Highlights
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1. Patients with depression had poorer subjective sleep quality than healthy subjects.
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2. Serum sTNF-αR1 positively correlated with sleep latency in treatment-resistant depression.
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3. Serum sIL-2R negatively associated with overall sleep quality in depression.
Introduction
Sleep disturbances represent a prevalent manifestation of major depressive disorder (MDD), with over 75% of afflicted individuals reporting significant disruptions in sleep patterns.Reference Nutt, Wilson and Paterson1 Patients with MDD exhibit decreased sleep efficiency, increased nocturnal awakenings, reduced latency to rapid eye movement (REM) sleep, and increased REM density compared to healthy controls.Reference Steiger and Pawlowski2 Notably, sleep disturbances not only serve as a primary risk factor for depression but also correlate with diminished quality of life, heightened suicide risk, and compromised response to conventional antidepressant therapies.Reference Nutt, Wilson and Paterson1 Inflammation emerges as a potential pathophysiological mechanism underlying sleep disturbances in depression, supported by accumulating evidence suggesting that activation of immune-inflammatory pathways contributes significantly to the development of MDD and may be relevant to its pathogenesis in specific subpopulations. Inflammatory markers, including acute phase proteins, cytokines, and their receptors, were found to access the central nervous system (CNS), thereby interfering with key physiological processes, including neuroplasticity, neurotransmitter metabolism, neuroendocrine function, and neuronal apoptosis.Reference Miller and Raison3 Inflammatory markers associated with depression include C-reactive protein (CRP), interleukin-2 (IL-2), interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and monocyte chemotactic protein-1 (MCP-1).Reference Capuron and Miller4-Reference Suarez, Krishnan and Lewis6 Bidirectional causative mechanisms intertwining inflammation and sleep disturbances have been posited,7 with cytokines such as IL-6 and TNF-α potentially affecting brain regions involved in sleep regulation, consequently altering sleep architecture.Reference Krueger, Obal and Fang8, Reference Krueger9 Preclinical models administering inflammatory cytokines to cancer patients have demonstrated diminished sleep quality and duration.Reference Capuron, Ravaud and Gualde10 Conversely, autonomic nervous system activation and heightened catecholamine levels during sleep deprivation may stimulate the production of inflammatory markers.Reference Irwin, Wang and Ribeiro11 Meta-analyses from cohort studies and sleep deprivation experiments have indicated a link between sleep disturbances and elevated systemic inflammatory markers: IL-6 and CRP.Reference Irwin, Olmstead and Carroll12 However, discrepancies exist within the literature, as certain investigations observed no associations between insomnia symptoms and IL-6.Reference Slavish, Graham-Engeland and Engeland13, Reference Fernandez-Mendoza, Baker and Vgontzas14 The variance in findings may stem from inconsistent insomnia symptom assessment methodologies and the inclusion of participants with diverse diagnoses. Additionally, scant attention has been paid to mood symptoms frequently co-occurring with poor sleep, such as depression, potentially contributing to the inconsistent findings regarding insomnia and inflammation.Reference Li, Wu and Gan15
Treatment-resistant depression (TRD) represents a subgroup of individuals with MDD who do not experience significant improvement in symptoms despite trying multiple standard antidepressant treatments, leading to chronic, severe, and debilitating symptoms that impair daily functioning and quality of life.Reference Berlim and Turecki16 While inflammation may contribute to the pathophysiology of depression, TRD was shown to be associated more with immune activation than with treatment-responsive depression,Reference Chamberlain, Cavanagh and de Boer17, Reference Strawbridge, Arnone and Danese18 and pro-inflammatory cytokines have been demonstrated to be a therapeutic target for patients with TRD.Reference Raison, Rutherford and Woolwine19 Investigating the relationship between sleep disturbance and inflammation among patients with MDD, especially TRD, is crucial for advancing our understanding of depression, identifying potential treatment targets, and enhancing clinical care. In a previous study, we examined peripheral levels of pro-inflammatory cytokines in MDD and found a higher serum concentration of soluble tumor necrosis factor-α receptor type 1 (sTNF-αR1) among patients with TRD compared to non-TRD patients and healthy subjects.Reference Huang, Chen and Tu20 In this paper, we extended our analysis to examine the role of peripheral inflammatory markers in subjective sleep quality among patients with TRD and treatment-responsive depression, utilizing the same dataset. This allows us to build upon our earlier findings and provide a more comprehensive understanding of how inflammation might contribute to adverse health outcomes in depression. We hypothesized that elevated peripheral pro-inflammatory markers would be associated with poor subjective sleep quality among patients with MDD.
