persistent depressive disorder case study

• Research article
• Open access
• Published: 10 February 2020

Persistent depressive disorder across the adult lifespan: results from clinical and population-based surveys in Germany

• Julia Nübel 1 ,
• Anne Guhn 2 ,
• Susanne Müllender 1 ,
• Hong Duyen Le 1 ,
• Caroline Cohrdes   ORCID: orcid.org/0000-0003-0063-4145 1 &
• Stephan Köhler 2  

BMC Psychiatry volume  20 , Article number:  58 ( 2020 ) Cite this article

10k Accesses

18 Citations

1 Altmetric

Metrics details

Although the individual and economic disease burden of depression is particularly high for long-term symptoms, little is known of the lifetime course of chronic depression. Most evidence derives from clinical samples, and the diagnostic distinction between persistent depressive disorder (PDD) and non-chronic major depression (NCMDD) is still debated. Thus, we examined characteristics of PDD among clinical vs. non-clinical cases, and the associated disease burden at a population level.

Data were drawn from the mental health module of the German Health Interview and Examination Survey for Adults (DEGS1-MH, 2009–2012, n  = 4483) and a clinical sample of PDD inpatients at Charité – Universitätsmedizin Berlin (2018–2019, n  = 45). The DSM-5 definition of PDD was operationalized a priori to the study using interview-based DSM-IV diagnoses of dysthymia and major depression lasting at least 2 years in both surveys. Additional depression characteristics (depression onset, self-classified course, suicidality, comorbid mental disorders, treatment history and current depressive symptoms [Patient Health Questionnaire-9]) were assessed. In the DEGS1-MH, health-related quality of life (Short Form Health Survey-36, SF-36), chronic somatic conditions, number of sick days (past 12 months) or days with limitations in normal daily life activities (past 4 weeks), and health service utilization (past 12 months) were compared for PDD vs. NCMDD.

PDD cases from the clinical sample had a significantly earlier depression onset, a higher proportion of self-classification as persistent course, and treatment resistance than PDD and NCMDD cases in DEGS1-MH. At a population level, PDD cases showed worse outcomes compared with NCMDD cases in terms of somatic comorbidity, SF-36 mental component score, and activity limitations owing to mental health problems, as well as a higher risk for outpatient mental health care contact.

Conclusions

The distinction between PDD and NCMDD proposed for DSM-5 seems warranted. Early onset depression, self-classification as persistent depressive course, and treatment resistance are suggested as markers of more severe and chronic depression courses. At a population level, PDD is associated with remarkably higher individual and economic disease burden than NCMDD, highlighting the need to improve medical recognition of chronic courses and establish specific treatment concepts for chronic depression.

Peer Review reports

More than 300 million people globally were affected by depression in 2015, reflecting an increase of about 18% since 2005 in clinical settings [ 1 ]. In terms of years lived with disability, depressive disorder is now a leading contributor to non-fatal health loss [ 2 ]. Owing to its individual and economic disease burden, depression has become a global core health challenge of the twenty-first century [ 3 , 4 , 5 , 6 , 7 ]. Social insurance agencies in Germany have reported an increase in the frequency of depression and growing health care costs owing to working days lost, early retirement, and increased health service provision [ 8 , 9 ].

However, there are individual differences in depression course (i.e., single episodes vs. recurrent episodes), type, and severity. The enormous economic impact of depression on the general population seems particularly related to its duration (i.e., long-term), rather than to its severity [ 10 , 11 , 12 , 13 , 14 ]. Primary data indicate that up to 30% of depression cases have a chronic course with symptoms that last for at least 2 years [ 12 , 15 , 16 , 17 ]. The 12-month prevalence of chronic depression is 1.5% [ 18 ] and its lifetime prevalence is 3 to 6% [ 16 , 17 , 18 ]. In Germany, there is a lack of population-based information on chronic vs. non-chronic depression courses. However, secondary data from national health insurance companies indicate that up to two-thirds of medical depression diagnoses take a chronic course over at least 2 years (repeated registration irrespective of type or severity) [ 19 ].

Furthermore, chronic depression may have an earlier onset (before 21 years of age) [ 14 , 20 , 21 , 22 ] and worse outcomes than non-chronic depression, such as single or recurrent depressive episodes with full inter-episode recovery. Chronic depression is characterized by higher comorbidity rates [ 12 , 13 , 14 , 15 , 18 , 20 , 22 ], somatic morbidity [ 14 , 15 ], suicidality [ 14 , 20 , 22 ], reduced somatic and psychological well-being and health-related quality of life [ 12 , 13 , 14 , 23 ], lower employment rates [ 24 ], longer delays for treatment [ 15 ], and limited effects of psychotherapeutic or psychopharmacological treatment [ 10 , 11 , 13 , 25 , 26 , 27 ], all of which indicate its enormous direct and indirect costs.

However, comparisons of the characteristics, prevalence, and disease burden of chronic vs. non-chronic depression is hampered by two facts: most knowledge derives from clinical samples [ 15 ] and prevalence estimates differ, because a generally accepted definition of chronic depression was lacking until the American Psychiatric Association in 2013 decided to include a new depressive subtype, persistent depressive disorder (PDD), in the latest version of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) [ 28 , 29 ]. PDD is defined as depression that persists for at least 2 years. The PDD subtype is thus a combination of the DSM-IV diagnoses of (lasting) major depressive disorder (MDD) and dysthymic disorder (DD). However, even the new PDD diagnostic category does not consider additional lifetime information [ 25 ]. Thus, little is known about chronic depression during the lifespan (e.g., regarding early vs. late onset depression). Furthermore, the DSM-5 PDD diagnosis relies predominantly on clinical data and the concept of PDD has been criticized [ 30 ], as its reliability has not been formally examined [ 31 ]. However, some researchers still argue for a diagnostic distinction between chronic and non-chronic forms of MDD [ 32 ].

In this study, we aimed to comparatively analyze and differentiate characteristics of PDD vs. non-chronic depression courses during the lifetime using population-based data from the German health monitoring program at the Robert Koch Institute and a clinical sample from Charité – Universitätsmedizin Berlin. We hoped to extend the knowledge of chronic depression beyond the data from clinical samples, provide frequency information at a population level, and quantify the individual and economic disease burden of chronic depression for the general population in Germany. The findings from clinical studies suggest that both the indirect costs (e.g., to health-related quality of life or sick (leave) days) and the direct costs of health service utilization and treatment resistance are much higher for PDD cases than for non-chronic cases.

The study objectives were 1) the classification of chronic vs. non-chronic depression courses at a population level, 2) the identification of PDD characteristics in a clinical vs. population-based sample, and 3) the comparison of PDD vs. non-chronic MDD (NCMDD) in terms of associations with health-related correlates at a population level.

Data basis and depression assessment

Data for the nationwide representative analyses were drawn from the first wave of the German Health Interview and Examination Survey for Adults (DEGS1, field work 2008–2011, n  = 7115) and its mental health module (DEGS1-MH, field work 2009–2012, n  = 4483), which included 18- to 79-year-old participants from statutory as well as private health insurances based on a two-stage clustered random sampling procedure (step 1: random sampling of study locations from all municipal communities; step 2: random sampling of participants from the population-registries in each sampled study location). The design and methods are described in detail elsewhere [ 33 , 34 , 35 ]. DEGS1 and DEGS1-MH were part of the German health monitoring program and provided data about the health of the non-institutionalized population in Germany based on self-rated questionnaires and a standardized computer-assisted Interview conducted by study physicians (CAPI). Mental disorders, including MDD and DD, were assessed by trained interviewers based on the World Health Organization Composite International Diagnostic Interview (CIDI). The CIDI is a standardized fully structured computer-assisted clinical face-to-face interview and is an internationally established measure of mental disorders [ 36 , 37 , 38 ]. A modified German version of the CIDI was used in DEGS1-MH [ 33 ] to assess mental disorders according to the DSM-IV-TR diagnostic criteria [ 39 ]. The CIDI provides lifetime information about symptoms (e.g., age of onset, recurrence and duration of episodes) that permits analysis of the course of depression over the lifespan. After participants with missing information on affective disorders were excluded ( n  = 75), the final study sample was n  = 4408.

Data were also obtained from a clinical sample recruited at the Charité – Universitätsmedizin Berlin ( n  = 60). Patients with a professional diagnosis of PDD according to DSM-5 [ 28 ] were treated for 12 weeks with a specialized chronic depression intervention: the Cognitive Behavioral Analysis System of Psychotherapy (CBASP; [ 27 , 40 ]). Patients were either directly referred from the outpatient clinic of the Charité, from inpatient wards of other hospitals from all parts of Germany, or from outpatient psychiatrists. Treatment was reimbursed by statutory health insurances. Exclusion criteria for the inpatient CBASP were a history of psychotic episodes, bipolar I or II disorders, comorbid substance dependence with less than 3 months of abstinence, severe forms of autism, and organic mental disorders. All patients treated at the ward from 2013 to 2018 were invited for a subsequent follow-up interview for the purpose of the present study. These interviews were conducted from October 2018 to March 2019 to collect lifetime information on course and type of depression and comorbid mental disorders using the Structured Clinical Interview for DSM-IV (SCID I; [ 41 ]) and self-rated questionnaires. To allow comparison with the epidemiological sample, additional questions based on the CIDI depression section were included. The final clinical study sample comprised n  = 45 patients, aged 24–66 years.

Definition and operationalization of (non-)chronic depression

For this study, the definition of chronic depression was based on the DSM-5 PDD diagnosis and drawn from the DSM-IV-based diagnoses of MDD or DD derived from the SCID I or CIDI. According to DSM-IV, MDD diagnosis requires the persistence of at least five out of nine depressive symptoms on nearly every day for 2 weeks or longer, of which at least one is depressed mood or decreased interest/pleasure (criterion A). Furthermore, clinically significant distress and impairment associated with these symptoms are necessary (criterion C). MDD exclusion criteria include lifetime manic/hypomanic episodes (criterion B) and depressive symptoms solely attributable to the direct physiological effects of a substance or a general medical condition (criterion D) or attributable to grief (criterion E). DD diagnosis requires depressed mood for most of the day and for at least 2 years (criterion A), and at least two out of six depression symptoms (criterion B). During the 2 years, the total recovery time should not have exceeded more than 2 months (criterion C) and the symptoms should have caused clinically significant distress or impairment (criterion H). Exclusion criteria include manic/hypomanic episodes (criterion E), symptoms owing to the direct physiological effects of a substance or a general medical condition (criterion G), or symptoms occurring during the course of a psychotic disorder (criterion F). Furthermore, DSM-IV DD diagnosis requires the absence of a major depressive episode during the first 2 years of occurrence (criterion D). However, DSM-5 no longer includes this criterion for PDD diagnosis, and MDD criteria may be continuously present for 2 years.

Thus, subjects with lifetime or 12-month MDD according to CIDI or SCID I who also report a lifetime maximum episode duration of at least 104 weeks, as well as subjects (concurrently) fulfilling the DD diagnostic criteria (irrespective of DSM-IV criterion D), were classified as lifetime PDD cases. The remaining MDD cases were categorized as non-chronic cases (NCMDD). The grouping of PDD and NCMDD was carried out a priori to the study. Cases with missing responses for maximum episode duration and missing information on diagnostic criteria of DD have been omitted. In the clinical sample, health professional-diagnosed PDD was validated via SCID I for all patients.

Depression characteristics

Age of depression onset and the number of depressive episodes were assessed in both diagnostic interviews. History of suicidality was also assessed in both surveys based on CIDI questions about thoughts of death or suicide, suicide plans, or attempted suicide.

Subjects of DEGS1-MH and patients of the clinical sample rated their course of depression based on CIDI depression section diagram on the following categories: single episode (remitted), single episode (acute), recurring episodes, single episode with chronic course, persistent depressive course, double depression, or other.

MDD symptoms according to DSM-IV were assessed using the German version of the internationally established Patient Health Questionnaire (PHQ-9). The PHQ-9 consists of nine items assessing the presence and frequency of depressive symptoms during the past 2 weeks. Summed scores ≥10 indicate current depressive symptoms [ 42 , 43 ].

The number of comorbid mental disorders (lifetime) was categorized as none, one, and at least two of the CIDI- or SCID I-based diagnoses of mental disorders during the lifetime. As some mental disorders were included in the exclusion criteria for the clinical sample, the following comorbid diagnoses were assessed: panic disorder, agoraphobia, generalized anxiety disorder, social phobia, specific phobias, obsessive–compulsive disorder, posttraumatic stress disorder, pain and somatoform disorders, substance abuse and dependence (excluding nicotine), anorexia nervosa, bulimia nervosa, and binge eating disorder.

Self-reported mental health treatment during the lifetime was assessed based on the CIDI questions in both DEGS1-MH and the clinical sample. The number of antidepressant treatments and the number of psychotherapies were each categorized as none, one, and at least two treatments. Treatment resistance was defined for cases with at least two reported antidepressant treatments, approaching the definition of Thase and Rush (medication resistance to two or more adequate trials of antidepressants) [ 44 ].

Health-related correlates

Several health-related correlates were assessed in DEGS1-MH: self-rated health (dichotomized into fair/poor vs. good/very good/excellent) and health-related quality of life (past 4 weeks) were assessed using the German version of the Short Form Health Survey-36 (SF-36) version 2 [ 45 , 46 ]). The physical component score (PCS) and the mental component score (MCS) were used as total scales with a mean value of 50 and a standard deviation of 10 (higher values indicate better health-related quality of life). The number of days with limitations in normal daily life activities owing to physical vs. mental health problems (including limitations owing to substance use) during the past 4 weeks were also assessed [see 23 ]. The self-reported number of sick days during the past 12 months was assessed in DEGS1 (irrespective of occupational status), as well as self-reported information on health service use during the past 12 months (number of outpatient physician visits, outpatient psychiatric or psychotherapeutic contacts, and number of nights in hospital). The number of chronic somatic conditions reported in DEGS1 was classified as none, one, and at least two of the following somatic conditions [see 47 ]: myocardial infarction (lifetime), chronic heart failure (lifetime), stroke (lifetime), osteoarthritis (lifetime), rheumatoid arthritis (past 12 months), osteoporosis (lifetime), gout (past 12 months), bronchial asthma (past 12 months), cirrhosis of the liver (lifetime), hepatitis (past 12 months), gastric-duodenal ulcer (past 12 months), cancer (lifetime), Parkinson’s disease (lifetime), epilepsy (past 12 months), hypertension (past 12 months), dyslipidemia (past 12 months), renal failure (lifetime), and inflammatory bowel disease (past 12 months).

Other measures

Sociodemographic variables included sex, age, marital status, and educational level. Age was assessed in years at the time of the clinical follow-up as well as of the DEGS1 mental health module assessment and categorized into age groups (18–34, 35–49, 50–64, and 65–79 years). Marital status was dichotomized into married and living with partner vs. married and not living with partner/single/never been married/divorced/widowed. The Comparative Analysis of Social Mobility in Industrial Nations (CASMIN) scale was used to classify responses on educational level into low, medium, and high. In DEGS1-MH, structural social support was assessed using the Oslo-3 Social Support Scale [ 48 ].

Statistical analysis

Frequency and mean estimates of the sample characteristics are reported with 95% confidence intervals (95% CI).

At a population level, prevalence estimates for lifetime MDD and DD are reported. Conditional frequencies for chronic vs. non-chronic courses among lifetime MDD cases are reported. Prevalence estimates for PDD and NCMDD could not be provided owing to many missing responses for self-reported maximum episode duration, resulting in a high proportion of MDD with unknown chronicity.

Frequency and mean estimates for depression characteristics are reported with 95% CI for PDD cases in the clinical sample and for PDD and NCMDD cases in the population-based sample. The significance ( p  < .01) of differences between the clinical sample and the DEGS1-MH sample was indicated by non-overlapping 95% Cis [ 47 ] and sizes of significant effects for independent groups with different sample size are indicated by Cohen’s d (small = 0.2, medium = 0.5, large = 0.8). Statistical significance of differences between PDD and NCMDD characteristics in DEGS1-MH were evaluated using the Rao–Scott chi-square test for categorical variables, and the Wilcoxon–Mann–Whitney test for continuous variables, using a two-sided significance level of 0.05.

Health-related correlates are shown for DEGS1-MH PDD vs. NCMDD cases with 95% CI, to enable the comparison of the associated individual and economic disease burden at a population level. Effect estimates for health-related correlates in cases with PDD vs. NCMDD were based on logistic, linear, negative binomial, or zero-inflated negative binomial regression models, including health-related correlates as dependent variables and depression course (PDD vs. NCMDD) as the independent variable (reference: NCMDD). All analyses were adjusted for sex, age group, educational level, marital status, social support, chronic somatic conditions (except for analysis of the number of chronic somatic conditions as an outcome variable), and PCS (except for analysis of PCS as an outcome variable) [see 49 ]. The results of the unadjusted regression analyses are included as supplementary data (see Additional file  1 ) and only described if divergent. Statistical significance was evaluated based on a two-sided significance level of 0.05.

