Next Article in Journal
Selective Delivery of Tofacitinib Citrate to Hair Follicles Using Lipid-Coated Calcium Carbonate Nanocarrier Controls Chemotherapy-Induced Alopecia Areata
Next Article in Special Issue
Depression among Patients with an Implanted Left Ventricular Assist Device: Uncovering Pathophysiological Mechanisms and Implications for Patient Care
Previous Article in Journal
Sex-Dependent Impairment of Endothelium-Dependent Relaxation in Aorta of Mice with Overexpression of Hyaluronan in Tunica Media
Previous Article in Special Issue
Probiotics as a Tool for Regulating Molecular Mechanisms in Depression: A Systematic Review and Meta-Analysis of Randomized Clinical Trials
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 9
10.3390/ijms24098437
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Relationship between Insomnia and the Pathophysiology of Major Depressive Disorder: An Evaluation of a Broad Selection of Serum and Urine Biomarkers

by
Tina Drinčić
1,
Jens H. van Dalfsen
1,*,
Jeanine Kamphuis
1,
Mike C. Jentsch
1,
Sjoerd M. van Belkum
1,
Marcus J. M. Meddens
2,
Brenda W. J. H. Penninx
3 and
Robert A. Schoevers
1
1
Department of Psychiatry, University Medical Centre Groningen, Hanzeplein 1, 9713 RB Groningen, The Netherlands
2
Independent Researcher, 7251 RT Vorden, The Netherlands
3
Department of Psychiatry, Amsterdam University Medical Centre (VUmc), De Boelelaan 1118, 1081 HZ Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(9), 8437; https://doi.org/10.3390/ijms24098437
Submission received: 28 February 2023 / Revised: 20 April 2023 / Accepted: 28 April 2023 / Published: 8 May 2023
(This article belongs to the Special Issue Depression: Molecular Pathology and Modern Therapy)
Versions Notes

Abstract

:
Insomnia exhibits a clinically relevant relationship with major depressive disorder (MDD). Increasing evidence suggests that insomnia is associated with neurobiological alterations that resemble the pathophysiology of MDD. However, research in a clinical population is limited. The present study, therefore, aimed to investigate the relationship between insomnia and the main pathophysiological mechanisms of MDD in a clinical sample of individuals with MDD. Data were extracted from three cohorts (N = 227) and included an evaluation of depression severity (Quick Inventory of Depressive Symptomatology, QIDS-SR16) and insomnia severity (QIDS-SR16 insomnia items) as well as serum and urine assessments of 24 immunologic (e.g., tumour necrosis factor α receptor 2 and calprotectin), neurotrophic (e.g., brain-derived neurotrophic factor and epidermal growth factor), neuroendocrine (e.g., cortisol and aldosterone), neuropeptide (i.e., substance P), and metabolic (e.g., leptin and acetyl-L-carnitine) biomarkers. Linear regression analyses evaluating the association between insomnia severity and biomarker levels were conducted with and without controlling for depression severity (M = 17.32), antidepressant use (18.9%), gender (59.0% female; 40.5% male), age (M = 42.04), and the cohort of origin. The results demonstrated no significant associations between insomnia severity and biomarker levels. In conclusion, for the included biomarkers, current findings reveal no contribution of insomnia to the clinical pathophysiology of MDD.

1. Introduction

Insomnia and major depressive disorder (MDD) are closely related. Insomnia is one of the primary symptoms of MDD with around 40–75% of the MDD population meeting the diagnostic criteria for clinical insomnia [1,2,3]. In addition to being a core symptom, insomnia has been widely recognised as an independent contributor to the aetiology of MDD. Recent meta-analyses have revealed that individuals with insomnia are two to three times as likely to develop MDD relative to people without sleep disturbances [4,5]. Clinical evidence further demonstrates that insomnia negatively impacts the clinical course of MDD, including a prolonged duration and greater severity of the depressive episode and an increased risk of relapse following successful treatment [6,7]. In line with these findings, systematic reviews conclude that the treatment of comorbid insomnia may improve treatment outcomes in MDD [8,9]. While the clinical relevance of insomnia has been well established, it remains unclear how insomnia relates to the underlying neurobiology of MDD.
The pathophysiology of MDD involves a diverse range of neurobiological mechanisms. This includes alterations in immunologic [10,11], neurotrophic [12,13], neuroendocrine [14], neuropeptide [15], and metabolic [16] functioning. Although these pathophysiological hypotheses have found support in studies comparing individuals with MDD to healthy controls, this field of research has yielded yet inconclusive results [17,18,19]. While these inconsistent findings may relate to methodological aspects in biomarker assessment [20], they also presumably reflect the marked heterogeneity in MDD. The diagnostic classification of MDD, according to the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM−5) [21], is based on a diverse range of symptoms. According to this classification, different combinations of sometimes even opposing symptoms may result in an MDD diagnosis [22]. Hence, MDD is a heterogenous disorder in both clinical presentation [22] and underlying pathophysiology [23]. Evaluating the association between symptoms with known clinical relevance, such as insomnia, and presumed pathophysiological mechanisms could therefore be crucial to advance the neurobiological understanding of MDD [24].
Previous research provides valuable support for an association between insomnia and neurobiological alterations that resemble the pathophysiology of MDD. This includes increased inflammation [25], decreased brain-derived neurotrophic factor (BDNF) [26], increased hypothalamic-pituitary-adrenal (HPA) axis (re)activity [27,28] as well as changes in neuropeptides [29] and metabolic functioning [30]. While these findings elucidate an association between insomnia and the pathophysiological mechanisms of MDD, research in individuals with MDD is limited. The initial findings on the level of inflammation have supported an association between insomnia and the underlying neurobiological processes of MDD in a clinical population [31,32,33,34,35]. It remains unclear, however, how insomnia relates to the broad spectrum of molecular mechanisms implicated in the clinical pathophysiology of MDD.
The present study, therefore, aimed to explore the relationship between insomnia and a diverse selection of immunologic (e.g., tumour necrosis factor-α receptor 2 and calprotectin), neurotrophic (e.g., brain-derived neurotrophic factor and epidermal growth factor), neuroendocrine (e.g., cortisol and aldosterone), neuropeptide (i.e., substance P), and metabolic (e.g., leptin and acetyl-L-carnitine) biomarkers in a clinical sample of individuals with MDD. Utilizing data from three cohorts in which blood and urine samples have been collected and analysed using the same assays, it was investigated whether the severity of insomnia is associated with biomarker levels. It was further examined whether depression severity, antidepressant use, gender, age, and the cohort of origin confounded this relationship. It is hypothesised that increasing insomnia severity exacerbates the neurobiological abnormalities associated with MDD, reflecting the potential involvement of insomnia in the clinical pathophysiology of MDD.

2. Results

2.1. Data

From the different research cohorts (see: Section 4.1), the total sample consisted of 227 individuals with MDD with available data on both insomnia severity and at least 1 biomarker level, making them eligible for inclusion in the univariate analyses. In this sample, the data on all relevant covariates were available for 189 individuals who were therefore included in the multivariate analyses. Notably, the number of participants included in the analyses varied depending on the availability of individual biomarker levels. The number of outliers for a given biomarker level ranged from 0 to 5, with no outliers detected for 13 biomarker levels. A total of 34 data points were identified as outliers and therefore excluded from all analyses. The actual number of participants included in each regression model is reported in the tables.

2.2. Demographic and Clinical Characteristics

The demographic and clinical characteristics are presented in Table 1. The total sample of individuals with MDD (N = 227) included 134 women (59.0%) and 92 men (40.5%). The age of participants ranged from 19 to 76, with a mean of 42.04 years (SD = 12.63). Current antidepressant use was present in 43 participants (18.9%). The total self-rated Quick Inventory of Depressive Symptomatology (QIDS-SR16) score ranged from 4 to 27. The mean QIDS-SR16 score was 17.32 (SD = 4.91), indicating that, on average, participants were experiencing severe depression. The mean QIDS-SR16 score with the sleep items excluded was 14.86 (SD = 4.57). The mean insomnia severity sub-score was 4.69 (SD = 2.47), ranging from 0 to 9. The duration of the current MDD episode was available for 177 participants, with 42.3% of the total sample experiencing an episode for less than a month, 8.8% for between one and six months, 4.8% for between six months and a year, and 21.6% for longer than a year. The total number of episodes ranged from 1 to 96, with a median of 2 and an interquartile range (IQR) from 1 to 5.