Methods
34 consecutive outpatients, aged 25 to 65 years, diagnosed with MDD using the Mini-International Neuropsychiatric Interview (MINI) and the Diagnostic and Statistical Manual of Mental Disorders-IV-Text Revision (DSM-IV-TR), were recruited from the psychiatric outpatient department of Taipei Veterans General Hospital. Among these individuals, 20 had a documented history of resistance to antidepressants (TRD group), while 14 did not (non-TRD group). To confirm antidepressant resistance, we adhered to established criteria (failure to respond to at least 2 different classes of antidepressant trials with adequate dosage and duration in the current or past episode). Exclusion criteria encompassed significant physical illness, alcohol or substance use disorder history, and major psychiatric comorbidities such as schizophrenia, bipolar disorder, or other psychotic disorders. Additionally, 34 healthy individuals, matched for age and sex, underwent the MINI assessment with a psychiatrist to ensure the absence of psychiatric illness. A comprehensive medical history review was conducted to rule out physical illness in all participants. The study adhered to the Declaration of Helsinki guidelines and received approval from the Institutional Review Board of Taipei Veterans General Hospital (V102B-033), with all participants providing written informed consent.
Clinical assessment
Demographic characteristics, encompassing age, sex, body mass index (BMI), and psychotropic medication use, were documented for each participant. Mood severity evaluations were conducted individually by an experienced psychiatrist, incorporating assessments derived from the 17-item Hamilton Rating Scale for Depression (HAMD-17).
Subjective sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI), a self-administered questionnaire designed to assess sleep quality over the preceding month. The PSQI encompasses 7 dimensions of sleep, namely component 1: subjective sleep quality, component 2: sleep latency, component 3: sleep duration, component 4: sleep efficiency, component 5: sleep disturbances, component 6: use of hypnotic medications, and component 7: daytime dysfunction. Each dimension is rated from 0 to 3, with higher scores suggesting poorer sleep quality. The aggregate of these scores yields the total PSQI score, ranging from 0 to 21. The PSQI is a validated tool, with reliability established against polysomnography, the gold standard for sleep measurement.Reference Buysse, Reynolds and Monk21 In this study, a PSQI total score > 5 indicates poor sleepers, and a total score ≥ 11 is indicative of severe sleep disturbance.Reference Zheng, Wang and Feng22
Laboratory measurement
IL-6 demonstrates a multifaceted impact, both neuroprotective and neurodegenerative, on inflammation and immune response within the CNS.Reference Ting, Yang and Tsai23 This cytokine exerts its biological influence through classical signaling and trans-signaling mechanisms, facilitated by its binding to both the IL-6 receptor and the soluble form of the IL-6 receptor (sIL-6R), respectively. Activation of the IL-6 receptor complex (the binding of IL-6 to IL-6 receptor) prompts the dimerization of the signal-transducing receptor subunit gp130, initiating downstream signaling cascades. While classical signaling exhibits both pro- and anti-inflammatory effects, IL-6 trans-signaling predominantly elicits pro-inflammatory responses.Reference Ting, Yang and Tsai23 Evaluation of serum levels of soluble IL-6 receptors may offer superior insights into inflammatory activity compared to IL-6 itself.Reference Bai, Su and Li24 TNF-α operates through interaction with 2 receptors, TNF-α receptor subtype 1 (TNF-αR1) and TNF-α receptor subtype 2 (TNF-αR2). TNF-αR1 engagement has been associated with apoptotic neuronal death, whereas TNF-αR2 plays a dominant role in dampening TNF-mediated inflammatory responses.Reference Ma, Zhang and Baloch25 Soluble forms of TNF-α receptors extend the half-life of TNF-α, with circulating levels serving as indicators of TNF-α production. Consequently, sTNF-αR1 emerges as a more dependable marker of pro-inflammatory activity.Reference Huang, Chan and Chen26 MCP-1, or C-C motif chemokine ligand 2 (CCL2), serves a critical function in inflammation by recruiting monocytes and other leukocytes to sites of inflammation. Beyond recruitment, MCP-1 activation of monocytes can lead to their differentiation into macrophages or dendritic cells, intensifying the inflammatory cascade through the release of additional cytokines.Reference Deshmane, Kremlev and Amini27
