of severity both in research and clinical settings, outlining
a functionally compromised group of patients irrespec-
tively of the diagnosis⁵³⁻⁵⁵. Yet, according to our results
mixed states do not invariantly represent a marker of
clinical severity. In fact, the statistical contribution to C2
in our study was observed for mixed affect defined as the
presence of at least three DSM-5 symptoms, but not as
determined according to other less conservative defini-
tions, i.e., RBDC criteria for mixed features and a mini-
mum of two DSM-5 symptoms. This could be explained
by the fact that RBDC criteria have a higher sensitivity for
detection of mixed states⁵⁶, but this does not directly
translate into a more severe MDE, bearing doubtful acute
prognostic implications. On the other hand, DSM-5 cri-
teria, specifically a minimum of three symptoms¹, seem to
have higher specificity in diagnosing more severe mixed
presentations, reflected by the statistical assignment to
our C2.

Last, the C2 association in the regression model with
previous suicide attempts and previous resistance to
antidepressant treatments could be considered the hall-
mark of mixed presentation, as previously outlined in this
very sample³³ and previous studies on suicidal ideation
and psychotic features marking more severely impaired
MDE patients⁵⁷. In particular, a history of previous

resistance to antidepressant treatments bears important
treatment implications. In fact, as suggested elsewhere,
mixed-features depressions not responding to anti-
depressants might benefit from coupling a mood-
stabilizing agent or even dropping the antidepressant
altogether⁵⁸.

Results coming from this post-hoc analysis should be
balanced against some limitations. First, due to the cross-
sectional design of the BRIDGE-II-MIX recall bias prob-
ably affected some of the variables' definitions, and
especially our definition of duration, whose esteem might
also depend on external factors (e.g., latency of referral
and psychiatric assessment). Also, the assessment of
symptom severity and functioning may have been some-
what hampered by the use of the GAF as a functional
outcome, as its anchor points rely also on symptoms
severity. This might have contributed to the strong cor-
relation between CGI and GAF scores in our sample.
Also, concerns about inter-rater reliability have been
raised in the past, but its validity and clinical usefulness on
the assessment of symptom and function dimensions have
been established⁵⁹. A deeper assessment of the overall
functioning of the patients should have accounted for
different dimensions that would have allowed for an
increased discriminant capacity⁶⁰. Second, the partici-
pating centers were not randomly selected, which may
have led to a bias through the inclusion of psychiatrists
with a particular interest in mixed states. However, this
could also represent a study strength as some expertise is
needed to detect mixed states in MDE patients. Another
limitation was the wide variation in the rates of hospita-
lized patients across countries, ranging from 1.0 to 57.8%,
which could reflect economically driven policies on the
use of hospitalization-based treatment rather than real
differences in clinical practice or patients' severity. The
main strengths of the BRIDGE-II-Mix study include the
large sample size and the wide range of care settings, both
hospital and community, from eight countries across
three continents. Furthermore, narrow exclusion criteria
increase the generalizability of the findings. Lastly, it
should be acknowledged that cluster analysis is a data
mining technique for finding patterns in data and is
exploratory in nature.

In conclusion, in this post-hoc cluster analysis on a large
international sample of 2314 acutely depressed patients,
approximately one third of the study population fitted
into a significantly more severe cluster of patients, inde-
pendently of the traditional, categorical diagnosis. Mixed
states seem to challenge the traditional unipolar–bipolar
dichotomy and bridge the gap between these two cate-
gories of mood disorders. Besides well-documented and
straightforward associations, such as current psychotic
symptoms, history of resistance to antidepressant treat-
ment and a history of suicide attempts, mixed affect
<figure><img src="image_2.png" /></figure>

**Fig. 1** Odds ratios (ORs) with 95% confidence intervals (95% CIs) are shown for the 7 variables with *p* value < 0.05 out of the 19 variables in the final stepwise Akaike information criterion (AIC) backward logistic regression model. ORs are expressed with respect to Cluster 2, i.e., the cluster displaying grater depressive burden.

defined as the co-occurrence of a minimum of three DSM-5 criteria for mixed features was significantly associated to more severe acute depression, supporting the usefulness of this definition for mixed features as significantly relevant to depression severity.

According to our results, a BD diagnosis does not automatically imply a worse severity of depression. In contrast, mixed features according to the current nosology should be considered as an intrinsic marker of severity, independently of a bipolar/unipolar diagnosis, in acutely depressed patients. This points to a continuum of the mood spectrum as a unitary phenomenon.

