You are viewing the site in preview mode

Skip to main content
Download PDF

Systemic inflammation as a mediator in the link between obesity and depression: Evidence from a nationwide cohort study

BMC Psychiatry volume 25, Article number: 449 (2025) Cite this article

Abstract

Background

Obesity and depression are major public health issues with a complex, bidirectional relationship potentially involving systemic inflammation.

Methods

Using a diverse sample from the National Health and Nutrition Examination Survey (NHANES) (n = 11,324; weighted population = 456,457,366), we examined the associations between obesity, systemic inflammation, and depression. Obesity was classified by Body Mass Index (BMI), depressive symptoms were assessed with the Patient Health Questionnaire-9 (PHQ-9), and systemic inflammation was measured using markers like Neutrophil-to-Lymphocyte Ratio (NLR), Systemic Inflammation Response Index (SIRI), and Systemic Immune-Inflammation Index (SII). Weighted logistic regression models were used to assess relationships between obesity, inflammation, and depression. Linear regression evaluated BMI’s association with inflammation markers, and Restricted Cubic Spline (RCS) analysis explored their interrelationships. Subgroup analyses and interaction tests were conducted, and mediation analysis examined the role of inflammation markers in mediating the obesity-depression association.

Results

Class III obesity was associated with higher inflammatory marker levels and increased depression risk. Mediation analysis showed NLR, SIRI, and SII mediated 5.2%, 5.9%, and 6.1% of the obesity-depression relationship.

Conclusions

Systemic inflammation partially mediates the relationship between obesity and depression.

Peer Review reports

Introduction

Major depressive disorder is often accompanied by reduced appetite and weight loss, but in certain conditions such as atypical depression, increased appetite and weight gain are observed. Obesity and depression have become significant public health concerns worldwide. According to a 2022 World Health Organization (WHO) report, around 890 million people globally are affected by obesity, with 20% of adults projected to be obese by 2025 [1, 2]. Obesity raises risks of cardiovascular disease, diabetes, metabolic disorders, and cancer, contributing to a 50% increase in mortality and making it the fifth leading global death risk [3,4,5,6,7]. Depression is one of the most common mood disorders, marked by low mood, loss of interest, and anhedonia [8]. According to WHO's 2017 survey, about 5% of the global population (350 million people) are affected by depression [9]. Depression can lead to self-harm or suicide, with 700,000 suicide deaths annually, making it a major cause of global disability [10].

The prevalence of obesity and depression significantly impairs quality of life and imposes a substantial global economic burden. By 2035, obesity-related costs are projected to reach $4.32 trillion annually, accounting for 2%−7% of global gross domestic product (GDP) [11, 12]. Additionally, Depression adds over $1 trillion in economic losses each year, covering both direct medical expenses and productivity losses [13, 14].

It is noteworthy that obesity and depression are not merely independent health issues. They frequently coexist, and their relationship is considered both complex and bidirectional. Individuals with obesity often face a higher risk of depression and anxiety. Studies have reported that the prevalence of depression in individuals with obesity ranges from 15 to 30%, approximately 1.5 to 2 times higher compared to those without obesity [15]. Conversely, obesity prevalence among individuals with depression ranges from 20 to 55%, roughly 1.5 to 2 times greater compared to those without depression [16]. Increasing evidence suggests that the association between obesity and depression is influenced by multiple factors, including both psychosocial and biological mechanisms [17]. Psychosocial factors such as body image dissatisfaction, diminished self-esteem, and social stigma may contribute to the increased risk of depression in individuals with obesity [18, 19]. Depressive symptoms like low mood, reduced energy, and lack of motivation often lead to decreased physical activity, potentially resulting in weight gain and obesity, creating a cycle where obesity and depression reinforce each other [20,21,22]. Above behavioral and psychological factors might lead us overlooking biological mechanisms that link obesity and depression. Investigating biological mechanisms is crucial as it could provide potential diagnostic and therapeutic targets.

Researches indicate that obesity is not merely a state of metabolic imbalance but also accompanied by a chronic low-grade systemic inflammatory response [23, 24]. This inflammation is often driven by excessive visceral fat expansion and dysfunction, releasing pro-inflammatory cytokines and chemokines that trigger systemic inflammation and lead to metabolic and immune dysregulation. [25,26,27]. Elevated circulating levels of inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and C-reactive protein (CRP), were reported in overweight and obese adults [28,29,30]. Chronic inflammation per se plays a key role in neural function including depression. Elevated inflammatory markers in depression suggested that inflammation may impact brain function through neuro-immune interactions [31], including disrupted neurotransmitter metabolism, activation of the hypothalamic–pituitary–adrenal (HPA) axis, and impaired neuroplasticity. Prolonged inflammation may alter central nervous system function, potentially triggering or worsening depressive symptoms [32, 33]. Meta-analyses show a strong link between depression and systemic inflammatory markers. One study of 3,213 depressed individuals and 2,798 healthy controls found a significant association between elevated IL-6 and TNF-α levels and depression [34]. Another meta-analysis of 107 studies, including 5,166 individuals with depression and 5,083 controls, found significantly higher levels of CRP, IL-6, interleukin-12 (IL-12), and TNF-α in those with depression [35].

Although much research has explored links between obesity, inflammatory markers, and depression, there is limited evidence on the mediating role of systemic inflammation (NLR, SIRI, and SII) in the relationship of obesity and depression. The objective of this study is to use NHANES data to investigate the interactions between obesity, systemic inflammatory markers (NLR, SIRI, and SII), and depression in a large, multi-ethnic adult cohort, and to explore the potential mediating role of these inflammatory markers in the association between obesity and depression. We hypothesize that obesity is positively associated with an increased risk of depression, with systemic inflammation acting as a critical mediator in this relationship.

Methods

Study design

The National Health and Nutrition Examination Survey (NHANES) is a health survey conducted by the National Center for Health Statistics (NCHS), under the Centers for Disease Control and Prevention (CDC) [36]. NHANES collects data on demographics, socioeconomic status, health-related issues, physical examinations, and laboratory tests. Since 1999, the survey has been conducted biennially to monitor the health and nutritional status of the U.S. population. NHANES employs a stratified, multistage probability sampling method to ensure national representativeness. In accordance with the updated Declaration of Helsinki, all NHANES protocols have been approved by the NCHS Research Ethics Review Board, and all participants provided written informed consent.

Participants

We conducted an exploratory cross-sectional study using publicly available data from the NHANES spanning 2007 to 2018, covering six cycles (2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018). Initially, a total of 59,842 participants were included. We excluded the following individuals: (1) those with missing BMI data (n = 7,151); (2) those with missing PHQ-9 scores (n = 21,411); (3) those with missing SII data (n = 1,276); (4) pregnant women (n = 292); and (5) individuals with a BMI of less than 30 kg/m2 (n = 18,388). Ultimately, 11,324 participants were included in the final analysis. A flowchart of the study participants is presented in Fig. 1. This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [37].

