Abstract
Sleep difficulties are common and often precede depressive disorders. We aimed to explore the associations between systemic inflammatory markers and depression risk in individuals with difficulty sleeping. We utilized data from the National Health and Nutrition Examination Survey (NHANES, 2005–2020), encompassing 7916 participants who reported having difficulty sleeping. The systemic inflammation response index (SIRI) and neutrophil‒platelet ratio (NPR) were calculated using peripheral blood cell counts. Odds ratios (ORs) and 95 % confidence intervals (CIs) of the SIRI/NPR for depression risk were calculated via logistic regression models. Restricted cubic spline (RCS) analysis was used to examine the dose‒response relationships between these indices and depression risk, whereas receiver-operating characteristic (ROC) analysis was used to evaluate their prognostic accuracy for depression risk. Participants in the highest SIRI and NPR quartile groups had significantly greater depression risk than those in the lowest quartile group did (OR (SIRI): 1.50, 95 % CI = 1.10–2.04; OR (NPR): 1.49, 95 % CI = 1.04–2.13). Subgroup analyses revealed consistent associations across different demographics and clinical subgroups. RCS analyses revealed a nonlinear association between depression risk and the SIRI (J-shaped, P nonlinearity <0.001) but not the NPR (P nonlinearity >0.05). ROC analysis revealed moderate discriminative ability for both the SIRI (AUC = 0.66, 95 % CI = 0.64–0.68) and the NPR (AUC = 0.65, 95 % CI = 0.63–0.67) in predicting depression among individuals with difficulty sleeping. These findings suggest that the SIRI and NPR are independently associated with increased depression risk among individuals with difficulty sleeping.
Keywords: Inflammatory indices, Depression, Sleep disorder, Cohort study, NHANES, Association
Highlights
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SIRI and NPR are positively associated with depression risk in individuals with sleep difficulties.
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A J-shaped relationship exists between SIRI and depression, with risk rising at moderate elevations.
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Findings underscore systemic inflammation as a potential target in sleep-related depression.
1. Introduction
Sleep disturbances are prevalent in modern society and are often characterized by difficulty falling asleep, staying asleep, or experiencing unrefreshing sleep [1]. The prevalence of difficulty sleeping was 29.8 % in a cross-sectional analysis among U.S. adults [2]. The consequences of chronic sleep disturbance extend far beyond fatigue and have significant impacts on physical health, such as metabolic, cardiovascular, and psychiatric disorders, especially depression [3,4]. In U.S. adults, 21.1 % of patients with clinically relevant depression (CRD) experience difficulty sleeping, and 33.5 % of individuals with difficulty sleeping develop CRD, highlighting the potential bidirectional relationship between these conditions [5]. This interaction also complicates the clinical treatment of both disorders and highlights the need for a comprehensive understanding of their shared pathophysiology.
Psychoneuroimmunology research highlights systemic inflammation as a key driver of depression [6]. Elevated levels of proinflammatory cytokines, such as interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and C-reactive protein (CRP), and the erythrocyte sedimentation rate (ESR) consistently associated with depressive symptoms [[7], [8], [9]]. Recent literature, including meta-analyses with large sample sizes, also indicates that depression is a pro-inflammatory state. This is evidenced by elevated levels of inflammatory markers such as TNF-α, CRP, interferon-gamma (INF-γ), and monocyte chemoattractant protein-1 (MCP-1) in individuals with depression, suggesting that these markers are elevated broadly across individuals with depression, not just specific subgroups [10,11]. Mechanistic research suggests that these biomarkers not only reflect peripheral inflammation but also indicate an inflammatory state in the central nervous system, which may contribute to mood swings and cognitive dysfunction [12]. Therefore, these traditional markers have been widely utilized to evaluate systemic inflammation [13].
Recently, interest in innovative composite indices that offer a more comprehensive assessment of the inflammatory environment has increased. The systemic inflammation response index (SIRI) and the neutrophil‒platelet ratio (NPR) are two such indices that have garnered attention in recent studies [14,15]. The SIRI is calculated via the peripheral blood cell count, which can capture the balance between proinflammatory and regulatory immune responses [16]. Similarly, the NPR reflects the interplay between innate immune activation and hemostatic regulation [17]. Both indices have shown promise in prognosticating outcomes in various inflammatory and neoplastic conditions [18,19]. As for depression, existing studies have explored the association between SIRI and depressive symptoms, yielding mixed findings. An analysis reported a slight but significant association between SIRI and the risk of depression (OR: 1.06, 95 % CI = 1.01 to 1.10), suggesting its potential as a biomarker for guiding anti-inflammatory treatment strategies in depression management [20]. Conversely, a recent randomized controlled trial (RCT) on adjunctive cyclooxygenase-2 (COX-2) inhibition in treatment-resistant bipolar depression (TRBDD) found no significant difference in baseline SIRI levels between TRBDD patients and healthy controls [21]. However, higher baseline SIRI levels were correlated with poorer depressive outcomes after treatment, particularly in patients with severe baseline depression [21]. These findings underscore the potential utility of SIRI in predicting treatment response, while its role in forecasting the risk of depression remains less consistent across studies. Higher NPR has also shown promise in understanding the inflammation-depression link, aligning with limited evidence of similar ratios like NLR being significantly associated with poor electroconvulsive therapy (ECT) efficacy [22]. These findings underscore the potential of markers such as SIRI and NPR in further exploring the inflammation-depression link.
Sleep disturbances alone constitute a significant stressor capable of exacerbating systemic inflammation [23]. Investigating the potential associations between inflammatory marker levels and the risk of developing depression in individuals experiencing sleep difficulties could yield valuable insights into novel therapeutic targets. Despite the growing body of literature exploring these links between systemic inflammation, sleep disturbances, and depression, most of these investigations have either assessed general populations or used traditional inflammatory markers such as CRP or IL-6 [24,25]. Few studies have specifically focused on individuals with self-reported sleep difficulties, a clinically vulnerable group, and even fewer have evaluated composite indices like SIRI or NPR. Our study addresses this gap by examining the association of SIRI and NPR with depression risk exclusively in individuals with sleep disturbances, using a large, representative cohort from the National Health and Nutrition Examination Survey (NHANES) spanning 2005–2020. This unique approach enables a more precise understanding of the inflammation–depression link in the context of impaired sleep.