Enzyme-linked immunosorbent assay (ELISA) kits sourced from R&D Systems, Minneapolis, MN, USA, were utilized to assess pro-inflammatory cytokines, namely CRP, soluble interleukin-6 receptor (sIL-6R), soluble interleukin-2 receptor (sIL-2R), sTNF-αR1, and MCP-1. The lower detection limit of CRP is 0.010 mg/L with a sensitivity of 0.005 ng/mL. Specificity: natural and recombinant human CRP. Cross-reactivity: <0.5% cross-reactivity was observed with available related molecules. <50% cross-species reactivity was observed with the species tested. The lower detection limit of sIL-6R was 6.5 pg/mL, and the sensitivity was 1.5 pg/mL. Specificity: natural and recombinant human sIL-6R. Cross-reactivity: <0.5% cross-reactivity was observed with available related molecules. Cross-species reactivity was not tested. The lower detection limit of sIL-2R was less than 10 pg/mL. Specificity: natural and recombinant human sIL-2R alpha. Cross-reactivity: <0.5% cross-reactivity observed with available related molecules. Cross-species reactivity not tested. The lower detection limit of sTNF-αR1 was 0.77 pg/mL, and the sensitivity was 0.43 pg/mL. Specificity: natural and recombinant human sTNF-αR1. Cross-reactivity: < 0.5% cross-reactivity was observed with available related molecules. < 50% cross-species reactivity was observed with the species tested. The lower detection limit of MCP-1 was 10 pg/mL, and the sensitivity was 1.7 pg/mL. Specificity: natural and recombinant human MCP-1. Cross-reactivity: < 0.5% cross-reactivity observed with available related molecules. < 50% cross-species reactivity observed with species tested. The fasting serum samples were collected in serum separator tubes, clotted for 30 minute, and stored at −80 °C until use. All assays were performed according to the vendor’s instructions. The final absorbance of the mixture was measured and analyzed at 450 nm using an ELISA plate reader with Bio-Tek Power Wave Xs and Bio-Tek’s KC junior software (Winooski, VT, USA). The standard range depended on the vendor’s instructions. A linear regression R-square value of ≥0.95 represented a reliable standard curve.
Statistical analyses
The assumption of normality for pro-inflammatory cytokine levels was checked using Shapiro–Wilk Test. We found that peripheral levels of sTNF-αR1, sIL-6R, and sIL-2R conformed to the normal distribution (all p > 0.05), while levels of CRP and MCP-1 were positively skewed; therefore, serum levels of pro-inflammatory markers were log-transformed to obtain normally distributed variables. Comparison of continuous variables was performed using Analysis of Variance (ANOVA) (or Student’s t test), while categorical data was evaluated using the chi-squared test. The Levene test was conducted to test differences in variances among groups. In cases of uneven distribution of variance homogeneity, Welch’s one-way analysis of variance was employed, followed by post-hoc pairwise comparisons between groups utilizing the Games-Howell test. Participants were stratified according to their disease, subjective ratings in PSQI total scores (≥11, <11, controls), or ratings in PSQI subscores (≥3, <3, controls). General linear models (GLMs) were used to assess serum levels of pro-inflammatory markers between groups, with the adjustment of age, sex, BMI, HAMD-17 total scores, duration of illness, psychotropic medication use. Linear regression models were used to examine the association between serum levels of pro-inflammatory markers and total/subscores of PSQI while controlling for age, sex, BMI, HAMD-17 total scores, duration of illness, psychotropic medication use, and disease group. For the management of multiple comparisons, the Benjamini–Hochberg false discovery method was employed. Significance was determined at a threshold of p < 0.05. Statistical analysis was performed using SPSS 22 (SPSS Inc., Chicago, IL, USA).