Fig.1
figure 1

Flowchart of participant selection

Full size image

Evaluation of obesity

In this study, we utilized the widely recognized indicator for assessing obesity: Body Mass Index (BMI) [3]. Anthropometric data were collected by trained health technicians at a Mobile Examination Center (MEC). All NHANES health technicians completed a two-day training program, which included evaluation and review by expert examiners to ensure accuracy and consistency of data collection. Height and weight were measured using standardized instruments: digital floor scales for weight and wall-mounted stadiometers for height. Standardization and calibration of all measurement equipment were also conducted to ensure data quality. BMI was calculated using height and weight measurements, with height (in meters) and weight (in kilograms) obtained using these calibrated, high-precision instruments. The BMI was calculated using the formula: BMI = weight (kg)/height (m2). Participants were categorized into obesity classes I, II, and III based on their BMI values.

Measurement of systemic inflammation

In this study the neutrophil-to-lymphocyte ratio (NLR), the Systemic Inflammation Response Index (SIRI), and the Systemic Immune-Inflammation Index (SII) were used as inflammatory markers because they are more comprehensive and effective in the body’s inflammatory and immune balance, providing a more holistic reflection of systemic inflammation. They are increasingly recognized as important tools for evaluating inflammatory responses and immune surveillance [38,39,40].Venous blood samples were collected from participants by professionally trained medical personnel following standardized procedures. Sampling was conducted in a controlled environment within the Mobile Examination Center (MEC) to ensure sample quality. Complete blood count (CBC) analysis was performed using an automated hematology analyzer. The analyzer classified leukocytes and platelets using techniques such as optical scatter, laser scatter, or electrical impedance, measuring the counts and percentages of different types of leukocytes, including neutrophils, lymphocytes, and monocytes, as well as platelets. The automated system provided differential counts for these cell types and reported absolute counts in cells per microliter of blood. Detailed analytical methods can be found in the NHANES Laboratory/Medical Technologists Procedures Manual (https://wwwn.cdc.gov/nchs/nhanes/). The NHANES laboratory follows strict quality control standards to ensure the accuracy and consistency of the results. Calibration was performed using control samples with known concentrations, and instrument performance was regularly monitored. Each assay was calibrated and subjected to quality assessment to ensure the reliability of the results.

The formulas for calculating NLR, SIRI, and SII are as follows:

NLR = Neutrophil Count/Lymphocyte Count.

SIRI = Neutrophil Count * Monocyte Count/Lymphocyte Count.

SII = Platelet Count * Neutrophil Count/Lymphocyte Count.

Assessment of depressive symptoms

This study utilized the Patient Health Questionnaire-9 (PHQ-9) to assess depressive symptoms among the population. The PHQ-9 is a widely recognized tool for depression screening, known for its accuracy and reliability [41]. The questionnaire consists of 9 items, each scored from 0 (not at all) to 3 (nearly every day), with a total score ranging from 0 to 27. A cutoff score of 10 was used to determine the presence of depression; participants scoring below 10 were considered not to have depressive symptoms, while those scoring 10 or above were classified as having depression. The sensitivity and specificity of a PHQ-9 score ≥ 10 for diagnosing depression are 85% and 89%, respectively [42].

Covariates

Based on prior literature, the covariates included in this study were age, sex, race/ethnicity, education level, marital status, family income-to-poverty ratio (RIP), time spent in physical activity, METs from physical activity, hemoglobin, WBC, lymphocytes, monocytes, neutrophils, platelets, creatinine, NLR, SIRI, SII, smoking status, alcohol intake, diabetes (DM), hypertension, chronic obstructive pulmonary disease (COPD), and coronary heart disease (CHD). Physical activity was assessed using the Global Physical Activity Questionnaire, which covers questions related to daily activities, leisure-time activities, and sedentary behaviors. Physical activity was defined as engaging in moderate or vigorous intensity activity, outside of work or transportation, for at least 10 min at a time. Smoking status was classified into three categories: never smokers (those who have smoked fewer than 100 cigarettes in their lifetime), former smokers (those who have smoked 100 or more cigarettes in their lifetime but have quit), and current smokers (those who have smoked 100 or more cigarettes and currently smoke either daily or on some days). Similarly, alcohol consumption was categorized into three groups: never drinkers (those who consumed fewer than 12 drinks in any year), former drinkers (those who consumed 12 or more drinks in any year but are currently not drinking), and current drinkers (those who consumed 12 or more drinks in any year and are still drinking). Among current drinkers, further classification was made into moderate and heavy drinkers: moderate drinkers were defined as women who consume at least 2 drinks per day or men who consume at least 3 drinks per day, or individuals who binge drink on at least 2 days per month; heavy drinkers were defined as women who consume 3 or more drinks per day, men who consume 4 or more drinks per day, or individuals who binge drink on 5 or more days per month. Self-reported questionnaires were used to assess the presence of DM, hypertension, COPD, and CHD. The measurement procedures for these variables are available on the CDC NHANES website: https://www.cdc.gov/nchs/nhanes/.

Statistical analysis

All analyses were conducted using NHANES weighting procedures to account for the complex sampling design. Participants were categorized into three BMI groups: Class I obesity (BMI < 35 kg/m2), Class II obesity (35 ≤ BMI < 40 kg/m2), and Class III obesity (BMI ≥ 40 kg/m2). Continuous variables were described as mean ± SD if normally distributed (assessed using the Kolmogorov–Smirnov test), with between-group comparisons using independent t-tests. Non-normally distributed variables were described as median (IQR) and compared using the Mann–Whitney U test. Categorical variables were reported as counts and percentages, with comparisons made using the chi-square or Fisher’s exact test. Before regression analyses, multicollinearity was assessed using variance inflation factors (VIFs), all of which were below 5. Weighted logistic regression models (Model 1, Model 2, and Model 3) evaluated the association between obesity and depression. Model 1 was unadjusted, Model 2 adjusted for age and sex, and Model 3 further adjusted for race/ethnicity, socioeconomic factors, and medical history. Restricted cubic splines (RCS) were used to explore dose–response relationships. Subgroup and interaction analyses were conducted using fully adjusted models to explore associations across age, sex, race/ethnicity, smoking status, and comorbidities. Mediation analysis was performed using the"mediation"package in R (v4.3.2), with 1,000 bootstrapped resamples to assess whether systemic inflammatory markers mediated the obesity-depression relationship. All results were expressed as odds ratios (ORs) with 95% confidence intervals (CIs), and statistical significance was defined as p < 0.05.