2. Methods
2.1. Study design and population
The NHANES is a continuous program that assesses the health and nutritional status of adults and children in the United States. NHANES investigators use a complex, multistage, probability sampling design to ensure that the sample is representative of the civilian and noninstitutionalized U.S. population. The data collection procedures were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants provided written informed consent.
This study was conducted in accordance with the Declaration of Helsinki and followed guidelines for the use of deidentified, publicly available data. Initially, 76,496 participants were considered from 7 survey cycles from 2005 to 2020. Participants younger than 20 years were excluded, along with those who had incomplete data on sleep disturbances, depression, inflammatory markers, and relevant covariates. After applying these exclusion criteria, a final sample of 7916 participants with difficulty sleeping was included in the analysis (Fig. 1).
Fig. 1.
Flowchart of participant inclusion and exclusion criteria.
Abbreviations: NHANES, National Health and Nutrition Examination Survey; PHQ-9, Patient Health Questionnaire-9; CBC, complete blood count.
2.2. Definitions of difficulty sleeping and depression
Trouble sleeping was defined on the basis of an affirmative response to the question “Have you ever told a doctor that you had difficulty sleeping?“, and participants could respond with either “Yes” or “No.” Individuals who responded “Yes” were asked to specify the category of difficulty sleeping, including difficulty falling asleep, night waking, early waking, feeling unrested during the daytime, daytime sleepiness, insufficient sleep, and taking sleep pills. Depression was assessed via the Patient Health Questionnaire-9 (PHQ-9). A PHQ-9 score of ≥10 was used to define the presence of clinically significant depressive symptoms.
2.3. Systemic inflammatory markers
Whole blood specimens were analyzed at NHANES Mobile Examination Centers (MECs) using the Beckman Coulter hematology analyzer. Strict quality control protocols, including duplicate testing and instrument calibration, were implemented to ensure data accuracy. These procedures provide reliable complete blood count (CBC) parameters for calculating inflammatory indices like SIRI and NPR used in this study. The formula for the SIRI is as follows: neutrophil count × monocyte count/lymphocyte count. The NPR was calculated as the ratio of the neutrophil count to the platelet count. These indices were chosen on the basis of their reported utility in reflecting systemic inflammatory status under various conditions. Additionally, due to the right-skewed distribution of inflammatory markers (Fig. 2A–C), the SIRI and NPR data were ln-transformed (Fig. 2B–D).
Fig. 2.
The distribution of the SIRI and NPR of the original values (A, C), and natural-logarithm (ln) converted values (B, D).
Abbreviations: SIRI, systemic inflammation response index; NPR, neutrophil‒platelet ratio.
2.4. Covariates
In this study, we accounted for various covariates, including demographic, socioeconomic, and lifestyle factors. Demographic and socioeconomic variables, such as age, sex, race/ethnicity, and body mass index (BMI), were assessed using standardized methods. Age was categorized into four groups (20–39, 40–59, 60–79, and ≥80 years). Race/ethnicity was classified as non-Hispanic white, non-Hispanic black, Mexican American, or other/multiracial. BMI was calculated based on measured weight and height and categorized as underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), or obesity (≥30.0 kg/m2). Health insurance status (insured or uninsured) was determined through self-reports. Comorbid conditions, including stroke, hypertension, diabetes, coronary heart disease (CHD), and heart failure, were also assessed based on participants self-reported history of physician-diagnosed conditions. All data were collected using standardized interviews and physical examinations conducted by trained personnel to ensure consistency and accuracy. These covariates were carefully selected to control for potential confounders and improve the robustness of the analysis.
2.5. Statistical analysis
Descriptive statistics summarized continuous variables as means ± SD and categorical variables as n (%). Differences between groups were assessed via t tests for continuous variables and Rao‒Scott chi-square tests for categorical variables. Survey-weighted logistic regression was conducted using the ‘survey’ package in R, accounting for strata, primary sampling unit (PSU), and sampling weights provided by NHANES. Three models were constructed: Model 1 was unadjusted; Model 2 was adjusted for demographic and socioeconomic variables; and Model 3 was further adjusted for comorbid conditions. A trend test was also conducted for the SIRI/NPR quartile groups. Subgroup analyses were performed to explore the consistency of associations across different demographic and clinical subgroups. Restricted cubic spline (RCS) regression was used to assess the dose‒response relationship between the SIRI/NPR and the risk of developing depression, allowing the detection of nonlinear associations. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the predictive ability of these indices for the development of depression, and the area under the ROC curve (AUC) was calculated to provide a measure of discriminative performance. All the statistical analyses were performed via R version 4.4.0 (Vienna, Austria). For all the analyses, a two-tailed p value < 0.05 was considered to indicate statistical significance.
3. Results
3.1. Participant characteristics
The demographic and clinical characteristics of the study population stratified by depression status are presented in Table 1. Among all the included participants, 629 (7.9 %) were diagnosed with depression on the basis of their PHQ-9 scores. Depressed participants were more often female (64.09 % vs 50.40 %) and obese (43.23 % vs 33.42 %; both p < 0.001). Depressed participants also had a greater prevalence of comorbidities, including stroke (7.53 % vs. 2.29 %, p < 0.001), hypertension (43.59 % vs. 29.13 %, p < 0.001), diabetes (12.31 % vs. 7.25 %, p = 0.002), heart failure (4.79 % vs. 1.85 %, p < 0.001), and cancer (13.04 % vs. 8.45 %, p < 0.001). As a comparison, the demographic characteristics of participants without difficulty sleeping are provided in Table S1. Additionally, Table 2 provides detailed information on the types of sleep difficulties reported by participants with and without depression.
Table 1.
