Introduction

In adult patients with type 1 diabetes, there is a high prevalence of comorbid mental disorders such as depression, anxiety, dysthymia, panic disorder and social phobia [1, 2]. Next to anxiety, depression constitutes the greatest problem, with much higher prevalence (4.6–13.6% [1, 3–7]) compared with reference groups (2.7–3.2% [5, 7]). The increased presence of depression in patients with type 1 diabetes might be partly attributable to a biological link (e.g. circulating cytokines [8]), or other shared mechanisms (e.g. dysregulation of the hypothalamic–pituitary–adrenal axis [9]). When present in patients with type 1 diabetes, depression is associated with fear of hypoglycaemia [10], complications [4, 11, 12], diabetes-related distress [13, 14], impaired quality of life [15], female sex [4], low socioeconomic status [4] and physical inactivity [13, 16]. One of the most important diabetes-related problems connected to depression is inadequate glycaemic control (high HbA1c levels). Recent research questions such a general association with type 1 diabetes because data were often retrieved from type 2 or mixed (types 1 and 2) diabetes cohorts. Studies focusing solely on type 1 diabetes have yielded inconsistent results. While depression was linked to poorer glycaemic control in some studies [16–19], in others it was not [2, 20, 21].

What is the course of depression following diabetes onset? When does it occur for the first time? Studies reporting the prevalence of depression tend to do so at a random time point during a patient’s disease. Longitudinal data can help us better understand the course of depression over time, and also unravel the relationship between depression and diabetes-related problems (e.g. poor glycaemic control).

While longitudinal data on type 2 diabetes has been available since 2008 [22], and data on the course of depression in type 2 diabetes has been available since 2012 [23], there is very little longitudinal data on type 1 diabetes, especially concerning adults.

A recent study by Rassart and colleagues presents longitudinal data concerning psychological functioning and glycaemic control on emerging adults [20]. Here, patient’s beliefs and perceptions about their diabetes were linked to diabetes-related problems (including depressive symptoms) 5 years later, but not to changes in glycaemic control. There is no longitudinal data on an onset cohort of adults with type 1 diabetes dealing with mental comorbidities.

In summary, there is a lack of research considering the natural incidence, as well as the trajectory, of depression after the diagnosis of type 1 diabetes in adults. Additionally, the predictors of depression in this group of patients are unknown. Hence, the aim of our study was to answer the following research questions: What is the long-term course (trajectory) of depressive symptoms over a period of 5 years? Is it possible to identify distinct classes of patients that develop or do not develop depression? If so, what are predictors of these trajectories of depressive symptoms? Finally, are there differences in outcome according to different depression classes or trajectories?

Methods

Two of this paper’s authors (F. Petrak, B. Kulzer) conducted a prospective, multicentre, longitudinal observational study on a cohort of adult inpatients with newly diagnosed type 1 diabetes (the German Multicenter Diabetes Cohort [GMDC] study). Study inclusion criteria were a recent diagnosis of type 1 diabetes (not more than 12 weeks prior to study enrolment), age between 17 and 40 years, and sufficient German language skills to complete the questionnaires. While the lower age limit was chosen to include adults only, we chose the upper age limit to avoid the unintentional enrolment of type 2 diabetes patients misdiagnosed with type 1 diabetes. Recruitment took place at 12 different hospitals: five acute care, five rehabilitation and two university hospitals. In total, 347 patients were eligible for inclusion; of these, 313 patients agreed (provided written informed consent) to participate. After 5 years, data was available for 258 patients (for a more detailed description of the study sample, see [5]).

At baseline (i.e. time of recruitment at the hospital) and at five consecutive yearly postal surveys, we collected data on patient characteristics, as well as behavioural and psychosocial data (e.g. coping styles, quality of life, disease distress), via questionnaires. Medical data, such as the type of treatment, HbA1c level or complications, were obtained from the treating physicians. See electronic supplementary material (ESM) Methods for further details.