Results

Baseline characteristics of the study participants

The baseline characteristics of the study participants are shown in Table 1. A total of 11,324 individuals with obesity were included (weighted population: 456,457,366, median age 49.00 years [IQR, 35.00–61.00 years]). Of these, 47.48% were male, and 52.52% were female. The racial/ethnic composition was as follows: 10.34% were Mexican American, 65.5% were non-Hispanic White, 13.49% were non-Hispanic Black, and 10.68% were classified as “other” (including Hispanic, American Indian/Alaska Native, Pacific Islander, Asian, and multiracial). Regarding education, 58.48% of participants had completed college or higher, and 63.74% were married or living with a partner. Figure 1 illustrates the trends in the prevalence of depression and obesity among adults aged 18 years and older in the United States. From 2007–2008 to 2017–2018, the prevalence of PHQ-9 scores ≥ 10 ranged from 10.1% to 13.6%. Over this period, the prevalence of Class II obesity increased from 24.7% to 26.2%, while the prevalence of Class III obesity rose from 16.8% to 21.1% (Fig. 2).

Table 1 Weighted baseline characteristics of the study participants
Full size table
Fig.2
figure 2

Trends in prevalence of depressive symptoms and obesity categories among US population, National Health and Nutrition Examination Survey 2007–2018

Full size image

Participants were categorized into 3 groups based on obesity class: Class I (BMI < 35 kg/m2; n = 6,196), Class II (BMI 35–39.9 kg/m2; n = 2,909), and Class III (BMI ≥ 40 kg/m2; n = 2,219). Compared with those in Class I, individuals in Class III were younger, more likely to be female, non-Hispanic Black, unmarried, and had lower socioeconomic status. They also had significantly higher depression scores, engaged in less physical activity, had lower physical activity metabolic equivalent of task (MET) values, and lower hemoglobin levels. Furthermore, they exhibited higher counts of white blood cells, lymphocytes, monocytes, neutrophils, and platelets, along with elevated NLR, SIRI, and SII. Smoking, diabetes, and hypertension were also more prevalent in this group (all P < 0.05).

Association between obesity and the risk of depression

We employed multivariable logistic regression models to assess the associations between obesity and the risk of depressive symptoms, as presented in Table 2. When BMI was analyzed as a continuous variable, BMI was significantly associated with depressive symptoms (Total: OR = 1.02, 95% CI = 1.01–1.03, P < 0.001; Male: OR = 1.03, 95% CI = 1.01–1.05, P = 0.002; Female: OR = 1.02, 95% CI = 1.01–1.03, P < 0.001) after adjusting for multiple covariates, including age, sex, race, diabetes mellitus, hypertension, poverty income ratio, and serum creatinine levels. Further analysis stratified participants into three groups based on BMI classification: Class I obesity, Class II obesity, and Class III obesity, with Class I obesity serving as the reference group. In the unadjusted model, the odds ratios (ORs) and 95% confidence intervals (CIs) for depressive symptoms were 1.17 (1.02–1.34) for Class II obesity and 1.61 (1.40–1.86) for Class III obesity compared with Class I obesity. In the fully adjusted model (Model III), after accounting for potential confounders, the adjusted ORs (95% CIs) were 1.02 (0.88–1.18) for Class II obesity and 1.30 (1.12–1.51) for Class III obesity, compared with Class I obesity. Next, we aim to investigate the association between BMI and depressive symptoms, stratified by sex. For both sexes, higher BMI as a continuous variable is significantly associated with increased odds of depressive symptoms, with odds ratios (ORs) slightly above 1 across all models and p-values less than 0.001. When BMI is categorized into Obesity Class I, II, and III, distinct patterns emerge. In males, those in Obesity Class III (BMI ≥ 40 kg/m2) show a significant increase in the odds of depressive symptoms across all models, with ORs ranging from 1.35 to 1.53, indicating a clear relationship between higher obesity levels and depression risk, while the association is weaker in Class II obesity. A similar pattern is seen in females, particularly in Obesity Class III, where the odds of depressive symptoms are significantly higher, with ORs ranging from 1.26 to 1.44. However, for Obesity Class II, the relationship is weaker and not consistently significant. In summary, higher BMI, particularly in Obesity Class III, is associated with an increased risk of depressive symptoms, with a stronger association observed in males compared to females. These findings suggest that as BMI increases, particularly in higher obesity classes (e.g., Class III obesity), the risk of depressive symptoms significantly increases. This trend was observed consistently in both male and female participants. The consistent results across all models highlight the robustness of the association between obesity and depressive symptoms, with the strength of this association increasing in parallel with the severity of obesity.

Table 2 The association between obesity and depressive symptoms
Full size table

Association between obesity and systemic inflammation markers

Table 3 demonstrate the association between BMI and inflammatory markers (NLR, SIRI, and SII) in the obese population. Across all markers, BMI as a continuous variable is positively correlated with higher inflammatory levels. For NLR, each 1-unit increase in BMI is associated with a 0.01 to 0.02 unit increase in NLR (P < 0.001). In the categorical analysis, Class III obesity (BMI ≥ 40 kg/m2) shows a significant elevation in NLR, with adjusted β values ranging from 0.19 to 0.26 (P < 0.001). Both males and females demonstrate a positive correlation between BMI and NLR, though the association is weaker and not significant in females with Class II obesity (P = 0.124). Similarly, for SIRI, a 1-unit increase in BMI is linked to a 0.01 to 0.02 unit rise in SIRI (P < 0.001). Class III obesity is associated with a significant increase in SIRI, with β values between 0.18 and 0.26 (P < 0.001). While both sexes exhibit a positive relationship between BMI and SIRI, the association is weaker and non-significant in females with Class II obesity (P = 0.154). For SII, BMI is significantly associated with elevated SII levels (P < 0.001). In the categorical analysis, Class III obesity shows a marked increase in SII, with β values ranging from 75.03 to 78.95 in males and 56.05 to 61.42 in females (P < 0.001). Overall, the higher the BMI, the greater the increase in SII, with stronger effects observed in individuals with higher obesity grades. In summary, higher BMI is consistently associated with increased levels of systemic inflammatory markers (NLR, SIRI, and SII), particularly in individuals with Class III obesity, with more pronounced effects seen in males. These findings highlight the robust link between obesity and systemic inflammation.

Table 3 The association between obesity and inflammatory markers
Full size table

Association of systemic inflammation markers with the risk of depression

Table 4 shows the association between inflammatory markers (NLR, SIRI, SII) and depressive symptoms in the obese population. In the total population, each 1-unit increase in NLR and SIRI is associated with an 8% (OR = 1.08, 95% CI = 1.02–1.14, P = 0.005) and 12% (OR = 1.12, 95% CI = 1.05–1.20, P = 0.001) higher risk of depression, respectively, while each 100-unit increase in SII raises depression risk by 3% (OR = 1.03, 95% CI = 1.01–1.05, P = 0.001). In males, the associations are stronger: NLR increases depression risk by 17% (OR = 1.17, 95% CI = 1.08–1.26, P < 0.001), SIRI by 19% (OR = 1.19, 95% CI = 1.08–1.31, P < 0.001), and SII by 6% (OR = 1.06, 95% CI = 1.03–1.10, P < 0.001). In females, these associations are weaker and not statistically significant for NLR (OR = 1.02, 95% CI = 0.95–1.10, P = 0.526), SIRI (OR = 1.09, 95% CI = 0.99–1.19, P = 0.067), and SII (OR = 1.01, 95% CI = 0.99–1.04, P = 0.2). In summary, systemic inflammatory markers are more strongly associated with depression risk in males, while the associations in females are weaker or non-significant.