Demographic characteristics of the included participants with different depression statuses.
| Characteristic | Overall (n = 7916) | Depression status (n = 629) | Nondepression status (n = 7287) | P value |
|---|---|---|---|---|
| Sex | <0.001 | |||
| Female | 4026 (51.30 %) | 398 (64.09 %) | 3628 (50.40 %) | |
| Male | 3890 (48.70 %) | 231 (35.91 %) | 3659 (49.60 %) | |
| Age | 46.21 (16.33) | 45.80 (14.20) | 46.24 (16.47) | 0.921 |
| Age group | <0.001 | |||
| 20–39 years | 2829 (37.94 %) | 215 (34.23 %) | 2614 (38.21 %) | |
| 40–59 years | 2551 (40.15 %) | 271 (48.90 %) | 2280 (39.53 %) | |
| 60–79 years | 2098 (18.61 %) | 133 (15.76 %) | 1965 (18.81 %) | |
| ≥80 years | 438 (3.30 %) | 10 (1.11 %) | 428 (3.45 %) | |
| Race/ethnicity | 0.005 | |||
| Mexican American | 1451 (7.83 %) | 114 (8.59 %) | 1337 (7.77 %) | |
| Other Hispanic | 563 (4.00 %) | 67 (6.16 %) | 496 (3.85 %) | |
| Non-Hispanic White | 4027 (73.11 %) | 287 (66.13 %) | 3740 (73.60 %) | |
| Non-Hispanic Black | 1589 (10.10 %) | 141 (14.60 %) | 1448 (9.78 %) | |
| Other/multiracial | 286 (4.97 %) | 20 (4.52 %) | 266 (5.00 %) | |
| BMI (kg/m2) | 28.61 (6.56) | 30.40 (7.78) | 28.48 (6.45) | 0.001 |
| BMI group | 0.001 | |||
| Underweight | 123 (1.55 %) | 8 (0.91 %) | 115 (1.59 %) | |
| Normal | 2186 (30.43 %) | 150 (25.22 %) | 2036 (30.80 %) | |
| Overweight | 2756 (33.96 %) | 187 (30.65 %) | 2569 (34.19 %) | |
| Obesity | 2851 (34.07 %) | 284 (43.23 %) | 2567 (33.42 %) | |
| Health insurance | <0.001 | |||
| Insured | 6128 (81.74 %) | 444 (73.49 %) | 5684 (82.32 %) | |
| Uninsured | 1788 (18.26 %) | 185 (26.51 %) | 1603 (17.68 %) | |
| Stroke status | <0.001 | |||
| Stroke | 267 (2.64 %) | 46 (7.53 %) | 221 (2.29 %) | |
| Nonstroke | 7649 (97.36 %) | 583 (92.47 %) | 7066 (97.71 %) | |
| Hypertension status | <0.001 | |||
| Hypertension | 2652 (30.08 %) | 279 (43.59 %) | 2373 (29.13 %) | |
| Nonhypertension | 5264 (69.92 %) | 350 (56.41 %) | 4914 (70.87 %) | |
| Diabetes status | 0.002 | |||
| Diabetes | 842 (7.58 %) | 98 (12.31 %) | 744 (7.25 %) | |
| Nondiabetes | 7074 (92.42 %) | 531 (87.69 %) | 6543 (92.75 %) | |
| CHD status | 0.086 | |||
| CHD | 307 (3.15 %) | 36 (4.36 %) | 271 (3.06 %) | |
| Non-CHD | 7609 (96.85 %) | 593 (95.64 %) | 7016 (96.94 %) | |
| Heart failure status | <0.001 | |||
| Heart failure | 236 (2.04 %) | 35 (4.79 %) | 201 (1.85 %) | |
| Nonheart failure | 7680 (97.96 %) | 594 (95.21 %) | 7086 (98.15 %) | |
| Cancer status | <0.001 | |||
| Cancer | 698 (8.33 %) | 82 (13.04 %) | 616 (8.45 %) | |
| Noncancer | 7218 (91.67 %) | 547 (86.96 %) | 6671 (91.54 %) |
Note: Continuous variables are displayed as the mean (SD), and classification variables are represented as n (%).
Abbreviations: BMI Body mass index, CHD coronary heart disease.
Table 2.
Types of sleep difficulties among participants stratified by depression status.
| Type of difficulty sleeping | Overall (n = 7916) | Depression status (n = 629) | Nondepression status (n = 7287) | P value |
|---|---|---|---|---|
| Difficulty falling asleep | <0.001 | |||
| Yes | 4639 (61.68 %) | 513 (83.03 %) | 4126 (60.17 %) | |
| No | 3277 (38.32 %) | 116 (16.97 %) | 3161 (39.83 %) | |
| Night waking | <0.001 | |||
| Yes | 5009 (65.58 %) | 526 (85.65 %) | 4483 (64.16 %) | |
| No | 2907 (34.42 %) | 103 (14.35 %) | 2804 (35.84 %) | |
| Early waking | <0.001 | |||
| Yes | 4433 (57.25 %) | 479 (75.04 %) | 3954 (55.99 %) | |
| No | 3483 (42.75 %) | 150 (24.96 %) | 3333 (44.01 %) | |
| Unrested daytime | <0.001 | |||
| Yes | 5403 (73.96 %) | 548 (90.72 %) | 4855 (72.78 %) | |
| No | 2513 (26.04 %) | 81 (9.28 %) | 2432 (27.22 %) | |
| Sleepy daytime | <0.001 | |||
| Yes | 5132 (69.33 %) | 544 (89.38 %) | 4588 (67.91 %) | |
| No | 2784 (30.67 %) | 85 (10.62 %) | 2699 (32.09 %) | |
| Insufficient sleep | <0.001 | |||
| Yes | 5468 (75.05 %) | 543 (88.91 %) | 4925 (74.07 %) | |
| No | 2448 (24.95 %) | 86 (11.09 %) | 2362 (25.93 %) | |
| Sleep pills | <0.001 | |||
| Yes | 5468 (75.05 %) | 543 (88.91 %) | 4925 (74.07 %) | |
| No | 2448 (24.95 %) | 86 (11.09 %) | 2362 (25.93 %) |
3.2. Associations between the SIRI/NPR and the risk of developing depression
The results of the weighted multivariate logistic regression analyses examining the associations between the selected systemic inflammatory indices (SIRI and NPR) and the risk of developing depression among individuals with difficulty sleeping are presented in Table 3. In the unadjusted model (Model 1), each unit increase in the SIRI was associated with 92 % greater odds of developing depression (OR: 1.92, 95 % CI = 1.26–2.91, p = 0.003). This association remained significant after adjusting for demographic factors and comorbidities in Model 3 (OR: 1.83, 95 % CI = 1.19–2.81; p = 0.009). Quartile analysis revealed a dose‒response relationship between the SIRI and the risk of developing depression. Compared with those in the lowest quartile, participants in the highest SIRI quartile had 50 % greater odds of developing depression in the fully adjusted model (OR: 1.50, 95 % CI = 1.10–2.04; p = 0.014). The trend across quartiles was statistically significant (P-trend<0.001). Similarly, in the fully adjusted model, each unit increase in the ln-transformed NPR was associated with 2.40 times greater odds of developing depression (OR: 2.40, 95 % CI = 1.15–4.99; p = 0.022). Compared with those in the lowest quartile, participants in the highest NPR quartile had 49 % greater odds of developing depression (OR: 1.49, 95 % CI = 1.04–2.13, p = 0.034), with a significant trend across quartiles (p-trend<0.001). Among individuals without sleep difficulties, the fully adjusted model revealed no significant associations between SIRI or NPR and depression risk, highlighting the unique interplay between systemic inflammation and depression specifically in the context of sleep disruptions (Table S2).