All statistical analyses were performed using Stata 15.1 (StataCorp, College Station, Texas, USA). For DEGS1-MH, all analyses were performed using the Stata survey design procedures to account for clustering and weighting of the study sample. Thus, survey-specific weighting factors were used to adjust the sample to the demographic distribution of the population in Germany as on 31st December, 2010, regarding sex, age, educational status, federal state, nationality, and the probability of participation in the mental health module subsequent to the core survey [ 33 , 50 ].

In addition, we calculated post-hoc power analyses to test for appropriate test power based on the present sample sizes.

Sample characteristics

Sample characteristics of the clinical sample and DEGS1-MH sample are shown in Table  1 . The DEGS1-MH sample was comparable to the clinical sample on age and sex, except for the proportion of participants aged 50–64 years (higher in the clinical sample) and 65–79 years (higher in DEGS1-MH participants). Clinical sample patients more frequently lived alone (88.9% vs. 39.2%) and demonstrated a significantly higher educational level than the DEGS1-MH sample (as indicated by non-overlapping 95% Cis).

Chronic depression at a population level

Among cases with lifetime MDD in DEGS1-MH (14.5%), 18.2% reported a maximum episode duration of at least 2 years, and 15.4% fulfilled the diagnostic criteria of concurrent DD (without considering criterion D). Overall, 36.5% of cases with a lifetime CIDI diagnosis of MDD were classified as chronic MDD cases; the remaining 63.5% were categorized as NCMDD cases. In addition to chronic MDD, PDD also comprised subjects with solely lifetime DD (1.3%, without considering criterion D).

Characteristics of chronic depression in a clinical sample and at a population level

PDD cases from the clinical sample had a significantly earlier disease onset than cases with PDD and NCMDD in DEGS1-MH (age of disorder onset < 21 years: 73.3% vs. 24.7% vs. 32.2%, see Table  2 ). Suicidality (thoughts of death/suicide, or having suicide plans/attempts) was reported more often by PDD cases in the clinical sample than by PDD or NCMDD cases in the DEGS1-MH sample (95.5% vs. 86.4% vs. 86.2%), as was attempted suicide (36.4% vs. 16.2% vs. 11.7%), but the significance of these differences remains unclear with one exception: the proportion of PDD patients in the clinical sample that attempted suicide was more than three times greater than the proportion of NCMDD cases in DEGS1-MH. Regarding self-reported depression course, PDD cases differed significantly from NCMDD cases in DEGS1-MH ( p  < 0.001). Both PDD groups showed significantly higher rates of a chronic course of a single episode compared with NCMDD cases (25.0 and 24.3% vs. 5.9%). Furthermore, a significantly higher proportion of clinical PDD patients showed a persistent depressive course compared with PDD and NCMDD DEGS1-MH cases (50.0% vs. 24.6% vs. 2.0%), and a smaller frequency of recurring episodes (2.3% vs. 20.9% vs. 55.1%; significant difference only for clinical PDD patients compared with NCMDD cases). Accordingly, cases with PDD in DEGS1-MH reported a significantly higher mean number of episodes in total (13.7) compared with both clinical PDD patients (2.8) and NCMDD cases in DEGS1-MH (7.4, p  < 0.001). Comorbid mental disorders seemed to be more pronounced among cases with PDD and NCMDD in DEGS1-MH compared with the clinical sample, but the significance of these differences remains unclear. There was a trend for higher comorbidity among PDD cases than among NCMDD cases in DEGS1-MH ( p  = 0.071) . The prevalence of current depressive symptoms was highest among the clinical PDD patients (PHQ-9 ≥ 10: 66.7%), and significantly higher among PDD cases compared with NCMDD cases in DEGS1-MH (44.9% vs. 18.6%, p  < 0.001). Furthermore, clinical PDD cases showed a significantly higher treatment resistance than PDD and NCMDD cases in DEGS1-MH, in terms of the proportion of cases reporting at least two psychotherapeutic treatments (90.9% vs. 2.7% vs. 0.9%) or antidepressant medications (81.0% vs. 9.1% vs. 12.2%) during the lifetime. Most PDD and NCMDD cases in DEGS1-MH reported no psychotherapeutic treatment (87.2 and 92.6%) or antidepressant medication (79.5 and 75.1%).

Health-related correlates of (non-)chronic depression at a population level

The associations of PDD vs. NCMDD with health-related correlates based on DEGS1-MH are shown in Tables  3 and 4 . The risk of experiencing fair or poor self-rated health was significantly higher among PDD cases (36.8%) than among NCMDD cases (20.4%, odds ratio [OR] = 2.0, p  = 0.041). Mean health-related quality of life (past 4 weeks) was lower among PDD cases for PCS (47.1 vs. 50.7, significant only for crude effect estimates, see Additional file 1 ) and MCS (34.5 vs. 43.8, β = − 8.2, p  < 0.001). Accordingly, the mean number of days with activity limitations (past 4 weeks) owing to mental health problems was higher for PDD than for NCMDD (5.4 vs. 2.4, incidence rate ratio [IRR] = 2.6, p  < 0.001). There was also a trend for more reported limitation days owing to physical health problems for PDD compared with NCMDD cases (5.3 vs. 3.1, IRR = 1.4, p  = 0.091). There was also a higher risk of sick days during the past 12 months for PDD cases (34.2 vs. 14.8), but this was only significant in the unadjusted analysis (see Additional file 1 ). Indicators of health service use during the past 12 months showed higher utilization rates for PDD than for NCMDD cases for the mean number of outpatient psychiatric or psychotherapeutic contacts (5.7 vs. 1.7, IRR = 2.7, p  = 0.006). There was also a trend for PDD cases to report a higher mean number of nights in hospital compared with NCMDD cases (3.9 vs. 0.9, IRR = 1.9, p  = 0.065). The mean number of outpatient physician visits (4.3 vs. 3.6) was only significantly higher for PDD cases in the unadjusted analysis (see Additional file 1 ). Furthermore, somatic comorbidity was significantly higher for PDD vs. non-chronic cases. The risk of having one chronic condition (31.0% vs. 20.6%, relative risk ratio [RRR] = 2.8, p  = 0.008) or at least two comorbid conditions (26.2% vs. 15.6%, RRR = 3.2, p  = 0.004) was approximately 3-fold for PDD. In contrast, most NCMDD cases (63.9%) had no somatic comorbidity at all (vs. 42.8% of PDD cases).

Post-hoc power analyses

Results from post-hoc power analyses with the help of G*Power 3 [ 51 ] suggest that that the present sample size of n  = 429 individuals was sufficient for the detection of moderate effects (ω = 0.30) within a chi-square goodness-of-fit test comparing PDD vs. NCMDD in clinical and population-based samples for each health-related correlate and an error probability of α = 0.05, at the power level of 1.00 (see Table 2 ). Moreover, results from post-hoc power calculation suggest that the present sample size of n  = 285 individuals was sufficient for the detection of moderate effects ( f 2  = 0.15) within a multiple regression design containing five predictors (PDD vs. NCMDD, age, sex, marital status, educational level) on each health-related correlate in a population-based sample, with an error probability of α = 0.05 and at the power level of 1.00 (see Table  4 ).

Based on a nationally representative sample of the general adult population in Germany, more than one-third (36.5%) of all subjects fulfilling MDD criteria showed a chronic depression course with maximum episode duration of at least 2 years and/or concurrent dysthymia at least once during the lifetime. This rate is slightly higher than previous international frequency estimates, which reported a chronic course for only 21 to 30% of depressed cases [ 12 , 15 , 16 , 17 ]. This inconsistency can be explained by different definitions of chronic depression: previous prevalence based solely on episode duration, without considering MDD cases with double depression (i.e., MDD and DD).

More severe PDD cases in the health care system

Overall, our estimated frequency for DEGS1-MH cases with MDD that had a chronic course during the lifetime (36.5%) was much lower than the proportion reported from national health insurance data (65%) [ 19 ]. However, previous findings show that among cases with CIDI-based MDD, 65.4% did not report any health service use for mental health problems [ 52 ]; and service use increased with depression severity [ 52 ]. Thus, particularly severe (and chronic) depression cases may eventually access the health care system, leading to higher proportions of chronic depression courses based on health insurance data [see 19 ] compared with frequency estimates for interview-based MDD cases at a population level.

Consequently, our comparisons of depression characteristics indicate that PDD cases in the health care system are more severely affected, since clinical sample PDD patients showed a pronounced long-term duration owing to earlier onset (73.3% vs. 24.7% with age onset ≤21 years) and significantly higher rates of treatment resistance (81.0% vs. 9.1% reported at least two antidepressant medication trials) compared with interview-defined PDD cases at a population level, as well as a higher proportion of self-classified persistence of depressive course. Furthermore, the prevalence of attempted suicide during lifetime was higher among clinical PDD patients as compared to the DEGS1-MH PDD cases (but nonsignificant) and more than three times higher than among NCMDD cases.

Considering the existing literature, our results are in line with clinical findings. For instance, lifetime prevalence of treatment resistance for depression was 81.8% in patients with long-term depression vs. 60.7% in patients with depression lasting less than 2 years [ 14 ]. In terms of inpatient treatment, a lifetime prevalence of 24.1% for hospitalization owing to mental health problems has been reported for PDD patients compared to 12.1% for non-PDD patients [ 17 ]. Furthermore, the average duration of past inpatient treatment is longer for PDD cases [ 53 ]. Patients with PDD also have higher rates of suicidal attempts and suicidal thoughts and are more likely to have a higher frequency of treatment approaches in general and a longer disorder duration [ 22 ].

Early depression onset seems a particular marker of a more severe PDD course: 73% of our clinical PDD patients showed an early onset, whereas the proportion was much lower among interview-defined PDD cases in DEGS1-MH (24.7%); and there was no significant difference between PDD and NCMDD cases at a population level. Similarly, international findings are heterogeneous: one meta-analysis found a significant relationship between early onset of depression and chronicity of the disorder [ 54 ]. However, in a recent review of 17 studies directly comparing age of onset in PDD vs. non-PDD cases, half the studies reported earlier onset for chronic vs. non-chronic depression whereas the other half reported no difference [ 22 ].

Recent reviews found that patients with PDD more often have psychiatric comorbidities than those with non-PDD, particularly personality disorders but also axis I and somatic comorbidities [ 22 ]. However, differences between PDD and NCMDD cases in DEGS1-MH have only been observed by trend, and our clinical sample of PDD patients demonstrated even less comorbidity than interview-defined cases. This may be related to differences in the diagnostic tools (SCID I vs. CIDI). Additionally, personality disorders, which account for a large proportion of comorbidities in the reviews, were not assessed in both samples. However, interpersonal problems as indicated by the social functioning subscale of the SF-36 were significantly reduced among PDD cases as compared to NCMDD cases in DEGS1-MH (post hoc sensitivity analysis; PDD: M = 61.47, 95%CI = 55.89–67.05; NCMDD: M = 76.77, 95%CI = 72.67–80.86). Consequently, only minor and non-significant differences in mental comorbidity have been observed between interview-defined PDD and NCMDD cases.

Higher disease burden for chronic vs. non-chronic depression

The comparison of interview-defined cases of PDD vs. NCMDD at a population level highlighted that several health-related correlates indicate higher individual and economic disease burden for chronic depression courses.

On the individual level, there was a remarkably higher prevalence of current depressive symptoms (as assessed by PHQ-9) among PDD cases than among NCMDD cases, as well as a higher mean number of depressive episodes (irrespective of episode severity or duration). Furthermore, higher levels of psychological and somatic comorbidity are in line with international findings on higher comorbidity rates [ 12 , 14 , 15 , 18 , 20 , 22 ] and somatic morbidity [ 14 , 15 ] for chronic depression courses. The present outcomes of worse self-rated health and reduced health-related quality of life for the MCS correspond to previous findings of reduced psychological well-being and health-related quality of life for individuals with chronic depression [ 12 , 13 , 14 , 23 ].

Accordingly, chronic depression is associated with higher indirect economic costs: PDD cases showed a higher risk of experiencing limitation days owing to mental health problems than non-chronic cases. Our findings of higher rates of outpatient mental health care utilization and the trend for a higher mean number of nights in hospital also indicate higher direct costs for the national economy and correspond to previous research findings [ 55 ].

Public health implications and future perspectives

Considering the growing frequency of depression and health care costs in Germany owing to working days lost, early retirement, and health service provision [ 8 , 56 , 57 , 58 ], our data strongly support the relevance of PDD as a specific course of depressive disorders. As long-term PDD is often associated with higher treatment resistance [ 59 ] there is a chance that if an early and tailored treatment of PDD and its specific psychopathological characteristics (e.g. CBASP) is carried out, a positive shift towards a more positive course of the disease can be achieved.

However, self-reported utilization rates [see 52 ] correspond with reported international treatment gaps for mental disorders in general [ 3 , 4 , 6 , 7 ]: most Germans with acute depression do not access mental health care. In addition, previous results indicate more frequent help-seeking with higher education [ 60 ]. The characteristics of our clinical sample also suggest that in particular PDD cases with lower educational levels do not seek help or receive (specialized) treatment: While international findings show that PDD is associated with low socioeconomic status [ 61 ], PDD cases in our clinical sample had a significantly higher education as compared to the general population (DEGS1-MH participants). This is important, as it raises the question of whether more educated patients are more willing to participate in a depression intervention, or more likely to be informed about specific treatment programs for PDD. If so, then PDD patients with lower education may be disadvantaged in this regard.

Moreover, findings from national health care data suggest that the validity of medical depression diagnoses are questionable, particularly in primary care [ 62 ], and that improving treatment targeting [ 63 , 64 ] and treatment quality [ 19 , 65 , 66 , 67 , 68 ] are desirable. In conclusion, these findings highlight the need for national public health initiatives in Germany to reduce barriers to accessing mental health care services in general and in individuals with low education in particular, to strengthen awareness using targeted information campaigns, and to improve the quality of medical recognition and specialized treatment provision for depression and its different courses.

There is thus a need to identify patients with PDD correctly and to tailor specific treatment strategies. Therefore, a focus on psychological characteristics [ 69 , 70 ] is warranted, as the DSM-5 diagnosis of PDD is very likely a heterogeneous umbrella diagnosis. For example, different studies could differentiate PDD and non-PDD in terms of psychopathological features and social functioning (e.g., cognitive and affective reactivity [ 69 , 70 ] and interpersonal behavior [ 71 ]. This is important for the development of new treatment approaches as well as for the empirical corroboration and refinement of existing treatment attempts. For instance, CBASP was specifically developed for the treatment of PDD [ 40 ]. CBASP particularly considers psychopathological features of PDD such as an early onset due to childhood maltreatment and interpersonal withdrawal and avoidance. Evidence for the effectiveness of CBASP is encouraging (e.g. [ 72 ]), especially in patients with childhood maltreatment [ 73 ]. There is also evidence that the improvement of interpersonal behavior through CBASP is associated with symptom reduction, thus providing an important treatment target for PDD [ 74 ]. In this regard, CBASP proved to be more effective than less specific psychotherapeutic treatments [ 75 , 76 ].

Limitations

In interpreting the findings of this study some potential limitations should be considered, such as the study design, response and reporting bias, and construct overlap.

The small number of PDD cases in both the clinical sample and the DEGS1-MH sample may have reduced the accuracy of the frequency and mean estimates. Thus, significant differences between the samples may not have been detected using non-overlapping 95% CI.

Comparisons between PDD cases of DEGS1-MH and cases of the clinical sample are limited for several reasons. Particularly severe and chronic depression cases may be underrepresented in DEGS1-MH owing to the exclusion of institutionalized subjects, selective non-responses of less healthy individuals, and the inclusion of participants with private health insurance as well as some longitudinal participants (with a potentially greater probability of re-participation among healthier persons) [ 33 , 34 , 35 ]. Moreover, we found that clinical PDD patients had a higher educational level than DEGS1-MH participants. This also limits the group comparison. However, it could indicate that patients with PDD and a higher educational level have easier access to specified treatment programs. The comparison between the DEGS1-MH and clinical samples is further limited in terms of psychological comorbidity, owing to the use of different diagnostic tools (CIDI vs. SCID I). Furthermore, recall bias may have been more pronounced for DEGS1-MH cases, since PDD was defined on the basis of lifetime information, whereas the clinical sample only included patients diagnosed by PDD within the last 6 years. Thus, recall bias may have led to an underestimation of depression characteristics particularly among DEGS1-MH cases, e.g. with regard to treatment resistance and a history of suicidality. Furthermore, recall bias, varying diagnostic accuracy, and participants’ reporting bias may also have led to the underestimation of comorbidity and chronic depression course during the lifetime in both surveys, particularly among male and older participants [ 64 , 77 ].