2.3. The Influence of Insomnia on Biomarker Levels

2.3.1. Univariate Analyses

The results of the univariate linear regression analyses are reported in Table 2 (serum biomarker levels) and Table 3 (urine biomarker levels). No significant associations between insomnia severity and the biomarker levels were found when controlling for multiple testing. Notably, prior to adjusting for multiple testing, a significant negative association between insomnia severity and serum resistin levels (b = −0.030; 95% confidence interval (CI) [−0.051, −0.009]; β = −0.186; unadjusted p = 0.006; adjusted p = 0.151; R2 = 0.035) and a significant positive association been insomnia severity and serum acetyl-L-carnitine levels (b = 0.039; 95% CI [.010, 0.068]; β = 0.178; unadjusted p = 0.009; adjusted p = 0.151; R2 = 0.032) were observed.

2.3.2. Multivariate Analyses

The results of the multivariate linear regression analyses are reported in Table 4 (serum biomarker levels) and Table 5 (urine biomarker levels). The regression statistics of each covariate are further provided in Supplementary Table S1 (serum biomarker levels) and Supplementary Table S2 (urine biomarker levels). Controlling for depression severity, antidepressant use, gender, age, and the cohort of origin, no significant associations between insomnia severity and the biomarker levels were observed when adjusting for multiple testing. Notably, prior to adjusting for multiple testing, a significant negative association between insomnia severity and serum endothelin-1 levels (b = −0.019; 95% CI [−0.037, −0.002]; β = −0.183; unadjusted p = 0.033; adjusted p = 0.528; R2 = 0.119) was found, and the significant positive association with serum acetyl-L-carnitine levels observed in the univariate analyses remained significant after controlling for relevant covariates (b = 0.036; 95% CI [.000, 0.073]; β = 0.167; unadjusted p = 0.049; adjusted p = 0.528; R2 = 0.134).

3. Discussion

The aim of the present study was to investigate the relationship between insomnia severity and the immunologic, neurotrophic, neuroendocrine, neuropeptide, and metabolic processes underlying MDD in a clinical sample of individuals with MDD. In contrast to the hypothesis, the results demonstrated no significant associations between insomnia and the biomarker levels for insomnia severity as a single predictor (univariate models) nor for insomnia severity corrected for depression severity, antidepressant use, gender, age, and the cohort of origin (multivariate models). Taken together, for the included biomarkers current findings do not support a profound influence of insomnia on the clinical pathophysiology of MDD.
The relationship between sleep disturbances and the proinflammatory markers represents the most widely explored relationship in a clinical sample of individuals with MDD relative to other pathophysiological mechanisms. The absence of a significant association between insomnia and immunologic biomarkers observed in the present study adds to the inconsistent findings of previous research. Multiple studies in individuals with MDD have demonstrated that sleep disturbances were associated with higher levels of circulating C-reactive protein (CRP) [31,32,33], serum tumour necrosis factor (TNF) α [34], interleukin(IL)−6, interferon(IFN)-α2, and IFN-γ [35]. However, this has not been consistently observed in a clinical sample for CRP, TNFα, and IL−6 [36]. Research on CRP suggests that several factors might confound this relationship as the effect of sleep disturbances was not maintained after controlling for relevant covariates in some [32], but not all [31], studies in individuals with MDD. Importantly, the research in non-depressed individuals found more conclusive relationships between insomnia and proinflammatory markers. A recent meta-analysis demonstrated that sleep disturbances are associated with higher levels of CRP and IL−6 [37], which also represent the most consistently identified immunologic abnormalities in MDD [18,20,38]. However, there is little evidence that inflammatory cytokines serve as valuable diagnostic biomarkers of insomnia [39]. Taken together, while studies in healthy populations suggest that sleep disturbances may exacerbate the immunologic abnormalities associated with MDD, the findings in clinical samples are inconsistent. Nonetheless, since previous studies reporting a significant association between insomnia and inflammation included biomarkers that were not included in the present study, it cannot be excluded that an association between insomnia and these biomarkers may exist in a clinical population.
Similar to the findings on immunologic biomarkers, the present study did not reveal an effect of insomnia on the levels of neurotrophic factors and neuropeptides. Previous research concerning the relationship between sleep disturbances and neurotrophic factors in clinical samples is limited and inconclusive. One study found that poorer sleep quality was associated with increased serum BDNF levels in MDD [40], whereas another relatively small intervention study observed that improvements in sleep quality were positively correlated with BDNF concentrations [41]. In contrast to the inconsistent findings in clinical populations, a meta-analysis of studies in healthy participants demonstrated that sleep disturbances were associated with lower levels of BDNF [26]. In line with this observation, there is evidence that BDNF may be a useful predictor of insomnia [39]. Although the direction of this association resembles the BDNF alterations observed in MDD [42], the present study could not verify a potential contribution of insomnia to neurotrophic abnormalities in a clinical sample. Concerning neuropeptides, the lack of a significant association between insomnia and substance P accords with a previous study on 186 individuals with MDD, which revealed that sleep disturbances were not correlated with the plasma levels of substance P and four other neuropeptides [43].
The relationship between insomnia and neuroendocrine biomarkers in an MDD population has not been studied extensively in previous research. The absence of a relationship observed in the present study is, however, in contrast to a recent finding of decreased levels of salivary cortisol in individuals with MDD and poor sleep quality [40]. The effect of insomnia on neuroendocrine function has been more elaborately investigated in non-depressed populations. Although serum cortisol levels seem to have a low diagnostic accuracy for insomnia [39], various meta-analyses and systematic reviews have identified that disturbed sleep is associated with elevated basal cortisol levels (e.g., [44]) as well as an increased stress reactivity of the HPA-axis (e.g., [27]). While these findings bare resemblance to neuroendocrine changes found in individuals with MDD relative to healthy controls [45,46], it is important to note that long-lasting MDD has also been associated with lower cortisol levels [47,48,49]. This may be due to adrenal exhaustion or the decreased feedback sensitivity of the HPA-axis due to chronic stress [50]. For this reason, the role of insomnia in increasing HPA-axis activity may not be observable in the more advanced stages of MDD. This could explain the absence of a significant association, as observed in the present study.
The present study did not observe an effect of insomnia severity on the levels of metabolic biomarkers. This relationship has not been previously examined in a clinical population of individuals with MDD, and research in healthy participants yielded inconclusive findings. While it has been shown that sleep restriction is related to a reduction in leptin levels [30], an association between insomnia and increased leptin has also been reported [51]. Since both leptin [16] and acetyl-L-carnitine [52] appear to be decreased in MDD, a negative association with insomnia would support a contribution of insomnia to metabolic changes in MDD. However, the present study does not confirm such a relationship in MDD.
To summarize, the current findings do not support a profound influence of insomnia on the pathophysiology of MDD in a clinical MDD population. This contrasts with previous findings in non-clinical populations, revealing that sleep disturbances are associated with a range of immunologic [18,20,37,38,42], neurotrophic [26,42], and neuroendocrine [27,44,45,46] alterations that resemble the pathophysiology of MDD. While more research is needed, these conflicting findings might suggest that the relationship between insomnia and pathophysiological mechanisms may be especially relevant for the aetiology of MDD. This postulation is supported by studies evaluating the mediating role of these pathophysiological processes in the relationship between sleep disturbance and the development of depressive symptoms [53,54]. However, based on the current findings it is unlikely that the influence of insomnia on the clinical course of MDD [6,7] relates to alterations in immunologic, neurotrophic, and neuroendocrine functioning.