The baseline prevalences of mental disorders according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) [24] were obtained using the Diagnostic Interview for Mental Disorders (DIMD short version) [25].

To assess the severity of depression, we used the Symptom Checklist 90-R (SCL-90-R) questionnaire [26]. Scores were transformed into sex-adjusted T-scores: T < 60, not depressed; 60 ≤ T < 70, moderately depressed; T ≥ 70, severely depressed [26]. Diabetes-related distress in daily life and diabetes-related quality of life (DQoL) were measured using the German Alltagsbelastungen bei Diabetes (FBD) and Lebensqualität bei Diabetes (LQD) questionnaires [27, 28]. The latter provides three scales of DQoL in type 1 diabetes: strain, satisfaction and strain due to blood glucose level. The adequacy of perceived familial support was measured by the revised Perceived Family Support and Communication Questionnaire (PFSQ-R) [29]. See ESM Methods for further details.

In order to explore the potential advantages of more advanced statistical methods and to ‘establish the best classification based on the longitudinal pattern of change’ [30], we applied growth mixture modelling (GMM) using Mplus version 7.3 statistical software to identify typical trajectories of depressive symptoms over time in a cohort of adults with newly diagnosed type 1 diabetes. Across different time points, GMM determines whether meaningful trajectories underlie the data to describe distinct (latent) classes. See ESM Methods for further details.

To gain a better understanding of the trajectories, we also sought distinctive characteristics that might characterise the final classes (outcomes at 5 year follow-up), or predict class membership (predictors at baseline or 1 year follow-up). To do this, data was analysed descriptively using one-way ANCOVA, χ2 and multinomial logistic regression (backward elimination) using IBM SPSS Statistics software version 22.0.

Predictors at baseline or 1 year follow-up for class membership

We made a distinction between baseline characteristics (sex, age, years of formal education, marital status, severity of depression [SCL-90-R], major depression [DIMD] and anxiety disorder [DIMD]) and those characteristics that might change after a diagnosis of type 1 diabetes (marital status, BMI, severity of depression [SCL-90-R] and DQoL [LQD], diabetes-related distress in daily life [FBD], and adequacy of perceived familial support [PFSQ-R]). Hence, multinomial logistic regression was either conducted with potential predictors measured at baseline (baseline characteristics), or at 1 year follow-up (sensitive to change). In addition, we checked whether patients had ever undergone psychotherapy during the 5 years of follow-up and whether this might predict class membership. Table 1 provides an overview on potential predictors (at baseline and 1 year follow-up) for depression class membership.

Table 1 Overview of potential predictors (at baseline and 1 year follow-up) for depression and outcomes at the 5 year follow-up
Full size table

Outcomes at 5 year follow-up

We tested medical outcomes such as HbA1c level, long-term complications, hypoglycaemia and type of treatment, as well as diabetes-related psychological outcomes such as DQoL, adequacy of perceived familial support and diabetes-related distress in daily life; the latter three would only be tested for class differences if they were unable to predict class membership. Table 1 gives an overview of observed outcomes at the 5 year follow-up.

Results

In total, 313 patients with newly diagnosed type 1 diabetes were included in our analyses at baseline. Because of dropouts, our patient sample comprised 291 patients for the 1 year follow-up, 267 patients for the 2 year follow-up, 267 patients for the 3 year follow-up, 254 patients for the 4 year follow-up and 258 patients for the 5 year follow-up. Figure 1 provides details about patients who dropped out completely and did not participate in any of the follow-ups. The dropouts included significantly more men (76.4%, n = 42) than women (23.6%, n = 13) compared with the sample that continued participation (men, 59.3%, n = 153; women, 40.7%, n = 105; χ2 (1) = 5.62, p = 0.018). Those who dropped out were also significantly younger at baseline (mean 26.4 years) than those who continued (mean 28.6 years, F (1,312) = 5.71, p = 0.017). There were no differences in years of formal education. At baseline, the average disease duration was 5.6 weeks (1 year follow-up, 63.5; 2 year follow-up, 120.0; 3 year follow-up, 176.4; 4 year follow-up, 231.9; 5 year follow-up, 288.6) and the mean age was 28.2 years. This sample comprised far more men than women, unmarried people, people living with a partner (and child[ren]) and people with a higher educational background. Diseases other than diabetes, complications and medication other than insulin were uncommon. Patient BMI ranged from underweight to obese. Detailed information on patient characteristics is found in Table 2.