In DEGS1-MH, the small number of PDD and NCMDD cases may also have led to low statistical power for detecting the effects of depression course on health-related outcomes. Furthermore, time lags between the core DEGS1 survey and its mental health supplement may have led to an underestimation of associations between PDD/NCMDD and health-related correlates, as well as differing reference time frames for CIDI-based depression course during the lifetime and outcome variables (e.g., health service utilization during the past 12 months). However, construct overlap between depressive symptoms and the examined outcome measures (e.g., SF-36 and limitation days) may have led to the overestimation of associations.

Finally, a chronic course of depression is challenging for both patients and practitioners. However, a knowledge gap remains regarding the lifetime characteristics and correlates of chronic depression and the reliability of the PDD concept itself.

By combining clinical and epidemiological perspectives, our study permitted a comparison of standardized characteristics of PDD among clinical vs. non-clinical cases and therefore extends existing knowledge about PDD. Our data suggest that the distinction between chronic and non-chronic depression proposed for DSM-5, in the form of PDD, is warranted. In particular, early onset depression, attempted suicide, self-classification as persistent depressive course, and treatment resistance are suggested as markers of more severe and chronic depression courses.

Furthermore, health-related correlates of PDD vs. non-chronic depression were compared at a population level. Thus, the associated individual and economic disease burden was evaluated for the general population in Germany for the first time. At a population level, chronic depression is associated with a remarkably higher disease burden than non-chronic courses, indicating enormous direct and indirect costs of chronic depression for the national economy and emphasizing its public health relevancy. In conclusion, these findings can inform the planning and targeting of prevention and health services. They highlight the need to further reduce barriers to accessing mental health care, improve awareness of different depression courses among health professionals, and implement specific treatment concepts for chronic depression.

Availability of data and materials

Population-based data from the German health monitoring program that support the findings of this study are available from the Robert Koch Institute (RKI) but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. The data set cannot be made publicly available because informed consent from study participants did not cover public deposition of data. However, a minimal data set is archived in the Health Monitoring Research Data Centre at the RKI and can be accessed by all interested researchers. On-site access to the data set is possible at the Secure Data Centre of the RKI’s Health Monitoring Research Data Centre. Requests should be submitted to the Health Monitoring Research Data Centre, Robert Koch Institute, Berlin, Germany (Email: [email protected] ). The patient data from the clinical population are available from the Charité – Universitätsmedizin Berlin. The data set cannot be made publicly available because informed consent from study participants did not cover public deposition of data.

Abbreviations

95% confidence interval

Comparative Analysis of Social Mobility in Industrial Nations

Cognitive Behavioral Analysis System of Psychotherapy

Composite International Diagnostic Interview

dysthymic disorder

German Health Interview and Examination Survey for Adults

Mental health module of DEGS1

Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision

incidence rate ratio

mental component score

major depressive disorder

non-chronic major depressive disorder

physical component score

persistent depressive disorder

Patient Health Questionnaire-9

relative risk ratio

Structured Clinical Interview for DSM-IV

Short Form Health Survey-36

GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1545–602.

Article   Google Scholar  

GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1789–858.

Alonso J, Codony M, Kovess V, Angermeyer MC, Katz SJ, Haro JM, et al. Population level of unmet need for mental healthcare in Europe. Br J Psychiatry. 2007;190:299–306.

Article   PubMed   Google Scholar  

Demyttenaere K, Bruffaerts R, Posada-Villa J, Gasquet I, Kovess V, Lepine JP, et al. Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization world mental health surveys. JAMA. 2004;291:2581–90.

Hewlett E, Moran V. Making mental health count: the social and economic costs of neglecting mental health care. OECD health policy studies. Paris: OECD Publishing; 2014.

Book   Google Scholar  

Kohn R, Saxena S, Levav I, Saraceno B. The treatment gap in mental health care. Bull World Health Organ. 2004;82:858–66.

PubMed   PubMed Central   Google Scholar  

Wittchen HU, Jacobi F, Rehm J, Gustavsson A, Svensson M, Jonsson B, et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol. 2011;21:655–79.

Article   CAS   PubMed   Google Scholar  

Statistisches Bundesamt (Destatis). Gesundheit: Krankheitskosten 2015. https://www.destatis.de/GPStatistik/servlets/MCRFileNodeServlet/DEHeft_derivate_00033175/2120721159004.pdf%3Bjsessionid%3DB19129ECF3A61F8DB331D847FC4E599C . Accessed 30 July 2019.

Google Scholar  

Gerste B, Roick C. Prävalenz und Inzidenz sowie Versorgung depressiver Erkrankungen in Deutschland - Eine Analyse auf Basis der in Routinedaten dokumentierten Depressionsdiagnosen. In: Klauber J, Günster C, Gerste B, Robra BP, Schmacke N, editors. Versorgungs-report 2013/2014. Stuttgart: Schattauer; 2014. p. 21–54.

Fostick L, Silberman A, Beckman M, Spivak B, Amital D. The economic impact of depression: resistance or severity? Eur Neuropsychopharmacol. 2010;20:671–5.

Sackeim HA. The definition and meaning of treatment-resistant depression. J Clin Psychiatry. 2001;62(Suppl 16):10–7.

CAS   PubMed   Google Scholar  

Gilmer WS, Trivedi MH, Rush AJ, Wisniewski SR, Luther J, Howland RH, et al. Factors associated with chronic depressive episodes: a preliminary report from the STAR-D project. Acta Psychiatr Scand. 2005;112:425–33.

Pettit JW, Lewinsohn PM, Roberts RE, Seeley JR, Monteith L. The long-term course of depression: development of an empirical index and identification of early adult outcomes. Psychol Med. 2009;39:403–12.

Angst J, Gamma A, Rossler W, Ajdacic V, Klein DN. Long-term depression versus episodic major depression: results from the prospective Zurich study of a community sample. J Affect Disord. 2009;115:112–21.

Rubio JM, Markowitz JC, Alegria A, Perez-Fuentes G, Liu SM, Lin KH, et al. Epidemiology of chronic and nonchronic major depressive disorder: results from the national epidemiologic survey on alcohol and related conditions. Depress Anxiety. 2011;28:622–31.

Article   PubMed   PubMed Central   Google Scholar  

Murphy JA, Byrne GJ. Prevalence and correlates of the proposed DSM-5 diagnosis of chronic depressive disorder. J Affect Disord. 2012;139:172–80.

Satyanarayana S, Enns MW, Cox BJ, Sareen J. Prevalence and correlates of chronic depression in the Canadian community health survey: mental health and well-being. Can J Psychiatr. 2009;54:389–98.

Blanco C, Okuda M, Markowitz JC, Liu SM, Grant BF, Hasin DS. The epidemiology of chronic major depressive disorder and dysthymic disorder: results from the National Epidemiologic Survey on alcohol and related conditions. J Clin Psychiatry. 2010;71:1645–56.

Melchior H, Schulz H, Härter M. Faktencheck Gesundheit - Regionale Unterschiede in der Diagnostik und Behandlung von Depressionen. Bertelsmann Stiftung: Gütersloh; 2014.

Mondimore FM, Zandi PP, Mackinnon DF, McInnis MG, Miller EB, Crowe RP, et al. Familial aggregation of illness chronicity in recurrent, early-onset major depression pedigrees. Am J Psychiatry. 2006;163:1554–60.

Klein DN, Schatzberg AF, McCullough JP, Dowling F, Goodmann D, Howland RH, et al. Age of onset in chronic major depression: relation to demographic and clinical variables, family history, and treatment response. J Affect Disord. 1999;55:149–57.

Köhler S, Chrysanthou S, Guhn A, Sterzer P. Differences between chronic and nonchronic depression: systematic review and implications for treatment. Depress Anxiety. 2018;36:18–30.

Mack S, Jacobi F, Beesdo-Baum K, Gerschler A, Strehle J, Höfler M, et al. Functional disability and quality of life decrements in mental disorders: results from the mental health module of the German health interview and examination survey for adults (DEGS1-MH). Eur Psychiatry. 2015;30:793–800.

OECD/EU. Health at a glance: Europe 2018. Paris: OECD Publishing; 2018.

Jobst A, Brakemeier EL, Buchheim A, Caspar F, Cuijpers P, Ebmeier KP, et al. European psychiatric association guidance on psychotherapy in chronic depression across Europe. Eur Psychiatry. 2016;33:18–36.

Cuijpers P, van Straten A, Schuurmans J, van Oppen P, Hollon SD, Andersson G. Psychotherapy for chronic major depression and dysthymia: a meta-analysis. Clin Psychol Rev. 2010;30:51–62.

Köhler S, Sterzer P, Normann C, Berger M, Brakemeier EL. Overcoming treatment resistance in chronic depression: the role of inpatient psychotherapy. Nervenarzt. 2016;87:701–7.

American Psychiatric Association. Diagnostic and statistical manual of mental disorders, fifth edition (DSM-5). Washington, DC: American Psychiatric Association; 2013.

Klein DN, Black SR. Persistent depressive disorder - dysthymia and chronic depression. In: Craighead EW, Miklowitz DJ, Craighead LW, editors. Psychopathology: history, diagnosis, and empirical foundations. Volume 2. Hoboken: Wiley; 2013.

Rhebergen D, Graham R. The re-labelling of dysthymic disorder to persistent depressive disorder in DSM-5: old wine in new bottles? Curr Opin Psychiatry. 2014;27:27–31.

Regier DA, Narrow WE, Clarke DE, Kraemer HC, Kuramoto SJ, Kuhl EA, et al. DSM-5 field trials in the United States and Canada, part II: test-retest reliability of selected categorical diagnoses. Am J Psychiatry. 2013;170:59–70.

van Randenborgh A, Hüffmeier J, Victor D, Klocke K, Borlinghaus J, Pawelzik M. Contrasting chronic with episodic depression: an analysis of distorted socio-emotional information processing in chronic depression. J Affect Disord. 2012;141:177–84.

Jacobi F, Mack S, Gerschler A, Scholl L, Hofler M, Siegert J, et al. The design and methods of the mental health module in the German health interview and examination survey for adults (DEGS1-MH). Int J Methods Psychiatr Res. 2013;22:83–99.

Scheidt-Nave C, Kamtsiuris P, Gößwald A, Hölling H, Lange M, Busch MA, et al. German health interview and examination survey for adults (DEGS) - design, objectives and implementation of the first data collection wave. BMC Public Health. 2012;12:730.

Gosswald A, Lange M, Kamtsiuris P, Kurth BM. DEGS: German health interview and examination survey for adults. A nationwide cross-sectional and longitudinal study within the framework of health monitoring conducted by the Robert Koch institute. Bundesgesundheitsbl. 2012;55:775–80.

Article   CAS   Google Scholar  

Kessler RC, Ustun TB. The world mental health (WMH) survey initiative version of the World Health Organization (WHO) composite international diagnostic interview (CIDI). Int J Methods Psychiatr Res. 2004;13:93–121.

Wittchen HU, Lachner G, Wunderlich U, Pfister H. Test-retest reliability of the computerized DSM-IV version of the Munich-composite international diagnostic interview (M-CIDI). Soc Psychiatry Psychiatr Epidemiol. 1998;33:568–78.

Wittchen HU. Reliability and validity studies of the WHO-composite international diagnostic interview (CIDI): a critical review. J Psychiatr Res. 1994;28:57–84.

American Psychiatric Association. Diagnostic and statistical manual of mental disorders, fourth edition, text revision (DSM-IV-TR). Washington, DC: American Psychiatric Association; 2000.

McCullough JP. Treatment for chronic depression: cognitive behavioral analysis system of psychotherapy (CBASP). New York: Guilford Press; 2000.

First MB, Gibbon M, Spitzer RL, Williams JB, Benjamin LS. Structured clinical interview for DSM-IV® axis ii personality disorders SCID-II. Washington, DC: American Psychiatric Press; 1997.

Löwe B, Spitzer RL, Zipfel S, Herzog W. Gesundheitsfragebogen für Patienten (PHQ-D). Komplettversion und Kurzform. Testmappe mit Manual, Fragebögen, Schablonen. 2. Auflage. Karlsruhe: Pfizer; 2002.

Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–13.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Thase M, Rush A. Treatment-resistant depression. In: Bloom FE, Kupfer DJ, editors. Psychopharmacology: the fourth generation of progress. New York: Raven Press; 1995. p. 1081–97.

Ellert U, Kurth BM. Health related quality of life in adults in Germany: results of the German health interview and examination survey for adults (DEGS1). Bundesgesundheitsbl. 2013;56:643–9.

Ware JE Jr. SF-36 health survey update. Spine. 2000;25:3130–9.

Cumming G, Finch S. Inference by eye: confidence intervals and how to read pictures of data. Am Psychol. 2005;60:170–80.

Dalgard OS, Bjork S, Tambs K. Social support, negative life events and mental health. Br J Psychiatry. 1995;166:29–34.

Maske U, Buttery A, Beesdo-Baum K, Riedel-Heller S, Hapke U, Busch M. Prevalence and correlates of DSM-IV-TR major depressive disorder, self-reported diagnosed depression and current depressive symptoms among adults in Germany. J Affect Disord. 2016;190:167–77.

Kamtsiuris P, Lange M, Hoffmann R, Schaffrath Rosario A, Dahm S, Kuhnert R, et al. The first wave of the German health interview and examination survey for adults (DEGS1). Sample design, response, weighting, and representativeness. Bundesgesundheitsbl. 2013;56:620–30.

Faul F, Erdfelder E, Lang AG, Buchner A. G* power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39:175–91.

Jacobi F, Becker M, Bretschneider J, Müllender S, Thom J, Hapke U, et al. Provision of outpatient specialist care for mental disorders: minor regional differences in treatment needs, major regional differences in availability. Nervenarzt. 2016;87:1211–21.

Köhler S, Wiethoff K, Ricken R, Stamm T, Baghai TC, Fisher R, et al. Characteristics and differences in treatment outcome of inpatients with chronic vs. episodic major depressive disorders. J Affect Disord. 2015;173:126–33.

Meng X. What characteristics are associated with earlier onset of first depressive episodes: a 16-year follow-up of a national population-based cohort. Psychiatry Res. 2017;258:427–33.

McMahon EM, Buszewicz M, Griffin M, Beecham J, Bonin EM, Rost F, et al. Chronic and recurrent depression in primary care: socio-demographic features, morbidity, and costs. Int J Family Med. 2012;2012:316–409.

BKK. Gesundheitsatlas 2015. Gesundheit in Regionen – Blickpunkt Psyche. Berlin: MWV Medizinisch Wissenschaftliche Verlagsgesellschaft; 2015.

DAK. DAK-Gesundheitsreport 2013. Berlin: IGES Institut GmbH; 2013.

Techniker Krankenkasse. Depressionsatlas. Auswertungen zu Arbeitsunfähigkeit und Arzneiverordnungen. Göttingen: AQUA - Institut für angewandte Qualitätsförderung und Forschung im Gesundheitswesen GmbH; 2015.

Köhler S, Chrysanthou S, Guhn A, Sterzer P. Differences between chronic and nonchronic depression: systematic review and implications for treatment. Depress Anxiety. 2019;36:18–30.

Schomerus G, Appel K, Meffert PJ, Luppa M, Andersen RM, GrabeH J, Baumeister SE. Personality-related factors as predictors of help-seeking for depression: a population-based study applying the behavioral model of health services use. Soc Psychiatry Psychiatr Epidemiol. 2013;48:1809–17.

Lorant V, Deliège D, Eaton W, Robert P, Philippot M. Ansseau, socioeconomic inequalities in depression: a meta-analysis. Am J Epidemiol. 2003;157:98–112.

Höfler M, Wittchen HU. Why do primary care doctors diagnose depression when diagnostic criteria are not met? Int J Methods Psychiatr Res. 2000;9:110–20.

Maske UE, Hapke U, Riedel-Heller SG, Busch MA, Kessler RC. Respondents’ report of a clinician-diagnosed depression in health surveys: comparison with DSM-IV mental disorders in the general adult population in Germany. BMC Psychiatry. 2017;17:39.

Bramesfeld A, Grobe T, Schwartz F. Who is diagnosed as suffering from depression in the German statutory health care system? An analysis of health insurance data. Eur J Epidemiol. 2007;22:397–403.

Bramesfeld A, Grobe T, Schwartz F. Who is treated, and how, for depression? Soc Psychiatry Psychiatr Epidemiol. 2007;42:740–6.

Gaebel W, Kowitz S, Zielasek J. The DGPPN research project on mental healthcare utilization in Germany: inpatient and outpatient treatment of persons with depression by different disciplines. Eur Arch Psychiatry Clin Neurosci. 2012;262(Suppl 2):51–5.