Results
In all, 34 patients with MDD, including 20 TRD patients and 14 non-TRD patients, and 34 age- and sex-matched healthy subjects were recruited. Both patients with TRD and non-TRD scored higher in PSQI total scores and subscores in sleep quality, sleep latency, sleep disturbances, use of sleeping medications, and daytime dysfunction than healthy individuals (Table 1). The percentage of severe sleep disturbance (PSQI total score ≥ 11) was 70%, 42.9%, and 2.9% among patients with TRD, non-TRD, and controls, respectively.
Table 1. Demographic Data, Inflammatory Markers, and Sleep Variables between Patients with Major Depression and Healthy Controls
Abbreviations: BMI, body mass index; CRP, C-reactive protein; HAMD-17, 17-item version of Hamilton depression rating scale; MDD, major depressive disorder; MCP-1, Monocyte chemoattractant protein-1; PSQI, Pittsburgh sleep quality index; sIL-2R, soluble interleukin-2 receptor; sIL-6R, soluble interleukin-6 receptor; sTNF-αR1, soluble tumor necrosis factor-α receptor type 1; TRD, treatment-resistant depression.
a Independent t-tests comparing MDD and HC groups.
b ANOVA comparing TRD, non-TRD and HC groups. Levene test was done to test differences of variances among groups. Welch one-way analysis of variance was performed when the homogeneity of variance was not equally distributed, and the post-hoc Games-Howell test was then performed to determine pairwise differences between groups.
Linear regression analyses adjusting for covariates showed that serum sTNF-αR1 level positively correlated with sleep latency, while serum sIL-2R level positively associated with PSQI total score among patients with MDD (Table 2). Sub-analyses stratified by disease group found positive associations between sTNF-αR1 and sleep latency in both the TRD and non-TRD group (both p < 0.05). However, after applying false discovery rate correction, the associations became statistically insignificant in each group. CRP, sIL-6R, and MCP-1 were not associated with subjective sleep quality in this study (Supplementary Table S1). Component 2 score (sleep latency) significantly correlated with component 3 score among patients with MDD (sleep duration; B = 0.419, 95% CI = 0.193–0.646, t = 3.768, R2 = 0.307). In addition, sex remained insignificant across all linear regression analyses between inflammation and subjective sleep quality.
Table 2. Correlation of sTNFaR1, sIL-2R, and PSQI Score Among Patients with Major Depression with the Adjustment of Age, Sex, BMI, HAMD-17 Total Scores, Duration of Illness, Psychotropic Medication Use, and Disease Group
Abbreviations: CI, confidence interval, GLM, general linear model; BMI, body mass index; HAMD-17, 17-item version of Hamilton depression rating scale; PSQI, Pittsburgh sleep quality index; sIL-2R, soluble interleukin-2 receptor; sTNF-αR1, soluble tumor necrosis factor-α receptor type 1.
Stratified by PSQI scores, GLM analyses adjusting for covariates showed that patients with severe sleep disturbance (PSQI ≥11) had significantly higher serum levels of sIL-2R and MCP-1 than other patients (Figure 1). MDD patients with difficulty initiating sleep (component 2: sleep latency score = 3) had higher serum levels of sIL-2R and sTNF-αR1 than other patients and controls (Figure 2). Patients stratified by other component scores of PSQI did not show differences in peripheral inflammatory markers between groups.
Figure 1. Comparison of pro-inflammatory cytokine levels among MDD patients with high (PSQI total score ≥ 11) and low (PSQI total score < 11) sleep disturbance and healthy controls. Cytokine levels were analyzed using a general linear model, adjusting for age, sex, BMI, HAMD-17 total score, duration of illness, psychotropic medication use, and disease group.
Abbreviation: BMI, body mass index; CRP, C-reactive protein; HAMD-17, 17-item version of Hamilton depression rating scale; MDD, major depressive disorder; MCP-1, Monocyte chemoattractant protein-1; PSQI, Pittsburgh sleep quality index; sIL-2R, soluble interleukin-2 receptor; sIL-6R, soluble interleukin-6 receptor; sTNF-αR1, soluble tumor necrosis factor-α receptor type 1; TRD, treatment-resistant depression.
Figure 2. Comparison of pro-inflammatory cytokine levels among 3 groups: patients with severe sleep latency disturbance (component 2 score = 3), patients with mild-to-moderate sleep latency disturbance (component 2 score < 3), and healthy subjects. The analysis was adjusted for age, sex, BMI, HAMD-17 total scores, duration of illness, psychotropic medication use, and disease groups. Adjusted cytokine levels were estimated using a general linear model with post-hoc comparisons.