## Acknowledgements

The authors thank the support of the Spanish Ministry of Science, Innovation (PI15/00283-PI18/00805) and cofinanced by the Instituto Carlos III integrated into the Plan Nacional de I+D+I (cofounded by European Regional Development Fund/European Social Fund) 'investing in your future'; the CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental); the Secretaría d'Universitats i Recerca del Departament d'Economia i Coneixement (2017 SGR 1365) and the CERCA Programme/Generalitat de Catalunya. We thank all the authors of the included papers and study participants. Prof Young's research is funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.

## Funding

This study is sponsored by Sanofi-Aventis. All investigators recruited received fees from the sponsor in recognition of their participation in the study on a per-patient basis. The present study received no funding.

## Author details

¹Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy. ²Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Hospital Clínic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain. ³Biomedical Research Networking Center for Mental Health (CIBERSAM), Barcelona, Spain. ⁴August Pi I Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain. ⁵Psychiatry B, Chaim Sheba Medical Center, Ramat-Gan, Israel. ⁶Department of Psychiatry, Sainte Marguerite Hospital, Marseille, France. ⁷Department of Psychiatry, University of Zurich, Zurich, Switzerland. ⁸Department of Psychiatry, University of Texas Health Science Center, San Antonio, TX, USA. ⁹Department for Therapy of Mental Disorders, Moscow Research Institute of Psychiatry, Moscow, Russia.

¹⁰Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, Centre for Affective Disorders, London, UK. ¹¹Clinica Psichiatrica, University of Pisa, Pisa, Italy

## Code availability

All R-code we developed for statistical modeling is available upon request.

## Conflict of interest

J.A., F.C., G.A., and N.V. declare no conflict of interest. I.P. has received CME-related honoraria, or consulting fees from ADAMED, Janssen-Cilag, and Lundbeck. L.S. has received grants, honoraria, or consulting fees from Janssen-Cilag, Lundbeck, and Otsuka. D.P. has served as a speaker and/or medical writer for Bristol-Myers Squibb, Merck Sharp & Dohme, Janssen-Cilag, and Ferrer. J.M. A. has received research support and has acted as a consultant and/or served on a speaker's bureau for Janssen, Lundbeck, Otsuka, Roche, Servier, and Takeda, and reports no financial or other relationship relevant to the subject of this article. C.L.B. has received grant support from Sunovion and the NIMH, and has consulted for Takeda, and reports no financial or other relationship relevant to the subject of this article. S.M. has received research grants from, and been involved in clinical trials for Servier, Eli Lilly, Lundbeck, AstraZeneca, Janssen-Cilag, Sanofi-Aventis, and Geodon Richter and reports no financial or other relationship relevant to the subject of this article. A.H.Y. is honorary Consultant SLaM (NHS, UK). He provided lectures and advisory boards for the following companies with drugs used in affective and related disorders: AstraZeneca, Eli Lilly, Janssen, Lundbeck, Sunovion, Servier, Livanova, and
Janssen. He does not share holdings in pharmaceutical companies. He is Lead Investigator for Embolden Study (AZ), BCI Neuroplasticity study and Aripiprazole Mania Study, he did investigator initiated studies from AZ, Eli Lilly, Lundbeck, Wyeth. Grant funding (past and present): NIMH (USA); CIHR (Canada); NARSAD (USA); Stanley Medical Research Institute (USA); MRC (UK); Wellcome Trust (UK); Royal College of Physicians (Edin); BMA (UK); UBC-VGH Foundation (Canada); WEDC (Canada); CCS Depression Research Fund (Canada); MSFHR (Canada); NIH (UK). Janssen (UK). He reports no financial or other relationship relevant to the subject of this article. G.P. has acted as consultant of Lundbeck, Angelini, FBHealth. He received grant/research support from Lundbeck and Angelini. He is on the speaker/advisory board of Sanofi-Aventis, Lundbeck, FBHealth, Angelini, and reports no financial or other relationship relevant to the subject of this article. E.V. has received grants and served as consultant, advisor or CME speaker for the following entities: AB-Biotics, Abbott, Allergan, Angelini, AstraZeneca, Bristol-Myers Squibb, Dainippon Sumitomo Pharma, Farmindustria, Ferrer, Forest Research Institute, Gedeon Richter, Glaxo-Smith-Kline, Janssen, Lundbeck, Otsuka, Pfizer, Roche, SAGE, Sanofi-Aventis, Servier, Shire, Sunovion, Takeda, the Brain and Behaviour Foundation, the Spanish Ministry of Science and Innovation (CIBERSAM), the EU Horizon 2020, and the Stanley Medical Research Institute. A.M. has received grants, honoraria, or consulting fees from, Janssen, Lundbeck, and Otsuka.

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**Supplementary Information** accompanies this paper at [https://doi.org/10.1038/s41398-020-00922-2](https://doi.org/10.1038/s41398-020-00922-2).

Received: 20 February 2020 Revised: 7 June 2020 Accepted: 9 June 2020

Published online: 19 July 2020

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