Table 4 The association between inflammatory markers and depressive symptoms in obese population
Full size table

Restricted Cubic Spline Analysis

We used restricted cubic spline (RCS) curves to evaluate potential nonlinearity in the association between BMI and the risk of depressive symptoms in the total population, as well as in males and females separately, as illustrated in Fig. 3. The results showed that higher BMI is significantly associated with an increased risk of depressive symptoms across all groups, particularly at BMI levels above 40 kg/m2. (Total: P = 0.003, P-nonlinear = 0.388; Male: P < 0.001, P-nonlinear = 0.427; Female: P = 0.002, P-nonlinear = 0.092). The association is mostly linear across groups, with a steady increase in the odds of depressive symptoms as BMI rises.

Fig.3
figure 3

The restricted cubic spline (RCS) analysis between BMI and the risk of depression in obese population

Full size image

We also applied RCS curves to assess the association between BMI and systemic inflammation markers among obese individuals. Figure 4 demonstrate a positive association between BMI and systemic inflammatory markers (NLR, SIRI, and SII) across the total population, males, and females. In all groups, increasing BMI is significantly correlated with higher levels of each marker (P < 0.001). The relationship between BMI and NLR is linear, with NLR increasing steadily as BMI rises (Overall: P < 0.001, P-nonlinear = 0.055; Male: P < 0.001, P-nonlinear = 0.193; Female: P < 0.001, P-nonlinear = 0.213). For SIRI, the pattern is similar, with a consistent increase in SIRI levels as BMI increases (Overall: P < 0.001, P-nonlinear = 0.057; Male: P < 0.001, P-nonlinear = 0.576; Female: P < 0.001, P-nonlinear = 0.120). For SII, the relationship is particularly strong, showing a steep linear increase with BMI across all groups (Overall: P < 0.001, P-nonlinear = 0.440; Male: P < 0.001, P-nonlinear = 0.154; Female: P < 0.001, P-nonlinear = 0.485). These findings suggest a consistent and significant link between higher BMI and increased systemic inflammation, as indicated by NLR, SIRI, and SII, across both sexes.

Fig.4
figure 4

The restricted cubic spline (RCS) analysis between BMI and systemic inflammation markers (NLR, SIRI, SII) in obese population

Full size image

We further investigated the potential nonlinear associations between systemic inflammation markers and depression risk in obese individuals using RCS models. Figure 5 show the associations between three inflammatory markers—NLR, SIRI, and SII—and the odds of depressive symptoms across the total population, males, and females. For all three markers, higher levels are significantly associated with an increased risk of depressive symptoms. NLR shows a linear association across all groups (Overall: P < 0.001, P-nonlinear = 0.935; Male: P < 0.001, P-nonlinear = 0.066; Female: P = 0.088, P-nonlinear = 0.033), while SIRI demonstrates a consistent positive trend, particularly in males and the total population, with females showing a weaker but still positive association (Overall: P < 0.001, P-nonlinear = 0.490; Male: P < 0.001, P-nonlinear = 0.144; Female: P = 0.056, P-nonlinear = 0.055). SII is also strongly associated with increased odds of depressive symptoms, especially in females, where the relationship appears more nonlinear (Overall: P < 0.001, P-nonlinear = 0.376; Male: P < 0.001, P-nonlinear = 0.546; Female: P = 0.001, P-nonlinear < 0.001). Overall, these findings suggest that elevated levels of systemic inflammation, as indicated by NLR, SIRI, and SII, are consistently linked to a higher risk of depressive symptoms across both sexes and the general population.

Fig.5
figure 5

The restricted cubic spline (RCS) analysis between systemic inflammation markers (NLR, SIRI, SII) and the risk of depression in obese population

Full size image

Subgroup Analysis

Stratified analyses and interaction tests were conducted to evaluate the association between obesity severity and depression risk across subgroups defined by sex, age (< 65 years vs. ≥ 65 years), race, marital status, education level, alcohol consumption, diabetes, hypertension, smoking status, coronary heart disease, and chronic obstructive pulmonary disease (COPD), as shown in Fig. 6. The results demonstrated that higher BMI was consistently associated with an increased risk of depression across all subgroups. Interaction tests (P for interaction) revealed no significant interactions between BMI and any of the stratified variables (P for interaction > 0.05), except for diabetes (P for interaction = 0.029), where a significant interaction was observed. This finding suggests that the association between obesity and depression risk is more pronounced among individuals with diabetes, indicating that diabetes may amplify the relationship between BMI and depression risk.

Fig.6
figure 6

Subgroup analysis for the association between BMI and the risk of depression in obese population

Full size image

Mediating effect of systemic inflammatory markers on the association between obesity and depressive symptoms

Given the observed interrelationships among obesity, systemic inflammatory markers, and depressive symptoms, we conducted a mediation analysis to further explore these associations. As presented in Table 5 and Fig. 7, NLR, SIRI, and SII each served as partial mediators in the relationship between BMI and depressive symptoms, with all mediation effects being statistically significant (P < 0.001). Specifically, NLR accounted for 5.2% of the mediation effect, SIRI for 5.9%, and SII for 6.1%, indicating that SII plays the largest mediating role in this relationship. These findings suggest that inflammatory markers (NLR, SIRI, and SII) may function as biological intermediaries, helping to explain the link between obesity (BMI) and the increased risk of depression.

Table 5 Inflammatory markers mediating the association between obesity and depressive symptoms
Full size table
Fig.7
figure 7

Analysis of the mediation by systemic inflammation markers (NLR, SIRI, SII) of the associations of BMI with the risk of depression

Full size image

When participants were stratified by sex and adjusted for all covariates, SIRI significantly mediated the association between obesity and depressive symptoms in both men and women (SIRI: Male, percentage mediated [PM] = 9.39%, P < 0.0001; Female, PM = 4.40%, P = 0.018). However, NLR and SII significantly mediated the association between obesity and depressive symptoms in men but not in women (NLR: Male, PM = 10.64%, P < 0.0001; Female, PM = 2.06%, P = 0.18. SII: Male, PM = 11.51%, P < 0.0001; Female, PM = 3.52%, P = 0.072).