Table 3.
Weighted multivariate logistic analysis for the associations between the SIRI/NPR and the risk of developing depression among patients with difficulty sleeping.
| Exposure | Model 1 |
Model 2 |
Model 3 |
||||||
|---|---|---|---|---|---|---|---|---|---|
| OR (95 % CI) | P value | P-trend | OR (95 % CI) | P value | P-trend | OR (95 % CI) | P value | P-trend | |
| SIRI | |||||||||
| Continuous | 1.92 (1.26, 2.91) | 0.003 | N/A | 1.90 (1.24, 2.91) | 0.005 | N/A | 1.83 (1.19, 2.81) | 0.009 | N/A |
| Quartile 1 | ref | <0.001 | ref | <0.001 | ref | <0.001 | |||
| Quartile 2 | 1.06 (0.78, 1.45) | 0.697 | 1.10 (0.79, 1.53) | 0.544 | 1.12 (0.79, 1.59) | 0.494 | |||
| Quartile 3 | 1.28 (1.01, 1.63) | 0.043 | 1.35 (1.02, 1.77) | 0.036 | 1.36 (1.04, 1.80) | 0.03 | |||
| Quartile 4 | 1.51 (1.14, 2.01) | 0.006 | 1.51 (1.11, 2.05) | 0.01 | 1.50 (1.10, 2.04) | 0.014 | |||
| NPR | |||||||||
| Continuous | 3.18 (1.70, 5.95) | 0.001 | N/A | 2.65 (1.29, 5.45) | 0.01 | N/A | 2.40 (1.15, 4.99) | 0.022 | N/A |
| Quartile 1 | ref | <0.001 | ref | <0.001 | ref | <0.001 | |||
| Quartile 2 | 1.00 (0.79, 1.28) | 0.971 | 0.94 (0.72, 1.22) | 0.609 | 0.92 (0.70, 1.20) | 0.497 | |||
| Quartile 3 | 1.31 (0.99, 1.75) | 0.062 | 1.24 (0.92, 1.67) | 0.148 | 1.24 (0.92, 1.67) | 0.141 | |||
| Quartile 4 | 1.74 (1.28, 2.37) | 0.001 | 1.56 (1.10, 2.21) | 0.015 | 1.49 (1.04, 2.13) | 0.034 |
Note: OR for continuous variables reflect per-unit increase in ln-transformed SIRI/NPR.
Model 1 was unadjusted; Model 2 was adjusted for age, sex, race/ethnicity, BMI, and health insurance status; and Model 3 was additionally adjusted for comorbidities, including hypertension, diabetes, stroke, CHD, cancer and heart failure.
Abbreviations: OR, odds ratio; SIRI, systemic inflammation response index; NPR, neutrophil‒platelet ratio; N/A, not available.
3.3. Subgroup analyses
The associations between the SIRI/NPR and the risk of developing depression were generally consistent across different subgroups, with some notable variations (Table 4). The association between the SIRI and the risk of developing depression was stronger in males (OR: 3.26, 95 % CI = 1.46–7.28, p = 0.006) than in females (OR: 1.44, 95 % CI = 0.80–2.59, p = 0.212). Both the SIRI and the NPR were more strongly associated with depression in individuals with obesity (SIRI: OR: 2.12, 95 % CI = 1.37–3.29, p = 0.002; NPR: OR: 3.79, 95 % CI = 2.04–7.04, p < 0.001) than in nonobese participants. Interaction analysis showed no statistically significant interactions between SIRI/NPR and variables such as sex, age, race, obesity status, or health insurance status in relation to depression risk (all p-interaction >0.05).
Table 4.
Subgroup analyses on the basis of the participants general characteristics.
| Subgroup | SIRI |
NPR |
||||
|---|---|---|---|---|---|---|
| OR (95 % CI) | P value | P-interaction | OR (95 % CI) | P value | P-interaction | |
| Sex | 0.127 | 0.295 | ||||
| Female | 1.44 (0.80, 2.59) | 0.212 | 2.45 (1.04, 5.77) | 0.041 | ||
| Male | 3.26 (1.46, 7.28) | 0.006 | 3.89 (1.18, 12.8) | 0.028 | ||
| Age | 0.714 | 0.395 | ||||
| 20–49 years | 1.94 (1.17, 3.22) | 0.013 | 2.25 (1.02, 5.00) | 0.046 | ||
| ≥50 years | 1.89 (0.87, 4.09) | 0.1 | 3.96 (1.18, 13.2) | 0.028 | ||
| Race | 0.436 | 0.557 | ||||
| White | 2.13 (1.26, 3.62) | 0.007 | 3.21 (1.34, 7.70) | 0.012 | ||
| Other | 1.85 (1.04, 3.28) | 0.038 | 2.60 (1.02, 6.66) | 0.046 | ||
| Obesity status | 0.902 | 0.209 | ||||
| Obesity | 2.12 (1.37, 3.29) | 0.002 | 3.79 (2.04, 7.04) | 0 | ||
| Nonobesity | 1.97 (1.05, 3.71) | 0.037 | 2.70 (0.94, 7.77) | 0.064 | ||
| Health insurance status | 0.711 | 0.631 | ||||
| Insured | 1.89 (1.10, 3.24) | 0.024 | 3.32 (1.46, 7.56) | 0.006 | ||
| Uninsured | 2.81 (1.22, 6.49) | 0.018 | 2.42 (0.66, 8.83) | 0.169 |
Note: OR for continuous variables reflect per-unit increase in ln-transformed SIRI/NPR. Subgroup analyses were conducted via the previously described Model 3.