Trautmann S, Beesdo-Baum K. The treatment of depression in primary care. Dtsch Arztebl Int. 2017;114:721–8.

Sundmacher L, Fischbach D, Schuettig W, Naumann C, Augustin U, Faisst C. Which hospitalisations are ambulatory care-sensitive, to what degree, and how could the rates be reduced? Results of a group consensus study in Germany. Health Policy. 2015;119:1415–23.

Guhn A, Sterzer P, Haack FH, Köhler S. Affective and cognitive reactivity to mood induction in chronic depression. J Affect Disord. 2018;229:275–81.

Guhn A, Steinacher B, Merkl A, Sterzer P, Köhler S. Negative mood induction: affective reactivity in recurrent, but not persistent depression. PLoS One. 2019;14:–e0208616.

Bird T, Tarsia M, Schwannauer M. Interpersonal styles in major and chronic depression: a systematic review and meta-analysis. J Affect Disord. 2018;239:93–101.

Keller MB, McCullough JP, Klein DN, Arnow B, Dunner DL, Gelenberg AJ, et al. A comparison of nefazodone, the cognitive behavioral-analysis system of psychotherapy, and their combination for the treatment of chronic depression. N Engl J Med. 2000;342:1462–70.

Nemeroff CB, Heim CM, Thase ME, Klein DN, Rush AJ, Schatzberg AF, Ninan PT, McCullough JP, Weiss PM, Dunner DL, Rothbaum BO. Differential responses to psychotherapy versus pharmacotherapy in patients with chronic forms of major depression and childhood trauma. PNAS. 2003;100:14293–6.

Wiersma JE, Van Schaik DJ, Hoogendorn AW, Dekker JJ, Van HL, Schoevers RA, et al. The effectiveness of the cognitive behavioral analysis system of psychotherapy for chronic depression: a randomized controlled trial. Psychother Psychosom. 2014;83:263–9.74.

Schramm E, Kriston L, Zobel I, Bailer J, Wambach K, Backenstrass M, et al. Effect of disorder-specific vs nonspecific psychotherapy for chronic depression: a randomized clinical trial. JAMA Psychiatry. 2017;74:233–42.

Schramm E, Zobel I, Dykierek P, Kech S, Brakemeier EL, Kulz A, et al. Cognitive behavioral analysis system of psychotherapy versus interpersonal psychotherapy for early-onset chronic depression: a randomized pilot study. J Affect Disord. 2011;129:109–16.

Karger A. Gender differences in depression. Bundesgesundheitsbl. 2014;57:1092–8.

Download references

Acknowledgments

We thank Kim Ackermann for assisting during the follow-up assessment at Charité – Universititäsmedizin Berlin. Furthermore, we thank Diane Williams, PhD, from Edanz Group ( www.edanzediting.com/ac ) for editing a draft of this manuscript.

This work was supported by the collaborative Focus Area DynAge project (Freie Universität Berlin, Charité – Universitätsmedizin Berlin, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) and Robert Koch Institute), the German Research Foundation (KO 5231/2–1), and by the German Ministry of Health (funding No. ZMV1–2516-FSB-703). DEGS1 and DEGS1-MH were funded primarily by the German Ministry of Health (Bundesministerium für Gesundheit, BMG, grant numbers for DEGS1-MH: 1368–1124 and 1501–54401). Supplementary funding for DEGS1-MH was provided by the Technische Universität Dresden, and by the Foundation for Mental Health (Stiftung Seelische Gesundheit) inaugurated by the German Association for Psychiatry, Psychotherapy and Psychosomatics (Deutsche Gesellschaft für Psychiatrie und Psychotherapie, Psychosomatik und Nervenheilkunde, DGPPN).

Author information

Authors and affiliations.

Department of Epidemiology and Health Monitoring, Unit 26 Mental Health, Robert Koch Institute, PO Box 650261, 13302 Berlin, Germany

Julia Nübel, Susanne Müllender, Hong Duyen Le & Caroline Cohrdes

Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany

Anne Guhn & Stephan Köhler

You can also search for this author in PubMed   Google Scholar

Contributions

JN and SM devised the main conceptual ideas. JN, SM, SK, and AG designed the study. JN and HDL prepared and analyzed the data. JN and SK interpreted the results and wrote a first draft of the manuscript. AG, SM, HDL, and CC contributed to the interpretation of results and critically revised the article. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Caroline Cohrdes .

Ethics declarations

Ethics approval and consent to participate.

Research using the DEGS1-MH and the clinical sample at Charité – Universitätsmedizin Berlin was conducted following the Federal and State Commissioners for Data Protection guidelines and the ethical principles of the Helsinki Declaration. Both surveys were approved by local ethics committees (Charité – Universitätsmedizin Berlin: No. EA2/047/08, No. EA4 140/13, Technische Universität Dresden: No. EK174062009). All participants provided written informed consent prior to participation.

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1..

Unadjusted effect estimates for health-related correlates in cases of PDD vs. NCMDD (ref.) during the lifetime.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Nübel, J., Guhn, A., Müllender, S. et al. Persistent depressive disorder across the adult lifespan: results from clinical and population-based surveys in Germany. BMC Psychiatry 20 , 58 (2020). https://doi.org/10.1186/s12888-020-2460-5

Download citation

Received : 13 August 2019

Accepted : 23 January 2020

Published : 10 February 2020

DOI : https://doi.org/10.1186/s12888-020-2460-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Chronic depression course
• Persistent depressive disorder
• Disease burden
• Health-related quality of life
• General population
• Epidemiology
• Cross-sectional studies
• Clinical studies

BMC Psychiatry

ISSN: 1471-244X

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Persistent Depressive Disorder (Dysthymia)

Reviewed by Psychology Today Staff

Persistent depressive disorder, known as dysthymia or low-grade depression , is less severe than major depression but more chronic. It occurs twice as often in women as in men.

Persistent depressive disorder (PDD) is a serious and disabling disorder that shares many symptoms with other forms of clinical depression. It is generally experienced as a less severe but more chronic form of major depression. PDD was referred to as dysthymia in previous versions of the DSM .

PDD is characterized by depressed mood experienced most of the time for at least two years. In children and adolescents, mood can be irritable rather than depressed. In addition to depression or irritable mood, at least two of the following must be present: insomnia or excessive sleep , low energy or fatigue, low self-esteem , poor appetite or overeating , poor concentration or indecisiveness, and feelings of hopelessness. More severe symptoms marking major depression are often absent in PDD—this includes anhedonia (the inability to feel pleasure), psychomotor symptoms (particularly lethargy or agitation), and thoughts of death or suicide .

PDD can occur alone or in conjunction with other mood or psychiatric disorders. For instance, more than half of people who suffer from PDD will experience at least one episode of major depression; this condition is known as double depression. Compared with people with major depressive disorder, those with PDD are at higher risk for anxiety and substance use disorders.

In a given 12-month period in the U.S., according to the National Institutes of Health, PDD is estimated to affect 1.5 percent of people.

The main sign of persistent depressive disorder is a low, dark, or sad mood that occurs for most of the day, for more days than not, for at least two years. People with PDD often describe their mood as consistently sad or "down in the dumps." Other symptoms can include:

• Poor appetite or overeating
• Sleep disturbances
• Low energy or fatigue
• Low self-esteem
• Poor concentration
• Feelings of hopelessness

In PDD, these symptoms are not directly a result of a general medical condition or the use of substances. In addition, they result in impaired functioning in work, social, or personal areas.

Yes, persistent depressive disorder is less severe than major depression, but as its name suggests, the condition is chronic and can be long-lasting. It can linger, and it is important to seek help for this condition.

Yes. Symptoms of persistent depressive disorder, also known as dysthymia, consist of mild depressiveness that can last more than two years. The symptoms might disappear, but then return in a matter of months. 

Double depression refers to the onset of a major depressive episode when one already suffers from chronic depression. In this case, the persistent symptoms remain but an individual experiences symptoms of major depression as well. Unfortunately, many people with PDD consider new symptoms to be an inevitable part of their life, or a natural progression of PDD, and do not seek help, even when their suffering becomes more acute. 

Persistent depressive disorder appears to have its roots in a combination of genetic, biochemical, environmental, and psychological factors. In addition, chronic stress and trauma can provoke PDD.

Stress is believed to impair one's ability to regulate mood and prevent mild sadness from deepening and persisting. Social circumstances, particularly isolation and the unavailability of social support, also contribute to the development of PDD. This cause can be especially debilitating given that depression often alienates those who are in a position to provide support, resulting in increased isolation and worsening symptoms. In addition, trauma, loss of a loved one, a difficult relationship, or any stressful situation may trigger a depressive episode. Subsequent depressive episodes may occur with or without an obvious trigger. In old age, PDD is more likely to be the result of medical illness, cognitive decline , bereavement , and physical disability.

Research indicates that depressive illnesses are disorders of the brain. Brain-imaging technologies, such as magnetic resonance imaging, have shown that the brains of people who have depression look different than those of people without depression. The parts of the brain responsible for regulating mood, thinking, sleep, appetite, and behavior appear to function abnormally. In addition, important neurotransmitters—chemicals that brain cells use to communicate—appear to be out of balance. But these images do not reveal why the depression has occurred.

Dysthymia, or persistent depressive disorder, is mild chronic depression. Cyclothymia is a mild case of bipolar disorder . A person with cyclothymia might be mildly depressed at one moment, then mildly manic at another moment.

Psychotherapy

Many people with persistent depressive disorder do not get the treatment they need; in many cases because they only see their family doctors, who often fail to diagnose the disorder. Part of the problem is that people suffering from PDD believe their symptoms are an inevitable part of life. In older people, dementia , apathy, or irritability can disguise PDD. Open-ended questions are helpful: "How has your mood been recently?"

Like major depression, PDD can be treated with supportive therapy that provides reassurance, empathy, education , and skill-building. Certain types of psychotherapy, such as supportive therapy, cognitive-behavioral therapy, psychodynamic therapy, and interpersonal therapy, can help relieve PDD. CBT helps identify and change the negative styles of thinking that promote self-defeating attitudes and behaviors. Additionally, individuals learn techniques that improve social skills and teach ways to manage stress and unlearn feelings of helplessness. IPT helps patients to cope with interpersonal disputes, loss and separation, and life transitions. Evidence from an NIMH-supported study indicates that IPT, in particular, may hold promise in the treatment of depressive disorders.

As with other forms of depression, there are a number of medication options for people with PDD. The most common drug treatments include selective serotonin reuptake inhibitors, SSRIs, such as fluoxetine (Prozac) and sertraline (Zoloft), or dual-action antidepressants such as venlafaxine (Effexor). Some patients may respond to tricyclic antidepressants such as imipramine (Tofranil). Antidepressant drugs have a number of side effects that can complicate treatment. For example, SSRIs may cause stomach upset, mild insomnia , and reduced sex drive.

For many patients, a long-term combination of medication and psychotherapy that includes a solid relationship with a mental health professional is the most effective course of treatment. Recovery from PDD can take time, and the symptoms often return. For this reason, many patients are encouraged to continue doing whatever made them well—whether it was a drug, therapy, or a combination of the two—after recovery.

• Find a Therapist
• Find a Treatment Center
• Find a Psychiatrist
• Find a Support Group
• Find Teletherapy
• United States
• Brooklyn, NY
• Chicago, IL
• Houston, TX
• Los Angeles, CA
• New York, NY
• Portland, OR
• San Diego, CA
• San Francisco, CA
• Seattle, WA
• Washington, DC
• Asperger's
• Bipolar Disorder
• Chronic Pain
• Eating Disorders
• Passive Aggression
• Personality
• Goal Setting
• Positive Psychology
• Stopping Smoking
• Low Sexual Desire
• Relationships
• Child Development
• Therapy Center NEW
• Diagnosis Dictionary
• Types of Therapy

Understanding what emotional intelligence looks like and the steps needed to improve it could light a path to a more emotionally adept world.

• Coronavirus Disease 2019
• Affective Forecasting
• Neuroscience

If you're seeing this message, it means we're having trouble loading external resources on our website.

If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.

To log in and use all the features of Khan Academy, please enable JavaScript in your browser.

Course: MCAT   >   Unit 4

• Patient with a missing cerebellum
• Could complete darkness be the cure for amblyopia?
• Is obesity contagious?
• Race, socioeconomic status, and nutrition
• Romantic and sexual networks of adolescents
• Marijuana usage as social behavior
• Why do we believe that there are universal emotions?
• Applications of operant conditioning in daily life
• Miller’s law, chunking, and the capacity of working memory
• Prosopagnosia - “You seem familiar, but I can’t place your face”
• Somatosensory effects of Temple Grandin’s squeeze box on hypersensitivity.
• Exploring clinical applications of classical conditioning
• Social conflict theory in an historical context
• Visual pathways in the brain, and what happens when they break
• Challenging beliefs to prevent eating disorders
• Do beliefs about healthy food inform consumer choices?
• Coping with cancer: novel methods of stress management
• Clinical vignette of an adult psychiatric patient
• Efficacy of cognitive behavioral therapy for social anxiety disorder
• Case study of panic disorder in an adult female
• Drug addiction treatment and relapse in incarcerated populations
• Navigating the stages of sleep
• Cocaine addiction in mice
• Changes in empathy throughout medical school and residency
• Big five personality traits and health behaviors
• Information processing and the discovery of iconic memory
• Gestalt principles and ratings of physical attractiveness
• Sensory adaptation and Weber's Law
• Feline night vision
• Sight (vision) - Passage 2
• Differential effectiveness of cochlear implants
• Functions of the basilar membrane
• Phantom limb pain among veterans
• Congenital analgesia
• Diminished sensory input and the enjoyment of food
• Taste disorders and their causes
• Matchboxes and problem solving
• Identifying intelligence types to increase student success
• Linguistic patterns of English and Navajo speakers
• Language deficits and traumatic brain injury
• Effects of sleep deprivation on memory
• Observing patients in a sleep clinic
• Differential memory loss and Alzheimer's Disease
• Remembering folk tales over time
• Responses to emotion induction
• Positive emotion and emotional eating
• Stress, coping, and surgical error
• PTSD and apoptosis
• How similar are the personalities of twins?
• Exploring an individual's temperament
• A virus which affects mouse muscles
• Diabetic peripheral neuropathy, pain, and acupuncture
• Stress responses and the endocrine system
• Effects of insulin deficiency
• Antisocial personality disorder and inkblot tests
• Freudian psychotherapy
• Physiological needs and pregnancy
• Smoking cessation and behavioral change
• Group dynamics in a graduate school classroom
• Dietary restriction and sorority dynamics
• Marriage and family preferences
• Marriage and family
• Cigarette use in teen populations
• Video game usage and aggressive behavior
• Alternative education models and child development
• Increasing Sarah's self-efficacy: A case study
• Urban growth models
• Social norms and deviant behavior
• Population growth and immigration
• The J-curve hypothesis
• Fertility rates and demographic change
• Socioeconomic status and mental illnesses
• Are suicidal behaviors "contagious" in adolescence?
• What are your chances of getting divorced?
• Malthusian population growth and population pyramids
• Disparities in healthcare access
• OCD and hoarding disorder

Clinical vignette of persistent depressive disorder

• Gender differences in symptoms of major depressive disorder
• Health after trauma- correlates of PTSD
• Demographic disparities in food resources
• Socioeconomic gradients in health
• To cohabit or not to cohabit?
• Educational attainment, income, and psychiatric disorders
• Theories of language acquisition
• Physiological stress response to cuss words?
• Tickle me Nim. Do primates speak language?
• Cats and dogs and conditioning
• Can tickling help us understand some symptoms of schizophrenia?
• Reciprocal altruism, vampire bats, and relatedness
• Structure of the central nervous system
• Spinal nerves and Guillain-Barre syndrome
• Simultaneous hermaphroditism, egg trading, and the Prisoner’s Dilemma
• Language location in the brain - ASL
• Embodied cognition, hockey, does expertise impact text understanding?
• Driving and talking on a cell phone
• Counting systems and the Pirahã tribe
• Perky effect
• (Choice A)   Victim mentality A Victim mentality
• (Choice B)   Neuroticism B Neuroticism
• (Choice C)   Learned helplessness C Learned helplessness
• (Choice D)   Internal locus of control D Internal locus of control

psychologyera

• Relationship/Marriage
• Suffering in Silence
• Separation Anxiety
• Hormone imbalance
• Stress Management

Case Study: Living with Persistent Depressive Disorder (PDD)

Table of Contents :

Meet Carrie and Grace: A Journey Through the Challenges of Persistent Depressive Disorder

Background :

Grace, a bright and talented young woman, grew up facing the hidden battle of Persistent Depressive Disorder (PDD). As a child, she displayed exceptional intellectual abilities and creativity. But her struggles with undiagnosed depression began to surface during her teenage years. Grace faced various school difficulties, significantly impacting her academic performance and emotional well-being.