Limitations and Future Directions

The main strength of the present study is the inclusion of a broad panel of high-quality biomarker assessments that cover all the neurobiological hypotheses of MDD. The present study is, however, subjected to the following limitations. First, the relatively small sample size may have limited the ability to observe significant differences. Hence, the high number of included biomarkers requires a relatively large sample to have sufficient statistical power to detect significant associations after adjusting for multiple testing. Nonetheless, unadjusted analyses also did not reveal significant associations in the expected direction, indicating that insomnia is unlikely to exacerbate the pathophysiology of MDD in a clinical sample. Second, the laboratory assessments did not include all biomarkers implicated in the pathophysiology of MDD. For future studies, it is therefore recommended to evaluate a narrow selection of biomarkers including CRP [18,37,38], IL−6 [18,20,37,38], BDNF [26,42], and more detailed measures of HPA-axis (re)activity [27,44,45,46]. Third, while the insomnia items of the QIDS-SR16 [36,55,56,57] and other depression scales [58,59,60] are commonly used to assess insomnia severity, this approach allows for relatively little variation compared to validated insomnia questionnaires. Future studies should therefore incorporate questionnaires that are specifically developed for the assessment of insomnia severity. Fourth, the time of biometric sample collection was only standardised in a part of the total sample. Hence, the accuracy of the reported associations may have been affected by diurnal fluctuations of biomarker levels [61]. Fifth, while the effect of insomnia was adjusted for important covariates in the multivariate analyses, information on other potentially relevant confounders known to affect biomarker levels, such as BMI [36,62] and the presence of obstructive sleep apnoea [63,64,65], was not available, and it is advised to control for these factors in future studies. Given the profound differences in studies evaluating the association between insomnia and pathophysiological mechanisms in healthy and clinical populations, further research should evaluate the presumed relevance of this relationship for the involvement of insomnia in the aetiology of MDD.

4. Materials and Methods

4.1. Data Source

The data used in the present study comprise previously collected data retrieved from three different studies: Pieken in de Delta Oost Nederland (PIDON) [66], transcranial pulsed electromagnetic fields (tPEMF) [67], and the Mood Treatment with Antidepressants or Running (MOTAR) study [68,69]. The baseline measurement included in the original investigations was utilized for the performed analyses (see Section 4.4). This includes an assessment of clinical depression (see Section 4.2) and depression symptoms (see Section 4.3.1). Biomarker levels were determined in the blood and urine samples collected at baseline as part of the initial study procedures of the included studies. Notably, biomarker assessment has been previously performed using the same assay in all research cohorts (see Section 4.3.3).
Data of the PIDON cohort (n = 38) were collected in a study aimed at the development of an algorithm to calculate a depression probability score differentiating individuals with MDD from healthy controls based on biomarker measurements [66]. Participants were recruited in the Netherlands via general practitioners, psychiatric care organisations, and newspaper advertisements. The study aimed to provide evidence for an association between a cluster of biomarkers and an MDD diagnosis. Blood and urine sampling was performed on the same day as the clinical interviews. Blood samples were collected between 8:00 AM and 6:00 PM. Urine samples were collected in the morning from the first urination. The tPEMF cohort (n = 56) comprised a sample of individuals with MDD included in a double-blind, randomised control trial comparing transcranial pulsed electromagnetic fields (tPEMF) to sham treatment [67]. Patients were recruited from major mental health care institutions in the northern part of the Netherlands and through media coverage. The primary aim was to investigate the antidepressant effect of tPEMF in treatment-resistant depression. Clinical interviews and biological sampling were performed on the same day in the morning or early afternoon. The MOTAR study comprises a 16-week intervention study comparing antidepressant medication and running therapy [68,69]. Individuals with MDD (n = 133) were recruited from a mental health organisation in the surroundings of Amsterdam. The main objective of the study was to investigate the effect of antidepressant medication and running therapy on depressive symptoms and biological ageing. Fasting blood and urine samples were collected between 8:30 and 9:30 AM during baseline assessment, which included a clinical interview and the completion of questionnaires.

4.2. Study Population

The study population (N = 227) comprises individuals with current MDD included in the research cohorts aged between 18 and 80 years. MDD diagnoses according to DSM-IV criteria were obtained using either the Mini International Neuropsychiatric Interview (MINI) [70] in the tPEMF [67] and PIDON [66] cohorts or the Composite International Diagnostic Interview (CIDI) [71] in the MOTAR cohort [68,69].
Shared inclusion criteria of the different cohorts include a current MDD diagnosis ascertained by the MINI or the CIDI and age 18–80. Importantly, the tPEMF cohort only included individuals with at least moderately severe depression who were non-responsive to one or more antidepressants provided for at least four weeks in an adequate dose. Shared exclusion criteria comprised pregnancy and the presence of another primary psychiatric disorder. Additionally, the tPEMF and MOTAR cohorts excluded individuals with evidence of (high) suicidal risk as well as individuals who recently or currently used other psychotropic medication. In the tPEMF cohort, individuals with a change in antidepressant medication in the four weeks prior to the study were additionally excluded. In the MOTAR cohort, individuals who used antidepressants in the previous two weeks, individuals who exercised regularly more than once a week, and individuals with medical contraindications to running therapy or antidepressants were excluded.
In the present study, participants were excluded from the univariate analysis if data were missing regarding any item on the scales used to measure insomnia symptoms (see Section 4.3.2) or if no data on biomarker levels were available. Patients were additionally excluded from the multivariate analysis if data were missing regarding any of the corresponding covariates.

4.3. Measurements

4.3.1. Depression

To assess the severity of depression, the self-rated Inventory of Depressive Symptomatology (IDS-SR30) [72] was used in the tPEMF [67] and MOTAR [68] cohorts, and the self-rated Quick Inventory of Depressive Symptomatology (QIDS-SR16) [73] was used in the PIDON cohort [67]. The QIDS-SR16 consists of items analogous to IDS-SR30. The total IDS-SR30 score was therefore converted to the total QIDS-SR16 score according to previously developed metrics [73] in the tPEMF and MOTAR cohorts.
The IDS-SR30 is a well-validated questionnaire that measures depression severity [72]. It inquires about all DSM-IV [74] melancholic and atypical criterion symptoms during the previous week and shows good internal consistency and interrater reliability [72]. As changes in appetite and weight are scored separately depending on the direction of change, the total score of the IDS-SR30 is based on 28 of 30 items, resulting in total scores ranging from 0 to 84. Each item measures one symptom construct and is scored as a Likert scale with points ranging from 0 to 3, with higher scores indicating greater depression severity. The QIDS-SR16 is based on a subset of the IDS-SR30, including 16 analogous items relevant to 9 DSM-IV [74] symptom domains [73]. It is well validated, shows high internal consistency, and its total score has been shown to highly correlate with the IDS-SR30 total score [73]. The total score is calculated by adding points of the maximally scored item in each symptom domain, yielding total scores ranging from 0 to 27. Higher scores are indicative of greater depression severity.
Items 1–4 of both the IDS-SR30 and the QIDS-SR16 relate to the sleep disturbance symptom domain. Therefore, in the performed analyses, the score on the respective sleep items was subtracted from the total score to derive depression severity irrespective of sleep disturbances.

4.3.2. Insomnia

Items 1–3 of the IDS-SR30 and the QIDS-SR16 assess the main night-time symptoms of insomnia pertaining to difficulty falling asleep, staying asleep, and early awakening. In line with previous research [36,55,56], the cumulative score of the three items in either scale was used as a continuous indicator of insomnia severity. This approach shows substantial concordance with assessment of insomnia severity using prospective sleep diary data [57].

4.3.3. Laboratory Assessments

Serum and urine levels of a broad panel of biomarkers have been previously assessed using Enzyme-Linked Immuno-Sorbent Assays (ELISA) kits. The selection of biomarker assessments was determined in two previous studies that aimed to identify general [66] and gender-specific [75] associations of biomarker levels with MDD. The panel included 24 biomarkers pertaining to immunologic, neurotrophic, neuropeptide, neuroendocrine, and metabolic functioning. ELISA kits were acquired from various vendors, and their reported intra- and inter-assay coefficient of variation (CV) is provided in Supplementary Table S3.
For each ELISA assay, specific standard operating procedures (SOPs) were set up, which included all experimental variables to assure full experimental traceability. Each SOP was based on the instructions guide of the manufacture and contained minor tweaks with respect to extra dilution steps, the addition of calibrators, and adjustments of pipetting volumes to control for pipetting errors during dilution steps. An ELISA plate washer (Biorad PW40) was used for all washing steps. TMB absorption measurements were performed on a Microtiter plate reader (thermos Multiskan Spectrum) at 450 nm using 620 nm as a reference wavelength. Biomarker concentrations were determined by use of a 4-parameter logistic regression (4-PL) model without weight factors. To control for potential variability in the ELISA kits, all parameter samples were randomised and divided over the various ELISA plates, and each biomarker was analysed within one batch. Each ELISA plate contained Quality Control (QC) samples with known concentrations within the low and high range. These QC samples were prepared by analysing previously unused sampled followed by selecting from two to four samples within the desired range, which were pooled and aliquoted. Calibrators and QC samples were measured in duplicate, whereas samples were measured in singlicate. For both calibrators and QC samples, a CV of 15% was used as run acceptance criteria.