Fig. 1
figure 1

Flow chart showing the number of complete dropouts at various follow-ups (this does not take into consideration missing follow-ups but dropouts at the final time point). Data includes dropout number, patient characteristics, information on severity of depression and HbA1c levels. One value is missing for education (years) of dropouts prior to1 year follow-up. Data are number, mean ± SD, or mean. Subsequent analysis (displayed in the main text) was carried out on participants who took part at the point of measurement, of which there were 291 at the 1 year follow-up, 267 at the 2 year follow-up, 267 at the 3 year follow-up, 254 at the 4 year follow-up and 258 at the 5 year follow-up

Full size image
Table 2 Patient demographics and clinical characteristics at baseline and the 5 year follow-up
Full size table

At baseline, 5.8% of patients were diagnosed with a major depressive episode and 7.7% with an anxiety disorder, according to DSM-IV. In total, 12.5% of patients were diagnosed with at least one mental disorder. A detailed description of the prevalence of mental disorders at baseline was previously reported [5].

Trajectories of depressive symptoms

The mean scores for depressive symptoms (T ± SD) were 51.0 ± 11.2 at baseline, 49.0 ± 11.6 at 1 year follow-up, 49.0 ± 11.7 at 2 year follow-up, 48.8 ± 11.9 at 3 year follow-up, 48.5 ± 11.3 at 4 year follow-up and 48.8 ± 12.0 at 5 year follow-up. However, at 5 year follow-up 7.8% (n = 20) of patients were classified as moderately depressed (60 ≤ T < 70), and 10.2% (n = 26) as severely depressed (T ≥ 70). We applied GMM to better understand the individual trajectories of depressive symptoms.

GMM statistics allowed for three possible trajectory models, but the two-class and five-class models were rejected because of higher clinical relevance of the three-class model (see Figure 2). See ESM for each class’s individual trajectories (ESM Figures 1–3).

Fig. 2
figure 2

GMM of trajectories of depression based on the depression subscale of the SCL-90-R. Solid line and square, class 1 (noD); dashed line and triangle, class 2 (improveD); dotted line and circle, class 3 (worseD)

Full size image

Class 1 comprises the majority of patients (n = 250, 79.9%), and incorporates those with no depressive symptoms at any time. Classes 2 and 3 identified those patients with (highly) relevant depressive symptoms. Class 3 (n = 43, 13.7%) comprises those with worsening symptoms over a 5 year period (mean ± SD: baseline, 61.4 ± 13.4, n = 43; 5 year follow-up, 73.8 ± 6.0, n = 36). The smallest class, class 2 (n = 20, 6.4%), incorporates those patients whose baseline depression score was close to the cut-off score, worsened over a period of 2 years and later improved (5 year follow-up, 53.3 ± 7.8, n = 18).

In short, class 1 comprises those patients with ‘no depressive symptoms’ (noD), class 2 those with ‘worsening depressive symptoms that improve after 2 years’ (improveD), and class 3 those with ‘worsening depressive symptoms’ (worseD).

We next investigated the probability of each individual belonging to a specific class. The average latent class probabilities for the most likely latent class membership were very high: worseD, 91%; improveD, 89%; and noD, 96%. However, estimated class membership was very imprecise for two patients (e.g. probability of being in worseD, 45%; probability of being in improveD, 21%; and probability of being in noD, 34% for one patient) and questionable for another four (e.g. probability of being in noD, 46%; probability of being in improveD, 51%; probability of being in noD, <1% for one patient). We removed these six patients from further analyses because their class membership was considered uncertain (new N = 307: worseD, n = 42; improveD, n = 18; noD, n = 247).

