Introduction

Depression is one of the most common mental health problems and a major public health issue worldwide. It is mainly characterized by frequent feelings of sadness, emptiness or hopelessness, loss of interest in previously enjoyed activities or things, as well as decreased energy or easy fatigue1, which not only affects the patient’s physical and mental health, but also leads to reduced productivity and impaired social functioning, thus increasing the economic burden. According to the World Health Organization (WHO), approximately 280 million people in the world suffer from depression, one of the major contributors to the burden of disease around the world2,3, and the emergence of COVID-19 has exacerbated the current state of poor mental health, resulting in a significant increase in the prevalence and burden of major depression and anxiety disorders4. Studies have shown that the incidence of depressive symptoms in the United States during the COVID-19 pandemic was more than three times higher compared to before the pandemic5. The proportion of the population with depression increased from 6.6% in 2005 to 9.2% in 20206,7. Additionally, a survey of U.S. adults in 2019 found that about 7.8% (19.4 million people) had experienced at least one major depressive episode8. Despite the existence of effective treatments, such as antidepressant medications and psychotherapy9, about one-third of patients are ineffective on antidepressant therapy, which in turn evolves into treatment-resistant depression. This suggests that there is a need to better understand the pathophysiology in order to find new therapeutic targets. Further studies have found that 50% of people with depression globally do not receive appropriate treatment, and in some low-income countries the proportion receiving treatment may be less than 10%10. This may be related to the failure to identify risk factors for depression in a timely manner. Therefore, the identification of effective predictors has a crucial role to play in the treatment and prevention of depression.

The Neutrophil Percentage to Albumin Ratio (NPAR) is a blood biochemical indicator that measures the ratio of the percentage of neutrophils to the serum albumin level. This ratio is an indicator for assessing a patient’s inflammatory state, infection and nutritional status. Inflammatory mechanisms have been implicated in depression. Higher NPAR scores generally indicate more severe inflammation. This is because a higher NPAR reflects an increase in the percentage of neutrophils and/or a decrease in serum albumin levels, both of which are indicators of heightened inflammatory responses in the body. Thus, this ratio can be a useful tool for assessing the severity of inflammation. Studying the relationship between NPAR and depression could enhance our understanding of the underlying inflammatory mechanisms in depression, which may help develop new treatment strategies. Meng et al.11 showed that neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR), which are indicators of inflammation, are associated with depression, and that essential nutrients12,13, fatty acids14, antioxidants15, dietary patterns16, and intestinal flora17,18 directly or indirectly affect brain function and mental health. NPAR, as a new inflammation and nutritional assessment index, integrates and adapts to the dual effects of inflammatory cytokines and nutritional markers. Zhou et al.’s study of inflammatory markers on chronic obstructive pulmonary disease (COPD) mortality confirmed that both NPAR and NLR predicted mortality, and that NPAR had a better predictive performance19. A study by Wang et al.20 found that the NPAR predicts mortality in patients at risk of acute kidney injury. NPAR has also been used as a predictor of inflammation-based prognosis in patients with cardiogenic shock, and palliative pancreatic cancer20,21,22.

Although NPAR is a composite indicator based on human blood components, its precise correlation with depression remains uncertain. In addition, the condition of depressed patients worsens over time, which makes valid indicators crucial for early identification of adverse health outcomes in depressed patients. Therefore, we conducted a cross-sectional study using data from the 2005–2018 National Health and Nutrition Examination Survey (NHANES) to explore the relationship between NPAR and depression to better provide effective guidance for the prevention and management of depression.

Methods

Study population

The study was a population-based cross-sectional study utilizing the National Health and Nutrition Examination Survey (NHANES) database. NHANES carried out by the Disease Control and Prevention (CDC), serves as a pivotal resource for assessing the health and nutritional status of the non-hospitalized U.S. population23. This survey collects a large amount of information, including detailed demographic data, physical examination results, and more. The data is meticulously collected via a complex, multi-stage stratified probability sampling method, ensuring national representation and is released in biennial cycles. This article extracts data from 7 independent NHANES cross-sectional waves (2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, 2017–2018). Comprehensive information on the research designs and the collected data is available on the National Center for Health Statistics (NCHS) website at (https://www.cdc.gov/nchs/nhanes/index.htm). Furthermore, the NHANES protocol has received approval from the National Health Statistics Ethics Review Committee of the CDC, with all participants providing written informed consent prior to participation. All research procedures were authorized by the Ethics Review Board of the National Center for Health Statistics. Informed consent was obtained from all participants, and the study followed the guiding principles of the Declaration of Helsinki.

This study analyzed participants with complete data on depression and NPAR. A total of 70,190 participants were initially recruited, with participants aged < 18 (n = 28,047) excluded, and exclude missing NPAR (n = 4473) and PHQ-9 (n = 3302) data and pregnancies (n = 600), the final analysis included 33,768 eligible participants (Fig. 1).