Abbreviation: BMI, body mass index; CRP, C-reactive protein; HAMD-17, 17-item version of Hamilton depression rating scale; MDD, major depressive disorder; MCP-1, Monocyte chemoattractant protein-1; PSQI, Pittsburgh sleep quality index; sIL-2R, soluble interleukin-2 receptor; sIL-6R, soluble interleukin-6 receptor; sTNF-αR1, soluble tumor necrosis factor-α receptor type 1; TRD, treatment-resistant depression.
Discussion
The current study found that higher serum levels of sTNF-αR1 were associated with longer sleep latency across TRD and non-TRD groups. Elevated serum sIL-2R levels correlated with poorer overall sleep quality among patients with MDD. Severe sleep disturbance correlated with increased sIL-2R and MCP-1 levels, while patients with sleep initiation difficulties or sleep loss had elevated sIL-2R and sTNF-αR1 levels. Other inflammatory markers tested were not associated with subjective sleep quality in this study.
In our study, serum sTNF-αR1 level was significantly associated with sleep latency in patients with MDD, including both TRD and non-TRD subgroups. However, after applying false discovery rate correction for multiple comparisons, the associations became statistically nonsignificant. This suggests that the observed associations should be interpreted with caution, as they may be influenced by sample size and multiple testing adjustments. Given the biological plausibility of TNF-α’s role in sleep disturbances and prior evidence in the literature, future studies with larger samples are needed to validate these findings. The TNF-α system has emerged as a crucial player in sleep regulation and circadian biology, evident in both animal and human studies.Reference Krueger9, Reference Rockstrom, Chen and Taishi28 In rodents, both acute sleep deprivation and chronic sleep restriction have been observed to elevate TNF-α expression in various brain regions, including the cortex, hippocampus, and brainstem, indicating diurnal fluctuations in TNF-α levels.Reference Zielinski, McKenna and McCarley29 In healthy individuals, acute sleep deprivation was shown to induce secretion of TNF-α, but not IL-6 or CRP.Reference Chennaoui, Sauvet and Drogou30 A reduction in total sleep duration (from 8 to 6 hours per night for 1 week in healthy subjects) has been associated with increased TNF-α levels the following day. In pathological conditions associated with shortened sleep duration, chronic elevations of systemic TNF-α have been observed.Reference Haack, Pollmacher and Mullington31 Our observational results in MDD correspond to previous studies that TNF-α may serve as a mediator of pathological or experimentally induced sleepiness. Conversely, TNF-α can modulate sleep, and experimental studies across species by intravenous administration or microinjections into certain brain areas have consistently shown that TNF-α enhances non-rapid eye movement sleep (NREMS) duration and induces sleepiness.Reference Mullington, Korth and Hermann32, Reference Terao, Matsumura and Yoneda33 Blockade of TNF-α has shown promise in reducing depressive symptoms in patients with increased inflammation, yet its effect on sleep in depressed patients remains underexplored. A polysomnography study indicated that TNF-α blockade with infliximab may improve sleep continuity in patients with MDD, particularly those with high inflammation levels.Reference Weinberger, Raison and Rye34 Additionally, levels of TNF-α messenger RNA (mRNA), sTNF-αR, and sTNF-αR mRNA in the CNS also vary with sleep propensity in a similar fashion. Irwin and colleagues demonstrated increased production of TNF-α mRNA by monocytes, notably observed the morning after sleep deprivation.Reference Irwin, Wang and Campomayor35, Reference Irwin, Carrillo and Olmstead36 This elevation in circulating TNF-α levels could stem from the activation of cellular TNF-α expression, potentially influencing the expression of other inflammatory cytokines within cells.Reference Irwin, Wang and Campomayor35 Furthermore, the heightened expression of inflammatory genes following sleep deprivation is attributed to the activation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) transcription control pathways, which play a pivotal role in regulating the expression of pro-inflammatory genes.Reference Irwin, Wang and Ribeiro11, Reference Irwin, Wang and Campomayor35 Regarding sTNF-αR, sTNF-αR1 levels rise after sleep loss in humans, while sTNF-αR2 remains unaffected.Reference Shearer, Reuben and Mullington37 Additionally, sleep deprivation boosts sTNF-αR1 mRNA expression.Reference Taishi, Gardi and Chen38 Further research is warranted to elucidate the intricate mechanisms underlying TNF-α’s role in sleep regulation and its potential therapeutic implications for sleep disorders and related conditions.