Discussion

In our study, we confirmed positive association between obesity and depressive symptoms, inflammatory markers (NLR, SIRI, SII) and both obesity and depression by NHANES data. Furthermore, mediation analyses showed that inflammatory markers mediated the relationship of obesity and depression, suggesting that systemic inflammation may be an underlying biological mechanism. However, when exploring potential sex differences, we found that SIRI significantly mediated the association in both genders while NLR and SII only existed in men, but not in women.

Obesity, characterized by chronic low-grade inflammation, can be quantitatively assessed using various inflammatory markers. Zhou et al., using data from NHANES, demonstrated that both SII and SIRI are significantly associated with obesity, with higher levels of these markers linked to an increased prevalence of obesity, underscoring their clinical relevance as inflammatory indicators [43]. Similarly, Nicoară et al. found that SII is closely related to childhood obesity and metabolic syndrome, with strong predictive value for cardiovascular risk [44]. Rodríguez-Rodríguez et al. highlighted that NLR is significantly associated with abdominal obesity and poor dietary habits in older adults, reflecting the increased systemic inflammation observed in obesity [45]. The result of our study was consistent with above findings indicating that obesity might induce systemic inflammatory responses, elevating SII, SIRI, and NLR levels, which hold significant predictive and diagnostic value.

There is growing evidence suggesting that systemic inflammation contributes to the onset and severity of depression. Previous research has shown that NLR, as a sensitive marker of systemic inflammation, is not only significantly elevated in patients with major depressive disorder (MDD) but also has important potential in assessing suicide risk in these individuals [46]. Additionally, NLR has demonstrated valuable predictive ability for early diagnosis of depression, with a sensitivity of 75% and specificity of 35% [47]. Similarly, SII, an integrated marker of systemic inflammation, has been widely used to assess the overall inflammatory burden, and its relevance to depression is receiving increasing attention. Several studies have confirmed the association between elevated SII levels and depression, particularly in COVID-19 survivors, where higher SII levels were significantly linked to increased rates of depression, supporting SII as a potential biomarker for depression [48,49,50,51]. Moreover, an analysis of NHANES data from 2005 to 2018 revealed that both SII and SIRI were associated with an increased risk of depression[52]. Unlike the previous studies, which examined the general U.S. population, our research focuses on the obese population in the U.S. and also found a positive correlation between systemic inflammation markers (SII, SIRI, NLR) and the risk of depression.

This study focuses on the mediating role of systemic inflammation markers (NLR, SII, SIRI) in the relationship between obesity and depression. Previous research has also explored similar mechanisms. For example, the"Surveillance and Management of Disability and Cognitive Impairment"(SUM-DCI) study found that overall obesity, rather than abdominal obesity, was significantly associated with both worsening and new onset of depressive symptoms in older adults, a relationship partly attributed to systemic inflammation, particularly C-reactive protein (CRP) [53]. Unlike our study, that research assessed depressive symptoms using the Geriatric Depression Scale (GDS-15). Further evidence comes from the English Longitudinal Study of Ageing (ELSA), which found in a cohort of 4,942 adults aged 50 and older that CRP partially mediated the relationship between being overweight and increased somatic symptoms (14.92%). However, in this study, CRP did not significantly mediate the relationship between overweight and cognitive-emotional symptoms or overall depressive symptoms [54]. Additionally, a meta-analysis of 15 large population-based studies showed that CRP mediated approximately 20% of the association between obesity and depressive symptoms, reinforcing the potential mediating role of systemic inflammation in this relationship [55]. Compared to CRP, NLR, SII, and SIRI offer a more comprehensive reflection of systemic inflammation. These indices integrate the relative ratios of various immune cells, including neutrophils, lymphocytes, and platelets, providing more detailed information on the immune response. CRP, though widely used as an acute-phase inflammatory marker, is a non-specific indicator that reflects overall inflammation but does not capture the complex interactions between immune cells during inflammation. Therefore, SII, SIRI, and NLR offer greater sensitivity and specificity in assessing complex inflammatory states.

In exploring the mediation index between obesity and depression, systemic inflammatory markers demonstrated notable gender differences. SIRI significantly mediated the obesity-depression link in both men and women, reflecting its ability to integrate the roles of neutrophils, monocytes, and lymphocytes. In women, specifically, SIRI appears to capture the complexity of immune responses, particularly involving monocytes, which may play a more active role. In contrast, SII and NLR were primarily associated with the obesity-depression link in men. This may be due to these markers reflecting neutrophil and lymphocyte activity, which could be modulated by estrogen’s anti-inflammatory effects in women. As a result, these markers may have a reduced impact in females. SIRI, however, appears better suited to capture the unique immune responses and metabolic inflammation observed in women, further supporting its role as a strong mediator of the obesity-depression association.

Our study has revealed that systemic inflammation markers mediate the relationship between obesity and depression, highlighting several complex neurobiological mechanisms. First, immune-inflammatory activation is a key factor, as obesity is accompanied by chronic low-grade inflammation, where macrophages and immune cells infiltrate adipose tissue, releasing pro-inflammatory cytokines such as TNF-α, IL-6, and CRP. These cytokines activate central nervous system inflammation through both humoral and neural pathways. This process manifests as elevated pro-inflammatory cytokine expression in regions such as the hippocampus and prefrontal cortex, damaging neurons and impairing emotional regulation [56,57,58,59]. Second, the disruption of the blood–brain barrier (BBB) by pro-inflammatory cytokines allows them to penetrate the brain, where they activate microglial cells and induce neuroinflammation. This leads to neuronal damage in the hippocampus and amygdala, reducing neural plasticity and connectivity, which exacerbates emotional dysregulation and contributes to depression [60,61,62]. Third, neurotransmitter imbalance is induced by pro-inflammatory cytokines, which activate the indoleamine 2,3-dioxygenase (IDO) pathway, reducing the conversion of tryptophan to serotonin (5-HT), thereby decreasing 5-HT synthesis [63, 64]. At the same time, these cytokines increase quinolinic acid production, which interacts with glutamate receptors to induce excitotoxicity, resulting in neuronal damage and further inhibition of neurotrophic factor synthesis, worsening emotional regulation [63]. Fourth, obesity-related inflammation activates the hypothalamic–pituitary–adrenal (HPA) axis, leading to elevated cortisol levels. Chronic high cortisol damages the hippocampus and prefrontal cortex, further impairing the brain's ability to regulate emotions [65, 66]. Finally, chronic inflammation alters neural circuits in the cortico-limbic system, weakening dopamine pathways, which leads to anhedonia and motivational deficits commonly seen in obese individuals [67,68,69]. In conclusion, the immune-inflammatory activation, neurotransmitter imbalance, and overactivation of the HPA axis in obesity contribute to neural circuit dysfunction, driving the development and progression of depression. These mechanisms provide a theoretical foundation for understanding the pathophysiology of obesity-related depression.