Fig. 1
figure 1

Flowchart of participant selection. NHANES, National Health and Nutrition Examination Survey; NPAR, neutrophil percentage-to-albumin ratio.

Full size image

Neutrophil percentage-to-albumin ratio

NPAR is a blood biochemical indicator of the percentage of neutrophils to albumin (NPAR) developed in recent years to measure the ratio of the percentage of neutrophils to the serum albumin level. Indicators are derived from human blood samples and include neutrophil percentage and albumin values, both measured in milligrams per deciliter (mg/dl); based on these relevant indicators, a value for NPAR is obtained. The formula for calculating NPAR is as follows: NPAR = Neutrophil percentage (in total WBC count) (%) × 100/Albumin (g/dL).

Assessment of depressive symptoms

The PHQ-9 is a widely used self-report scale for identifying the severity of depressive symptoms and is designed based on the DSM-IV diagnostic criteria for depression. The scale consists of nine questions with scores ranging from 0 to 27, with high scores indicating greater severity of depressive symptoms. The study showed that the PHQ-9 is highly sensitive and specific, with a sensitivity and specificity of 88 percent. In addition, the scale includes a 10-point severity rating system to further differentiate the severity of depression24. Depression in this study was defined by PHQ-9 ≥ 1025. The PHQ-9 can be used not only for the initial screening of patients with depression, but also for the follow-up evaluation of treatment effects.

Covariates

The covariates in this study were selected based on factors found to be associated with NPAR and depression in previous studies. This encompasses factors such as age, sex, ethnicity, level of education, ratio of poverty to income (PIR), body mass index (BMI) tobacco use, alcohol intake, history of hypertension, diabetes and CVD history. Race was categorized into four groups: Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, and other races. PIR was a continuous variable. Education level was categorized into three levels: < high school, high school, and > high school. Participants were categorized as past smokers, current smokers, and never smokers based on their responses to the questions “SMQ020—Have smoked at least 100 cigarettes in your lifetime” and “SMQ040—Do you currently smoke cigarettes” on the questionnaire. Alcohol consumption status was determined by the question “ALQ130—Average # of alcoholic beverages/day—past 12 months”. CVD questionnaire was assessed. Hypertension was determined by a triple mean systolic blood pressure (SBP) of ≥ 140 mmHg or triple mean diastolic blood pressure (DBP) of ≥ 90 mmHg, or use of antihypertensive medications. Diabetes mellitus was determined by questionnaire or other criteria, including a glycosylated hemoglobin (HbA1c) level of ≥ 6.5%, a fasting blood glucose level of ≥ 126 mg/dl, or current use of diabetes medication or insulin.

Statistical analysis

In this study, we meticulously express continuous variables using their mean ± standard deviation (SD) and delineate categorical variables by frequencies or percentages. Using chi-square and t-tests, we can discern differences between groups. NPAR was categorized into quartiles (Q1: 0.18–11.98 Q2: 12.00–13.64 Q3: 13.65–15.33 Q4: 15.34–59.67), with the lowest quartile considered the reference group. We further investigate the independent association between NPAR scores and depression through logistic regression analysis across three distinct models: the first model operates without any adjustment for covariates; the second model incorporates adjustments for sex, age, and race; and the third model extends these adjustments to include education, smoking status, alcohol consumption, hypertension, diabetes mellitus, BMI, PIR and history of CVD. Subgroup analyses to explore the relationship between NPAR and depression are conducted using stratified factors such as sex, ethnicity, level of education, BMI, tobacco use, hypertension, diabetes and CVD. We assess the heterogeneity of associations across these subgroups using interaction terms between subgroup indicators. Furthermore, we also utilized curve fitting with smoothing splines to detect the nonlinear impact of NPAR on the prevalence of depression, and employed piecewise linear regression models to further investigate their threshold effects. We set the statistical significance threshold at p < 0.05 and conducted all analyses using the Empower software, supplied by X&Y Solutions, Boston, MA.

Results

Baseline characteristics

A total of 33,768 participants were enrolled in this study, and Table 1 displays the foundational data, where the mean age is noted as 48.31 ± 18.67, with 50.21% men and 49.79% women. Participants were categorized into quartiles based on the distribution of NPAR ranging from 0.18 to 11.98 (first quartile), 12.00–13.64 (second quartile), 13.65–15.33 (third quartile) and 15.34–59.67 (fourth quartile). Overall, 2386 (7.07%) participants had prevalent depression, and the higher NPAR quartiles had a higher ratio of females and a relatively higher prevalence of depression in them compared to the lower quartiles (quartile 1: 5.89%; quartile 2: 6.10%; quartile 3: 6.76%; quartile 4: 9.52%, P < 0.001). The NPAR quartiles differed significantly in terms of age, sex, ethnicity, level of education, ratio of PIR, BMI, tobacco use, CVD, hypertension, and diabetes (P < 0.05). Participants in the higher NPAR group were more likely to be female, non-Hispanic white, never smoker, and higher levels of education, and were more likely to have hypertension, diabetes and CVD than those in the lower NPAR subgroup. No statistically significant differences were found in alcohol consumption (P > 0.05).