Difficulties Faced in School :

During her early years in school, Grace’s teachers noticed her declining enthusiasm for learning and participation in class activities. She often struggled to concentrate, leading to a decline in her grades despite her evident intelligence. Social interactions became increasingly challenging for her, and she withdrew from her peers, feeling overwhelmed by a sense of isolation. Grace’s parents, unaware of her underlying condition, attributed these changes to typical teenage mood swings, unaware of the deeper issue at hand.

Impact on Academic Performance:

Her academic difficulties became more pronounced as Grace progressed through middle and high school. She found it hard to complete assignments, often feeling paralyzed by hopelessness and low energy. Grace’s test scores suffered, and she started to miss classes frequently, unable to muster the motivation to attend. Her declining academic performance became a source of immense frustration and self-doubt, leading to a cycle of negative thoughts and emotions.

Late Diagnosis and Academic Setbacks:

It wasn’t until Grace’s late teens that her condition was diagnosed correctly as Persistent Depressive Disorder. By this time, she had already faced significant setbacks in her education. Due to her undiagnosed depression, Grace struggled to cope with the demands of college. Her inability to focus and engage in her studies led to academic probation, and eventually, she had to repeat a year in college. This experience dealt a severe blow to her self-esteem and confidence, exacerbating her depressive symptoms.

Family Support and Intervention :

Upon Grace’s diagnosis, her family rallied around her, seeking professional help and educational support. Grace started attending therapy sessions with a skilled psychologist, where she learned coping mechanisms and strategies to manage her symptoms. Her family actively participated in her therapy, gaining insights into her struggles and learning how to provide the necessary support. Grace’s therapist recommended accommodations in her educational setting, such as extended time for exams and counseling services, to help her navigate the challenges of academia.

Steps Taken to Manage PDD:

With the diagnosis and appropriate interventions in place, Grace began her journey toward recovery. Therapy sessions provided her with a safe space to express her feelings and fears, and medication prescribed by her healthcare provider helped stabilize her mood. With the understanding and support of her family, Grace slowly started rebuilding her academic confidence and personal identity.

The Road to Recovery :

While living with Persistent Depressive Disorder continues to pose challenges, Grace’s story is one of resilience and determination. With ongoing therapy, medication, and the unwavering support of her family, she has made significant progress. Grace’s academic performance has improved, and she has found new ways to cope with her condition, allowing her to engage more actively in her studies and social interactions.

In Conclusion :

Grace’s journey highlights the critical importance of early intervention and understanding in the face of mental health challenges. With the proper support, individuals like Grace can overcome the hurdles posed by Persistent Depressive Disorder, allowing them to rediscover their potential and lead fulfilling lives. Carrie’s dedication to her sister’s well-being showcases the profound impact that family support can have on the journey to recovery, offering hope to others facing similar struggles.

Karuna Kaul (Psycho, Social, & Clinical Psychologist, London University)

Karuna Kaul is psycho socio clinical psychologist, who works with all age group people. Her profession motivates her to serve people who are facing behavioral issues. She has over 8 years of experience and has successfully established credibility in the areas of counselling and wellness. Assessment and behavioral analysis and training and coaching. She has been an active advocate of mental health awareness. And all her endeavors in the field are primarily focused on educating more and more people about Mental Health concerns and promoting Holistic Wellbeing. She has done master in clinical psychology PG Diploma in counselling and guidance and certified in drug addiction counselling Also she has done neuro medicine psychology from London University, Kent College of United Kingdom. With an experience of six years, she had worked with various organization which provides mental health services.

Related Posts

Understanding Exhibitionist Disorder: Causes, Prevalence, and Treatment

Case Study: Patrick’s Struggle with Voyeuristic Disorder

A Case Study on Agoraphobia: Teresa’s Journey

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 08 April 2022

Depressive disorders are associated with increased peripheral blood cell deformability: a cross-sectional case-control study (Mood-Morph)

• Andreas Walther   ORCID: orcid.org/0000-0003-4516-1783 1 , 2 ,
• Anne Mackens-Kiani 1 ,
• Julian Eder 1 ,
• Maik Herbig   ORCID: orcid.org/0000-0001-7592-7829 3 , 4 ,
• Christoph Herold 3 , 5 ,
• Clemens Kirschbaum 1 ,
• Jochen Guck   ORCID: orcid.org/0000-0002-1453-6119 3 , 4 ,
• Lucas Daniel Wittwer 4 , 6 ,
• Katja Beesdo-Baum 7 &
• Martin Kräter 3 , 4  

Translational Psychiatry volume  12 , Article number:  150 ( 2022 ) Cite this article

5495 Accesses

13 Citations

142 Altmetric

Metrics details

• Diagnostic markers

Pathophysiological landmarks of depressive disorders are chronic low-grade inflammation and elevated glucocorticoid output. Both can potentially interfere with cytoskeleton organization, cell membrane bending and cell function, suggesting altered cell morpho-rheological properties like cell deformability and other cell mechanical features in depressive disorders. We performed a cross-sectional case-control study using the image-based morpho-rheological characterization of unmanipulated blood samples facilitating real-time deformability cytometry (RT-DC). Sixty-nine pre-screened individuals at high risk for depressive disorders and 70 matched healthy controls were included and clinically evaluated by Composite International Diagnostic Interview leading to lifetime and 12-month diagnoses. Facilitating deep learning on blood cell images, major blood cell types were classified and morpho-rheological parameters such as cell size and cell deformability of every individual cell was quantified. We found peripheral blood cells to be more deformable in patients with depressive disorders compared to controls, while cell size was not affected. Lifetime persistent depressive disorder was associated with increased cell deformability in monocytes and neutrophils, while in 12-month persistent depressive disorder erythrocytes deformed more. Lymphocytes were more deformable in 12-month major depressive disorder, while for lifetime major depressive disorder no differences could be identified. After correction for multiple testing, only associations for lifetime persistent depressive disorder remained significant. This is the first study analyzing morpho-rheological properties of entire blood cells and highlighting depressive disorders and in particular persistent depressive disorders to be associated with increased blood cell deformability. While all major blood cells tend to be more deformable, lymphocytes, monocytes, and neutrophils are mostly affected. This indicates that immune cell mechanical changes occur in depressive disorders, which might be predictive of persistent immune response.

Similar content being viewed by others

Machine learning assisted real-time deformability cytometry of CD34+ cells allows to identify patients with myelodysplastic syndromes

Maik Herbig, Angela Jacobi, … Ekaterina Balaian

Accelerated epigenetic aging and decreased natural killer cells based on DNA methylation in patients with untreated major depressive disorder

Ryota Shindo, Takaki Tanifuji, … Akitoyo Hishimoto

A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm

Zahra Mousavi Kouzehkanan, Sepehr Saghari, … Reshad Hosseini

Introduction

Depressive disorders including major depressive disorder (MDD) and persistent depressive disorder (PDD; formerly dysthymia) are the leading causes of disability worldwide [ 1 ]. To diagnose MDD a two-week phase is required. During this phase, at least one of the two cardinal symptoms “depressive mood” or “anhedonia” in combination with four or more of seven other symptoms (e.g., changes in appetite, insomnia/hypersomnia, increased fatigue, feelings of worthlessness) have to be present for most of the day and must cause functional impairment. PDD is diagnosed based on a period of depressive mood over 2-years in combination with at least two of six additional symptoms similar to MDD [ 2 ]. To date, physiological manifestations only play a theoretical role in diagnostics. This is due to the fact that the pathophysiology of depressive disorders remains insufficiently understood.

The two most consistent and salient physiological abnormalities are a hyperactive hypothalamus-pituitary-adrenal (HPA) axis and chronic low-grade inflammation associated with elevated cortisol and proinflammatory cytokine levels, respectively [ 3 ]. In line with this, an increased lymphocyte count has been identified in MDD and PDD [ 4 ]. Interestingly, increased levels of natural killer cells were rescued after antidepressant treatment in MDD and PDD patients suggesting a depressive disorder-dependent state of increased lymphocyte cell count. Corroborating this, in a recent study including a sample of 206 depression cases and 77 healthy controls, increased neutrophil and monocyte counts were described in cases as compared to controls [ 5 ]. These findings support a depressive disorder-dependent state of increased immune cell count pointing toward a hyperactive immune system in subjects with depressive disorders. It is suggested that the underlying mechanism leading to increased immune cell counts in depressed individuals is rooted in the effect of elevated glucocorticoid levels remodeling the actin cytoskeleton of blood cells and thereby softening leukocytes and enabling them to demarginate from the vessel wall [ 6 ].

Increased cortisol levels and chronic low-grade inflammation in individuals suffering from depressive disorders not only increase immune cell count but directly influence blood cells by crucially affecting actin cytoskeleton, lipid metabolism and composition, and cell membrane formation [ 6 ]. These processes cause softening and increased bending and destabilization of the cell [ 7 , 8 , 9 , 10 ], which ultimately reduces blood cell function. Therefore, we hypothesize depressive disorders to be associated with altered peripheral blood cell function, which might be represented by the cell’s morpho-rheological properties such as increased cell deformability.

Blood is a poly-disperse suspension of a number of different cell types, representing multiple functions from metabolite transport to overall blood flow. The morpho-rheological properties including cell mechanical features or cell size of each cell can be predictive of its specific physiological or pathological function [ 11 , 12 ]. It was recently highlighted, that the assessment of the blood cell mechanical status, measured by cell deformability under constant shear stress, is appropriate to detect and classify human disease conditions [ 13 ]. For example, in this series of experiments, it was observed that individuals suffering from spherocytosis exhibited reduced erythrocyte deformability, which was also documented for erythrocytes exposed to Plasmodium falciparum the principal malaria-causing parasite [ 13 ]. By contrast, inhalation of lipopolysaccharide from E. coli , infecting blood in vitro with Staphylococcus aureus, having an acute lung injury, or suffering from viral respiratory tract infections or COVID-19 infection led all to increased neutrophil deformation [ 13 , 14 ]. Similarly, patients with Epstein-Barr-virus infection showed increased monocyte and lymphocyte deformability [ 13 ]. This suggests that in certain disease conditions altered blood cell function may be present being measurable via blood cell mechanical properties such as cell deformability. Cell deformability has so far never been investigated in relation to depressive disorders.

Furthermore, proof-of-concept studies have used optical traps [ 15 ], atomic force microscopy [ 11 , 12 ], and micropipette aspiration [ 16 ] to show immune cell mechanical alterations during physiological and pathological conditions. Most likely due to the overall predominance of erythrocytes in the blood, a correlation between blood cell mechanics and mental disorders has so far only been examined by measuring erythrocyte deformability [ 17 , 18 ]. These studies have used different methods to investigate aspects of erythrocyte deformability and have identified that chronic fatigue patients have lower erythrocyte deformability than healthy controls, while children with more severe symptoms in the autism spectrum also exhibited lower erythrocyte deformability [ 17 , 18 ]. However, immune cells seem to be more likely effectors of increased cortisol levels in depressed individuals through specific interactions with membrane-bound glucocorticoid receptors and also increased chronic low-grade inflammation through immune cells’ interaction with cytokines [ 19 , 20 ]. Therefore, the parallel study of cell deformability of all blood cell types with respect to a particular condition provides broader insight into the underlying pathophysiology and the potential for internal replication. Thus, progress towards clinical application has yet not been achieved, potentially due to the lack of measurement throughput with only a couple of hundred cells per hour [ 21 ].

Here, we used state-of-the-art real-time deformability cytometry (RT-DC) together with artificial intelligent-based image processing in order to overcome the throughput limitations (Fig. 1 ). We measured the morpho-rheological properties of more than 16 × 10 6 single blood cells of 139 individuals at high risk for depressive disorders and matched healthy controls (HCs). RT-DC facilitates microfluidics and high-throughput imaging to assess up to 1000 cells per second. Based on the cell image, RT-DC quantifies multiple parameters including the cell’s deformability under shear stress and cell size without the need for blood preparation, like cell staining or erythrocyte depletion [ 22 ]. The observed deformability is dependent on the cell´s mechanical properties. These properties are representative of the molecular composition and the cytoskeletal state [ 23 , 24 ], which are arguably the most crucial aspects to fulfill tissue and cell-specific functionality. Thus, we argue that the precise control of mechanical features of blood cells is indispensable to keep physical and psychological homeostasis. Thus, cell mechanical properties potentially comprise crucial pathophysiological information in mental disorders and particularly depressive disorders.

A A schematic illustration of an RT-DC measurement chip is shown. Whole blood was resuspended in measurement buffer (CellCarrierB), drawn in a syringe, and connected to the sample inlet. CellCarrierB was used as a sheath fluid within a second syringe and the sample and sheath were flushed through the chip at a ratio of flow rates of 1:3 under constant flow (0.06 µL/s). The chip was mounted to an inverted microscope and an image of every cell was recorded at the end of a 600 µm long constriction cannel. B Using ShapeOut, an open-source software tool, data were plotted. The dot-plot shows a measurement of 38,517 blood cells plotted in cell size (projected area [µm²]) and cell deformability. For better representation, the ratio of leukocytes (IV, V i - iii ) to erythrocytes and thrombocytes (I–III) is artificially increased. In order to identify the different blood cell types, the images were imported to AIDeveloper an open-source software tool to train, evaluate, and apply neural networks for image classification. A neural net based on the LeNet5 architecture, readily trained for the classification of blood cells, was loaded into AID and used to classify I = thrombocytes, II = erythrocytes, III = erythrocyte doublets, IV = lymphocytes, V i  = eosinophils, V ii  = neutrophils, and V iii  = monocytes [ 24 ]. Finally, the mean values for cell deformability and cell size were extracted for every cell type individually.

Study design and setting

The study entitled mood-related morpho-rheological changes in peripheral blood cells (Mood-Morph) included a prescreening to select participants suffering from depressive disorders and healthy control subjects matched by age and sex, followed by a cross-sectional case-control study. Study visits included a clinical diagnostic interview, psychometric testing, and blood sampling. The study was approved by the local ethics committee of the Dresden University of Technology (EK182042019) and all participants gave written informed consent to participate in the study.

Participants

Recruitment was performed from the participant pool of the large prospective cohort study on stress-related mental disorders (Dresden Burnout Study [DBS]) with respect to depression scores (measured with the Patient Health Questionnaire [PHQ-9]) in the most recent examination wave from October to December 2018. Subjects with a score >10 (high risk of depression) as well as age- and sex-matched subjects with a score <5 (low risk of depression) were invited. This procedure’s aim was to achieve a sample of participants suffering from depressive disorders and a matched sample of healthy control subjects (Fig. 2 ). Based on previous studies by our group that investigated the deformability of cells by real-time deformability cytometry (RT-DC) with respect to different somatic conditions [ 13 ], we used a sample size for the present study that is sufficiently powered to identify medium to large effects.

DIA-X-5 The DIA-X-5/Composite International Diagnostic Interview (DIA-X-5/CIDI) is a standardized clinical interview for the assessment of mental disorders, LTPDD lifetime persistent depressive disorder, 12-month PDD 12-month persistent depressive disorder, LMDD lifetime major depressive disorder, 12-month MDD 12-month major depressive disorder.

The required age for participation was between 18 and 68 years. Study participants were excluded if they suffered from a blood disease that could affect the deformability of blood cells. Potential participants contacted were informed in the study invitation and informational email that certain diseases, such as spherocytosis and, in general, diseases related to blood, precluded participation [ 13 ]. To exclude acutely infectious participants, potential participants with a cold or another infection no longer ago than two weeks were not included. In addition, participants answered a questionnaire at the beginning of the study (before the clinical diagnostic interview) to ensure that there were no physical diseases that could affect cell deformability. However, conditions that were prevalent in the population, such as hypertension, did not lead to exclusion, and the specific drug categories (e.g., for hypertension—antihypertensive drugs) were then examined in the analyses. Individuals who reported psychopharmaceutical treatment or mental disorders in the questionnaire or one was identified in the clinical interview were excluded from the healthy control group. For data analysis, individuals screening positive for any other mental disorder or for mania were excluded from the PDD and MDD groups.

Depressive symptom severity, as well as general health, were measured online via the DBS-homepage one week prior to examination using the depression section of the PHQ-9 and the Short Form Health Questionnaire (SF-12) [ 25 , 26 ]. The PHQ is a self-report questionnaire for the assessment of common mental disorders with the PHQ-9 being the module designed for the measurement of depression severity. The SF-12 measures physical and psychological health-related quality of life.