Abbreviations: OR, odds ratio; 95 % CI, 95 % confidence interval; SIRI, systemic inflammation response index; NPR neutrophil‒platelet ratio.
Subgroup analyses based on the category of difficulty sleeping are shown in Table 5. For those reporting difficulty falling asleep, the SIRI (OR: 2.24, 95 % CI = 1.28–3.93, p = 0.007) and NPR (OR: 3.13, 95 % CI = 1.30–7.56, p = 0.014) were strongly associated with the risk of developing depression. Similar associations were observed between the SIRI/NPR and the risk of experiencing night waking, early waking, and feeling unrested during the day. The associations were generally weaker or nonsignificant in participants who did not report these specific sleep problems.
Table 5.
Subgroup analyses based on the category of difficulty sleeping.
| Subgroup | SIRI |
NPR |
||
|---|---|---|---|---|
| OR (95 % CI) | P value | OR (95 % CI) | P value | |
| Difficulty falling asleep | ||||
| Yes | 2.24 (1.28, 3.93) | 0.007 | 3.13 (1.30, 7.56) | 0.014 |
| No | 0.61 (0.23, 1.58) | 0.289 | 1.69 (0.51, 5.57) | 0.369 |
| Night waking | ||||
| Yes | 2.01 (1.27, 3.18) | 0.005 | 3.55 (1.68, 7.49) | 0.002 |
| No | 1.06 (0.30, 3.75) | 0.918 | 0.89 (0.22, 3.60) | 0.866 |
| Early waking | ||||
| Yes | 2.01 (1.14, 3.53) | 0.018 | 3.36 (1.60, 7.08) | 0.003 |
| No | 1.39 (0.63, 3.06) | 0.388 | 1.59 (0.46, 5.44) | 0.442 |
| Unrested daytime | ||||
| Yes | 2.01 (1.23, 3.28) | 0.008 | 3.18 (1.51, 6.72) | 0.004 |
| No | 0.84 (0.31, 2.28) | 0.711 | 0.34 (0.07, 1.56) | 0.155 |
| Sleepy daytime | ||||
| Yes | 1.90 (1.18, 3.05) | 0.011 | 2.86 (1.37, 5.99) | 0.008 |
| No | 1.37 (0.42, 4.43) | 0.580 | 1.38 (0.20, 9.54) | 0.728 |
| Insufficient sleep | ||||
| Yes | 1.97 (1.25, 3.13) | 0.006 | 3.06 (1.38, 6.78) | 0.009 |
| No | 1.58 (0.51, 4.92) | 0.410 | 1.74 (0.28, 10.8) | 0.534 |
| Sleep pills | ||||
| Yes | 1.54 (1.13, 2.32) | 0.002 | 2.02 (1.18, 4.54) | 0.001 |
| No | 1.12 (0.65, 4.25) | 0.320 | 0.89 (0.45, 6.81) | 0.223 |
Note: OR for continuous variables reflect per-unit increase in ln-transformed SIRI/NPR. Subgroup analyses were conducted with the previously described Model 3.
Abbreviations: OR, odds ratio; 95 % CI, 95 % confidence interval; SIRI systemic inflammation response index; NPR neutrophil‒platelet ratio.
3.4. RCS analysis
The results of the RCS analysis are shown in Fig. 3. For the SIRI, the analysis revealed a J-shaped nonlinear relationship with the risk of developing depression (P nonlinearity <0.001), with a threshold effect observed at higher SIRI values. The risk of developing depression increased significantly at SIRI values above 0.021, suggesting that even moderate elevations in these indices are associated with a marked increase in the risk of developing depression (Fig. 3A–C). On the other hand, the analyses did not reveal a nonlinear relationship between the NPR and the risk of developing depression (P nonlinearity >0.05) (Fig. 3D–F).
Fig. 3.
Restricted cubic spline (RCS) curves depicting the associations between systemic inflammatory indices and the risk of depression among participants with self-reported trouble sleeping. Shaded areas denote 95 % confidence intervals. All models are survey-weighted logistic-regression models 1–3.
A–C: SIRI shows a J-shaped nonlinear relationship with depression risk (non-linearity P < 0.001), with an inflection threshold at approximately ln-SIRI = 0.021 (original SIRI = 1.021; dashed vertical line); D–F: NPR demonstrates a linear dose-response relationship with depression risk (non-linearity P > 0.05).
Abbreviations: SIRI, systemic inflammation response index; NPR, neutrophil‒platelet ratio.
3.5. ROC analysis
The ROC curves for the usefulness of the SIRI and NPR for predicting depression risk among individuals with difficulty sleeping are shown in Fig. 4. The AUC for the SIRI was 0.68 (95 % CI = 0.65–0.71), indicating moderate discriminative ability (Fig. 4A–C). Similarly, the AUC for the NPR was 0.66 (95 % CI = 0.63–0.69) (Fig. 4D–F).
Fig. 4.
Receiver-operating characteristic (ROC) curves illustrating the discriminatory ability of systemic inflammatory indices for detecting clinically significant depression among adults with self-reported trouble sleeping.
Each plot shows the area under the curve (AUC) with 95 % CI in the legend and marks the optimal Youden-index threshold. The red dashed line denotes the reference line (AUC = 0.50).
A–C: ROC curves for SIRI in model 1–3; D–F: ROC curves for NPR in model 1–3.
Abbreviations: SIRI, systemic inflammation response index; NPR, neutrophil‒platelet ratio.
4. Discussion
In our study, we examined the relationships between systemic inflammatory indices, specifically the SIRI and NPR, and the risk of developing depression in individuals with difficulty sleeping. Using data from the NHANES database, the present study revealed significant associations of higher SIRI and NPR values with an increased risk of developing depression among individuals with difficulty sleeping. This association was consistent across different sleep disturbance categories, including difficulty falling asleep, night waking, early waking, and feeling unrested during the day. Our results reinforce systemic inflammation as a key factor linking sleep disturbances to depression.