We found that serum sIL-2R level was positively associated with PSQI total score among patients with MDD, and patients with problems in sleep latency had higher serum sIL-2R levels. IL-2, primarily synthesized by helper T cells, serves as a T cell growth factor with extensive immunoregulatory functions and CNS regulatory roles.Reference Malek39 The exact nature of IL-2’s inflammatory activity remains ambiguous. While historical literature has predominantly depicted IL-2 as pro-inflammatory, recent clinical investigations have suggested its anti-inflammatory properties. Researchers have observed IL-2’s capacity to suppress inflammatory responses, leading to its classification as both pro- and anti-inflammatory.Reference Boerrigter, Weickert and Lenroot40, Reference Lan, Selmi and Gershwin41 Limited human studies have explored the relationship between sleep and IL-2. Previous studies in humans found that sleep deprivation increased IL-2 activity, with increased IL-2 production during sleep recovery.Reference Born, Lange and Hansen42, Reference Irwin, McClintick and Costlow43 Sleep deprivation may result in an upregulation of TNF-α, which in turn stimulates NF-κB and subsequently increases IL-2 production.Reference Krueger, Majde and Obal44 This could explain our observation of the association between high sIL-2R levels and longer sleep latency. The sleep-inducing effects of IL-2 may be mediated through various mechanisms, including opioid receptor antagonism, induction of sleep regulatory substances such as TNF-α, and activation of neuronal nitric oxide (NO) synthase. These findings suggest a complex interplay between IL-2 and other sleep regulatory substances, highlighting the need for further research to elucidate the precise mechanisms underlying IL-2-induced sleep regulation.
This cross-sectional study suggests a potential interplay between sleep disturbance and inflammation in depression. However, the causal directionality between sleep and inflammation remains uncertain. From an evolutionary perspective, cytokines are crucial regulators of host defense against infection, and the sleep-inducing effects of cytokines, along with the activation of immune cells that stimulate cytokine production, may lead individuals with elevated inflammation levels to longer sleep durations.Reference Vgontzas, Papanicolaou and Bixler45 Experimental studies have demonstrated that cytokines can modulate sleep architecture, implying that alterations in inflammation could plausibly contribute to sleep abnormalities and subsequently elevate the risk of depression.Reference Haack, Schuld and Kraus46 Conversely, sleep disturbances might also contribute to increased systemic inflammation compared to periods of uninterrupted sleep.Reference Vgontzas, Zoumakis and Bixler47 For example, sleep reduction by 2 hours per night for a week in non-depressed individuals has been shown to increase circulating TNF-α.Reference Vgontzas, Zoumakis and Bixler47 Sleep disturbance is believed to influence the sympathetic nervous system and hypothalamic–pituitary–adrenal axis, thereby promoting inflammation. Specifically, β-adrenergic signaling has been implicated in inducing the expression of inflammatory genes, cytokine production, and systemic inflammation.Reference Hall, Smagula and Boudreau48
We did not find sex-specific effects of subjective sleep quality on inflammatory responses. The association between sleep patterns and health outcomes seems to exhibit sex-specific differences, with recent evidence indicating that women may be more susceptible to the consequences of poor sleep, showing heightened inflammatory responses.Reference Irwin, Carrillo and Olmstead36, Reference Suarez49 However, population-based studies have yielded conflicting results, with some showing no association between poor sleep and peripheral inflammatory markers when pooling men and women, while others suggest a stronger link in women when analyzed separately.Reference Taheri, Austin and Lin50 These findings underscore the need for further investigation into the sex-specific effects of sleep disturbances on inflammatory mechanisms and subsequent health outcomes.
Our study offers valuable insights into the potential underlying mechanisms of MDD and its related symptoms, namely sleep disturbance. It can inform the development of targeted interventions aimed at improving both sleep quality and inflammation, thereby potentially ameliorating the burden of depression and reducing the risk of related health complications.Reference Mallon, Broman and Hetta51 Chronic inflammation, linked to adverse health conditions like cardiovascular disease and diabetes, underscores the importance of investigating how poor sleep quality, inflammation, and depression interact. In addition, our team’s past research on patients with MDD found that inflammation is associated with cognitive impairment and suicidal ideation;Reference Huang, Chen and Hsu5, Reference Huang, Chan and Chen26 this study further expands the clinical influences of inflammation on sleep disturbance in MDD.