In our study, the mediating role of systemic inflammation in the relationship between obesity and depression sheds light on the complex mechanisms underlying obesity-related mental health issues. This finding carries significant public health and clinical implications, particularly by pointing toward new directions for early intervention and prevention strategies for both depression and obesity. Obesity not only increases the risk of metabolic disorders but also exacerbates the incidence of depression through inflammatory processes. Thus, public health policies should place greater emphasis on managing weight and reducing inflammation through healthy diets and physical activity to lower the risk of depression. Additionally, clinical practice should involve a comprehensive assessment of inflammation levels and mental health in obese patients. Early intervention, including weight management and proactive treatment of inflammatory conditions, could help prevent depression and other comorbidities. Such an integrated approach would be instrumental in alleviating the global burden of both obesity and depression.

Strengths and limitations of the study

Our study has several key strengths. First, it is the first to demonstrate that systemic inflammatory markers mediate the relationship between obesity and depression. Second, we used a nationally representative dataset with a large sample size and diverse racial and ethnic composition, enhancing the generalizability of our findings. Third, we employed comprehensive systemic inflammatory markers (SII, SIRI, NLR), which are cost-effective and clinically feasible, providing a more thorough understanding of inflammation's role in this relationship. Additionally, we adjusted for a wide range of covariates, including demographic, socioeconomic, and lifestyle factors, reducing potential confounding bias. Sensitivity and stratified analyses further supported the robustness of our findings.

However, there are limitations. First, due to the cross-sectional design, causal relationships cannot be established. Second, the PHQ-9 assessment of depressive symptoms is self-reported and may be subject to bias. Additionally, the results should be verified in individuals with a clinical diagnosis of depression. Third, BMI alone may not fully capture obesity's complexity, particularly regarding fat distribution and metabolic health. Fourth, medication use was not included as a covariate, which may influence the results and lead to some bias. Finally, as the study used NHANES data, which primarily represents the U.S. population, the external validity may be limited for other populations.

Conclusion

In conclusion, this study provides evidence of a positive association between obesity and risk of depression, while underscoring the critical role of systemic inflammation as a mediator in this relationship. Elevated inflammatory markers, such as NLR, SIRI, and SII, appear to be key biological pathways linking obesity to depressive symptoms. These findings offer important insights into the mechanisms underlying the comorbidity of obesity and depression. Targeting inflammation in therapeutic interventions may therefore present a promising approach for managing both conditions. By focusing on inflammation, clinicians may develop more effective strategies to reduce the psychological burden of obesity, ultimately enhancing both mental and physical health outcomes. Future research should further investigate this mediating role, with an emphasis on the potential for personalized treatment approaches.

Data availability

The publicly available datasets used in this study can be accessed at https://www.cdc.gov/nchs/nhanes/index.htm.

References

  1. Lingvay I, Cohen RV, Roux CWL, Sumithran P. Obesity in adults. Lancet. 2024;404(10456):972–87.

    Article  CAS  PubMed  Google Scholar 

  2. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6–10.

    Article  CAS  PubMed  Google Scholar 

  3. Elmaleh-Sachs A, Schwartz JL, Bramante CT, Nicklas JM, Gudzune KA, Jay M. Obesity management in adults: a review. JAMA. 2023;330(20):2000–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Roth GA, Nguyen G, Forouzanfar MH, Mokdad AH, Naghavi M, Murray CJ. Estimates of global and regional premature cardiovascular mortality in 2025. Circulation. 2015;132(13):1270–82.

    Article  PubMed  Google Scholar 

  5. Lega IC, Lipscombe LL. Review: diabetes, obesity, and cancer-pathophysiology and clinical implications. Endocr Rev. 2020;41(1):33.

    Article  Google Scholar 

  6. Avgerinos KI, Spyrou N, Mantzoros CS, Dalamaga M. Obesity and cancer risk: Emerging biological mechanisms and perspectives. Metabolism. 2019;92:121–35.

    Article  CAS  PubMed  Google Scholar 

  7. Aron-Wisnewsky J, Warmbrunn MV, Nieuwdorp M, Clément K. Metabolism and metabolic disorders and the microbiome: the intestinal microbiota associated with obesity, lipid metabolism, and metabolic health-pathophysiology and therapeutic strategies. Gastroenterology. 2021;160(2):573–99.

    Article  CAS  PubMed  Google Scholar 

  8. Hasin DS, Sarvet AL, Meyers JL, Saha TD, Ruan WJ, Stohl M, Grant BF. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiat. 2018;75(4):336–46.

    Article  Google Scholar 

  9. Reynolds CF 3rd, Cuijpers P, Patel V, Cohen A, Dias A, Chowdhary N, Okereke OI, Dew MA, Anderson SJ, Mazumdar S, et al. Early intervention to reduce the global health and economic burden of major depression in older adults. Annu Rev Public Health. 2012;33:123–35.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Mann JJ, Michel CA, Auerbach RP. Improving suicide prevention through evidence-based strategies: a systematic review. Am J Psychiatry. 2021;178(7):611–24.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Benros ME, Waltoft BL, Nordentoft M, Ostergaard SD, Eaton WW, Krogh J, Mortensen PB. Autoimmune diseases and severe infections as risk factors for mood disorders: a nationwide study. JAMA Psychiat. 2013;70(8):812–20.

    Article  Google Scholar 

  12. Koliaki C, Dalamaga M, Liatis S. Update on the obesity epidemic: after the sudden rise, is the upward trajectory beginning to flatten? Curr Obes Rep. 2023;12(4):514–27.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Arias D, Saxena S, Verguet S. Quantifying the global burden of mental disorders and their economic value. EClinicalMedicine. 2022;54: 101675.

    Article  PubMed  PubMed Central  Google Scholar 

  14. The Lancet Global H: Mental health matters. Lancet Glob Health. 2020;8(11):e1352.

  15. Simon GE, Von Korff M, Saunders K, Miglioretti DL, Crane PK, van Belle G, Kessler RC. Association between obesity and psychiatric disorders in the US adult population. Arch Gen Psychiatry. 2006;63(7):824–30.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Luppino FS, de Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BW, Zitman FG. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry. 2010;67(3):220–9.

    Article  PubMed  Google Scholar 

  17. Atlantis E, Goldney RD, Wittert GA. Obesity and depression or anxiety. Bmj. 2009;339:b3868.

    Article  PubMed  Google Scholar 

  18. Sarwer DB, Polonsky HM. The Psychosocial Burden of Obesity. Endocrinol Metab Clin North Am. 2016;45(3):677–88.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Fulton S, Décarie-Spain L, Fioramonti X, Guiard B, Nakajima S. The menace of obesity to depression and anxiety prevalence. Trends Endocrinol Metab. 2022;33(1):18–35.