Our findings are in line with those of previous studies that highlighted the role of systemic inflammation in depression. For example, a series of systemic inflammatory indices, including the SIRI, were shown to be valuable predictors for differentiating individuals with mental illnesses from healthy control participants [26]. A study using NHANES data from 2005 to 2018 revealed that an increase in the SIRI is associated with a 6 % higher risk of developing depression in the general population [20]. These findings underscore the systemic inflammatory response as a critical factor in the development and persistence of depressive symptoms. Another study demonstrated that higher pretreatment SIRI levels were predictive of poorer depressive outcomes after treatment, further highlighting systemic inflammation's role in depression management [21]. Our findings are also consistent with those of studies on other neutrophil-based systemic inflammatory markers, even though the association between the NPR and the risk of developing depression has not been explored previously. One study involving a large NHANES community sample established that sleep disturbances and depressive symptoms are associated with an elevated neutrophil-to-lymphocyte ratio (NLR), indicating a potential mediating role of inflammation in the relationship between sleep problems and the risk of developing depression [27]. Similarly, the findings of another study revealed that the NLR tends to be greater in patients with depressive disorders, and a high NLR value supports the view that inflammation is a critical factor in the etiology of major depressive disorder [28]. Emerging evidence not only supports the link between inflammatory processes and depressive disorders but also suggests that anti-inflammatory treatments, such as omega-3 fatty acids, may modestly improve depressive symptoms, particularly in youth with depression [29]. These studies collectively suggest that systemic inflammatory markers such as the SIRI and NPR can be used to obtain valuable insights into the inflammatory processes underlying various health conditions, especially mental health disorders such as depression.
Inflammatory status is associated with difficulty sleeping [30]. Given the high incidence of depression among those with sleep disturbances, increasing research efforts are focused on elucidating the relationship between inflammation status and the risk of developing depression in this context [25]. The exact mechanisms through which systemic inflammation contributes to depression are complex and multifactorial. Inflammatory processes have been implicated in the pathophysiology of depression through several mechanisms. One proposed mechanism involves the activation of the hypothalamic‒pituitary‒adrenal (HPA) axis, leading to increased secretion of cortisol, a stress hormone that has been linked to depression [31,32]. Additionally, systemic inflammation can alter the synthesis, release, and reuptake of neurotransmitters such as serotonin and dopamine, which are critical for mood regulation [33]. Inflammation can also disrupt the blood‒brain barrier, allowing proinflammatory cytokines, such as TNF, IL-17A and IL-23, to enter the central nervous system and influence brain regions involved in mood regulation, such as the hypothalamus and hippocampus [34]. Consistent with this mechanism, our supplementary analyses revealed that SIRI and NPR were significantly associated with the risk of depression only in individuals with sleep disturbances, while no such associations were observed in those without sleep disturbances. The observed J-shaped association between SIRI and depression risk may reflect immune dysregulation at only high levels of systemic inflammation. Previous studies have suggested that mild inflammation may be adaptive, while excessive inflammation could trigger neurotoxic cascades, leading to mood disturbances [12,34]. These findings suggest that sleep disturbances may amplify the relationship between systemic inflammation and depression, highlighting the potential role of sleep as a mediator or modifier in this interplay [35]. This underscores the importance of considering sleep disturbances when investigating the inflammation-depression link and tailoring interventions for affected individuals.
Our subgroup analysis revealed that in males, the association between the SIRI and the risk of developing depression was highly significant (OR: 3.26, 95 % CI = 1.46–7.28), much greater than that in females (OR: 1.44, 95 % CI = 0.80–2.59), as was the association between the NPR and the risk of developing depression. Interestingly, the literature in the general population suggests that females have a greater overall risk of developing depression [36]. This discrepancy might be explained by the specific focus of our study on individuals with chronic systemic inflammation and difficulty sleeping. First, neuroimaging studies reveal sex-specific brain changes in depression, with males having larger brain volumes in regions such as the amygdala and cerebellum, whereas females show greater regional homogeneity in orbitofrontal areas related to emotional perception [36]. In addition, females typically exhibit a greater prevalence of depression, characterized by symptoms such as appetite disturbances, impaired sleep, and depressed mood. Males, on the other hand, often exhibit anger, aggression, substance use, and risk-taking behaviors, which may not fit traditional depression criteria [37]. Notably, the impact of inflammation on depression varies by sex, potentially due to the immune response and hormonal differences [13]. This might explain the stronger SIRI/NPR-depression link in males in our study. In our study, we also revealed that high SIRI and NPR values are associated with a greater risk of developing depression in patients with obesity than in nonobese individuals. Obesity status is associated with the development of systemic inflammation, which can contribute to depressive symptoms [38]. Addressing specific needs on the basis of these differences might be essential for developing effective, personalized treatments and improving outcomes.
Despite the strengths of this study, including the use of a large, nationally representative sample and comprehensive measures of inflammation and depression, several limitations should be noted. First, due to the cross-sectional nature of this study, the observed associations between inflammatory indices and depression risk should be interpreted with caution, as causality cannot be established. Longitudinal studies are needed to validate these findings. Second, both sleep difficulties and depression were self-reported, which may introduce recall or reporting bias. Objective assessments such as polysomnography or structured clinical interviews would provide more robust measurements. Finally, this study did not account for all potential confounders, such as genetic predispositions and environmental factors, which could influence the observed associations. Additionally, we did not include smoking, physical activity, sedentary behavior, or other psychiatric comorbidities (e.g., anxiety, substance use disorders) as covariates in our analyses, despite their potential relevance to both inflammation and depression. Future studies should aim to incorporate these variables to provide a more comprehensive understanding of the relationship between inflammation, sleep disturbances, and depression.
5. Conclusion
In conclusion, this study demonstrated that systemic inflammatory indices are associated with depressive symptoms in individuals with difficulty sleeping. These findings highlight the potential role of inflammation in the interplay between sleep disturbances and depression. Further research is warranted to confirm these associations, explore underlying mechanisms, and better understand the clinical implications of these findings.
CRediT authorship contribution statement
Ruolin Zhu: Writing – review & editing, Writing – original draft, Software, Investigation, Conceptualization. Lu Wang: Visualization, Project administration, Funding acquisition. Xingqi Wu: Writing – review & editing, Visualization, Investigation, Funding acquisition. Kai Wang: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Funding acquisition, Formal analysis.
Disclaimer
The findings and conclusions presented in this study are solely those of the authors and do not necessarily reflect the views of the National Center for Health Statistics or the Centers for Disease Control and Prevention.