This study has several limitations. First, the sample size was relatively small. Studies with a larger sample size are required to avoid a type II error. Second, a cross-sectional design limits causality establishment between variables, only allowing for associations to be observed. To better understand the mediating role of inflammation in the relationship between MDD incidence and sleep disturbances, longitudinal studies or experimental manipulations of sleep and/or cytokines are necessary.Reference Prather, Rabinovitz and Pollock52 Third, other inflammatory markers, such as IL-1β or neutrophil-to-lymphocyte ratio, may also be relevant to the pathophysiology of depression and sleep disturbance, but were not included in this study.Reference Norman, Karelina and Zhang53, Reference Fusar-Poli, Natale and Amerio54 Also, inflammatory markers were measured only once in the current study, whereas repeated measurements would have provided a more robust validation of inflammatory activity. Fourth, the associations between sleep disturbance and inflammation varied by the method of sleep assessment,Reference Irwin, Olmstead and Carroll12 as polysomnography or actigraphic assessment was not utilized. Objective sleep parameters and sleep apnea were not evaluated in this study, potentially impacting the results, given the known association between obstructive sleep apnea and increased inflammatory activity, including alterations in TNF-α production.Reference Tamaki, Yamauchi and Fukuoka55 Additionally, lifestyle factors such as diet and physical activity,Reference Kiecolt-Glaser56, Reference Gleeson, Bishop and Stensel57 which could influence inflammation and sleep quality, were not collected in our study. Last, a considerable portion of patients were prescribed psychotropic medications in our study, particularly antidepressants, known to impact REM latency and REM sleep duration; some psychotropic medications carry anti-inflammatory effects.Reference Beurel and Jope58 Cautious interpretation of our results due to potential confounding effects of medications on sleep quality and inflammation is needed,Reference Pompili, Venturini and Palermo59 but preserving patients’ ongoing medication prevents disease relapse and exacerbation of symptoms throughout the study period and was ethically imperative.
This study elucidates the intricate relationship between sleep disturbance and inflammation among patients with MDD, particularly focusing on inflammatory markers such as sTNF-αR1 and sIL-2R. Patients with TRD exhibited higher levels of serum sTNF-αR1. Serum sTNF-αR1 levels positively correlated with sleep latency, and serum sIL-2R levels were associated with poorer overall sleep quality and specific sleep disturbances. These findings indicate the interplay between inflammation and sleep in MDD. Further longitudinal research is warranted to elucidate causal relationships, so as to inform potential therapeutic interventions targeting both domains.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1017/S1092852925000227.
Data availability statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Acknowledgments
We would like to thank all the patients who kindly participated in this study and all the research assistants, physicians, and staff who assisted with the study and imaging procedures. This study was sponsored by grants from Taipei Veterans General Hospital (V108D44-003-MY3-1, V111-B-019) [CTL], University System of Taiwan Joint Research Program (VGHUST111-G1-2-1), and the National Science and Technology (MOST108-2321-B-075-004-MY2; NSC 111-2314-B-075-085-MY3) [CTL] (NSTC112-2314-B-A19-001, NSTC113-2314-B-350-003) [MHH]. This work was also supported by the Brain Research Center, National Yang Ming Chiao Tung University, from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. Neither of the aforementioned organizations had any role in the study design, data collection, analysis, interpretation of results, writing of the report, or ultimate decision to submit the paper for publication.
Author contribution
Mao-Hsuan Huang: conceptualization, formal analysis, investigation, writing—original draft; Mu-Hong Chen: methodology; Pei-Chi Tu: supervision; Ya Mei Bai: data curation, funding acquisition, project administration, resources; Tung-Ping Su: supervision; Yee-Lam E Chan: visualization; Cheng-Ta Li: data curation, funding acquisition, project administration, resources, writing—review & editing.
Financial support
All authors have no financial relationships relevant to this article to disclose.
Disclosures
All authors have no conflict of interest.
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