    Article  CAS  PubMed  Google Scholar 

  20. Chiu LY, Stewart K, Woo C, Yatham LN, Lam RW. The relationship between burnout and depressive symptoms in patients with depressive disorders. J Affect Disord. 2015;172:361–6.

    Article  PubMed  Google Scholar 

  21. Sutin AR, Zonderman AB. Depressive symptoms are associated with weight gain among women. Psychol Med. 2012;42(11):2351–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Thompson EJ, Krebs G, Zavos HMS, Steves CJ, Eley TC. The relationship between weight-related indicators and depressive symptoms during adolescence and adulthood: results from two twin studies. Psychol Med. 2024;54(3):527–38.

    Article  PubMed  Google Scholar 

  23. Saltiel AR, Olefsky JM. Inflammatory mechanisms linking obesity and metabolic disease. J Clin Invest. 2017;127(1):1–4.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Gregor MF, Hotamisligil GS. Inflammatory mechanisms in obesity. Annu Rev Immunol. 2011;29:415–45.

    Article  CAS  PubMed  Google Scholar 

  25. Reilly SM, Saltiel AR. Adapting to obesity with adipose tissue inflammation. Nat Rev Endocrinol. 2017;13(11):633–43.

    Article  CAS  PubMed  Google Scholar 

  26. Kolb H. Obese visceral fat tissue inflammation: from protective to detrimental? BMC Med. 2022;20(1):494.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. de Heredia FP, Gómez-Martínez S, Marcos A. Obesity, inflammation and the immune system. Proc Nutr Soc. 2012;71(2):332–8.

    Article  PubMed  Google Scholar 

  28. Vgontzas AN, Bixler EO, Papanicolaou DA. Chrousos GP: Chronic systemic inflammation in overweight and obese adults. Jama. 2000;283(17):2235 author reply 2236.

    CAS  PubMed  Google Scholar 

  29. Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Elevated C-reactive protein levels in overweight and obese adults. JAMA. 1999;282(22):2131–5.

    Article  CAS  PubMed  Google Scholar 

  30. Park HS, Park JY, Yu R. Relationship of obesity and visceral adiposity with serum concentrations of CRP, TNF-alpha and IL-6. Diabetes Res Clin Pract. 2005;69(1):29–35.

    Article  CAS  PubMed  Google Scholar 

  31. Haapakoski R, Mathieu J, Ebmeier KP, 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;49:206–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zunszain PA, Anacker C, Cattaneo A, Carvalho LA, Pariante CM. Glucocorticoids, cytokines and brain abnormalities in depression. Prog Neuropsychopharmacol Biol Psychiatry. 2011;35(3):722–9.

    Article  CAS  PubMed  Google Scholar 

  33. Leff-Gelman P, Mancilla-Herrera I, Flores-Ramos M, Cruz-Fuentes C, Reyes-Grajeda JP, García-Cuétara Mdel P, Bugnot-Pérez MD, Pulido-Ascencio DE. The immune system and the role of inflammation in perinatal depression. Neurosci Bull. 2016;32(4):398–420.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Köhler CA, Freitas TH, Maes M, de Andrade NQ, Liu CS, Fernandes BS, Stubbs B, Solmi M, Veronese N, Herrmann N, et al. Peripheral cytokine and chemokine alterations in depression: a meta-analysis of 82 studies. Acta Psychiatr Scand. 2017;135(5):373–87.

    Article  PubMed  Google Scholar 

  35. Osimo EF, Pillinger T, Rodriguez IM, Khandaker GM, Pariante CM, Howes OD. Inflammatory markers in depression: a meta-analysis of mean differences and variability in 5,166 patients and 5,083 controls. Brain Behav Immun. 2020;87:901–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Johnson CL, Dohrmann SM, Burt VL, Mohadjer LK. National health and nutrition examination survey: sample design, 2011–2014. Vital Health Stat. 2014;2(162):1–33.

    Google Scholar 

  37. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326(16):1614–21.

    Article  PubMed  Google Scholar 

  38. Bhikram T, Sandor P. Neutrophil-lymphocyte ratios as inflammatory biomarkers in psychiatric patients. Brain Behav Immun. 2022;105:237–46.

    Article  CAS  PubMed  Google Scholar 

  39. Cao Y, Zheng X, Hu Y, Li J, Huang B, Zhao N, Liu T, Cai K, Tian S. Levels of systemic inflammation response index are correlated with tumor-associated bacteria in colorectal cancer. Cell Death Dis. 2023;14(1):69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Zhang K, Hua YQ, Wang D, Chen LY, Wu CJ, Chen Z, Liu LM, Chen H. Systemic immune-inflammation index predicts prognosis of patients with advanced pancreatic cancer. J Transl Med. 2019;17(1):30.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Costantini L, Pasquarella C, Odone A, Colucci ME, Costanza A, Serafini G, Aguglia A, Belvederi Murri M, Brakoulias V, Amore M, et al. Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review. J Affect Disord. 2021;279:473–83.

    Article  PubMed  Google Scholar 

  42. Levis B, Benedetti A, Thombs BD. Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis. BMJ. 2019;365: l1476.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Zhou Y, Wang Y, Wu T, Zhang A, Li Y. Association between obesity and systemic immune inflammation index, systemic inflammation response index among US adults: a population-based analysis. Lipids Health Dis. 2024;23(1):245.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Nicoară DM, Munteanu AI, Scutca AC, Mang N, Juganaru I, Brad GF, Mărginean O. Assessing the relationship between systemic immune-inflammation index and metabolic syndrome in children with obesity. Int J Mol Sci. 2023;24(9):8414.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Rodríguez-Rodríguez E, López-Sobaler AM, Ortega RM, Delgado-Losada ML, López-Parra AM, Aparicio A. Association between neutrophil-to-lymphocyte ratio with abdominal obesity and healthy eating index in a representative older Spanish population. Nutrients. 2020;12(3):855.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Velasco Á, Rodríguez-Revuelta J, Olié E, Abad I, Fernández-Peláez A, Cazals A, Guillaume S, de la Fuente-Tomás L, Jiménez-Treviño L, Gutiérrez L, et al. Neutrophil-to-lymphocyte ratio: a potential new peripheral biomarker of suicidal behavior. Eur Psychiatry. 2020;63(1):e14.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Ghafori SS, Yousefi Z, Bakhtiari E, Mohammadi Mahdiabadi Hasani MH, Hassanzadeh G: Neutrophil-to-lymphocyte ratio as a predictive biomarker for early diagnosis of depression: a narrative review. Brain Behav Immun Health. 2024;36:100734.