Predictors at baseline and 1 year follow-up

For explanatory purposes, we applied multinomial logistic regression models (backward elimination) despite the low class frequency of improveD. The reference category was noD. At baseline and 1 year follow-up, only severity of depression significantly predicted class membership of either improveD or worseD vs noD (see Tables 3 and 4).

Table 3 Covariates assessed at baseline for predicting depression class membership at 5 year follow-up: multinomial logistic regression models
Full size table
Table 4 Covariates assessed at 1 year follow-up for predicting depression class membership at 5 year follow-up: multinomial logistic regression models
Full size table

In total, 34 patients received psychotherapy. Receiving psychotherapy significantly predicted class membership of worseD and noD, favouring worseD (worseD, 35.7%; noD, 4.5%; b = 2.6, Wald χ2 (1) = 32.0, p < 0.001). Although improveD was not significantly predicted, it is notable that 44.4% (n = 8) of this class received psychotherapy.

Class membership for worseD and improveD was not predicted by any characteristics measured at baseline or 1 year after diagnosis of type 1 diabetes, nor by undergoing psychotherapy within 5 years.

Outcomes at 5 year follow-up

Although we observed no difference between depression classes for glycaemic control at 1 year follow-up (HbA1c level [mean ± SD]: worseD, 6.5 ± 1.1%, 48 mmol/mol; improveD, 6.4 ± 1.1%, 46 mmol/mol; noD, 6.5 ± 1.3%, 46 mmol/mol; F (2, 260) = 0.23, p = 0.793, adjusted for sex and age), we did detect significant differences at the 5 year follow-up. Here, worseD had the higher HbA1c values (see also Table 5). For DQoL, we found significant class differences for strain, strain due to blood glucose and satisfaction (see also Table 5). For DQoL, patients classified as worseD class experienced a significantly higher strain, as well as lower satisfaction compared with those classified as improveD and noD. As with the higher strain due to blood glucose, differences were only significant between patients classified as worseD and as noD. Here, patients classified as worseD demonstrated higher strain due to blood glucose than those classified as noD.

Table 5 Outcomes at 5 year follow-up: differences between three classes
Full size table

Although neither diabetes-related distress nor adequacy of perceived familial support predicted depression class membership, we identified significant differences in these scores between classes at 5 years after the first diagnosis of type 1 diabetes (see also Table 5). Patients classified as worseD exhibited more diabetes-related distress compared with those classified as improveD or noD, and experienced less adequate familial support than those classified as noD (see also Table 5).

Only about 6% of patients suffered any long-term complications during the 5 year follow-up period. There were no significant between-class differences in the number of hypoglycaemic events experienced within the last 5 years or the type of treatment (conventional, intensive, insulin pump; see also Table 5).

Discussion

We present long-term (5 year) data on the course of depressive symptoms in adults with newly diagnosed type 1 diabetes. Taken together, this population proved to be relatively healthy with average depression scores that did not become clinically significant at any time point. Also, although the diabetes burden was high in some individuals, the overall burden was considerably low. This is surprising, as one would expect that a disease with lifelong implications in terms of medication and lifestyle, and with a potential risk of secondary complications, would have a stronger impact.

However, 5.8% of patients were initially diagnosed with major depression; this percentage is more than twice as high as the percentage in a comparison group of a national representative sample without diabetes (2.7%) [5].

In addition to their increased prevalence of major depression, a small sample of our diabetes cohort became continuously more depressed over time (about 14%). These patients exhibited worse glycaemic control and less satisfaction with DQoL, received less perceived familial support, and exhibited more diabetes-related distress, strain on DQoL and strain due to blood glucose levels on DQoL. In all, 18.0% (46 out of 255) of patients exhibited depressive symptoms 5 years after diagnosis; 26 of those (10.2%) had severe depressive symptoms. While depressive symptoms themselves require therapy, they also contribute negatively to diabetes-related medical, social and psychological outcomes, and therefore need to be addressed.