4.4. Statistical Analyses

Statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) 28 [76] and R [77]. Demographic and clinical characteristics were evaluated using descriptive statistics. A linear regression analysis was performed for each biomarker to examine the association between insomnia severity (independent variable) and biomarker level (dependent variable). To evaluate the relationship between insomnia severity and biomarker levels when controlling for potential confounders, a multivariate regression analysis was performed for each biomarker with depression severity, medication use, gender, age, and cohort of origin as additional predictors of biomarker levels. The R package ‘stats’ [78] was used to adjust p-values in the univariate and multivariate regression models using the Benjamini–Hochberg method [79,80]. Both unadjusted and adjusted p-values are reported.
Dependent variables were assessed for normality prior to analyses and ln-transformation was applied to non-normal distributions. Outliers were detected according to the outlier labelling rule [81,82] and were treated as missing data. Regression diagnostics were applied to confirm that the assumptions of linear regression were not violated. Robust confidence intervals were computed for variables demonstrating heteroscedasticity in either the univariate of multivariate model [83].
Results are reported in numbers (n) with percentages (%), means (M) with standard deviation (SD), medians (Mdn) with interquartile range (IQR), unstandardized beta values (b) with 95% confidence intervals (95% CI), standardized beta values (β), and R2-statistic (R2). For all analyses, a two-tailed 5% significance level was adopted.

5. Conclusions

In conclusion, for the included biomarkers current findings do not support an influence of insomnia on the clinical pathophysiology of MDD. This is in contrast with previous findings in non-clinical populations. While more research is necessary, these divergent findings might suggest that the relationship between insomnia and pathophysiological mechanisms may pertain more to the aetiology rather than the clinical course of MDD.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24098437/s1.

Author Contributions

Conceptualization, T.D., J.H.v.D. and R.A.S.; methodology, T.D., J.H.v.D., J.K., B.W.J.H.P. and R.A.S.; software, T.D. and J.H.v.D.; validation, M.C.J.; formal analysis, T.D. and J.H.v.D.; investigation, T.D., J.H.v.D., M.C.J., S.M.v.B., M.J.M.M., B.W.J.H.P. and R.A.S.; resources, M.C.J., S.M.v.B., M.J.M.M., B.W.J.H.P. and R.A.S.; data curation, J.H.v.D., M.C.J., S.M.v.B., M.J.M.M., B.W.J.H.P. and R.A.S.; writing—original draft preparation, T.D. and J.H.v.D.; writing—review and editing, J.K., M.C.J., S.M.v.B., M.J.M.M., B.W.J.H.P. and R.A.S.; visualization, T.D. and J.H.v.D.; supervision, J.H.v.D., J.K. and R.A.S.; project administration, T.D., J.H.v.D. and R.A.S.; funding acquisition, N/A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The PIDON, tPEMF, and MOTAR studies were conducted according to the guidelines of the Declaration of Helsinki. The PIDON study was approved by the Medical Ethical Committee of the Isala Clinics, Zwolle, the Netherlands (METC Isala registration number 11.0563, 11 November 2011). The tPEMF study was approved by the Medical Ethical Committee of the UMCG, Groningen, the Netherlands (UMCG METC registration number 2012.039, 16 April 2012). The MOTAR study was approved by the Medical Ethics Committee of VU Medical Centre, Amsterdam, the Netherlands (VUmc METC registration number 2012–064, 23 May 2012).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Acknowledgments