During an examination, participants completed a subject questionnaire regarding age, sex, weight, height, medication, drug use, psychopharmacological treatment, and psychotherapeutic treatment. After completion of the questionnaire, the depression section of the Composite International Diagnostic Interview (DIA-X-5/CIDI) was conducted by trained interviewers [ 27 ]. The DIA-X-5 is a standardized clinical interview for the assessment of mental disorders. The DIA-X-5 first asks about the presence of lifetime symptoms, followed by questions about the worst lifetime episode, and finally which of these symptoms also occurred in the past 12 months. A mania-screening questionnaire, consisting of the initial questions of the DIA-X-5 mania section, was conducted to detect any lifetime (hypo-) mania symptoms. Lifetime and 12-month diagnoses were subsequently generated for PDD and MDD according to DSM-5 criteria [ 2 ]. Subsequently, a 20 µl capillary blood sample was extracted from the fingertip using a safety lancet. The blood was immediately diluted with 380 µl of measurement buffer (CellCarrierB, Zellmechanik Dresden, Germany) in a microcentrifuge tube. All subjects signed consent forms regarding data privacy, clinical data collection and saving, and blood extraction. Subjects received 15 € compensation for expenses. The duration of the examination was ~1 h. After examination, blood samples were transferred to the Department of Cellular Machines at the Biotechnology Center of the TU Dresden, where the samples were measured using an RT-DC device.

Real-time deformability cytometry (RT-DC)

A 20 µl blood drop was taken from study participants by finger pricking using a lancet (Safety-Lancet Normal 21, Sarstedt, Nümbrecht, Germany) and harvested in a capillary (Minivette POCT, 20 µl, Sarstedt, Nümbrecht, Germany). Blood was immediately resuspended in 380 µl RT-DC measurement buffer containing 0.6% methylcellulose (CellCarrierB; Zellmechanik Dresden, Germany), maintained at room temperature, and measured within 3 h according to a protocol published elsewhere [ 13 ]. Overnight fasting of individuals is not required for this method [ 14 , 22 ]. In brief, blood was flushed through a microfluidic channel constriction of 20 µm × 20 µm in cross-section (Flic20, Zellmechanik Dresden, Germany) by applying a constant flow rate. An image of every measured blood cell was taken by a high-speed camera (Fig. 1A ) and besides other parameters, cell deformability and projected area (cell size) were calculated [ 22 ]. RT-DC measurements were controlled by the acquisition software Shape-In2 (Zellmechanik Dresden, Germany). The different blood cell types were classified by utilizing artificial intelligence-based image classification as published elsewhere [ 28 ] and mean values for cell deformability and cell size of every donor and blood cell type were extracted (Fig. 1B ).

Statistical analysis

Data were analyzed using R 3.4.3 [ 29 ]. Two-tailed independent t -tests and Mann–Whitney U -tests were performed to compare deformability and cell size (projected area [µm 2 ]) of each blood cell type between healthy controls and depressed individuals. Data were checked for normality using Shapiro–Wilk tests and p values were adjusted for multiple testing using a step-down Holm–Bonferroni method to control for familywise error rate of committing type I errors [ 30 ]. Moderating confounders (sex, age, BMI, psychopharmaceutical intake, medication category) were either adjusted for or tested using two-tailed independent t -tests and univariate ANOVA.

Case-control distribution for the group contrasts

A total of 139 pre-screened individuals scoring above 10 ( n  = 69) or below 5 ( n  = 70) in the PHQ-9 were examined in the study. Individuals meeting a positive screen for mania were excluded from PDD and MDD groups. Individuals reporting psychopharmaceutical treatment or mental disorders in the subject questionnaire were excluded from the healthy control group. Exclusion criteria and the DIA-X-5 diagnosis led to the following groups: Lifetime persistent depressive disorder (LTPDD) ( n  = 30), 12-month persistent depressive disorder (12-month PDD) ( n  = 15), lifetime major depressive disorder (LTMDD) ( n  = 42), 12-month major depressive disorder (12-month MDD) ( n  = 12), and healthy controls (HC) ( n  = 62). PDD and MDD groups partially overlap. Each group of participants suffering from depression was compared to an age- and sex-matched healthy control group. For detailed sample characteristics see Table 1 .

While no significant difference in cell size was detected for any disease group compared to healthy controls (see Supplementary Table 1 ), cell deformability was altered in a disease-specific way. Two-tailed independent t -tests and Mann–Whitney U -tests showed increased cell deformability in the granulo-monocyte cell fraction, especially in monocytes ( t (58) = 3.105, p  = 0.003) and neutrophils ( t (58) = 2.887, p  = 0.005) in participants with lifetime PDD compared to healthy controls (Fig. 3 ). These results remained significant after applying Holm–Bonferroni corrections for multiple comparisons for both monocytes ( p  = 0.018) and neutrophils ( p  = 0.02) suggesting large effects (Cohen’s d : monocytes = 0.80, neutrophils = 0.74). In 12-month PDD an increased cell deformability in erythrocytes ( t (28) = 2.082, p  = 0.047), but no association with lymphocyte or granulo-monocyte deformability was detected (Fig. 4 ). 12-month MDD (Fig. 5 ) was associated with increased cell deformability in lymphocytes ( U (22) = 32, p  = 0.0224), while we found no significant effect, but only trends towards increased cell deformability in lymphocytes and myeloid cells in lifetime MDD (Fig. 6 ). However, significant effects vanished for the association between 12-month PDD and erythrocyte deformability ( p  = 0.282) and for the association between 12-month MDD and lymphocyte deformability ( p  = 0.132) after Holm–Bonferroni correction. For detailed statistics see Table 2 .

Boxplots include interquartile range (boxes), median (lines), range (whiskers), and outliers (black dots). Erythrocyte deformability ( A ), monocyte deformability ( B ), neutrophil deformability ( C ), lymphocyte deformability ( D ), granulo-monocyte deformability ( E ), thrombocyte deformability ( F ). * indicates significantly different deformability values at p < 0.05 (two-tailed), ** indicates significantly different deformability values at p < 0.01 (two-tailed).

Depressive disorder and healthy control groups showed no significant differences regarding age, sex, and BMI (see Table 1 ). Depressive disorder and healthy control groups significantly differed in PHQ-9 scores, SF-12 scores, and usage of psychopharmaceutic treatment (see Table 1 ). These differences remained significant after Holm–Bonferroni correction. Psychopharmaceutical treatment was not associated with changes in cell deformability of erythrocytes ( t (137) = −0.776, p  = 0.439), monocytes ( t (137) = −0.463, p  = 0.644), neutrophils ( t (137) = −0.603, p  = 0.243), lymphocytes ( t (137) = −0.096, p  = 0.924), granulo-monocytes ( t (137) = 1.138, p  = 0.257), and thrombocytes ( t (137) = −0.333, p  = 0.3695). Subject questionnaire information on drug intake was used to create five groups according to medication: No medication ( n  = 75), psychopharmaceutical medication ( n  = 5), antihypertensive drugs ( n  = 16), thyroid dysfunction medication ( n  = 17), others ( n  = 4), and participants regularly taking in a combination of the preceding categories ( n  = 10). Univariate ANOVA showed no association between medication intake groups and cell deformability of erythrocytes ( F (5, 137)  = 1.112, p  = 0.352), monocytes ( F (5, 137)  = 0.915, p  = 0.474), neutrophils ( F (5, 137)  = 0.774, p  = 0.570), lymphocytes ( F (5, 137)  = 1.051, p  = 0.391), granulo-monocytes ( F (5, 137)  = 0.904, p  = 0.481), or thrombocytes ( F (5, 137)  = 1.075, p  = 0.377).

Correlation analysis for diagnostic groups and matched controls

Tables 3 , 4 , and 5 present Pearson, Spearman, and partial correlations for the association between PHQ-9, SF-12 physical health, SF-12 mental health, and cell deformability for each blood cell type. Partial correlations correcting for age, sex, BMI, and psychopharmaceutical treatment showed for the group consisting of lifetime PDD and matched control subjects significant correlations for higher depressive symptomatology and increased cell deformability for monocytes ( r p  = 0.306; p  = 0.022), neutrophils ( r p  = 0.292; p  = 0.028), and granulo-monocytes ( r p  = 0.293; p  = 0.028). In addition, 12-month PDD and 12-month MDD groups with matched controls showed significant positive partial correlations for depressive symptomatology and monocytes ( r p  = 0.389; p  = 0.050) and erythrocytes ( r p  = 0.452; p  = 0.046), respectively (see Table 3 ). However, these significant partial correlations faded when correcting for multiple comparisons using Holm–Bonferroni correction. With respect to Pearson and Spearman correlations only Spearman correlations for neutrophils ( p  = 0.038) and granulo-monocytes ( p  = 0.025) remained significant. No significant partial correlations emerged for the SF-12 physical health scale and cell deformability of any cell type (see Table 4 ). For the SF-12 mental health scale, as shown in Table 5 , significant partial correlations with cell deformability emerged for the group consisting of lifetime PDD subjects and controls for neutrophils ( r p  = −0.306; p  = 0.022) and granulo-monocytes ( r p  = −0.304; p  = 0.022). Negative correlations indicate higher cell deformability being associated with lower self-reported mental health. These partial correlations did not remain significant after the Holm–Bonferroni correction. No significant correlations with the SF-12 mental health scale were identified for cell deformability of the 12-month PDD and matched control group as well as the lifetime MDD and matched control group. In the 12-month MDD group and matched controls a significant partial correlation emerged for erythrocytes ( r p  = −0.581; p  = 0.008). This single correlation remained significant after correcting for multiple comparisons using Holm–Bonferroni correction ( p  = 0.048).

To our knowledge, this is the first study providing insights into the association between depressive disorders and cell morpho-rheological features of all major blood cell types. Our results suggest depressive disorders and in particular PDD to be associated with an overall increase in blood cell deformability, while for cell size no difference was observed. Hereby, the most consistent differences were found in lymphocytes, monocytes, and neutrophils highlighting the impact of depressive disorders on the mechanical properties of primary immune cells. However, correction for multiple testing highlights differences in cell deformability in the granulo-monocyte cell fraction and neutrophils in individuals with lifetime PDD compared to healthy controls to be most pronounced.

Morpho-rheological assessment of blood cells can provide crucial health status information, as changes in the cell’s mechanical constitution are associated with physiological or pathological function [ 13 ]. Thus, increased blood cell deformability in individuals with depressive disorders compared to controls (see Figs. 3 – 6 and Table 3 ) provides novel insight into the pathophysiology of depressive disorders. However, due to the complex interplay between morpho-rheological features and cell function, we can only speculate on the underlying causal link. A large body of evidence indicates depressive disorders to be associated with increased HPA-activity resulting in elevated cortisol levels [ 31 ]. Moreover, chronic low-grade inflammation with increased levels of proinflammatory cytokines and proteins such as interleukin-6 and C-reactive protein were described [ 32 ]. Increased immune cell deformability in depressed patients might be a direct response to elevated cortisol levels, which induce actin cytoskeleton reorganization and thereby increase cell deformability [ 6 ]. Additionally, an acute immune activation with lipopolysaccharide together with an increase in proinflammatory markers were shown to increase monocyte deformability [ 16 ], suggesting the often described chronic low-grade inflammation in depressed individuals contributes to increased leukocyte deformability. Furthermore, elevated glucocorticoid levels and inflammatory markers are suggested to influence the general lipid composition leading to impaired membrane formation, stability, and increased membrane bending and destabilization [ 7 ]. Supporting this line of research, individuals exhibiting increased inflammatory signaling such as healthy individuals after inhalation of lipopolysaccharide from E. coli , or individuals suffering from an acute lung injury, viral respiratory tract infections, or Epstein-Barr-virus infection show all increased neutrophil, monocyte, or lymphocyte deformation measured with RT-DC [ 13 ]. Furthermore, in the acute phase of a COVID-19 infection, with elevated levels of cytokines or even cytokine storm, neutrophils showed higher deformability during the acute phase but also 7 months after acute symptoms indicating that an activated cell state can also be identified in the long term [ 14 ].

Hyperactivity of the HPA-axis, chronic low-grade inflammation, and disturbed lipid composition combined resulting in increased blood cell deformability potentially lead to overall reduced integrity and altered functionality of blood cells [ 6 , 7 , 16 , 23 ]. Thus, the association of increased cell deformability observed in depressive disorders is in accordance with current pathophysiological models of depressive disorders. In addition, Lynall et al. (2019) reported elevated numbers of immune cells in depressed individuals compared to controls. It is known that increased levels of glucocorticoids and catecholamines result in increased white blood cell count, as cells demarginate from the vessel walls. Interestingly, these observations were recently associated with cellular softening [ 6 ]. In our study, elevated levels of circulating white blood cells in individuals suffering from depressive disorders cannot be confirmed, presumably due to the smaller sample size and the resulting lower power to detect the relatively small differences in immune cell count.

On the other hand, we also found increased erythrocyte deformability in individuals with current PDD. Tight control of homeostatic erythrocyte deformability is arguable of high importance in order to provide passage through narrow capillaries and tissue oxygen supply, critical for various organs including the central nervous system. Whether an altered lipid composition interferes with erythrocyte oxygen transport due to increased erythrocyte deformability or whether increased erythrocyte deformability is a body response to keep oxygen supply constant needs to be further examined. However, since the relation between erythrocyte deformability and current PDD does not survive correction for multiple testing, these findings need further replication and should be interpreted with caution. Nevertheless, our results highlight altered blood cell morpho-rheological properties in depressed patients to play a critical role in the pathophysiological processes; however, to what extent blood cell deformability is involved in the disease progression is yet far from understood. Furthermore, we did not identify differences in cell size of any of the investigated blood cells underlining the potential to readout cell deformability as an indicator for altered cell function. Importantly, since participants were not drug naïve, the potential association of specific medication types such as psychopharmacological agents, thyroid dysfunction medication, or antihypertensives with cell deformability was examined. No association was identified for cell deformability with any specific medication type suggesting that depressive disorders and not the examined medication types affect cell deformability.

When the correlation analysis is considered, it is noticeable that a consistent pattern emerges for the correlations between cell deformability and the PHQ-9 and the SF-12 mental health scale, while no correlations appear for cell deformability and the SF-12 physical health scale. However, it is important to emphasize that the significant correlations found do not withstand correction for multiple testing and should therefore be interpreted with caution. Nevertheless, comparable to the group comparisons, it appears that the strongest positive correlations between cell deformability and depressive symptomatology emerge in the group of lifetime PDD and HCs. Although, in general, in all groups and for all cell types positive correlations emerge, only for the lifetime PPD-HCs-subsample significant correlations were detected. More specifically, only for monocytes, neutrophils, and granulo-monocytes significant correlations between cell deformability and depressive symptoms or impaired mental health were observed suggesting these immune cells to be the most sensitive cells to react to depressive disorders with morpho-rheological changes.

Another point to be considered is that there are different ways to assess cell deformability. While the applied RT-DC method represents a versatile tool to measure specifically blood cell deformability on short time scales [ 13 , 22 ], other approaches provide important insights into cell mechanical properties on longer time scales. A recent study reports reduced erythrocyte deformability in 16 patients with myalgic encephalomyelitis/chronic fatigue syndrome compared to age-matched healthy controls [ 17 ]. They examined how long erythrocytes take to cross a 5 μm × 5 μm channel at a negative pressure of −13.79 kPa. The cells deform tactile to fit through the channel, which took ~13 ms. This is more than three times as long as cells are deformed in RT-DC. Thus, in the study by Saha et al. (2019), the cell’s viscous properties might play a more pronounced role in order to pass through the channel compared to RT-DC. Interestingly, the erythrocytes derived from myalgic encephalomyelitis / chronic fatigue syndrome patients were found to be larger compared to healthy controls. Indicating again a disease-specific alteration of the morpho-rheological features. A similar report applying centrifugation of erythrocytes through a filter of 5 µm pores quantifies erythrocyte deformability as the ratio of filtered erythrocytes to initial cell number. They examined 54 children with autism spectrum disorders and identified impaired erythrocyte deformability associated with more severe restricted and repetitive symptomatology [ 18 ].

Strengths and limitations

Strengths of our work are the pioneering character of the blood cell deformability measurement carried out using RT-DC in individuals with depressive disorders and age- and sex-matched healthy controls and the acquisition of an average of 115,000 cell images of each of the 139 subjects representing a yet not achieved large dataset. For fast, automated cell classification of the resulting dataset of over 16 million images, an artificial intelligence-based analysis was leveraged (Fig. 1 ). It needs to be noted, that blood samples were immediately measured using RT-DC within a time frame of 3 h after sampling since longer storage times or freezing of the cells could potentially alter cell deformability properties. In addition, the standardized clinical evaluation of the individuals provides the highest level of diagnostic validity and reliability. A limitation of our study is the fact that the participants were not drug naïve. However, since we intended to investigate depressive disorders in general including individuals with a lifetime or current PDD or MDD diagnosis, it is very difficult to include drug naïve individuals only. Therefore, in our analyses, we consistently controlled for the potential influence of medication on cell deformability. Another limitation is that PDD and MDD groups partially overlap raising the question of whether findings are related to the combination of disorders, the general severity of the symptoms of depression, or to chronicity. Further, the relatively low number of male individuals in our study renders a generalization to the male population difficult.