Availability of data and materials
The datasets utilized in this investigation are publicly accessible and can be found on the NHANES website at https://www.cdc.gov/nchs/nhanes/.
Ethics approval and consent to participate
The study protocol was approved by the Ethics Review Board of the National Center for Health Statistics (NCHS). Additional information is available on the NHANES website (https://www.cdc.gov/nchs/nhanes/participant.htm). Written informed consent was obtained from all participants prior to their inclusion in the study.
Funding
We thank the support of the Hefei Comprehensive National Science Center Hefei Brain Project. This work was supported by the National Natural Science Foundation of China (No. 82101498 to XW); STI2030-Major Projects of China (No. 2021ZD0201801 to PH); Clinical Medical Research and Transformation Project of Anhui (Nos. 202304295107020039 to PH and 202204295107020028 to KW), and the 2021 Youth Foundation Training Program of the First Affiliated Hospital of Anhui Medical University (No. 2021kj19 to XW).
Declaration of competing interest
The authors declare that they have no competing interests.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.cpnec.2025.100299.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
References
- 1.Wilkinson K., Shapiro C. Nonrestorative sleep: symptom or unique diagnostic entity? Sleep Med. 2012;13(6):561–569. doi: 10.1016/j.sleep.2012.02.002. [DOI] [PubMed] [Google Scholar]
- 2.Di H., Guo Y., Daghlas I., Wang L., Liu G., Pan A., et al. Evaluation of sleep habits and disturbances among US adults, 2017-2020. JAMA Netw. Open. 2022;5(11) doi: 10.1001/jamanetworkopen.2022.40788. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Lalley-Chareczko L., Segal A., Perlis M.L., Nowakowski S., Tal J.Z., Grandner M.A. Sleep disturbance partially mediates the relationship between intimate partner violence and physical/mental health in women and men. J. Interpers Violence. 2017;32(16):2471–2495. doi: 10.1177/0886260515592651. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Grandner M.A., Jackson N.J., Pak V.M., Gehrman P.R. Sleep disturbance is associated with cardiovascular and metabolic disorders. J. Sleep Res. 2012;21(4):427–433. doi: 10.1111/j.1365-2869.2011.00990.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Chunnan L., Shaomei S., Wannian L. The association between sleep and depressive symptoms in US adults: data from the NHANES (2007-2014) Epidemiol. Psychiatr. Sci. 2022;31:e63. doi: 10.1017/s2045796022000452. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Lee C.H., Giuliani F. The role of inflammation in depression and fatigue. Front. Immunol. 2019;10:1696. doi: 10.3389/fimmu.2019.01696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Gimeno D., Kivimäki M., Brunner E.J., Elovainio M., De Vogli R., Steptoe A., et al. Associations of C-reactive protein and interleukin-6 with cognitive symptoms of depression: 12-year follow-up of the Whitehall II study. Psychol. Med. 2009;39(3):413–423. doi: 10.1017/s0033291708003723. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Moieni M., Irwin M.R., Jevtic I., Olmstead R., Breen E.C., Eisenberger N.I. Sex differences in depressive and socioemotional responses to an inflammatory challenge: implications for sex differences in depression. Neuropsychopharmacology. 2015;40(7):1709–1716. doi: 10.1038/npp.2015.17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Tan M., Luo Y., Hu J., Hu K., Li X., Yang J., et al. Elevated C-reactive protein and erythrocyte sedimentation rate correlates with depression in psoriasis: a Chinese cross-sectional study. Clin. Cosmet. Invest. Dermatol. 2023;16:397–405. doi: 10.2147/ccid.S401934. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Osimo E.F., Pillinger T., Rodriguez I.M., Khandaker G.M., Pariante C.M., Howes O.D. Inflammatory markers in depression: a meta-analysis of mean differences and variability in 5,166 patients and 5,083 controls. Brain Behav. Immun. 2020;87:901–909. doi: 10.1016/j.bbi.2020.02.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Islam M.R., Sohan M., Daria S., Masud A.A., Ahmed M.U., Roy A., et al. Evaluation of inflammatory cytokines in drug-naïve major depressive disorder: a systematic review and meta-analysis. Int. J. Immunopathol. Pharmacol. 2023;37 doi: 10.1177/03946320231198828. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Miller A.H., Raison C.L. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat. Rev. Immunol. 2016;16(1):22–34. doi: 10.1038/nri.2015.5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Beydoun M.A., Obhi H.K., Weiss J., Canas J.A., Beydoun H.A., Evans M.K., et al. Systemic inflammation is associated with depressive symptoms differentially by sex and race: a longitudinal study of urban adults. Mol. Psychiatr. 2020;25(6):1286–1300. doi: 10.1038/s41380-019-0408-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Wang W., Yao W., Tang W., Li Y., Lv Q., Ding W. Systemic inflammation response index is associated with increased all-cause and cardiovascular mortality in US adults with rheumatoid arthritis. Prev. Med. 2024;185 doi: 10.1016/j.ypmed.2024.108055. [DOI] [PubMed] [Google Scholar]
- 15.Somaschini A., Cornara S., Demarchi A., Mandurino-Mirizzi A., Fortuni F., Crimi G., et al. Neutrophil to platelet ratio: a novel prognostic biomarker in ST-elevation myocardial infarction patients undergoing primary percutaneous coronary intervention. Eur. J. Prev. Cardiol. 2020;27(19):2338–2340. doi: 10.1177/2047487319894103. [DOI] [PubMed] [Google Scholar]
- 16.Qi Q., Zhuang L., Shen Y., Geng Y., Yu S., Chen H., et al. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122(14):2158–2167. doi: 10.1002/cncr.30057. [DOI] [PubMed] [Google Scholar]
- 17.Wei X.B., Liu Y.H., He P.C., Yu D.Q., Tan N., Zhou Y.L., et al. The impact of admission neutrophil-to-platelet ratio on in-hospital and long-term mortality in patients with infective endocarditis. Clin. Chem. Lab. Med. 2017;55(6):899–906. doi: 10.1515/cclm-2016-0527. [DOI] [PubMed] [Google Scholar]