The authors acknowledge all individuals that contributed to the data collection in the included cohorts.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Stewart, R.; Besset, A.; Bebbington, P.; Brugha, T.; Lindesay, J.; Jenkins, R.; Singleton, N.; Meltzer, H. Insomnia comorbidity and impact and hypnotic use by age group in a national survey population aged 16 to 74 years. Sleep 2006, 29, 1391–1397. [Google Scholar] [CrossRef] [PubMed]
  2. Troxel, W.M.; Kupfer, D.J.; Reynolds, C.F., III; Frank, E.; Thase, M.E.; Miewald, J.M.; Buysse, D.J. Insomnia and objectively measured sleep disturbances predict treatment outcome in depressed patients treated with psychotherapy or psychotherapy-pharmacotherapy combinations. J. Clin. Psychiatry 2012, 73, 478–485. [Google Scholar] [CrossRef] [PubMed]
  3. Geoffroy, P.A.; Hoertel, N.; Etain, B.; Bellivier, F.; Delorme, R.; Limosin, F.; Peyre, H. Insomnia and hypersomnia in major depressive episode: Prevalence, sociodemographic characteristics and psychiatric comorbidity in a population-based study. J. Affect. Disord. 2018, 226, 132–141. [Google Scholar] [CrossRef] [PubMed]
  4. Baglioni, C.; Battagliese, G.; Feige, B.; Spiegelhalder, K.; Nissen, C.; Voderholzer, U.; Lombardo, C.; Riemann, D. Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies. J. Affect. Disord. 2011, 135, 10–19. [Google Scholar] [CrossRef] [PubMed]
  5. Hertenstein, E.; Feige, B.; Gmeiner, T.; Kienzler, C.; Spiegelhalder, K.; Johann, A.; Jansson-Fröjmark, M.; Palagini, L.; Rücker, G.; Riemann, D.; et al. Insomnia as a predictor of mental disorders: A systematic review and meta-analysis. Sleep Med. Rev. 2019, 43, 96–105. [Google Scholar] [CrossRef]
  6. Franzen, P.L.; Buysse, D.J. Sleep disturbances and depression: Risk relationships for subsequent depression and therapeutic implications. Dialogues Clin. Neurosci. 2008, 10, 473–481. [Google Scholar] [CrossRef]
  7. Riemann, D. Does effective management of sleep disorders reduce depressive symptoms and the risk of depression? Drugs 2009, 69 (Suppl. 2), 43–64. [Google Scholar] [CrossRef]
  8. Cunningham, J.E.A.; Shapiro, C.M. Cognitive Behavioural Therapy for Insomnia (CBT-I) to treat depression: A systematic review. J. Psychosom. Res. 2018, 106, 1–12. [Google Scholar] [CrossRef]
  9. Gebara, M.A.; Siripong, N.; DiNapoli, E.A.; Maree, R.D.; Germain, A.; Reynolds, C.F.; Kasckow, J.W.; Weiss, P.M.; Karp, J.F. Effect of insomnia treatments on depression: A systematic review and meta-analysis. Depress. Anxiety 2018, 35, 717–731. [Google Scholar] [CrossRef]
  10. Maes, M. Evidence for an immune response in major depression: A review and hypothesis. Prog. Neuropsychopharmacol. Biol. Psychiatry 1995, 19, 11–38. [Google Scholar] [CrossRef]
  11. Smith, R.S. The macrophage theory of depression. Med. Hypotheses 1991, 35, 298–306. [Google Scholar] [CrossRef] [PubMed]
  12. Duman, R.S.; Heninger, G.R.; Nestler, E.J. A molecular and cellular theory of depression. Arch. Gen. Psychiatry 1997, 54, 597–606. [Google Scholar] [CrossRef] [PubMed]
  13. Duman, R.S.; Monteggia, L.M. A neurotrophic model for stress-related mood disorders. Biol. Psychiatry 2006, 59, 1116–1127. [Google Scholar] [CrossRef] [PubMed]
  14. Pariante, C.M.; Lightman, S.L. The HPA axis in major depression: Classical theories and new developments. Trends Neurosci. 2008, 31, 464–468. [Google Scholar] [CrossRef]
  15. Werner, F.M.; Coveñas, R. Classical neurotransmitters and neuropeptides involved in major depression: A review. Int. J. Neurosci. 2010, 120, 455–470. [Google Scholar] [CrossRef]
  16. Lu, X.Y. The leptin hypothesis of depression: A potential link between mood disorders and obesity? Curr. Opin. Pharm. 2007, 7, 648–652. [Google Scholar] [CrossRef]
  17. Cao, B.; Chen, Y.; Brietzke, E.; Cha, D.; Shaukat, A.; Pan, Z.; Park, C.; Subramaniapillai, M.; Zuckerman, H.; Grant, K.; et al. Leptin and adiponectin levels in major depressive disorder: A systematic review and meta-analysis. J. Affect. Disord. 2018, 238, 101–110. [Google Scholar] [CrossRef]
  18. Haapakoski, R.; Mathieu, J.; Ebmeier, K.P.; Alenius, H.; Kivimäki, M. Cumulative meta-analysis of interleukins 6 and 1β, tumour necrosis factor α and C-reactive protein in patients with major depressive disorder. Brain Behav. Immun. 2015, 49, 206–215. [Google Scholar] [CrossRef]
  19. WHO Team. Depression and Other Common Mental Disorders. Booklet. Geneva: WHO Document Production Services; WHO: Geneva, Switzerland, 2017. [Google Scholar]
  20. Hiles, S.A.; Baker, A.L.; de Malmanche, T.; Attia, J. A meta-analysis of differences in IL-6 and IL-10 between people with and without depression: Exploring the causes of heterogeneity. Brain Behav. Immun. 2012, 26, 1180–1188. [Google Scholar] [CrossRef]
  21. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5-TR, 5th ed.; American Psychiatric Association: Washington, DC, USA, 2022. [Google Scholar]
  22. Fried, E.I.; Nesse, R.M. Depression is not a consistent syndrome: An investigation of unique symptom patterns in the STAR*D study. J. Affect. Disord. 2015, 172, 96–102. [Google Scholar] [CrossRef]
  23. Charney, D.S.; Barlow, D.H.; Botteron, K.; Cohen, J.D.; Goldman, D.; Gur, R.E.; Lin, K.-M.; López, J.F.; Meador-Woodruff, J.H.; Moldin, S.O.; et al. Neuroscience research agenda to guide development of a pathophysiologically based classification system. In A Research Agenda for DSM—V; American Psychiatric Association: Arlington, VA, USA, 2002; pp. 31–83. [Google Scholar]
  24. Hasler, G. Pathophysiology of depression: Do we have any solid evidence of interest to clinicians? World Psychiatry 2010, 9, 155–161. [Google Scholar] [CrossRef] [PubMed]
  25. Irwin, M.R. Sleep and inflammation: Partners in sickness and in health. Nat. Rev. Immunol. 2019, 19, 702–715. [Google Scholar] [CrossRef] [PubMed]
  26. Ballesio, A.; Zagaria, A.; Curti, D.G.; Moran, R.; Goadsby, P.J.; Rosenzweig, I.; Lombardo, C. Peripheral brain-derived neurotrophic factor (BDNF) in insomnia: A systematic review and meta-analysis. Sleep Med. Rev. 2023, 67, 101738. [Google Scholar] [CrossRef] [PubMed]
  27. Van Dalfsen, J.H.; Markus, C.R. The influence of sleep on human hypothalamic-pituitary-adrenal (HPA) axis reactivity: A systematic review. Sleep Med. Rev. 2018, 39, 187–194. [Google Scholar] [CrossRef]
  28. Meerlo, P.; Sgoifo, A.; Suchecki, D. Restricted and disrupted sleep: Effects on autonomic function, neuroendocrine stress systems and stress responsivity. Sleep Med. Rev. 2008, 12, 197–210. [Google Scholar] [CrossRef] [PubMed]
  29. Huang, Q.; Liao, J.; Liu, Y.; Liang, H.; Ma, P.; Pan, J. Plasma neuropeptide Y levels in Chinese patients with primary insomnia. Sleep Breath. 2015, 19, 617–622. [Google Scholar] [CrossRef]
  30. Van Cauter, E.; Spiegel, K.; Tasali, E.; Leproult, R. Metabolic consequences of sleep and sleep loss. Sleep Med. 2008, 9 (Suppl. 1), S23–S28. [Google Scholar] [CrossRef]
  31. Cho, H.J.; Savitz, J.; Dantzer, R.; Teague, T.K.; Drevets, W.C.; Irwin, M.R. Sleep disturbance and kynurenine metabolism in depression. J. Psychosom. Res. 2017, 99, 1–7. [Google Scholar] [CrossRef]
  32. Figueroa-Hall, L.K.; Xu, B.; Kuplicki, R.; Ford, B.N.; Burrows, K.; Teague, T.K.; Sen, S.; Yeh, H.W.; Irwin, M.R.; Savitz, J.; et al. Psychiatric symptoms are not associated with circulating CRP concentrations after controlling for medical, social, and demographic factors. Transl. Psychiatry 2022, 12, 279. [Google Scholar] [CrossRef]