Conclusions

To conclude, this study provides to our knowledge the first evidence of a relationship between peripheral blood cell deformability and depressive disorders. As the pathophysiology of depressive disorders is only poorly understood, and HPA-hyperactivity and chronic low-grade inflammation represent landmarks of the current pathophysiological model, our results further point toward a persistently activated immunity in depressive disorders. In combination with altered lipid metabolism and blood cell membrane assembly, cell functional changes mediated by cytoskeletal adaptations are very likely to occur. In agreement with other reports, we found, that these cell functional changes can be detected disease-specific by morpho-rheological measurements, potentially leading to a co-diagnostic marker. Thus, our study broadens the understanding of the current physiological underlying causes of depressive disorders in blood cells and delivers a clinical-grade method to assess erythrocyte and immune cell functionality. Future research will be needed to confirm our findings in larger cohorts, in order to render the discriminant potential of cell morpho-rheological properties in depressive disorders more specific. Moreover, the potential to reverse increased peripheral blood cell deformability might be harnessed to develop new pharmacological treatments restoring optimal levels of cell deformability, and cell function and thereby reducing depressive burden.

Data availability

The anonymized data will be made available to all interested parties upon request.

Code availability

The anonymized code will be made available to all interested parties upon request.

World Health Organisation (WHO). Depression and other common mental disorders global health estimates. 2017.

American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5®). 5th ed. Arlington: American Psychiatric Pub; 2013.

Pariante CM. Why are depressed patients inflamed? A reflection on 20 years of research on depression, glucocorticoid resistance and inflammation. Eur Neuropsychopharmacol. 2017;27:554–9.

Article   CAS   Google Scholar  

Ravindran AV, Griffiths J, Merali Z, Anisman H. Lymphocyte subsets associated with major depression and dysthymia: Modification by antidepressant treatment. Psychosom Med. 1995;57:555–63.

Lynall M-E, Turner L, Bhatti J, Cavanagh J, Boer P de, Mondelli V, et al. Peripheral blood cell immunophenotyping reveals distinct subgroups of inflamed depression. Biol Psychiatry. 2019;88:185–96.

Fay ME, Myers DR, Kumar A, Turbyfield CT, Byler R, Crawford K, et al. Cellular softening mediates leukocyte demargination and trafficking, thereby increasing clinical blood counts. Proc Natl Acad Sci USA. 2016;113:1987–92.

Walther A, Cannistraci CV, Simons K, Durán C, Gerl MJ, Wehrli S, et al. Lipidomics in major depressive disorder. Front Psychiatry. 2018;9:459.

Liu X, Zheng P, Zhao X, Zhang Y, Hu C, Li J, et al. Plasma lipidomics reveals potential lipid markers of major depressive disorder. Anal Bioanal Chem. 2016;408:6497–507.

Demirkan A, Isaacs A, Ugocsai P, Liebisch G, Struchalin M, Rudan I, et al. Plasma phosphatidylcholine and sphingomyelin concentrations are associated with depression and anxiety symptoms in a Dutch family-based lipidomics study Ays. J Psychiatr Res. 2013;47:357–62.

Article   Google Scholar  

Knowles EEM, Huynh K, Meikle PJ, Göhring HHH, Olvera RL, Mathias SR, et al. The lipidome in major depressive disorder: shared genetic influence for ether-phosphatidylcholines, a plasma-based phenotype related to inflammation, and disease risk. Eur Psychiatry. 2017;43:44–50.

Lam WA, Rosenbluth MJ, Fletcher DA. Increased leukaemia cell stiffness is associated with symptoms of leucostasis in paediatric acute lymphoblastic leukaemia. Br J Haematol. 2008;142:497–501.

Rosenbluth MJ, Lam WA, Fletcher DA. Analyzing cell mechanics in hematologic diseases with microfluidic biophysical flow cytometry. Lab a Chip. 2008;8:1062–70.

Toepfner N, Herold C, Otto O, Rosendahl P, Sta J, Menschner L, et al. Detection of human disease conditions by phenotyping of blood. eLife. 2018;7:e29213.

Kubánková M, Hohberger B, Hoffmanns J, Fürst J, Herrmann M, Guck J, et al. Physical phenotype of blood cells is altered in COVID-19. Biophysical J. 2021;120:2838–47.

Ekpenyong AE, Whyte G, Chalut K, Pagliara S, Lautenschläger F, Fiddler C, et al. Viscoelastic properties of differentiating blood cells are fate-and function-dependent. PLoS ONE. 2012;7:e45237.

Ravetto A, Wyss HM, Anderson PD, den Toonder JMJ, Bouten CVC. Monocytic cells become less compressible but more deformable upon activation. PLoS ONE. 2014;9:e92814.

Saha AK, Schmidt BR, Wilhelmy J, Nguyen V, Abugherir A, Do JK, et al. Red blood cell deformability is diminished in patients with Chronic Fatigue Syndrome. Clin Hemorheol Microcirc. 2019;71:113–6.

Jasenovec T, Radosinska D, Celusakova H, Filcikova D, Babinska K, Ostatnikova D, et al. Erythrocyte deformability in children with autism spectrum disorder: correlation with clinical features. Physiological Res. 2019;68:S307–S313.

Buttgereit F, Scheffold A. Rapid glucocorticoid effects on immune cells. Steroids. 2002;67:529–34.

Rossi L, Serafini S, Pierigé F, Antonelli A, Cerasi A, Fraternale A, et al. Erythrocyte-based drug delivery. Expert Opin Drug Deliv. 2005;2:311–22.

Wu P-H, Aroush DR-B, Asnacios A, Chen W-C, Dokukin ME, Doss BL, et al. A comparison of methods to assess cell mechanical properties. Nat Methods. 2018;15:491–8.

Otto O, Rosendahl P, Mietke A, Golfier S, Herold C, Klaue D, et al. Real-time deformability cytometry: on-the-fly cell mechanical phenotyping. Nat Methods. 2015;12:199.

Bashant KR, Topefner N, Day CJ, Mehta NN, Kaplan MJ, Summers C, et al. The mechanics of myeloid cells. Biol Cell. 2020;112: 103–12.

Fletcher DA, Mullins RD. Cell mechanics and the cytoskeleton. Nature. 2010;463:485–92.

Kroenke K, Spitzer RL, Williams JBW. The phq‐9. J Gen Intern Med. 2001;16:606–13.

Ware Jr JE, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care 1996;34:220–33.

Hoyer J, Voss C, Strehle J, Venz J, Pieper L, Wittchen H-U, et al. Test-retest reliability of the computer-assisted DIA-X-5 interview for mental disorders. BMC Psychiatry. 2020;20:1–16.

Google Scholar  

Kräter M, Abuhattum S, Soteriou D, Jacobi A, Krüger T, Guck J, et al. AIDeveloper: deep learning image classification in life science and beyond. Adv Sci. 2021;2003743:1–12.

Team RC. R Core Team. R: a language and environment for statistical computing. R Found Stat Comput Vienna, Austria . 2017. Available from http://www.R-project.org/

Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat. 1979;6:65–70.

Stetler C, Miller G. Depression and hypothalamic-pituitary-adrenal activation: a quantitative summary of four decades of research. Psychosom Med. 2011;126:114–26.

Goldsmith DR, Rapaport MH, Miller BJ. A meta-analysis of blood cytokine network alterations in psychiatric patients: comparisons between schizophrenia, bipolar disorder and depression. Mol Psychiatry. 2016;21:1696–709.

Download references

Author contribution

AW and MK designed the study, performed experiments and data analysis, and co-wrote the manuscript. AM-K, JE, and MH performed experiments and data analysis and co-wrote the manuscript. CH and LDW supported data analysis. CK, JG, and KB-B provided funding, infrastructure/equipment, and co-wrote the manuscript.

Research pool TU Dresden (F-004242-552-848-1040103) awarded to AW. Faculty of Psychology of the TU Dresden (MK201911) awarded to AW. Alexander von Humboldt Professorship awarded to JG. German Research Foundation (DFG) – (399422891) awarded to MH and JG. Federal Ministry of Education and Research (BMBF) (01ER1307) awarded to KB-B. AW was further supported by the Swiss National Science Foundation (Grant: PZPGP1_201757) and received funding for open access publication by the Publication Fund of the University of Zurich and University Library Open Science Services of Zurich. The funding sources are national and university funding sources and had no influence on the writing of the manuscript or the decision to submit it for publication. None of the authors received financial incentives from industrial companies to write this publication. The corresponding author had full access to all study data and had the final responsibility for the decision to submit the paper for publication.

Author information

Authors and affiliations.

Biopsychology, TU Dresden, Dresden, Germany

Andreas Walther, Anne Mackens-Kiani, Julian Eder & Clemens Kirschbaum

Clinical Psychology and Psychotherapy, University of Zurich, Zurich, Switzerland

Andreas Walther

Center for Molecular and Cellular Bioengineering, Biotechnology Center, TU Dresden, Dresden, Germany

Maik Herbig, Christoph Herold, Jochen Guck & Martin Kräter

Max Planck Institute for the Science of Light & Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany

Maik Herbig, Jochen Guck, Lucas Daniel Wittwer & Martin Kräter

Zellmechanik Dresden GmbH, Dresden, Germany

Christoph Herold

Faculty of Informatics/Mathematics, HTW Dresden, Dresden, Germany

Lucas Daniel Wittwer

Behavioral Epidemiology, TU Dresden, Dresden, Germany

Katja Beesdo-Baum

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Andreas Walther .

Ethics declarations

Competing interests.

Christoph Herold owns shares of, and is employed at Zellmechanik Dresden GmbH, a company selling devices based on real-time deformability cytometry. Zellmechanik Dresden GmbH did not have any role in the conception and planning of this study or its preparation for publication. All other authors state that they have no actual or potential conflict of interest to declare, including any financial, personal, or other relationships with other people or organizations within 3 years of beginning the submitted work that could influence or bias their work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplemental material, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Walther, A., Mackens-Kiani, A., Eder, J. et al. Depressive disorders are associated with increased peripheral blood cell deformability: a cross-sectional case-control study (Mood-Morph). Transl Psychiatry 12 , 150 (2022). https://doi.org/10.1038/s41398-022-01911-3

Download citation

Received : 22 March 2021

Revised : 17 March 2022

Accepted : 22 March 2022

Published : 08 April 2022

DOI : https://doi.org/10.1038/s41398-022-01911-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Relatively low fluoride in drinking water increases risk of knee osteoarthritis (koa): a population-based cross-sectional study in china.

• Xinyue Meng

Environmental Geochemistry and Health (2023)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Persistent Depressive Disorder Intervention: 30-Year-Old Male Patient Case Study

Description of the case.

Mordecai is a male who is 30 years old. He suffers from a persistent depressive disorder, which is a common type of depression. Persons with this condition report low self-esteem, failure to enjoy things they once found pleasurable, and fatigue (Lam, Michalak, & Swinson, 2006). Mordecai has been suffering from the condition for the past year. The condition and its contextual factors have affected his level of functioning. It has impaired his daily activities and social life.

Patients with this disorder have impaired social cognition, perspective-taking, and social sensitivity (Wilkinson, 2013). They lack concentration and have a negative attitude towards life. According to family and friends, over the past year, Mordecai has complained of persistent feelings of sadness and hopelessness. When carrying out his activities, the patient shows little or no interest (Lam et al., 2006).

Mordecai’s disorder has had a significant impact on his life. He cannot maintain a stable relationship with his relatives or friends. The reason for this is his negative attitude and consistent changes in moods. In addition, he cannot maintain a job ( National mental health report, 2013).

Description and Justification of an Intervention

As a medical practitioner with extensive experience in dealing with depression, my primary duty will be to help Mordecai manage his problem (Mpofu & Oakland, 2010). A number of interventions relevant to the problem will be adopted. I will adopt these measures using the ICF framework. To this end, I will start by analysing Mordecai’s mental functions, his major life skills, and significant interactions. In addition, I will assess the structures of his nervous systems. I will also evaluate the dynamics and relationships between his behaviour and social systems (Manincor, Bensoussan, Smith, Fahey, & Bourchier, 2015).

Mordecai exhibits a wide range of behaviours. They include indecisiveness, restlessness, and problems with decision making. In addition, he reports recurring thoughts of suicide and death (Lam et al., 2006). I have evaluated these traits in relation to the provisions of the ICF framework. My assessment reveals that Mordecai has restricted interpersonal interactions. The patient also suffers from significant constraints in relation to his community and social life. It is noted that suicidal thoughts and the inability to maintain stable relationships are the greatest challenges facing patients with persistent depressive disorder (Manincor et al., 2015). The case is evident in Mordecai’s situation. According to ICF, the problem can be classified as moderately difficult Class II impairment.

There are a number of interventions that can be used to help patients suffering from a persistent depressive disorder. The success of the treatment procedure depends on various factors. One of them is the severity of the illness. Another factor is the willingness of the patient to take part in the management procedure (Corey, 2010). As an experienced medical practitioner, I have come up with a set of what I believe to be the best treatment interventions for Mordecai. My decisions are based on the requirements of the ICF framework. The preferred interventions would be medication and talking therapy.

Medication entails administering different classes of antidepressants based on the severity of the condition (Lam et al., 2006). To find the right and most effective medication, which has the least side effects, a number of factors will be evaluated. The aspects include physical and mental condition, as well as other medical conditions.

Talking Therapy

There are a number of talking therapies that can be used on this patient. They include Cognitive Behavioural Therapy (CBT) and Mentalisation- Based Therapy (MBT). Both techniques will be used to help Mordecai change his negative attitudes towards others, ease death and suicidal thoughts, and enhance self-esteem (Manincor et al., 2015).

Risks Involved with the Interventions

The main risk associated with the therapies is exploring the patient’s painful feelings, experiences, and emotions (Wilkinson, 2013). As a result, the treatment sessions can be uncomfortable to the individual. The patient may develop stress and anxiety (Manincor et al., 2015). Another risk is lack of commitment and cooperation on the part of the patient and failure to take antidepressants as prescribed.

Risk Minimisation Strategies

The first risk minimisation strategy is working with a team of skilled medical practitioners (Corey, 2010). The professionals will help me create a working treatment environment. Their participation will make it possible to effectively engage with Mordecai. The second strategy is to equip the team of medical practitioners with coping skills to address the negative attitudes portrayed by the patient. Finally, a family member will be requested to monitor Mordecai to ensure that he takes the medications as required.

Identification of another Health Professional

Another health professional in the inter-professional team will be a psychologist. According to the ICF framework, the role of the practitioner is to analyse the problems presented by the patient in relation to four primary components (Mpofu & Oakland, 2010). The aspects include body functions and structures, activities and participation, as well as environmental factors.

Goal Setting

One of the main roles of a psychologist when dealing with persistent depressive disorder patients is goal setting (Corey, 2010). In Mordecai’s case, the psychologist will help in setting such goals as maintaining stable relationships, improving work performance, anger management, and enhancing self-esteem.

According to the ICF framework, psychologists should regularly test their patients. The aim is to diagnose the persistent depressive condition of the client. In relation to Mordecai, the professional will carry out routine check-ups to understand the link between his thoughts, feelings, and actions (Manincor et al., 2015). The assessments will be used in evaluations during the treatment sessions.

Ongoing Care

Psychologists offer long-term counselling to patients suffering from depression disorders (Wilkinson, 2013). In relation to Mordecai, the professional will meet the patient twice a week.

One of the methods to be used in managing the patient’s problem is therapy. As a result, the professional’s help will be of great importance (Wilkinson, 2013). The psychologist will work with other practitioners to offer mutual support to Mordecai.

Specification of Professionalism Characteristics

Maintaining ethical standards.

Healthcare professionals are guided by codes of ethics and standards of practice ( National mental health report, 2013). When treating Mordecai, ethics as a characteristic of professionalism will be demonstrated by maintaining confidentiality, integrity, and accountability. Confidentiality is a key ethical principle in healthcare (Corey, 2010). The reason is that the medical practitioner acquires sensitive and private information about a patient. In Mordecai’s case, I will maintain his right to privacy. In addition, I will demonstrate honesty and accountability.

Clinical Maturity

As a characteristic of professionalism, clinical maturity will be demonstrated by managing personal emotional states and empathy. I will also protect Mordecai from negative, aggressive acts from ‘their self’ (Corey, 2010). Failure to control personal emotions may impact negatively on intervention practices (Wilkinson, 2013). The feature may affect the relationship between the patient and the doctor.

Clinical maturity will be demonstrated by showing empathy towards Mordecai while remaining objective about the importance and meaning of various manifestations of the mental condition. According to Wilkinson (2013), patients who feel that medical practitioners have created a genuine and empathetic connection with them experience a reduction in levels of stress, pain, and anxiety. Empathy makes the patient feel cared for by the doctor. Consequently, they open up to the practitioner and become committed to the treatment process.