Table 1 Baseline characteristics of the study population stratified according to neutrophil percentage-to-albumin ratio.
Full size table

The relationship between NPAR and depression

Table 2 shows the results of the multivariate logistic regression model describing the relationship between NPAR and depression. After adjusting for NPAR as a continuous variable, both model 1 model 2 and model 3 demonstrated a strong positive association (OR = 1.03, 95% CI: 1.01, 1.05, P < 0.001 for model 3), suggesting that Strong positive correlation between NPAR and odds of depression. When NPAR was divided into quartiles, a positive association was observed between NPAR quartiles and the odds of depression, with all models showing statistical significance. Using the lowest quartile (quartile 1) as a reference, the adjusted OR for the highest quartile (quartile 4) was 1.20 (95% CI: 1.06, 1.36, P < 0.05). This suggests that 20% increase in odds of depression in Q4 compared to Q1, thus confirming a stable, positive, and statistically significant association between an increase in NPAR and the prevalence of depression.

Table 2 Association between NPAR and depression.
Full size table

Furthermore, the application of curve smoothing techniques demonstrated the non-linear correlation between NPAR and the prevalence of depression, as depicted in Fig. 2. Then, threshold analysis then revealed a saturation effect of NPAR at the inflection point of 12.65. A positive association between NPAR and the prevalence of depression was observed when NPAR > 12.65 (OR = 1.06, 95% CI: 1.04, 1.09, P < 0.001); whereas, we did not find a statistically significant relationship, when NPAR < 12.65 (OR = 0.96, 95% CI: 0.91, 1.00, P = 0.045). This result suggests that there is a correlation between NPAR and the prevalence of depression when NPAR > 12.65 (Table 3).

Fig. 2
figure 2

Smooth curve fitting for NPAR and depression. The solid red line represents the smooth curve fit between variables. Blue dotted lines represent the 95% CI from the fit.

Full size image
Table 3 Threshold effect analysis of the correlation between NPAR and depression.
Full size table

Subgroup analyses

Analysis of subgroups and interaction tests were conducted by sex, ethnicity, level of education, smoke, BMI, CVD, hypertension and diabetes to examine the consistency of the association between NPAR and depression in the overall population and to assess whether effect modifiers had any influence on this association. In our analysis, smoking was found to alter the association between NPAR and depression (P for interaction < 0.05), we observed no significant interactions between the other subgroups. But NPAR scores remained significantly and positively associated with depression among participants who age ≥ 42 years, male, non-Hispanic white and black, never smoked and never smoked, BMI > 25, had diabetes or hypertension and no CVD (Fig. 3).

Fig. 3
figure 3

Verification of the association between NPAR and depression by subgroup analyses.

Full size image

Discussion

The aim of our study was to examine the association between NPAR and the prevalence of depression by analyzing data from 33,768 participants from the 2005–2018 NHANES through cross-sectional analysis. We found a positive association between NPAR and the prevalence of depression, suggesting that a rise in NPAR per unit is linked to higher prevalence of depression. We observed a different pattern of association on both sides of a specific breakpoint (NPAR-12.65). On the right side of this breakpoint, a positive correlation was found between NPAR and the prevalence of depression, whereas the association on the left side of the breakpoint was not statistically significant. Therefore, we find a positive correlation between NPAR and the prevalence of depression. These results highlight the importance of considering NPAR levels in the assessment and management of depression.

This study marks a pioneering effort to evaluate the relationship between the NPAR and the prevalence of depression. Previous studies have delved into the link between inflammation and depression. And numerous studies have been conducted to show that inflammation has a potentially critical role in the development of depression26,27. People with depression may experience a bi-directional causal relationship between an inflammatory state in the body28, which in turn affects brain function and behavior through a variety of pathways29. And a cross-sectional study11 showed have shown that three important indicators of inflammation, NLR, PLR, and MLR, are nonlinearly correlated with depressive symptoms. These studies are similar to our findings of a positive correlation between NPAR and depression. This suggests that, in addition to traditional indicators of inflammation, NPAR can be used as a novel indicator of inflammation that may be associated with psychoneurological disorders.