  33. Frank, P.; Jokela, M.; Batty, G.D.; Cadar, D.; Steptoe, A.; Kivimäki, M. Association Between Systemic Inflammation and Individual Symptoms of Depression: A Pooled Analysis of 15 Population-Based Cohort Studies. Am. J. Psychiatry 2021, 178, 1107–1118. [Google Scholar] [CrossRef]
  34. Reddy, A.; Thootkur, M.; Li, L. Association Between Major Depressive Disorder and Sleep Disturbances through Inflammation in Adolescents. Front. Psychiatry 2020, 11, 559272. [Google Scholar] [CrossRef] [PubMed]
  35. Wang, M.; Wei, J.; Yang, X.; Ni, P.; Wang, Y.; Zhao, L.; Deng, W.; Guo, W.; Wang, Q.; Li, T.; et al. The level of IL-6 was associated with sleep disturbances in patients with major depressive disorder. Neuropsychiatr. Dis. Treat. 2019, 15, 1695–1700. [Google Scholar] [CrossRef] [PubMed]
  36. Lamers, F.; Milaneschi, Y.; de Jonge, P.; Giltay, E.J.; Penninx, B. Metabolic and inflammatory markers: Associations with individual depressive symptoms. Psychol. Med. 2018, 48, 1102–1110. [Google Scholar] [CrossRef]
  37. Irwin, M.R.; Olmstead, R.; Carroll, J.E. Sleep Disturbance, Sleep Duration, and Inflammation: A Systematic Review and Meta-Analysis of Cohort Studies and Experimental Sleep Deprivation. Biol. Psychiatry 2016, 80, 40–52. [Google Scholar] [CrossRef] [PubMed]
  38. Colasanto, M.; Madigan, S.; Korczak, D.J. Depression and inflammation among children and adolescents: A meta-analysis. J. Affect. Disord. 2020, 277, 940–948. [Google Scholar] [CrossRef]
  39. Dikeos, D.; Wichniak, A.; Ktonas, P.Y.; Mikoteit, T.; Crönlein, T.; Eckert, A.; Kopřivová, J.; Ntafouli, M.; Spiegelhalder, K.; Hatzinger, M.; et al. The potential of biomarkers for diagnosing insomnia: Consensus statement of the WFSBP Task Force on Sleep Disorders. World J. Biol. Psychiatry 2023, 1–29. [Google Scholar] [CrossRef] [PubMed]
  40. De Menezes Galvão, A.C.; Almeida, R.N.; de Sousa, G.M., Jr.; Leocadio-Miguel, M.A.; Palhano-Fontes, F.; de Araujo, D.B.; Lobão-Soares, B.; Maia-de-Oliveira, J.P.; Nunes, E.A.; Hallak, J.E.C.; et al. Pathophysiology of Major Depression by Clinical Stages. Front. Psychol. 2021, 12, 641779. [Google Scholar] [CrossRef] [PubMed]
  41. Gourgouvelis, J.; Yielder, P.; Clarke, S.T.; Behbahani, H.; Murphy, B.A. Exercise Leads to Better Clinical Outcomes in Those Receiving Medication Plus Cognitive Behavioral Therapy for Major Depressive Disorder. Front. Psychiatry 2018, 9, 37. [Google Scholar] [CrossRef]
  42. Molendijk, M.L.; Spinhoven, P.; Polak, M.; Bus, B.A.; Penninx, B.W.; Elzinga, B.M. Serum BDNF concentrations as peripheral manifestations of depression: Evidence from a systematic review and meta-analyses on 179 associations (N = 9484). Mol. Psychiatry 2014, 19, 791–800. [Google Scholar] [CrossRef]
  43. Hidese, S.; Yoshida, F.; Ishida, I.; Matsuo, J.; Hattori, K.; Kunugi, H. Plasma neuropeptide levels in patients with schizophrenia, bipolar disorder, or major depressive disorder and healthy controls: A multiplex immunoassay study. Neuropsychopharmacol. Rep. 2022, 43, 57–68. [Google Scholar] [CrossRef]
  44. Dressle, R.J.; Feige, B.; Spiegelhalder, K.; Schmucker, C.; Benz, F.; Mey, N.C.; Riemann, D. HPA axis activity in patients with chronic insomnia: A systematic review and meta-analysis of case-control studies. Sleep Med. Rev. 2022, 62, 101588. [Google Scholar] [CrossRef] [PubMed]
  45. Lopez-Duran, N.L.; Kovacs, M.; George, C.J. Hypothalamic-pituitary-adrenal axis dysregulation in depressed children and adolescents: A meta-analysis. Psychoneuroendocrinology 2009, 34, 1272–1283. [Google Scholar] [CrossRef] [PubMed]
  46. Stetler, C.; Miller, G.E. Depression and hypothalamic-pituitary-adrenal activation: A quantitative summary of four decades of research. Psychosom. Med. 2011, 73, 114–126. [Google Scholar] [CrossRef] [PubMed]
  47. Bremmer, M.A.; Deeg, D.J.; Beekman, A.T.; Penninx, B.W.; Lips, P.; Hoogendijk, W.J. Major depression in late life is associated with both hypo- and hypercortisolemia. Biol. Psychiatry 2007, 62, 479–486. [Google Scholar] [CrossRef]
  48. De Menezes Galvão, A.C.; de Almeida, R.N.; Silva, E.; Freire, F.A.M.; Palhano-Fontes, F.; Onias, H.; Arcoverde, E.; Maia-de-Oliveira, J.P.; de Araújo, D.B.; Lobão-Soares, B.; et al. Cortisol Modulation by Ayahuasca in Patients With Treatment Resistant Depression and Healthy Controls. Front. Psychiatry 2018, 9, 185. [Google Scholar] [CrossRef]
  49. Oldehinkel, A.J.; van den Berg, M.D.; Flentge, F.; Bouhuys, A.L.; ter Horst, G.J.; Ormel, J. Urinary free cortisol excretion in elderly persons with minor and major depression. Psychiatry Res. 2001, 104, 39–47. [Google Scholar] [CrossRef]
  50. Heim, C.; Ehlert, U.; Hellhammer, D.H. The potential role of hypocortisolism in the pathophysiology of stress-related bodily disorders. Psychoneuroendocrinology 2000, 25, 1–35. [Google Scholar] [CrossRef]
  51. Samad, N.; Yasmin, F.; Naheed, S.; Bari, A.Z.; Ayaz, M.M.; Zaman, A.; Akbar, A.; Saeed, S.; Ullah, N. Serum levels of leptin, zinc and tryptophan in obese subjects with sleep deficits. Pak. J. Pharm. Sci. 2017, 30, 1431–1438. [Google Scholar]
  52. Nasca, C.; Bigio, B.; Lee, F.S.; Young, S.P.; Kautz, M.M.; Albright, A.; Beasley, J.; Millington, D.S.; Mathé, A.A.; Kocsis, J.H.; et al. Acetyl-l-carnitine deficiency in patients with major depressive disorder. Proc. Natl. Acad. Sci. USA 2018, 115, 8627–8632. [Google Scholar] [CrossRef]
  53. Ballesio, A.; Zagaria, A.; Ottaviani, C.; Steptoe, A.; Lombardo, C. Sleep disturbance, neuro-immune markers, and depressive symptoms in older age: Conditional process analysis from the English Longitudinal Study of Aging (ELSA). Psychoneuroendocrinology 2022, 142, 105770. [Google Scholar] [CrossRef]
  54. Kuhlman, K.R.; Chiang, J.J.; Bower, J.E.; Irwin, M.R.; Seeman, T.E.; McCreath, H.E.; Almeida, D.M.; Dahl, R.E.; Fuligni, A.J. Sleep problems in adolescence are prospectively linked to later depressive symptoms via the cortisol awakening response. Dev. Psychopathol. 2020, 32, 997–1006. [Google Scholar] [CrossRef] [PubMed]
  55. Mason, B.L.; Davidov, A.; Minhajuddin, A.; Trivedi, M.H. Focusing on insomnia symptoms to better understand depression: A STAR*D report. J. Affect. Disord. 2020, 260, 183–186. [Google Scholar] [CrossRef] [PubMed]
  56. Sonmez, A.I.; Kucuker, M.U.; Lewis, C.P.; Kolla, B.P.; Doruk Camsari, D.; Vande Voort, J.L.; Schak, K.M.; Kung, S.; Croarkin, P.E. Improvement in hypersomnia with high frequency repetitive transcranial magnetic stimulation in depressed adolescents: Preliminary evidence from an open-label study. Prog. Neuropsychopharmacol. Biol. Psychiatry 2020, 97, 109763. [Google Scholar] [CrossRef] [PubMed]
  57. Manber, R.; Blasey, C.; Arnow, B.; Markowitz, J.C.; Thase, M.E.; Rush, A.J.; Dowling, F.; Koscis, J.; Trivedi, M.; Keller, M.B. Assessing insomnia severity in depression: Comparison of depression rating scales and sleep diaries. J. Psychiatr. Res. 2005, 39, 481–488. [Google Scholar] [CrossRef] [PubMed]
  58. Deligiannidis, K.M.; Citrome, L.; Huang, M.Y.; Acaster, S.; Fridman, M.; Bonthapally, V.; Lasser, R.; Kanes, S.J. Effect of Zuranolone on Concurrent Anxiety and Insomnia Symptoms in Women With Postpartum Depression. J. Clin. Psychiatry 2023, 84, 45307. [Google Scholar] [CrossRef]
  59. Deng, Z.; Jiang, X.; Liu, W.; Zhao, W.; Jia, L.; Sun, Q.; Xie, Y.; Zhou, Y.; Sun, T.; Wu, F.; et al. The aberrant dynamic amplitude of low-frequency fluctuations in melancholic major depressive disorder with insomnia. Front. Psychiatry 2022, 13, 958994. [Google Scholar] [CrossRef]