- 18.Zhou E., Wu J., Zhou X., Yin Y. Systemic inflammatory biomarkers are novel predictors of all-cause and cardiovascular mortality in individuals with osteoarthritis: a prospective cohort study using data from the NHANES. BMC Public Health. 2024;24(1):1586. doi: 10.1186/s12889-024-19105-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Yin Y., Zhu W., Xu Q. The systemic inflammation response index as a risk factor for hepatic fibrosis and long-term mortality among individuals with metabolic dysfunction-associated steatotic liver disease. Nutr. Metabol. Cardiovasc. Dis. 2024 doi: 10.1016/j.numecd.2024.04.018. [DOI] [PubMed] [Google Scholar]
- 20.Li X., Huan J., Lin L., Hu Y. Association of systemic inflammatory biomarkers with depression risk: results from national health and nutrition examination survey 2005-2018 analyses. Front. Psychiatr. 2023;14 doi: 10.3389/fpsyt.2023.1097196. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Murata S., Baig N., Decker K., Halaris A. Systemic inflammatory response index (SIRI) at baseline predicts clinical response for a subset of treatment-resistant bipolar depressed patients. J. Personalized Med. 2023;13(9) doi: 10.3390/jpm13091408. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Geng D., Wang W., Du N., Niwenahisemo L.C., Xu H., Wang Y., et al. Association of the neutrophil-to-platelet ratio with response to electroconvulsive therapy in adolescents with major depressive disorder. Front. Psychiatr. 2024;15 doi: 10.3389/fpsyt.2024.1413608. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Garbarino S., Lanteri P., Bragazzi N.L., Magnavita N., Scoditti E. Role of sleep deprivation in immune-related disease risk and outcomes. Commun. Biol. 2021;4(1):1304. doi: 10.1038/s42003-021-02825-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Seizer L., Martínez-Albert E., Löchner J. Nonlinear and symptom specific associations between chronotype and depression. Sci. Rep. 2024;14(1) doi: 10.1038/s41598-024-79868-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Yin J., Gong R., Zhang M., Ding L., Shen T., Cai Y., et al. Associations between sleep disturbance, inflammatory markers and depressive symptoms: mediation analyses in a large NHANES community sample. Prog. Neuropsychopharmacol. Biol. Psychiatry. 2023;126 doi: 10.1016/j.pnpbp.2023.110786. [DOI] [PubMed] [Google Scholar]
- 26.Wei Y., Wang T., Li G., Feng J., Deng L., Xu H., et al. Investigation of systemic immune-inflammation index, neutrophil/high-density lipoprotein ratio, lymphocyte/high-density lipoprotein ratio, and monocyte/high-density lipoprotein ratio as indicators of inflammation in patients with schizophrenia and bipolar disorder. Front. Psychiatr. 2022;13 doi: 10.3389/fpsyt.2022.941728. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Kinoshita H., Takekawa D., Kudo T., Sawada K., Mikami T., Hirota K. Higher neutrophil-lymphocyte ratio is associated with depressive symptoms in Japanese general male population. Sci. Rep. 2022;12(1):9268. doi: 10.1038/s41598-022-13562-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Demir S., Atli A., Bulut M., İbiloğlu A.O., Güneş M., Kaya M.C., et al. Neutrophil-lymphocyte ratio in patients with major depressive disorder undergoing no pharmacological therapy. Neuropsychiatric Dis. Treat. 2015;11:2253–2258. doi: 10.2147/ndt.S89470. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Vöckel J., Markser A., Wege L., Wunram H.L., Sigrist C., Koenig J. Pharmacological anti-inflammatory treatment in children and adolescents with depressive symptoms: a systematic-review and meta-analysis. Eur. Neuropsychopharmacol. 2024;78:16–29. doi: 10.1016/j.euroneuro.2023.09.006. [DOI] [PubMed] [Google Scholar]
- 30.Motivala S.J., Sarfatti A., Olmos L., Irwin M.R. Inflammatory markers and sleep disturbance in major depression. Psychosom. Med. 2005;67(2):187–194. doi: 10.1097/01.psy.0000149259.72488.09. [DOI] [PubMed] [Google Scholar]
- 31.Iob E., Kirschbaum C., Steptoe A. Persistent depressive symptoms, HPA-axis hyperactivity, and inflammation: the role of cognitive-affective and somatic symptoms. Mol. Psychiatr. 2020;25(5):1130–1140. doi: 10.1038/s41380-019-0501-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Pariante C.M., Lightman S.L. The HPA axis in major depression: classical theories and new developments. Trends Neurosci. 2008;31(9):464–468. doi: 10.1016/j.tins.2008.06.006. [DOI] [PubMed] [Google Scholar]
- 33.Leonard B.E. Impact of inflammation on neurotransmitter changes in major depression: an insight into the action of antidepressants. Prog. Neuropsychopharmacol. Biol. Psychiatry. 2014;48:261–267. doi: 10.1016/j.pnpbp.2013.10.018. [DOI] [PubMed] [Google Scholar]
- 34.Medina-Rodriguez E.M., Beurel E. Blood brain barrier and inflammation in depression. Neurobiol. Dis. 2022;175 doi: 10.1016/j.nbd.2022.105926. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Berk M., Williams L.J., Jacka F.N., O'Neil A., Pasco J.A., Moylan S., et al. So depression is an inflammatory disease, but where does the inflammation come from? BMC Med. 2013;11:200. doi: 10.1186/1741-7015-11-200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Altemus M., Sarvaiya N., Neill Epperson C. Sex differences in anxiety and depression clinical perspectives. Front. Neuroendocrinol. 2014;35(3):320–330. doi: 10.1016/j.yfrne.2014.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Mohammadi S., Seyedmirzaei H., Salehi M.A., Jahanshahi A., Zakavi S.S., Dehghani Firouzabadi F., et al. Brain-based sex differences in depression: a systematic review of neuroimaging studies. Brain Imaging Behav. 2023;17(5):541–569. doi: 10.1007/s11682-023-00772-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Tully P.J., Baumeister H., Bengel J., Jenkins A., Januszewski A., Martin S., et al. The longitudinal association between inflammation and incident depressive symptoms in men: the effects of hs-CRP are independent of abdominal obesity and metabolic disturbances. Physiol. Behav. 2015;139:328–335. doi: 10.1016/j.physbeh.2014.11.058. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets utilized in this investigation are publicly accessible and can be found on the NHANES website at https://www.cdc.gov/nchs/nhanes/.