There is a strong association between inflammation and depression. Previous studies have shown that some inflammatory markers such as tumor necrosis factor-alpha (TNF-alpha), IL-6, and CRP30, which have higher levels in patients with depression, are able to cross the blood–brain barrier or indirectly affect the brain by affecting the functioning of the brain’s capillaries, and that these cytokines can influence neuronal activity in the brain, including the synthesis and release of neurotransmitters, such as serotonin and dopamine. These neurotransmitters are closely associated with symptoms of depression31,32,33. Chronic stress can activate the body’s inflammatory response, which may increase inflammation by activating the HPAA axis ( the hypothalamic–pituitary–adrenal axis)34,35, and this sustained state of inflammation may have deleterious effects on the brain, for example, causing a reduction in the volume of the hippocampus, a structural change in the brain common to people with depression. Inflammation also affects the medial and lateral prefrontal cortex of the brain36, which affects its executive control and emotion regulation functions. Under stress, this dysfunction can lead to persistent changes in the emotion regulation system, triggering symptoms of irritability and anxiety, also known as “sickness behavior”, which is a natural bio-behavioral response to fatigue, lack of motivation, sleep disturbances and depressed mood. This is a natural biobehavioral response that includes fatigue, lack of motivation, sleep disturbances, and depressed mood, which are highly similar to the behavioral manifestations of depression37. At this point, people may engage in unhealthy self-medicating behaviors to cope with these symptoms, but this can exacerbate inflammation, which in turn can worsen depressive symptoms. Inflammation can also affect the brain’s neuroplasticity29, which is the alteration of connections between neurons and the formation of new neural connections. Diminished neuroplasticity is recognized as a key pathological change in depression38.

Albumin, the main protein in plasma, usually reflects an individual’s nutritional status and level of inflammation. Its low level is usually indicative of malnutrition, which may be caused by dietary deficiencies, chronic disease, or inflammation. Such declines may affect the transportation and distribution of a variety of substances in the body, including specific nutrients. Neurotransmitters in the brain, such as serotonin, dopamine, and norepinephrine are essential for mood regulation, and their synthesis relies on specific nutrients such as tryptophan, tyrosine, B vitamins, and omega-3 fatty acids39,40. Therefore, a diet rich in antioxidant, anti-inflammatory foods may help reduce inflammation and depressive symptoms41,42. There is a strong link between gut health and brain health, known as the “gut-brain axis”43,44. Malnutrition may lead to dysbiosis, which further affects brain health. Imbalances in gut flora have been shown to be associated with mood disorders, including depression45,46. Meanwhile, nutritional deficiencies of micronutrients such as iron, zinc, magnesium, and vitamins have been associated with increased depressive symptoms13,47,48. These elements play a key role in biochemical reactions that affect brain function and neurotransmission. Changes in NPAR levels, reflecting an increase in the proportion of neutrophils and a decrease in albumin levels, are associated with an increased prevalence of depression. Therefore, the important role of albumin levels should also be taken into account when exploring the relevance of depression. Our study identified an inflection point where the relationship between NPAR and the prevalence of depression shifted when NPAR = 13.52. When NPAR > 13.52, the prevalence of depression were positively correlated with NPAR, suggesting that for every unit increase in NPAR, the prevalence of depression also increased. The existence of this nonlinear relationship may reflect the differential effects of different levels of NPAR on the prevalence of depression. These findings suggest that NPAR could potentially serve as a predictive biomarker for depression, offering a novel avenue for early identification and intervention. Large-scale prospective studies are necessary to confirm these results and to explore the mechanisms underlying this association. Clinically, if these findings are validated, NPAR could be integrated into routine screenings, providing a cost-effective tool for identifying individuals at higher risk of depression. This could facilitate earlier therapeutic interventions, potentially improving patient outcomes and reducing the burden of depression on healthcare systems. Further research should also consider the impact of variables such as medication use and other comorbid conditions to refine the predictive accuracy of NPAR.

The current study has several strengths. First, this study used the widely representative and rigorously validated NHANES database. Second, the study adjusted for potential confounders and performed subgroup analyses to examine the robustness of the relationship between NPAR levels and the prevalence of depression in different populations. Despite this, the study has limitations. As a beginning, while a cross-sectional design of study facilitates rapid data collection and identification of relationships between variables, this design does not allow for the determination of the temporal sequence of events and therefore causality. Second, although multiple confounders were considered, we still could not rule out the effects of all potential confounders. Additionally, this study did not include detailed medication information for patients with depression, so the influence of medication on depression cannot be ruled out. Third, the sample selected may not be fully representative of more diverse regions and populations, which may affect the generalizability and accuracy of the findings.

Conclusion

Our results indicate a positive association between NPAR levels and the prevalence of depression. NPAR may become a new predictor of depression. This finding has important value for primary prevention in populations with a high incidence of depression. Evidence of the potential predictive and diagnostic utility of NPAR may help clinicians refine the diagnosis and treatment of depression.