However, when is the appropriate time to screen for depression and start treatment? Our analyses of depression trajectories revealed that the majority of non-depressed patients at the time of diagnosis did not become depressed. The strongest predictors for distinguishing patients with worsening symptoms or those with initially worsening and steadily improving symptoms of depression from those who did not become depressed was an increase in the severity of depression (as measured by a subscale of the SCL-90-R). Clinicians may benefit from this knowledge. Depression could and probably should be targeted as soon as an initial diagnosis of type 1 diabetes is made, especially as depression treatments such as cognitive behavioural therapy (CBT) or pharmacotherapy (sertraline) are known to be effective in patients with type 1 diabetes [31]. There is evidence for positive effects of psychotherapy including mindfulness-based cognitive therapy [32] and CBT [32–34] (the latter often combined with a diabetes self-management intervention [34]), of pharmacotherapy [32, 33], and of collaborative care approaches [34] on depressive symptoms in mixed samples comprising patients with type 1 and type 2 diabetes. Time- and cost-effective screening instruments such as the Patient Health Questionnaire (PHQ)-2 [35] could be routinely applied by physicians at the start of diabetes treatment, and patients could be encouraged to seek a proper diagnosis and treatment by psychologists or psychiatrists when required. However, there is limited potential for detecting depression early via positive screening results, especially when they are not followed by clear treatment pathways (also compare [31]).

In all, 11% of our total sample and over a third of patients with worsening depressive symptoms received psychotherapy during the 5 year follow-up period, suggesting a need to consider depressive symptoms (at least partially) in patients with type 1 diabetes. However, although psychotherapy can alleviate depressive symptoms, patients in the worseD trajectory tended to express worsening depressive symptoms. This raises the question of whether patients with type 1 diabetes have special requirements that need to be addressed for treating their depression appropriately, and indeed whether diabetes-specific treatment approaches might be more beneficial. However, this proposal should be interpreted cautiously because our sample sizes are small.

Irrespective of depression, glycaemic control should be closely monitored because patients exhibited much better glycaemic control at 1 year after initial diagnosis than at 5 years. Glycaemic control was worst in patients with worsening depressive symptoms. Hence, routine check-ups should always monitor glycaemic control, especially if the patient presents symptoms of depression.

This study had several limitations. Patients were recruited consecutively from January 1996 to May 1999, thus our data is not recent. As a history of depression was not assessed at baseline, there is no way of telling whether a patient’s depression preceded or co-occurred with the diagnosis of type 1 diabetes. Also, we did not investigate possible interaction effects between predictors. While acceptable for GMM, our study sample was still quite small for statistical analysis. This became more obvious when looking at the class frequencies of trajectories: WorseD consisted of 43 and improveD of only 20 patients. This is an important consideration in general, but even more so when interpreting multinomial regression models. We tolerated our model’s lack of statistical robustness with the aim of identify tendencies that might guide further research. Further, two of our assessment instruments (LQD, FBD) remain to be validated. This should also be considered when interpreting our results.

Conclusion

The prevalence of major depression in patients newly diagnosed with type 1 diabetes is more than twice as high as that in reference groups. We were able to model three trajectories of depressive symptoms in which a majority of patients remaining undepressed (class 1) and the others had either worsening depressive symptoms over a 2 year period, improving steadily thereafter (class 2), or became more depressed over time (class 3). Severity of depression symptoms at baseline (subscale of the SCL-90-R questionnaire) significantly predicted whether patients remained undepressed during the 5 years of follow-up or if they either worsened continuously during that time or got worse at first and improved after two 2 years. We found significant differences in outcome between trajectories at the 5 year follow-up: those patients with worsening depressive symptoms exhibited poorer scores for glycaemic control, DQoL and adequacy of perceived familial support, as well as more diabetes-related distress. This study has shown that longitudinal data can enhance our understanding of the underlying mechanisms of depression development in patients with type 1 diabetes.