Consulting other Professionals

Professionals in medical fieldwork with different colleagues. They share ideas regarding the conditions of patients and preferred interventions (Lam et al., 2006). When treating Mordecai, I will regularly consult with other experts, such as psychologists. The major aim of this approach is to share information on what is working well and what needs to be changed (Mpofu & Oakland, 2013).

Description and Justification of Person-Centred Strategies

Person-centred practice entails adopting a humanistic approach to help patients understand their ability to resolve their problems and realise their potential (Corey, 2010). It also helps them to transform their lives in positive ways. The two strategies I will implement to ensure the person-centred approach include focusing on thinking and planning. The approaches promote patient participation.

Person-Centred Thinking Strategy

In Mordecai’s case, I will use this strategy to develop a set of values, tools, and skills to know him better ( National mental health report, 2013). The primary aim of the approach is to determine what he considers important in life. My main objective will be to support the client based on his weaknesses, strengths, abilities, and aspirations. Mordecai will then be helped to make meaningful decisions about his life based on the concepts.

Person-Centred Planning Strategy

The approach entails discovering patients’ goals and supporting them to accomplish the targets (Wilkinson, 2013). The strategy is evidence-based. In the case scenario, I will use the technique to assist Mordecai plan and lead an inclusive and independent life. Resources will be provided from the patient’s network, service providers, and non-specialists. The aim is to help Mordecai support himself and manage some of his problems as he plans to lead a new life.

Corey, G. (2010). Theory and practice of counselling and psychotherapy . Auckland, N.Z.: Royal New Zealand Foundation of the Blind.

Lam, R., Michalak, E., & Swinson, R. (2006). Assessment scales in depression, mania and anxiety . Oxfordshire, UK: Taylor & Francis.

Manincor, M., Bensoussan, A., Smith, C., Fahey, P., & Bourchier, S. (2015). Establishing key components of yoga interventions for reducing depression and anxiety, and improving well-being: A Delphi method study. BMC Complementary and Alternative Medicine , 15 (85), 1-10.

Mpofu, E., & Oakland, T. (2010). Rehabilitation and health assessment: Applying ICF guidelines . New York: Springer.

National mental health report: Tracking progress of mental health reform, 1993-2011. (2013). Web.

Wilkinson, M. (2013). Depression . Web.

• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2022, December 6). Persistent Depressive Disorder Intervention: 30-Year-Old Male Patient. https://ivypanda.com/essays/persistent-depressive-disorder-mordecai-case/

"Persistent Depressive Disorder Intervention: 30-Year-Old Male Patient." IvyPanda , 6 Dec. 2022, ivypanda.com/essays/persistent-depressive-disorder-mordecai-case/.

IvyPanda . (2022) 'Persistent Depressive Disorder Intervention: 30-Year-Old Male Patient'. 6 December.

IvyPanda . 2022. "Persistent Depressive Disorder Intervention: 30-Year-Old Male Patient." December 6, 2022. https://ivypanda.com/essays/persistent-depressive-disorder-mordecai-case/.

1. IvyPanda . "Persistent Depressive Disorder Intervention: 30-Year-Old Male Patient." December 6, 2022. https://ivypanda.com/essays/persistent-depressive-disorder-mordecai-case/.

Bibliography

IvyPanda . "Persistent Depressive Disorder Intervention: 30-Year-Old Male Patient." December 6, 2022. https://ivypanda.com/essays/persistent-depressive-disorder-mordecai-case/.

• Depression in a 30-Year-Old Female Client
• Career Development Program for 30-Year-Old Population
• The Concept of Person-Centred Care
• ICF International: Background and Nowadays
• Healthy Lifestyle Against Negative Physical Conditions
• Psychiatric Evaluation: Sadness and Anxiety
• Vulnerability in Perioperative Patients: A Case Study
• Down's Syndrome Recurrence Discussion
• Compare and Contrast Mordecai Kaplan's Judaism as a Civilization and Abraham Joshua Heschel's the Sabbath
• Hypertension Care Plan for a Young Woman
• Physiological Mechanism of Stress
• Collaboration: Legal and Ethical Issues
• Posttraumatic Stress Disorders: Psychological Assessment
• Self-Fashioning in Society and Solitude
• Inevitable Prejudice in Social Psychology

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Integr Med Insights

Four Cases of Dysthymic Disorder and General Malaise Successfully Treated with Traditional Herbal (Kampo) Medicines: Kamiuntanto

Toshiaki kogure.

1 Department of Integrated Japanese Oriental Medicine, School of Medicine, Gunma University, Japan

2 Department of Japanese Oriental Medicine, Gunma Central General Hospital, Gunma, Japan

Takeshi Tatsumi

3 Department of Internal Medicine, Gunma Central General Hospital, Gunma, Japan

Traditional herbal (Kampo) medicines have been used since ancient times to treat patients with mental disorders. In the present report, we describe four patients with dysthymia successfully treated with Kampo medicines: Kamiuntanto (KUT). These four patients fulfilled the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria for dysthymic disorder with easy fatigability and sleeplessness, but did not fulfill the criteria for major depressive disorder. Treatment with KUT relieved depressive status, fatigue and sleeplessness in these patients. As a result, their QOL (quality of life) was considerably improved. KUT may be useful as an additional or alternative treatment for dysthymia, especially in the field of primary health care.

Introduction

Dysthymic disorder (dysthymia) is a disabling psychiatric disorder characterized by mild but persistent depressive symptoms. In the USA, it is reported that the lifetime prevalence of dysthymia ranges from 3% to 6% in the general population, 1 , 2 and up to 36% in psychiatric outpatient clinics. 3 In Japan, a low lifetime prevalence (1.4%) 4 and a 12-month prevalence of 0.7% 5 for dysthymia have been reported. It has been suggested that the low rate of dysthymia in Japan is due to a lack of familiarity with operational diagnostic criteria, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM). Early diagnosis is of vital important for the successful treatment of dysthymia, especially in primary health care. Additionally, about 25% of patients with dysthymia experience a chronic unchangeable status, and a subset of these patients develop major depressive disorder despite various treatments, such as antidepressants and antianxiety drugs.

In Japan, traditional herbal (Kampo) medicines (THM) are covered by national health insurance and play an important role in primary care, and several kampo formulae have been prescribed for mental disorders. 6 , 7 THM has two points that differ from Western Medicine, i) the Kampo formula is a crude drug, not a purified chemical product; ii) the diagnostic system in Kampo Medicine differs from that of Western medicine. A Kampo formula is generally composed of several herbal components and is generally considered safe. Pseudoaldosteronism due to licorice root is a well known adverse effect of THM. There have also been allergic effects, such as skin eruption and liver injury, induced by crude drugs. Furthermore, it is crucial to understand that the Kampo diagnostic system is constructed from a paradigm that differs from the paradigm underlying Western natural science. When we treat a patient with dysthymia using Kampo Medicine, Kampo diagnosis is required in addition to that of Western Medicine. These characteristics make it difficult to perform controlled clinical trials. Therefore, there is no evidence supporting the use of Kampo formulae for dysthymia although Kampo formulae are often applied for mental disorders in Japan. However, it is a fact that there are responders to THM among patients with dysthimia. In this regard, we prescribed Kamiuntanto (KUT), one of these Kampo formulae, for the treatment of dysthymia with several physical and mental symptoms diagnosed by DSM-4th edition (DSM-IV).

Here, we describe four patients with dysthymia who were successfully treated with KUT.

Case Reports

Treatment of each patient was approved under the comprehensive agreement of Gunma University. Informed consent was obtained from all four cases before treatment with Kampo therapy. Further, each patient was treated before 2008. The authors have received training in performing clinical trials at Gunma University.

A 63-year-old male consulted the Department of Japanese Oriental (Kampo) Medicine (DJOM), Gunma University in October 200X requesting traditional herbal medicine (Kampo) for dysthymic disorder with sleeplessness and malaise that had persisted for about 5 years despite treatment with antidepressants ( Table 1 ). He worked at a bakery as a full-regular employee on ordinary days. He was neither a smoker nor a drinker. At the initial examination, there were no remarkable findings in the chest or abdomen, and hepatorenal and thyroid functions appeared normal on both blood analysis and image diagnosis. Additionally, he had not complained of any clinical features indicating the dementia. He had been taking time off work several days a month due to fatigue. Treatment with Kamikihito, one of the kampo formulae, for 4 weeks failed to improve his symptoms. Kamiuntanto (KUT; Table 2 ) was therefore administered in addition to antidepressants. After KUT therapy for 12 weeks, sleeplessness and malaise improved. The patient was accordingly relieved from dysthymia and estazolam was discontinued, and the patient became able to commute every day. The improvement in social activity has continued for 3 years with KUT. In addition, we evaluated the improvement of depressive symptoms using global assessment of functioning (GAF) scale by DSM-4th ed. Text revision (DSM-IV TR) 8 in all cases. Her GAF scale changed from 70–61 to 90–81.

Clinical features of four patients with dysthymia.

Herbal components of Kamiuntanto (KUT).

The herbs were mixed with 600 ml of water and boiled down to 300 ml, and the aqueous extract was filtered through a sieve. The extract, called a decoction, was administered twice a day before meals in the morning and evening.

A 62-year-old female consulted DJOM requesting Kampo treatment for general malaise and lack of volition that had persisted for 2 years despite conventional Western therapy, which consisted of benzodiazepines. She was not regarded as having senile dementia. She was a housewife and barely able to perform housework. Her status was diagnosed as dysthymic disorder by operational diagnostic criteria; DSM-IV. Saikokeishikankyoto (decoction) therapy in addition to Western medicines for 4 months failed to improve her symptoms. Therefore, we changed the Kampo formula from Saikokeishikankyoto to KUT. Dysthymia, consisting of general malaise and depressive symptoms was reduced by 80% after KUT therapy for about 4 months, along with the occasional use of Kousosan (TJ-70, 2.5 g TSUMURA Co. Ltd Japan) to relieve her anxiety. Relief from dysthymia has continued for about 2 years with KUT treatment. Her GAF scale changed from 70–61 to 90–81.

A 61-year-old female developed a feeling of heavy head and sleeplessness in April 200X. She was receiving atorvastatin for hyperlipidemia, and had also received a sleeping drug from a local hospital. However, her symptoms persisted, followed by the development of depressive symptoms and malaise, although she continued to work as a pharmacist. She consulted our hospital requesting Kampo treatment in November 200X + 2. She was diagnosed as having dysthymic disorder based on DSM-IV. Kousosanryo (decoction) therapy for 8 weeks failed to improve dysthymia. Her symptoms relieved by 80% after 4 months of KUT administration, and thereafter she became able to concentrate on work and housekeeping. Improvement of dysthymia has continued for 18 months with KUT treatment. Her GAF scale changed from 80–71 to 100–91.

A 53-year-old female (menopause: 51-year-old) came to DJOM requesting Kampo treatment for dysthymic disorder with sleeplessness, malaise and nervousness without vasomotor symptoms, consisting of hot flashes and sweating, which had persisted for about 5 years. She was a housewife and barely performed housework. She had not been taking antidepressant therapy, although she was taking hypotensive drugs for essential hypertension. Depressive symptoms were relieved after 4 months of KUT treatment and the improvement continued for 6 months. However, sleeplessness, easy fatigability and appeteite loss reappeared. Therefore, we changed KUT to another kampo formula (Kamikihito: decoction) and have obtained improvement by 50%. Her GAF scale changed from 70–61 to 90–81.

In all cases, there were no remarkable findings in the chest or abdomen, hepato-renal and thyroid functions appeared normal on both blood analysis and image diagnosis, and dysthymic disorder had been diagnosed by a psychiatrist based on DSM-IV criteria. During the follow-up periods, there were no adverse reaction attributable to Kampo medicines.

Dysthymia is defined in DSM-IV as follows: mild depressive mood continued nearly all day for 2 years, and there were no major depressive episodes observed during at least the first 2 years. It has been reported that youth are susceptible to dysthymia, while elderly people demonstrate symptoms closer to major depressive disorder. Three (No. 1.2.3 in Table 1 ) of our cases were elderly patients, however kampo treatment with KUT resulted in an improvement of depressive mood. However, one of the patients (No. 4 in Table 1 .) experienced dysthymic symptoms in the postmenopausal period, and her status was categorized as a climacteric mental disorder. It is well known that depressive symptoms in climacterium are associated with a decrease in estrogen (E2). Although an E2-like action of KUT has not been recognized, it is possible that KUT may also be effective for dysthymia in postmenopausal females.

Although dysthymia is apt to be regarded as a mild depressive disorder by non-psychiatrists, social loss due to dysthymia is serious. Cassano et al have reported that social activity shows greater reduction in patients with dysthymia than in patients with major depressive disorders. 9 The clinical features of dysthymia are characterized by low ADL despite mild depressive symptom. The etiology of low ADL remains unclear, but it is possible that it may be difficult to diagnose dysthymia early because the depressive symptoms are mild. In addition, the physical symptoms such as general malaise as well as emotional symptoms probably contribute to decreasing ADL in dysthymia. The clinical characteristics of each patient in this series are summarized in Table 1 . Each patient complained of fatigue and sleeplessness. Furthermore, it is well known that dysthymia in climacterium is characterized by severe malaise. KUT treatment resulted in the improvement of depressive status, as well as easy fatigability and sleeplessness, and so ADL would probably improve. These clinical courses suggest that KUT (Kampo medicine) may be useful as an additional or alternative treatment for dysthymia, especially in the field of primary health care. During this period, we encountered 2 other patients with depressive symptoms, who did not fulfill the criteria for dysthymia because the period of mild depressive symptoms was less than 2 years. Howevere, these patients were also successfully treated with KUT (data not shown). Kampo treatment generally aims not only at improving or regaining physical health, but also taking the patient’s psychic and emotional imbalance into account. 10 However, the efficacy is limited among responders to KUT treatement. To confirm this efficacy, further clinical trial such as N of 1 clinical study, 11 will be required.

In Japan, traditional herbal medicines (Kampo) are covered by national health insurance, and are generally used in primary health care. KUT (kamiuntanto) is one of the kampo formulae used for the treatment of mental disorders, such as insomnia or dementia. 12 Kampo formula is administered following traditional diagnosis, in addition to diagnosis by Western medicine. The traditional target group for KUT comprises patients with sleeplessness, anxiety, and malaise after a serious illness as well as depressive status in patients lacking physical strength. 9 Since patients with dysthymia who complain of general malaise are close to the target group for KUT, we therefore treated 4 dysthymia patients with KUT and achieved good outcomes. Further, the traditional target group of Kamikihito (a kampo formula), which was administered in case nos. 1 and 4, comprises patients characterized by appetite loss in addition to other symptoms.

It is still not clear whether KUT improves the status of dysthymia, but several actions of KUT on the nervous system have been demonstrated. It has been reported that KUT potentiates the brain cholinergic system in an aged mouse model and its effect may be attributed to an increase in the activity of choline acetyltranseferase (ChAT). 13 Those effects have also been demonstrated in thiamine-deficient mice that demonstrate impairment of learning and memory. 14 It has been considered that the beneficial effect of KUT on Alzheimer’s disease (AD) is due to the potentiation of ChAT, but not inhibition of cholinesterase (ChE). 12 Although an excess of HPA axis was observed in the patients with dysthymia, suppression of the HPA axis by KUT has not been demonstrated. However, recently it has been reported that AD and depression are significant associated in the aging population, and interestingly ChAT polymorphism is significantly associated with depression. 15 Three of our patients were elderly, and KUT might improve dysthymic status through action on ChAT as in dementia. Furthermore, Smith et al have demonstrated that cholinergic neurons were also decreased in the cerebral cortex such as the frontal lobe in postmenopausal females, and estrogen replacement therapy (ERT) suppressed the decrease in cholinergic neurons using SPECT and 123 I-iodobenzovesamicol. 16 Therefore, it is possible that KUT treatment may potentiate ChAT in the postmenopausal female. We consider that the dysthymic patient (Pt. no. 4 in Table 1 ) in climacterium was also successfully treated with KUT due to its effects on ChAT. Thus, it is considered that KUT may be useful for various patients with dysthymia.

Finally, we present 4 patients with dysthymia successfully treated with the Kampo formula: KUT. KUT may be useful and safe as an additional or alternative treatment for dysthymia. These observations encourage us to proceed further with controlled trials to confirm the efficacy of KUT.

Disclosures

This manuscript has been read and approved by all authors. This paper is unique and is not under consideration by any other publication and has not been published elsewhere. The authors and peer reviewers of this paper report no conflicts of interest. The authors confirm that they have permission to reproduce any copyrighted material.

This work was supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science.