  48. Cui S, Li J, Liu Y, Yao G, Wu Y, Liu Z, Sun L, Sun L, Liu H. Correlation of systemic immune-inflammation index and moderate/major depression in patients with depressive disorders: a large sample cross-sectional study. Front Psychiatry. 2023;14: 1159889.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wang J, Zhou D, Dai Z, Li X. Association between systemic immune-inflammation index and diabetic depression. Clin Interv Aging. 2021;16:97–105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mazza MG, De Lorenzo R, Conte C, Poletti S, Vai B, Bollettini I, Melloni EMT, Furlan R, Ciceri F, Rovere-Querini P, et al. Anxiety and depression in COVID-19 survivors: Role of inflammatory and clinical predictors. Brain Behav Immun. 2020;89:594–600.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zheng Y, Yin K, Li L, Wang X, Li H, Li W, Fang Z. Association between immune-inflammation-based prognostic index and depression: An exploratory cross-sectional analysis of NHANES data. J Affect Disord. 2024;362:75–85.

    Article  CAS  PubMed  Google Scholar 

  52. 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 Psychiatry. 2023;14: 1097196.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Guo YX, Wang AQ, Gao X, Na J, Zhe W, Zeng Y, Zhang JR, Jiang YJ, Yan F, Yunus M, et al. Obesity is positively Associated with Depression in Older Adults: Role of Systemic Inflammation. Biomed Environ Sci. 2023;36(6):481–9.

    CAS  PubMed  Google Scholar 

  54. Chu K, Cadar D, Iob E, Frank P. Excess body weight and specific types of depressive symptoms: Is there a mediating role of systemic low-grade inflammation? Brain Behav Immun. 2023;108:233–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Frank P, Jokela M, Batty GD, 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(12):1107–18.

    Article  PubMed  Google Scholar 

  56. Zhu X, Tu Y, Chen H, Jackson AO, Patel V, Yin K. Micro-environment and intracellular metabolism modulation of adipose tissue macrophage polarization in relation to chronic inflammatory diseases. Diabetes Metab Res Rev. 2018;34(5): e2993.

    Article  PubMed  Google Scholar 

  57. Thomas D, Apovian C. Macrophage functions in lean and obese adipose tissue. Metabolism. 2017;72:120–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Salvador AF, de Lima KA, Kipnis J. Neuromodulation by the immune system: a focus on cytokines. Nat Rev Immunol. 2021;21(8):526–41.

    Article  CAS  PubMed  Google Scholar 

  59. Capuron L, Miller AH. Immune system to brain signaling: neuropsychopharmacological implications. Pharmacol Ther. 2011;130(2):226–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Varatharaj A, Galea I. The blood-brain barrier in systemic inflammation. Brain Behav Immun. 2017;60:1–12.

    Article  CAS  PubMed  Google Scholar 

  61. Huang X, Hussain B, Chang J. Peripheral inflammation and blood-brain barrier disruption: effects and mechanisms. CNS Neurosci Ther. 2021;27(1):36–47.

    Article  CAS  PubMed  Google Scholar 

  62. Zheng ZH, Tu JL, Li XH, Hua Q, Liu WZ, Liu Y, Pan BX, Hu P, Zhang WH. Neuroinflammation induces anxiety- and depressive-like behavior by modulating neuronal plasticity in the basolateral amygdala. Brain Behav Immun. 2021;91:505–18.

    Article  CAS  PubMed  Google Scholar 

  63. Müller N, Schwarz MJ. The immune-mediated alteration of serotonin and glutamate: towards an integrated view of depression. Mol Psychiatry. 2007;12(11):988–1000.

    Article  PubMed  Google Scholar 

  64. Chaves Filho AJM, Lima CNC, Vasconcelos SMM, de Lucena DF, Maes M, Macedo D. IDO chronic immune activation and tryptophan metabolic pathway: A potential pathophysiological link between depression and obesity. Prog Neuropsychopharmacol Biol Psychiatry. 2018;80(Pt C):234–49.

    Article  CAS  PubMed  Google Scholar 

  65. van den Heuvel JK, Boon MR, van Hengel I, Peschier-van der Put E, van Beek L, van Harmelen V, van Dijk KW, Pereira AM, Hunt H, Belanoff JK, et al. Identification of a selective glucocorticoid receptor modulator that prevents both diet-induced obesity and inflammation. Br J Pharmacol. 2016;173(11):1793–804.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Frodl T, O’Keane V. How does the brain deal with cumulative stress? A review with focus on developmental stress, HPA axis function and hippocampal structure in humans. Neurobiol Dis. 2013;52:24–37.

    Article  PubMed  Google Scholar 

  67. Rengasamy M, Brundin L, Griffo A, Panny B, Capan C, Forton C, Price RB. Cytokine and reward circuitry relationships in treatment-resistant depression. Biol Psychiatry Glob Open Sci. 2022;2(1):45–53.

    Article  PubMed  Google Scholar 

  68. Sinha R, Jastreboff AM. Stress as a common risk factor for obesity and addiction. Biol Psychiatry. 2013;73(9):827–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Li G, Hu Y, Zhang W, Wang J, Ji W, Manza P, Volkow ND, Zhang Y, Wang GJ. Brain functional and structural magnetic resonance imaging of obesity and weight loss interventions. Mol Psychiatry. 2023;28(4):1466–79.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the staff and the participants of the NHANES study for their valuable contributions.

Clinical trial number

Not applicable.

Funding

This study was financially supported by the Construction Project of Shanghai Clinical Key Specialty (No. shsiczdzk03603).

the Construction Project of Shanghai Clinical Key Specialty,No. shsiczdzk03603,No. shsiczdzk03603,No. shsiczdzk03603,No. shsiczdzk03603,No. shsiczdzk03603,No. shsiczdzk03603,No. shsiczdzk03603

Author information

Authors and Affiliations

  1. Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China

    Xiaoqing Wang, Xiaoqing Wang & Jing Cang

  2. Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China

    Xiaoqing Wang, Xinyue Liang, Ling Wei, Xiangpeng Shi & Jing Cang

  3. State Key Laboratory of Medical Neurobiologyand, MOE Frontiers Center for Brain Science, Institutes of Brain Science , Fudan University, Shanghai, China

    Xiaoqing Wang

  4. Department of Pain Management, Zhongshan Hospital, Fudan University, Shanghai, China

    Ming Jiang, Fang Fang & Jing Cang

Authors
  1. Xiaoqing Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xinyue Liang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ming Jiang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ling Wei
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xiangpeng Shi
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Fang Fang
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Jing Cang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

XQ.W conceived the idea. M.J and XY.L designed the study and wrote the original manuscript. XP.S and L.W analyzed and interpreted data. J.C and F.F performed the validation analyses. All authors have read and agree to the published version of the manuscript.

Corresponding authors

Correspondence to Fang Fang or Jing Cang.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interest

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Liang, X., Jiang, M. et al. Systemic inflammation as a mediator in the link between obesity and depression: Evidence from a nationwide cohort study. BMC Psychiatry 25, 449 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12888-025-06892-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12888-025-06892-3

Keywords

BMC Psychiatry

ISSN: 1471-244X

Contact us