  60. Yi, P.C.; Qin, Y.H.; Zheng, C.M.; Ren, K.M.; Huang, L.; Chen, W. Tumor markers and depression scores are predictive of non-suicidal self-injury behaviors among adolescents with depressive disorder: A retrospective study. Front. Neurosci. 2022, 16, 953842. [Google Scholar] [CrossRef]
  61. Lange, T.; Dimitrov, S.; Born, J. Effects of sleep and circadian rhythm on the human immune system. Ann. N. Y. Acad. Sci. 2010, 1193, 48–59. [Google Scholar] [CrossRef]
  62. Khanna, D.; Khanna, S.; Khanna, P.; Kahar, P.; Patel, B.M. Obesity: A Chronic Low-Grade Inflammation and Its Markers. Cureus 2022, 14, e22711. [Google Scholar] [CrossRef]
  63. Li, X.; He, J. The Association Between Serum/Plasma Leptin Levels and Obstructive Sleep Apnea Syndrome: A Meta-Analysis and Meta-Regression. Front. Endocrinol. (Lausanne) 2021, 12, 696418. [Google Scholar] [CrossRef]
  64. Nadeem, R.; Molnar, J.; Madbouly, E.M.; Nida, M.; Aggarwal, S.; Sajid, H.; Naseem, J.; Loomba, R. Serum inflammatory markers in obstructive sleep apnea: A meta-analysis. J. Clin. Sleep Med. 2013, 9, 1003–1012. [Google Scholar] [CrossRef] [PubMed]
  65. Sweetman, A.; Lack, L.; McEvoy, R.D.; Smith, S.; Eckert, D.J.; Osman, A.; Carberry, J.C.; Wallace, D.; Nguyen, P.D.; Catcheside, P. Bi-directional relationships between co-morbid insomnia and sleep apnea (COMISA). Sleep Med. Rev. 2021, 60, 101519. [Google Scholar] [CrossRef] [PubMed]
  66. Van Buel, E.M.; Meddens, M.J.M.; Arnoldussen, E.A.; van den Heuvel, E.R.; Bohlmeijer, W.C.; den Boer, J.A.; Muller Kobold, A.; Boonman-de Winter, L.J.M.; van Rumpt, D.; Timmers, L.F.J.; et al. Major depressive disorder is associated with changes in a cluster of serum and urine biomarkers. J. Psychosom. Res. 2019, 125, 109796. [Google Scholar] [CrossRef] [PubMed]
  67. Van Belkum, S.M.; de Boer, M.K.; Opmeer, E.M.; Kortekaas, R.; Mulder, T.; Woonings, F.; Hoenders, H.J.R.; Kamphuis, H.; Aleman, A.; Schoevers, R.A. No antidepressant effects of low intensity transcranial pulsed electromagnetic fields for treatment resistant depression. J. Affect. Disord. 2021, 294, 679–685. [Google Scholar] [CrossRef] [PubMed]
  68. Lever-van Milligen, B.A.; Verhoeven, J.E.; Schmaal, L.; van Velzen, L.S.; Révész, D.; Black, C.N.; Han, L.K.M.; Horsfall, M.; Batelaan, N.M.; van Balkom, A.; et al. The impact of depression and anxiety treatment on biological aging and metabolic stress: Study protocol of the MOod treatment with antidepressants or running (MOTAR) study. BMC Psychiatry 2019, 19, 425. [Google Scholar] [CrossRef]
  69. Verhoeven, J.E.; Han, L.K.M.; Lever-van Milligen, B.A.; Hu, M.X.; Révész, D.; Hoogendoorn, A.W.; Batelaan, N.M.; van Schaik, D.J.F.; van Balkom, A.; van Oppen, P.; et al. Antidepressants or running therapy: Comparing effects on mental and physical health in patients with depression and anxiety disorders. J. Affect. Disord. 2023, 329, 19–29. [Google Scholar] [CrossRef]
  70. Sheehan, D.V.; Lecrubier, Y.; Sheehan, K.H.; Amorim, P.; Janavs, J.; Weiller, E.; Hergueta, T.; Baker, R.; Dunbar, G.C. The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 1998, 59 (Suppl. 20), 22–33, quiz 34–57. [Google Scholar]
  71. Kessler, R.C.; Ustün, T.B. 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. [Google Scholar] [CrossRef]
  72. Rush, A.J.; Carmody, T.; Reimitz, P.-E. The Inventory of Depressive Symptomatology (IDS): Clinician (IDS-C) and Self-Report (IDS-SR) ratings of depressive symptoms. Int. J. Methods Psychiatr. Res. 2000, 9, 45–59. [Google Scholar] [CrossRef]
  73. Rush, A.J.; Trivedi, M.H.; Ibrahim, H.M.; Carmody, T.J.; Arnow, B.; Klein, D.N.; Markowitz, J.C.; Ninan, P.T.; Kornstein, S.; Manber, R.; et al. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): A psychometric evaluation in patients with chronic major depression. Biol. Psychiatry 2003, 54, 573–583. [Google Scholar] [CrossRef]
  74. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th ed.; APA: Washington, DC, USA, 1994. [Google Scholar]
  75. Jentsch, M.C.; Burger, H.; Meddens, M.B.M.; Beijers, L.; van den Heuvel, E.R.; Meddens, M.J.M.; Schoevers, R.A. Gender Differences in Developing Biomarker-Based Major Depressive Disorder Diagnostics. Int. J. Mol. Sci. 2020, 21, 3039. [Google Scholar] [CrossRef] [PubMed]
  76. IBM Corp. IBM SPSS Statistics for Windows, 28.0; IBM Corp: Armonk, NY, USA, 2021. [Google Scholar]
  77. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2022. [Google Scholar]
  78. R Core Team and Contributors Worldwide. The R Stats Package, 4.2.2 ed.; R Foundation for Statistical Computing: Vienna, Austria, 2022. [Google Scholar]
  79. Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B. (Methodol.) 1995, 57, 289–300. [Google Scholar] [CrossRef]
  80. Wright, S.P. Adjusted P-Values for Simultaneous Inference. Biometrics 1992, 48, 1005–1013. [Google Scholar] [CrossRef]
  81. Hoaglin, D.C.; Iglewicz, B. Fine-Tuning Some Resistant Rules for Outlier Labeling. J. Am. Stat. Assoc. 1987, 82, 1147–1149. [Google Scholar] [CrossRef]
  82. Hoaglin, D.C.; Iglewicz, B.; Tukey, J.W. Performance of Some Resistant Rules for Outlier Labeling. J. Am. Stat. Assoc. 1986, 81, 991–999. [Google Scholar] [CrossRef]
  83. Hayes, A.F.; Cai, L. Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behav. Res. Methods 2007, 39, 709–722. [Google Scholar] [CrossRef]
Table
Table 1. Demographic and clinical characteristics.
Table 1. Demographic and clinical characteristics.
Total Sample, N227 a
Sexn%
Female13459.0
Male9240.5
AgeMSD
Years42.0412.63
Antidepressant usen%
Yes4318.9
No15668.7
Cohort of originn%
PIDON3816.7
tPEMF5624.7
MOTAR13358.6
Depression severityMSD
QIDS-SR1617.324.91
QIDS-SR16 (sleep items excluded)14.864.57
QIDS-SR16 insomnia severity4.692.47
Current episode durationn%
Less than 1 month9642.3
1–6 months208.8
6 months–1 year114.8
More than 1 year4921.6
RecurrenceMdnIQR
Lifetime number of episodes21–5
Note: IQR = interquartile range; tPEMF = transcranial pulsed electromagnetic fields; PIDON = Pieken in de Delta Oost Nederland; MOTAR = Mood Treatment with Antidepressants or Running; QIDS-SR16 = self-rated Quick Inventory of Depressive Symptomatology. a Total number of participants may differ among performed analyses due to missing data.
Table
Table 2. Association between insomnia severity and levels of serum biomarkers.
Table 2. Association between insomnia severity and levels of serum biomarkers.
BiomarkerUnit of Measurementn b p
b95% CIβR2
LLUL UnadjustedAdjusted
Immunologic
α1-antitrypsinmg/L213−0.073−4.7974.650−0.0020.9760.9880.000
Calprotectin aμg/mL216−0.005−0.0410.031−0.0190.7790.9800.000
cAMPpmol/mL214−0.292−1.0580.475−0.0510.4540.9050.003
Endothelin-1 apg/mL215−0.006−0.0190.008−0.0570.4080.8680.003
Myeloperoxidase ang/mL216−0.007−0.0400.026−0.0280.6870.9370.001
Resistin ang/mL216−0.030−0.051−0.009−0.1860.0060.1510.035
Thromboxane a ng/mL216−0.043−0.1020.016−0.0980.1530.7810.010
TNFαR2 ang/mL2150.000−0.0150.014−0.0010.9880.9880.000
Zonulin bng/mL2140.168−0.0710.4070.1000.1680.7810.010
Neurotrophic
BDNFng/mL215−0.112−0.5400.316−0.0350.6070.9370.001
BDNF freeng/mL215−0.132−0.5240.259−0.0460.5060.9050.002
BDNF totalng/mL215−0.012−0.3920.368−0.0040.9510.9880.000
EGFpg/mL215−2.205−15.62011.210−0.0220.7460.9760.000
Neuropeptide
Substance P apg/mL213−0.005−0.0210.011−0.0410.5490.9340.002
Neuroendocrine
Cortisol bμg/dL214−0.207−0.5830.168−0.0750.2780.7810.006
Metabolic
Acetyl-L-carnitine ang/mL2150.0390.0100.0680.1780.0090.1510.032
Apolipoprotein A1mg/mL215−0.006−0.0240.011−0.0480.4850.9050.002
Leptin ang/mL2160.034−0.0240.0930.0790.2490.7810.006
Prolactin aμIU/mL2140.023−0.0140.0600.0840.2220.7810.007
Note: BDNF = brain-derived neurotrophic factor; cAMP = cyclic adenosine monophosphate; CI = confidence interval; EGF = epidermal growth factor; TNFαR2 = tumour necrosis factor α receptor 2. A significant b-coefficient indicates the β-coefficient is also significant. b represents unstandardised regression coefficients; β indicates standardised regression coefficients. LL and UL indicate the lower and upper limit of the confidence interval, respectively. a Ln-transformation was applied. b Heteroscedasticity-adjusted confidence interval was computed.
Table
Table 3. Association between insomnia severity and levels of urine biomarkers.
Table 3. Association between insomnia severity and levels of urine biomarkers.
BiomarkerUnit of Measurementn b p
b95% CIβR2
LLUL UnadjustedAdjusted
Immunologic
α1-antitrypsinμg/L2100.031−0.0230.0860.0780.2600.7810.006
Calprotectin abng/mL210−0.005−0.1260.115−0.0060.9310.9880.000
cGMP apmol/mL210−0.013−0.0430.017−0.0590.3910.8680.004
HVEM ang/mL210−0.017−0.0500.015−0.0730.2890.7810.005
Isoprostane−2 ang/mL209−0.007−0.0410.027−0.0300.6700.9370.001
Lipocalin−2 ang/mL2090.047−0.0280.1210.0860.2160.7810.007
LTB4 apg/mL210−0.007−0.0360.021−0.0350.6120.9370.001
Resistin ang/mL209−0.035−0.0870.016−0.0940.1760.7810.009
Thromboxane ang/mL210−0.001−0.0360.034−0.0050.9470.9880.000
Neurotrophic
EGF ang/mL209−0.030−0.0640.003−0.1230.0770.7810.015
Midkine abpg/mL2070.005−0.0400.0490.0170.8320.9880.000
Neuropeptide
Substance P bpg/mL205−2.503−8.4353.428−0.0580.4060.8680.003
Neuroendocrine
Aldosterone ang/mL207−0.019−0.0540.017−0.0730.2990.7810.077
Cortisol abμg/dL209−0.010−0.0590.039−0.0320.6890.9370.001
Metabolic
Acetyl-L-carnitine ang/mL210−0.003−0.0430.037−0.0100.8830.9880.000
Note: cGMP = cyclic guanosine monophosphate; CI = confidence interval; EGF = epidermal growth factor; HVEM = herpes virus entry mediator; LTB4 = leukotriene B4. A significant b-coefficient indicates the β-coefficient is also significant. b represents unstandardised regression coefficients; β indicates standardised regression coefficients. LL and UL indicate the lower and upper limit of the confidence interval, respectively. a Ln-transformation was applied. b Heteroscedasticity-adjusted confidence interval was computed.
Table
Table 4. Association between insomnia severity and levels of serum biomarkers adjusted for relevant covariates a.
Table 4. Association between insomnia severity and levels of serum biomarkers adjusted for relevant covariates a.
BiomarkerUnit of Measurementn b p
b95% CIβR2
LLUL UnadjustedAdjusted
Immunologic
α1-antitrypsinmg/L175−0.717−7.1215.686−0.0200.8250.9350.040
Calprotectin bμg/mL1780.021−0.0200.0620.0910.3110.9060.025
cAMPpmol/mL176−0.290−1.2230.644−0.0500.5410.9060.196
Endothelin-1 bpg/mL177−0.019−0.037−0.002−0.1830.0330.5280.119
Myeloperoxidase bng/mL1780.010−0.0340.0550.0410.6500.9060.021
Resistin bng/mL178−0.016−0.0450.013−0.0960.2660.9060.104
Thromboxane b ng/mL178−0.030−0.1090.049−0.0670.4480.9060.044
TNFαR2 bng/mL177−0.018−0.0380.001−0.1660.0620.5280.050
Zonulin cng/mL176−0.123−0.4330.187−0.0700.4350.9060.176
Neurotrophic
BDNFng/mL178−0.148−0.7250.430−0.0440.6150.9060.054
BDNF freeng/mL178−0.225−0.7360.286−0.0770.3860.9060.049
BDNF totalng/mL178−0.106−0.6170.406−0.0360.6840.9060.043
EGFpg/mL177−5.792−24.02512.441−0.0560.5310.9060.063
Neuropeptide
Substance P bpg/mL175−0.009−0.0310.013−0.0710.4340.9060.013
Neuroendocrine
Cortisol cμg/dL1760.056−0.4040.5170.0190.8090.9350.192
Metabolic
Acetyl-L-carnitine bng/mL1780.0360.0000.0730.1670.0490.5280.134
Apolipoprotein A1mg/mL1770.001−0.0210.0230.0060.9420.9650.060
Leptin bng/mL178−0.020−0.0830.044−0.0430.5390.9060.399
Prolactin bμIU/mL1780.009−0.0300.0480.0360.6530.9060.205
Note: BDNF = brain-derived neurotrophic factor; cAMP = cyclic adenosine monophosphate; EGF = epidermal growth factor; CI = confidence interval; TNFαR2 = tumour necrosis factor α receptor 2. A significant b-coefficient indicates the β-coefficient is also significant. b represents unstandardised regression coefficients; β indicates standardised regression coefficients. LL and UL indicate the lower and upper limit of the confidence interval, respectively. a Covariates included in the model: non-sleep depression severity, antidepressant use, gender, age, and cohort of origin. b Ln-transformation was applied. c Heteroscedasticity-adjusted confidence interval was computed.
Table
Table 5. Association between insomnia severity and levels of urine biomarkers adjusted for relevant covariates a.
Table 5. Association between insomnia severity and levels of urine biomarkers adjusted for relevant covariates a.
BiomarkerUnit of Measurementn b p
b95% CIβR2
LLUL UnadjustedAdjusted
Immunologic
α1-antitrypsinμg/L1740.025−0.0410.0910.0600.4540.9060.256
Calprotectin bcng/mL174−0.017−0.1190.084−0.0200.7350.9060.564
cGMP bpmol/mL174−0.007−0.0450.031−0.0300.7290.9060.095
HVEM bng/mL174−0.010−0.0500.031−0.0400.6450.9060.118
Isoprostane−2 bng/mL1730.008−0.0350.0510.0320.7130.9060.113
Lipocalin−2 bng/mL1730.073−0.0180.1640.1270.1150.7840.254
LTB4 bpg/mL1740.001−0.0350.0370.0050.9590.9650.063
Resistin bng/mL173−0.012−0.0790.055−0.0310.7190.9060.109
Thromboxane bng/mL1740.003−0.0410.0470.0110.9010.9650.145
Neurotrophic
EGF bng/mL174−0.007−0.0480.034−0.0270.7460.9060.183
Midkine bcpg/mL1720.025−0.0260.0760.0840.3360.9060.221
Neuropeptide
Substance P cpg/mL170−1.710−8.4555.034−0.0380.6170.9060.294
Neuroendocrine
Aldosterone bng/mL1710.001−0.0440.0460.0040.9650.9650.074
Cortisol bcμg/dL1740.020−0.0380.0780.0640.5080.9060.057
Metabolic
Acetyl-L-carnitine bng/mL174−0.048−0.0960.001−0.1680.0550.5280.105
Note: cGMP = cyclic guanosine monophosphate; CI = confidence interval; EGF = epidermal growth factor; HVEM = herpes virus entry mediator; LTB4 = leukotriene B4. A significant b-coefficient indicates the β-coefficient is also significant. b represents unstandardised regression coefficients; β indicates standardised regression coefficients. LL and UL indicate the lower and upper limit of the confidence interval, respectively. a Covariates included in the model: non-sleep depression severity, antidepressant use, gender, age, and cohort of origin. b Ln-transformation was applied. c Heteroscedasticity-adjusted confidence interval was computed.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Drinčić, T.; van Dalfsen, J.H.; Kamphuis, J.; Jentsch, M.C.; van Belkum, S.M.; Meddens, M.J.M.; Penninx, B.W.J.H.; Schoevers, R.A. The Relationship between Insomnia and the Pathophysiology of Major Depressive Disorder: An Evaluation of a Broad Selection of Serum and Urine Biomarkers. Int. J. Mol. Sci. 2023, 24, 8437. https://doi.org/10.3390/ijms24098437

AMA Style

Drinčić T, van Dalfsen JH, Kamphuis J, Jentsch MC, van Belkum SM, Meddens MJM, Penninx BWJH, Schoevers RA. The Relationship between Insomnia and the Pathophysiology of Major Depressive Disorder: An Evaluation of a Broad Selection of Serum and Urine Biomarkers. International Journal of Molecular Sciences. 2023; 24(9):8437. https://doi.org/10.3390/ijms24098437

Chicago/Turabian Style

Drinčić, Tina, Jens H. van Dalfsen, Jeanine Kamphuis, Mike C. Jentsch, Sjoerd M. van Belkum, Marcus J. M. Meddens, Brenda W. J. H. Penninx, and Robert A. Schoevers. 2023. "The Relationship between Insomnia and the Pathophysiology of Major Depressive Disorder: An Evaluation of a Broad Selection of Serum and Urine Biomarkers" International Journal of Molecular Sciences 24, no. 9: 8437. https://doi.org/10.3390/ijms24098437

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop