The Moderating Effect of Physical Activity on the Relationship Between Neutrophils and Depression: A Cross-sectional Analysis Based on the NHANES Database

The Moderating Effect of Physical Activity on the Relationship Between Neutrophils and Depression: A Cross-sectional Analysis Based on the NHANES Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Moderating Effect of Physical Activity on the Relationship Between Neutrophils and Depression: A Cross-sectional Analysis Based on the NHANES Database Zhaohui GUO, Zhenwen XIE, Peng WANG, Shufan LI, Xin XIN, Xing WANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3850398/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective: To analyze the relationship between neutrophil count and depression based on the NHANES database and to explore the moderating effect of physical activity on the relationship between neutrophils and depression. Methods: Cross-sectional data from the NHANES database were extracted, including demographic information, neutrophil count, scores from the PHQ-9 depression self-assessment scale, and scores from the GPAQ (Global Physical Activity Questionnaire). Pearson correlation, binary logistic regression, restricted cubic spline models, and linear regression equation models were used to analyze the association among physical activity, neutrophil count, and depression. Results: There was a positive correlation between neutrophil count and depression, with an OR[95%CI] of 1.12[1.011,1.24]. The effect of physical activity in moderating the impact of neutrophils on depression was statistically significant (β=-0.1691, P<0.05). Conclusion: Identifying depression in adults can be informed by monitoring neutrophil counts, which may be related to the "inflammation-induced depression" theory. Physical activity, as an effective moderator, significantly reduces the impact of neutrophils on depression, offering potential value in future prevention and intervention strategies for depression. Biological sciences/Cell biology Biological sciences/Immunology Biological sciences/Psychology Health sciences/Diseases Health sciences/Medical research Physical activity Neutrophils Depression Moderation model Figures Figure 1 Figure 2 Figure 3 Introduction Depression has become the most significant challenge in global mental health, accounting for 10.3% of the overall disease burden [1] . According to statistics from the World Health Organization, the total number of individuals suffering from depression worldwide has surpassed 350 million [2–3] , marking a 49.86% increase compared to the past. Research suggests that depression is a complex disorder influenced by a multitude of factors. These factors, in various combinations, can trigger different disease trajectories and are characterized by clinical manifestations such as low mood, sleep deprivation, and tendencies towards self-harm and suicide [4] . The prolonged absence of effective treatment continuously impacts patients' physical health and quality of life [1] . This places a tremendous strain on healthcare resources, making the prevention and mitigation of depression an urgent societal issue to address. Depression is not only influenced by factors such as family environment, educational level, and marital status [5–7] , but an increasing body of research [8–10] suggests that inflammatory responses play a crucial role in the pathogenesis of depression. Depressive individuals exhibit significant specificity in inflammatory factors, metabolic markers, and oxidative stress in their blood compared to healthy populations [11–12] . Changes in blood immune cell composition, particularly in the white blood cell subtypes, have been observed in individuals with depression. White blood cells, known for generating reactive oxygen species as part of the immune response, can, when overly activated, lead to increased oxidative stress, potentially causing damage to normal cells, including neurons in the brain. If the white blood cell count is elevated in individuals with depression, it may contribute to heightened oxidative stress levels in the brain, impacting neuronal function and further exacerbating the patient's condition. Moreover, previous studies have revealed positive correlations between depression and peripheral blood lymphocytes, white blood cells, and myeloid cells [13–14] . Meta-analysis results indicate that an increase in the relative percentage or absolute count of neutrophils can aid in identifying depression patients associated with inflammation [15] . Therefore, we propose that peripheral blood immune cell counts can serve as a valuable indicator for identifying a subgroup of depression related to inflammation and assessing the risk of the disease. Physical activity (PA) is an approach that, through various exercise behaviors, aims to promote health, enhance physical fitness, and improve the quality of life [16] . Studies have found that PA can not only effectively influence physiological mechanisms but also elevate positive emotional levels, significantly alleviating depressive symptoms [17] . Additionally, research indicates that physical activity can effectively reduce the risk of depression. This is attributed to the increased expression of adult hippocampal neurons and Brain-Derived Neurotrophic Factor (BDNF) through PA [18] , which in turn prevents the onset of depression. Furthermore, studies have confirmed that appropriate PA has anti-inflammatory effects [19] . It can not only reduce the circulating count of white blood cells [20] but also enhance antioxidant capacity, mitigating oxidative stress [21–22] , thereby reducing the risk factor for depression. Reviewing previous research, we found that both blood inflammatory cells and physical activity can individually influence depression. Physical activity has been widely accepted as an effective strategy to reduce the risk of depression. However, the exact mechanism of its action remains unclear, especially regarding whether there's an interactive regulatory effect between physical activity and inflammatory cells in reducing the risk of depression. In light of this, our study intends to conduct statistical analysis based on samples from the NHANES database, observing the relationship between the counts of various inflammatory cells in whole blood and depression. We aim to explore whether the level of physical activity can mitigate the risk of depression caused by changes in the count of inflammatory cells and evaluate its impact. This research seeks to provide evidence-based recommendations for preventing the onset of depression and reducing the increase in inflammatory cell counts, ultimately promoting both physical and mental health in individuals. Subjects and Methods 2.1 Research Subjects This study is based on data from the NHANES database (National Health and Nutrition Examination Survey, NHANES). Managed by the U.S. Centers for Disease Control and Prevention (CDC), the NHANES has been surveying population health since 1999 through questionnaires and physical examinations. The questionnaire covers demographics, socio-economics, diet, and health information, while the physical examination includes physiological measurements and checks. The aim is to assess the health and nutritional status of adults and children. All research has been approved by the National Center for Health Statistics (NCHS) Institutional Review Board, and informed consent has been obtained from all participants. 2.2 Data Inclusion and Exclusion Criteria This study selected data from the NHANES database for the years 2017–2020 that included demographic information, physical activity levels, depression, and complete blood count. Based on this, a cross-sectional survey study was conducted. After excluding data for duplication, loss, and strong subjective awareness, a total of 634 subjects were included for analysis. 2.3 Data Collection 2.3.1 Demographic Information The study included consolidated demographic information such as gender, age, education level (below 9th grade, 9-11th grade, high school graduate, undergraduate and above), marital status (married, single, widowed, cohabiting, divorced or separated), number of family members, annual family income, smoking, drinking, and Body Mass Index (BMI). The BMI is calculated as weight (kg) divided by the square of height (meters). 2.3.2 Depression Status The Patient Health Questionnaire-9 (PHQ-9) is commonly used to assess the mental health and depression status of subjects over a two-week period. Based on the PHQ-9, the nine questions range from "not at all" to "nearly every day," representing scores from 0 to 3. The total score is the sum of all questions: 0–4 indicates no depression; 5–9 mild depression; 10–14 moderate depression; 15–19 moderate to severe depression; and 20–27 severe depression. The Cronbach's alpha coefficient of the scale is 0.839. The Pearson correlation coefficient between the nine items ranges from 0.160–0.578 (P < 0.01), indicating that the questionnaire has high sensitivity and specificity for diagnosing depression [23] . 2.3.3 Blood Indicators The Beckman Coulter DxH 800 instrument was used to produce a complete blood count (CBC) on blood specimens, examining the blood indicators in subjects. The CBC is a routine blood test used to assess overall health [24] and detect diseases, including anemia, infections, and leukemia. The sample collection process adhered to the Westgard rules [25] , which include quarterly progress reports, calibration of instruments and reagents, and controlling any variables to ensure data authenticity and reliability. 2.3.4 Physical Activity Level MET (Metabolic Equivalent of Task) represents the oxygen consumption required for metabolism by a healthy adult at rest. It is based on the energy expenditure of sitting quietly [26] . Different types of physical activities have different MET values, and the NHANES data provides recommended MET values for various exercises. The GPAQ (Global Physical Activity Questionnaire) survey includes vigorous work-related activities (MET = 8), moderate work-related activities (MET = 4), walking or cycling for transportation (MET = 4), vigorous leisure-time physical activities (MET = 8), and moderate leisure-time physical activities (MET = 4) (source). PA can be calculated based on MET values, type of activity, frequency per week, and duration. PA (MET-h/wk) = MET × frequency per week × duration of each physical activity session. Subjects with PA = 0 ( 48 MET-h/wk). For this cross-sectional study, based on PA values, all subjects were divided into two groups: no physical activity group (PA = 0) and physical activity group (PA = 1). 3. Statistical Analysis All continuous variables are represented by mean ± standard deviation (Mean ± SD), while categorical variables are represented by N (%). Difference tests were used to assess the differences in continuous variables between patients with and without depression, aiming to identify "targets". The chi-square test was employed to evaluate differences in categorical demographics. When the dependent variable was binary, Pearson correlation, binary logistic regression, and restricted cubic spline models were used to analyze the relationship between the independent variable and the risk of depression. The results of the study are represented by odds ratios (OR) and 95% confidence intervals (95%CI). When examining whether the level of physical activity as a moderating variable affects depression, the dependent variable was transformed into a continuous variable based on the scale scoring criteria. Linear regression analysis was used to explore whether there was an interaction effect between neutrophil count and physical activity level on depression. All research data were statistically processed and analyzed using SPSS 26.0 and R language 4.2.2, with P < 0.05 indicating significant differences. Regression analysis equation: Depression = β0 + β1 × segmented neutrophil count + β2 × physical activity level + β3 × (segmented neutrophil count × physical activity level) (Note: β0 is the intercept, β1 is the coefficient for "segmented neutrophil count", β2 is the coefficient for "physical activity level", and β3 is the coefficient for the interaction term). Results 4.1 Basic Demographic Characteristics As shown in Table 1 , a total of 634 participants were included in this study, of which 357 (56.30%) were male and 277 (43.7%) were female. All participants were adults aged ≥ 20 years, with 162 individuals (25.6%) aged between 20–46 years, 166 individuals (26.2%) aged between 47–61 years, and the rest were older than 61 years. There were 235 smokers (37.1%) and 617 long-term alcohol drinkers (97.3%). Among them, 591 (93.2%) did not engage in physical activity (PA = 0), and only 43 (6.8%) participated in low or high-intensity physical activities (PA = 1). The average counts for lymphocytes, monocytes, neutrophils, and basophils (all units are represented as 1000 cells/uL) were 2.26 ± 1.42, 0.61 ± 0.21, 4.37 ± 1.78, and 0.06 ± 0.05, respectively. The prevalence of depression among all participants was 28.5% (PHQ-9 ≥ 5). The prevalence of depression in males was 22.4%, in married individuals was 20.4%, in the age group 47–61 years was 36.7%, in those with a BMI > 32 was 40%, and in those without physical activity was 29.6%. All these differences were statistically significant (P < 0.05). There was no significant difference in the prevalence of depression among other factors. Table 1 Basic Demographic Characteristics N(%) Non-depressed (%) Depressed (%) P Total Number of Participants 634(100) 453(71.5) 181(28.5) / Gender 0.000099 * Male 357(56.3) 277(71.6) 80(22.4) Female 277(43.7) 176(63.5) 101(36.5) Age 0.055 * ≤ 46 62(25.6) 113(69.8) 49(30.2) 47–61 166(26.2) 105(63.3) 61(36.7) 62–71 149(23.5) 111(74.5) 38(25.5) > 72 157(24.7) 124(79) 33(21) Body Mass Index(BMI) 0.036 * ≤ 24 185(29.2) 138(74.6) 47(25.4) 25–27 136(21.5) 100(73.5) 36(26.5) 28–31 173(27.3) 131(75.7) 42(26.5) > 32 140(22) 84(60) 56(40) Education Level 0.13 Less than 9th grade 43(6.8) 30(69.8) 13(30.2) 9-11th grade 101(15.9) 68(67.3) 33(32.7) High school graduate 161(25.4) 113(70.2) 48(29.8) Bachelor's degree 221(34.9) 158(71.5) 63(28.5) Less than 9th grade 108(17) 84(77.8) 24(22.2) Family size 0.85 1 126(19.9) 83(65.9) 43(34.1) 2–4 420(66.2) 309(73.6) 111(26.4) > 4 88(13.9) 61(69.3) 27(30.7) Marital Status 0.02 * Married 294(46.4) 234(79.6) 60(20.4) Single 66(10.4) 49(74.2) 17(34.1) Widowed 88(13.9) 58(65.9) 30(34.1) Cohabiting 58(9.1) 41(70.7) 17(29.3) Divorced or Separated 128(20.2) 71(55.5) 57(44.5) Smoking 0.3 Yes 235(37.1) 158(67.2) 77(32.8) No 399(62.9) 295(71.5) 104(28.5) Alcohol drinking 0.076 Yes 617(97.3) 443(71.8) 174(28.2) No 17(2.7) 10(58.8) 7(41.2) Physical Activity Level 0.007 * No physical activity (PA = 0) 591(93.2) 416(70.4) 175(29.6) Engaged in physical activity (PA = 1) 43(6.8) 37(86) 6(14) *P < 0.05. 4.2 Blood Cell Predictors for Depression Risk Through differential testing to evaluate the differences in continuous variables between depressed and non-depressed patients, the "target" showed that the counts of neutrophils and basophils in the non-depressed group were 4.24 ± 1.74 and 0.06 ± 0.05 (1000 cells/uL) respectively, while in the depressed group, the counts were 4.72 ± 1.85 and 0.07 ± 0.05 (1000 cells/uL) respectively. The differences were statistically significant (P < 0.05). There was no significant difference in lymphocytes and monocytes. Analysis through Table 2 and the restricted cubic spline model (Fig. 1 ) revealed a positive correlation between inflammatory cells, specifically neutrophils and basophils, with depression (P < 0.01). Other blood cells did not show a direct association with depression. Therefore, when treating the dependent variable as a binary variable for binary logistic regression, the results showed that only neutrophils could predict and identify the risk of depression with statistical significance (OR = 1.12, 95%CI: 1.011–1.24). Table 2 Inflammatory Cells and the Risk of Depression Inflammatory Cells r P1 OR 95%Cl P2 Monocyte 0.033 0.401 - - - Lymphocyte 0.025 0.525 - - - Neutrophil 0.121 ** 0.002 1.12 [1.011,1.24] 0.03 Eosinophil 0.104 ** 0.009 - - - **. indicates significance at the 0.01 level (two-tailed). OR: Odds Ratio; CI: 95% Confidence Interval; All dependent variables are depression-related. 4.3 Modulatory Role of Physical Activity Level on Depression and Neutrophils To explore whether there is an interactive effect between physical activity level and neutrophils on depression, the binary variable of depression was transformed into a continuous variable based on the PHQ-9 score for linear regression analysis. The results showed that the F-statistic was 10.79 (Table 3 ), with P < 0.05. This indicates that the research results can reject the null hypothesis (i.e., all regression coefficients are equal to zero), and the modulatory variable model is statistically significant. The regression results (Table 4 ) revealed a significant positive correlation between segmented neutrophil count and depression level (β = 0.1083, P < 0.05). This means that, while keeping the physical activity level constant, as the neutrophil count increases, the depression score rises. Conversely, when controlling for segmented neutrophil count, there is a negative correlation between physical activity level and depression level, but this is not statistically significant (β=-0.1644, P = 0.28). Therefore, to explore the specific relationship between the three variables, physical activity level was treated as a modulating variable. The research results (Table 4 , Figs. 2 and 3 ) showed that there is an interactive effect between physical activity level and neutrophils on depression, and the modulatory effect is statistically significant (β=-0.1691, p < 0.05). Specifically, as the physical activity level transitions from none to some, the positive correlation between segmented neutrophil count and depression level weakens. Table 3 Model Summary R R 2 F P 0.2211 0.0489 10.79 0 Table 4 Linear Regression Results β(Regression Coefficient) SE P 95%Cl Neutrophil Count 0.1083 0.216 0 [0.0659,0.1507] Physical Activity Level -0.1644 0.1535 0.2846 [-0.4659,0.1371] Neutrophil Count * Physical Activity Level -0.1691 0.0458 0.0002 [-0.2591,-0.0791] Dependent Variable: Depression Score Discussion The prevalence of depression in the study population was found to be 28.5%. Notably, specific demographics such as females, middle-aged to elderly individuals (47–61 years old), individuals with a BMI greater than 32, and married individuals exhibited a higher incidence of depression. These findings are consistent with previous studies [27–28] . Biologically, fluctuations in female hormone levels [29] and gender differences in brain structures [30–31] may elevate the risk of depression. Some research suggests that females, due to their smaller hippocampal spatial structure, are more susceptible to stress. Additionally, factors such as obesity, age, and marital status have been linked to depression, likely influenced by lifestyle choices and psychological stressors [32–35] . These insights underscore the importance for researchers and mental health professionals to prioritize depression prevention and treatment in these specific populations, advocating for healthier lifestyles to mitigate depression risks. Furthermore, our study identified a significant positive correlation between neutrophil counts and depression, suggesting that an increase in these counts can aid in identifying patients with inflammation-associated depression. This aligns with prior research [15] .The underlying biological mechanism supports the "inflammation-induced depression" theory [36–37,39] . Chronic inflammatory states can potentially impact brain functions through various pathways, including neurotransmitter metabolism, neuroplasticity, and neuroendocrine functions, leading to the onset of depression[40]. Neutrophils, a category of white blood cells, serve as primary markers for the body's response to infections [41] . An excessive increase in their numbers might reflect heightened inflammation, subsequently affecting neurobiological mechanisms in the brain. Studies have confirmed [42] that neutrophils can activate glial cells in the brain, such as astrocytes and microglia, leading to the release of a plethora of cytokines, like TNF-α and IL-1β. These cytokines, crossing the blood-brain barrier, intensify the inflammatory response, impacting neurotransmitter synthesis, release, and neuronal growth and survival, thereby inducing depression [36–39] . From a clinical perspective, while the diagnosis of depression primarily relies on symptom assessment, the use of biological markers like neutrophils can offer a more precise diagnostic foundation. Interestingly, our study revealed that the level of physical activity (PA) did not have a direct influence on the incidence rate of depression (P > 0.05). However, when combined with neutrophil counts, PA exhibited a significant modulatory effect, effectively mitigating the impact of neutrophils on depression. This might be attributed to physical activity modulating the quantity and activity of neutrophils by influencing immune functions. In an "anti-inflammatory" state, physical activity, by increasing the production of anti-inflammatory cytokines (e.g., IL-4 and IL-10), suppresses the activation of neutrophils and the production of inflammatory cytokines [43] . Moreover, from a neurotransmitter and hormonal perspective, physical activity can modulate serotonin and dopamine levels in the brain [44] , neurotransmitters believed to regulate mood and suppress inflammation [45] , providing both analgesic and euphoric effects. Additionally, physical activity can enhance the secretion of hormones like cortisol and growth hormone, which inhibit the activation of neutrophils and the production of inflammatory cytokines. From an antioxidative mechanism standpoint, physical activity can bolster the body's antioxidative capacity, such as enhancing the activity of superoxide dismutase and hydrogen peroxide enzymes [46] , aiding in neutralizing free radicals, thereby reducing cellular damage and inflammation. In terms of neuroplasticity, physical activity can effectively enhance neuroplasticity [47–49] , promoting neuronal growth, survival, and the formation and repair of synapses through the elevation of neurotrophic factors like brain-derived neurotrophic factor (BDNF). In summary, engaging in physical activity can, through various biological mechanisms—including immune function modulation, neurotransmitter and hormonal influences, antioxidative effects, and enhanced neuroplasticity—suppress the activation of neutrophils and the production of inflammatory cytokines, thereby reducing the risk of depression. Conclusion The findings of this study suggest a positive correlation between neutrophil counts and depression in adults. An excessive increase in neutrophil counts and percentages emerges as one of the risk factors contributing to depression. Furthermore, the study identifies the level of physical activity as a significant modulatory factor, attenuating the impact of neutrophils on depression. Consequently, future diagnostic approaches for depression should emphasize monitoring inflammatory cells like neutrophils, inhibiting the production of inflammatory cytokines, enhancing the psychological well-being of high-risk populations, and preventing the onset of mental disorders. 6. Limitations and Future Directions The primary foundation of this study is a cross-sectional analysis based on the NHANES database, making it challenging to discern causal relationships between the variables. In analyzing data related to physical activity, the Global Physical Activity Questionnaire (GPAQ) did not categorize activity into low, medium, or high intensities. As a result, this study could not delve into the specific effects of varying activity intensities on mitigating the influence of neutrophils on depression. The PHQ-9, utilized in this study, is a self-reported depression scale and not a clinical diagnosis, potentially introducing a bias risk. Future research should focus on larger sample sizes to further elucidate the intricate relationships between physical activity levels, neutrophils, and depression. There is a pressing need to delve deeper into the efficacy of neutrophils in predicting and identifying depression. Incorporating the five essential elements of exercise into longitudinal studies could pave the way for crafting precise strategies for depression prevention and management. Declarations Data availability All NHANES data for this study are publicly available and can be found here: https://wwwn.cdc.gov/nchs/nhanes.The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Author contributions Mr. Z.G.: data collect and manuscript editing；Mrs. Z.X.: data analysis and original draft writing；Mr. P.W.: data analysis；Mrs. S.L.: data analysis；Mrs. X.X: data collect； Mr. X.W.: review and editing. 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Tikka S K, Garg S, Siddiqui M A, 2021.Prescribing Physical Activity in Mental Health: A Focused Review on the Latest Evidence, Recommendations, Challenges, and Relevance to India[J].SAGE Publications Sage India: New Delhi, India, 2021(6). Eliyahu D, Csatlósová Kristína, Čuriová Barbora,et al.,2017. Effect of Physical Exercise and Acute Escitalopram on the Excitability of Brain Monoamine Neurons: In Vivo Electrophysiological Study in Rats[J].International Journal of Neuropsychopharmacology,2017(7):585-592. Magdalena W, Jadwiga S, Marcin M, et al.,2018. Anaerobic Exercise-Induced Activation of Antioxidant Enzymes in the Blood of Women and Men[J].Frontiers in Physiology,2018,9:1006. Li X, Luo P,Wang Q,et al.,2016.Hurdle Aerobic Exercise Induces Neuroplasticity and Angiogenesis in Hippocampus of Mice[J].Neural Plasticity,2016:1-9. Valero J, Mastrella G, Neiva I, et al.,2019. Neural Stem Cells and Adult Neurogenesis: The Role of Inflammation[J]. Frontiers in Aging Neuroscience,11:137. Esposito G, Scuderi C, Valenza M, et al.,2011. Cannabidiol Reduces Aβ-Induced Neuroinflammation and Promotes Hippocampal Neurogenesis through PPARγ Involvement[J].PLoS ONE,6(12):e28668. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Apr, 2024 Reviews received at journal 21 Mar, 2024 Reviewers agreed at journal 11 Mar, 2024 Reviewers agreed at journal 26 Feb, 2024 Reviewers invited by journal 16 Feb, 2024 Editor assigned by journal 16 Feb, 2024 Editor invited by journal 17 Jan, 2024 Submission checks completed at journal 17 Jan, 2024 First submitted to journal 10 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3850398","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267800409,"identity":"b1c5a2ae-cd90-4198-8330-0aef8ea74b69","order_by":0,"name":"Zhaohui GUO","email":"","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":false,"prefix":"","firstName":"Zhaohui","middleName":"","lastName":"GUO","suffix":""},{"id":267800410,"identity":"aff271e0-45c1-4149-a2bb-b3681702c0c3","order_by":1,"name":"Zhenwen XIE","email":"","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":false,"prefix":"","firstName":"Zhenwen","middleName":"","lastName":"XIE","suffix":""},{"id":267800411,"identity":"b2db3154-353d-479e-836d-a74b3136295c","order_by":2,"name":"Peng WANG","email":"","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"WANG","suffix":""},{"id":267800412,"identity":"bcb2adfb-7a27-4698-9b13-c5f69f6d8c82","order_by":3,"name":"Shufan LI","email":"","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":false,"prefix":"","firstName":"Shufan","middleName":"","lastName":"LI","suffix":""},{"id":267800413,"identity":"5be75eb1-a72f-4149-a1d9-ba1ade25acbf","order_by":4,"name":"Xin XIN","email":"","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"XIN","suffix":""},{"id":267800414,"identity":"1f550501-9a23-46fe-b35d-1c6f39073060","order_by":5,"name":"Xing WANG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYNACAwYGNvb2ww8+wLnEaOHjOZNmOIN4LUAgJ5FgIM1DjBaD42cPv+YpuGPXJpGQYGzz57A9A3vzNgmGmju4tZzJS7OcYfAsuY3n4YHHuW2HExt4jpVJMBx7hlOL2YEcM4MPBoeT2diBtuQ2HE5gkMgxk2BsOIxby/k3ZgYJIC0MQL9YgBwm/4aAlhs5xg+AttixcQC1MLAdZmyQ4MGvxf7GGzPGGQaHE9hAgdzblp7YxpNWbJFwDLcWyf4c4888QPfItwOj8scfa3t+9sMbb3yowa0FCNgkgERiA5wLIhLwaWBgYAYlE3v8akbBKBgFo2BEAwDKZVZHy/E9BQAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":true,"prefix":"","firstName":"Xing","middleName":"","lastName":"WANG","suffix":""}],"badges":[],"createdAt":"2024-01-10 13:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3850398/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3850398/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49894166,"identity":"658fda78-8324-4ad4-b59a-c58a4a3b1da3","added_by":"auto","created_at":"2024-01-19 21:26:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":690264,"visible":true,"origin":"","legend":"\u003cp\u003eResults of restricted cubic spline model\u003c/p\u003e\n\u003cp\u003e(The independent variables of the restricted cubic spline chart were, in order, neutrophils, basophils, lymphocytes, monocytes /1000 cells /uL)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3850398/v1/9812408b2d58b953cf514061.png"},{"id":49894167,"identity":"153d9faf-c0b5-4a0b-82c9-e5fc973dfbd3","added_by":"auto","created_at":"2024-01-19 21:26:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22832,"visible":true,"origin":"","legend":"\u003cp\u003eStructural relationship among physical activity level, depression, and neutrophil count\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3850398/v1/5a0ac85cb61b12f4e7f25720.png"},{"id":49894168,"identity":"b5e5c18a-7f39-4983-8533-295ca223c6db","added_by":"auto","created_at":"2024-01-19 21:26:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":210398,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effect of physical activity level and neutrophil count on depression.\u003c/p\u003e\n\u003cp\u003e(X: Neutrophil count; Y: Depression score; M: Physical activity level; Blue line: Neutrophil count; Red line: Physical activity level; Green line: Neutrophil count *Physical activity level* indicates statistical significance, P \u0026lt; 0.05.)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3850398/v1/1bacf1390ac0fb9b8fe88774.png"},{"id":49894832,"identity":"cd3cc277-fe93-40ab-8b12-085496058379","added_by":"auto","created_at":"2024-01-19 21:34:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":732892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3850398/v1/645762fe-aaff-48a6-8b08-b3ceb21d0fee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Moderating Effect of Physical Activity on the Relationship Between Neutrophils and Depression: A Cross-sectional Analysis Based on the NHANES Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepression has become the most significant challenge in global mental health, accounting for 10.3% of the overall disease burden\u003csup\u003e[1]\u003c/sup\u003e. According to statistics from the World Health Organization, the total number of individuals suffering from depression worldwide has surpassed 350 million\u003csup\u003e[2\u0026ndash;3]\u003c/sup\u003e, marking a 49.86% increase compared to the past. Research suggests that depression is a complex disorder influenced by a multitude of factors. These factors, in various combinations, can trigger different disease trajectories and are characterized by clinical manifestations such as low mood, sleep deprivation, and tendencies towards self-harm and suicide\u003csup\u003e[4]\u003c/sup\u003e. The prolonged absence of effective treatment continuously impacts patients' physical health and quality of life\u003csup\u003e[1]\u003c/sup\u003e. This places a tremendous strain on healthcare resources, making the prevention and mitigation of depression an urgent societal issue to address.\u003c/p\u003e \u003cp\u003eDepression is not only influenced by factors such as family environment, educational level, and marital status\u003csup\u003e[5\u0026ndash;7]\u003c/sup\u003e, but an increasing body of research\u003csup\u003e[8\u0026ndash;10]\u003c/sup\u003e suggests that inflammatory responses play a crucial role in the pathogenesis of depression. Depressive individuals exhibit significant specificity in inflammatory factors, metabolic markers, and oxidative stress in their blood compared to healthy populations\u003csup\u003e[11\u0026ndash;12]\u003c/sup\u003e. Changes in blood immune cell composition, particularly in the white blood cell subtypes, have been observed in individuals with depression. White blood cells, known for generating reactive oxygen species as part of the immune response, can, when overly activated, lead to increased oxidative stress, potentially causing damage to normal cells, including neurons in the brain. If the white blood cell count is elevated in individuals with depression, it may contribute to heightened oxidative stress levels in the brain, impacting neuronal function and further exacerbating the patient's condition. Moreover, previous studies have revealed positive correlations between depression and peripheral blood lymphocytes, white blood cells, and myeloid cells\u003csup\u003e[13\u0026ndash;14]\u003c/sup\u003e. Meta-analysis results indicate that an increase in the relative percentage or absolute count of neutrophils can aid in identifying depression patients associated with inflammation\u003csup\u003e[15]\u003c/sup\u003e. Therefore, we propose that peripheral blood immune cell counts can serve as a valuable indicator for identifying a subgroup of depression related to inflammation and assessing the risk of the disease.\u003c/p\u003e \u003cp\u003ePhysical activity (PA) is an approach that, through various exercise behaviors, aims to promote health, enhance physical fitness, and improve the quality of life\u003csup\u003e[16]\u003c/sup\u003e. Studies have found that PA can not only effectively influence physiological mechanisms but also elevate positive emotional levels, significantly alleviating depressive symptoms\u003csup\u003e[17]\u003c/sup\u003e. Additionally, research indicates that physical activity can effectively reduce the risk of depression. This is attributed to the increased expression of adult hippocampal neurons and Brain-Derived Neurotrophic Factor (BDNF) through PA\u003csup\u003e[18]\u003c/sup\u003e, which in turn prevents the onset of depression. Furthermore, studies have confirmed that appropriate PA has anti-inflammatory effects\u003csup\u003e[19]\u003c/sup\u003e. It can not only reduce the circulating count of white blood cells\u003csup\u003e[20]\u003c/sup\u003ebut also enhance antioxidant capacity, mitigating oxidative stress\u003csup\u003e[21\u0026ndash;22]\u003c/sup\u003e, thereby reducing the risk factor for depression.\u003c/p\u003e \u003cp\u003eReviewing previous research, we found that both blood inflammatory cells and physical activity can individually influence depression. Physical activity has been widely accepted as an effective strategy to reduce the risk of depression. However, the exact mechanism of its action remains unclear, especially regarding whether there's an interactive regulatory effect between physical activity and inflammatory cells in reducing the risk of depression. In light of this, our study intends to conduct statistical analysis based on samples from the NHANES database, observing the relationship between the counts of various inflammatory cells in whole blood and depression. We aim to explore whether the level of physical activity can mitigate the risk of depression caused by changes in the count of inflammatory cells and evaluate its impact. This research seeks to provide evidence-based recommendations for preventing the onset of depression and reducing the increase in inflammatory cell counts, ultimately promoting both physical and mental health in individuals.\u003c/p\u003e"},{"header":"Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research Subjects\u003c/h2\u003e \u003cp\u003eThis study is based on data from the NHANES database (National Health and Nutrition Examination Survey, NHANES). Managed by the U.S. Centers for Disease Control and Prevention (CDC), the NHANES has been surveying population health since 1999 through questionnaires and physical examinations. The questionnaire covers demographics, socio-economics, diet, and health information, while the physical examination includes physiological measurements and checks. The aim is to assess the health and nutritional status of adults and children. All research has been approved by the National Center for Health Statistics (NCHS) Institutional Review Board, and informed consent has been obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eThis study selected data from the NHANES database for the years 2017\u0026ndash;2020 that included demographic information, physical activity levels, depression, and complete blood count. Based on this, a cross-sectional survey study was conducted. After excluding data for duplication, loss, and strong subjective awareness, a total of 634 subjects were included for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Demographic Information\u003c/h2\u003e \u003cp\u003eThe study included consolidated demographic information such as gender, age, education level (below 9th grade, 9-11th grade, high school graduate, undergraduate and above), marital status (married, single, widowed, cohabiting, divorced or separated), number of family members, annual family income, smoking, drinking, and Body Mass Index (BMI). The BMI is calculated as weight (kg) divided by the square of height (meters).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Depression Status\u003c/h2\u003e \u003cp\u003eThe Patient Health Questionnaire-9 (PHQ-9) is commonly used to assess the mental health and depression status of subjects over a two-week period. Based on the PHQ-9, the nine questions range from \"not at all\" to \"nearly every day,\" representing scores from 0 to 3. The total score is the sum of all questions: 0\u0026ndash;4 indicates no depression; 5\u0026ndash;9 mild depression; 10\u0026ndash;14 moderate depression; 15\u0026ndash;19 moderate to severe depression; and 20\u0026ndash;27 severe depression. The Cronbach's alpha coefficient of the scale is 0.839. The Pearson correlation coefficient between the nine items ranges from 0.160\u0026ndash;0.578 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that the questionnaire has high sensitivity and specificity for diagnosing depression\u003csup\u003e[23]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Blood Indicators\u003c/h2\u003e \u003cp\u003eThe Beckman Coulter DxH 800 instrument was used to produce a complete blood count (CBC) on blood specimens, examining the blood indicators in subjects. The CBC is a routine blood test used to assess overall health\u003csup\u003e[24]\u003c/sup\u003eand detect diseases, including anemia, infections, and leukemia. The sample collection process adhered to the Westgard rules\u003csup\u003e[25]\u003c/sup\u003e, which include quarterly progress reports, calibration of instruments and reagents, and controlling any variables to ensure data authenticity and reliability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Physical Activity Level\u003c/h2\u003e \u003cp\u003eMET (Metabolic Equivalent of Task) represents the oxygen consumption required for metabolism by a healthy adult at rest. It is based on the energy expenditure of sitting quietly\u003csup\u003e[26]\u003c/sup\u003e. Different types of physical activities have different MET values, and the NHANES data provides recommended MET values for various exercises. The GPAQ (Global Physical Activity Questionnaire) survey includes vigorous work-related activities (MET\u0026thinsp;=\u0026thinsp;8), moderate work-related activities (MET\u0026thinsp;=\u0026thinsp;4), walking or cycling for transportation (MET\u0026thinsp;=\u0026thinsp;4), vigorous leisure-time physical activities (MET\u0026thinsp;=\u0026thinsp;8), and moderate leisure-time physical activities (MET\u0026thinsp;=\u0026thinsp;4) (source). PA can be calculated based on MET values, type of activity, frequency per week, and duration. PA (MET-h/wk)\u0026thinsp;=\u0026thinsp;MET \u0026times; frequency per week \u0026times; duration of each physical activity session. Subjects with PA\u0026thinsp;=\u0026thinsp;0 (\u0026lt;\u0026thinsp;1MET-h/wk) are defined as having no physical activity, while the rest are categorized as engaging in low-intensity physical activity (1-48MET-h/wk) or high-intensity physical activity (\u0026gt;\u0026thinsp;48 MET-h/wk). For this cross-sectional study, based on PA values, all subjects were divided into two groups: no physical activity group (PA\u0026thinsp;=\u0026thinsp;0) and physical activity group (PA\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e3. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eAll continuous variables are represented by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), while categorical variables are represented by N (%). Difference tests were used to assess the differences in continuous variables between patients with and without depression, aiming to identify \"targets\". The chi-square test was employed to evaluate differences in categorical demographics. When the dependent variable was binary, Pearson correlation, binary logistic regression, and restricted cubic spline models were used to analyze the relationship between the independent variable and the risk of depression. The results of the study are represented by odds ratios (OR) and 95% confidence intervals (95%CI). When examining whether the level of physical activity as a moderating variable affects depression, the dependent variable was transformed into a continuous variable based on the scale scoring criteria. Linear regression analysis was used to explore whether there was an interaction effect between neutrophil count and physical activity level on depression. All research data were statistically processed and analyzed using SPSS 26.0 and R language 4.2.2, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significant differences.\u003c/p\u003e \u003cp\u003eRegression analysis equation: Depression\u0026thinsp;=\u0026thinsp;β0\u0026thinsp;+\u0026thinsp;β1 \u0026times; segmented neutrophil count\u0026thinsp;+\u0026thinsp;β2 \u0026times; physical activity level\u0026thinsp;+\u0026thinsp;β3 \u0026times; (segmented neutrophil count \u0026times; physical activity level)\u003c/p\u003e \u003cp\u003e(Note: β0 is the intercept, β1 is the coefficient for \"segmented neutrophil count\", β2 is the coefficient for \"physical activity level\", and β3 is the coefficient for the interaction term).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Basic Demographic Characteristics\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 634 participants were included in this study, of which 357 (56.30%) were male and 277 (43.7%) were female. All participants were adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years, with 162 individuals (25.6%) aged between 20\u0026ndash;46 years, 166 individuals (26.2%) aged between 47\u0026ndash;61 years, and the rest were older than 61 years. There were 235 smokers (37.1%) and 617 long-term alcohol drinkers (97.3%). Among them, 591 (93.2%) did not engage in physical activity (PA\u0026thinsp;=\u0026thinsp;0), and only 43 (6.8%) participated in low or high-intensity physical activities (PA\u0026thinsp;=\u0026thinsp;1). The average counts for lymphocytes, monocytes, neutrophils, and basophils (all units are represented as 1000 cells/uL) were 2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42, 0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21, 4.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78, and 0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, respectively. The prevalence of depression among all participants was 28.5% (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;5). The prevalence of depression in males was 22.4%, in married individuals was 20.4%, in the age group 47\u0026ndash;61 years was 36.7%, in those with a BMI\u0026thinsp;\u0026gt;\u0026thinsp;32 was 40%, and in those without physical activity was 29.6%. All these differences were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was no significant difference in the prevalence of depression among other factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic Demographic Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-depressed (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepressed (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Number of Participants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e634(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e453(71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181(28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000099\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e357(56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e277(71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80(22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277(43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176(63.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101(36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62(25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113(69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u0026ndash;61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166(26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105(63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e62\u0026ndash;71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149(23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111(74.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157(24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124(79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody Mass Index(BMI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185(29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138(74.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100(73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u0026ndash;31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173(27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131(75.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42(26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113(70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor's degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221(34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158(71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63(28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108(17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84(77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(65.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43(34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e420(66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e309(73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111(26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88(13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e294(46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234(79.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88(13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(65.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(70.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced or Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57(44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e235(37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158(67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77(32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e399(62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295(71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104(28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol drinking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e617(97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e443(71.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174(28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical Activity Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo physical activity (PA\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e591(93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416(70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175(29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngaged in physical activity (PA\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Blood Cell Predictors for Depression Risk\u003c/h2\u003e \u003cp\u003eThrough differential testing to evaluate the differences in continuous variables between depressed and non-depressed patients, the \"target\" showed that the counts of neutrophils and basophils in the non-depressed group were 4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74 and 0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 (1000 cells/uL) respectively, while in the depressed group, the counts were 4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85 and 0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 (1000 cells/uL) respectively. The differences were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was no significant difference in lymphocytes and monocytes.\u003c/p\u003e \u003cp\u003eAnalysis through Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and the restricted cubic spline model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) revealed a positive correlation between inflammatory cells, specifically neutrophils and basophils, with depression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Other blood cells did not show a direct association with depression. Therefore, when treating the dependent variable as a binary variable for binary logistic regression, the results showed that only neutrophils could predict and identify the risk of depression with statistical significance (OR\u0026thinsp;=\u0026thinsp;1.12, 95%CI: 1.011\u0026ndash;1.24).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInflammatory Cells and the Risk of Depression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInflammatory Cells\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%Cl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.121\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.011,1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.104\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e**. indicates significance at the 0.01 level (two-tailed).\u003c/p\u003e \u003cp\u003eOR: Odds Ratio; CI: 95% Confidence Interval; All dependent variables are depression-related.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Modulatory Role of Physical Activity Level on Depression and Neutrophils\u003c/h2\u003e \u003cp\u003eTo explore whether there is an interactive effect between physical activity level and neutrophils on depression, the binary variable of depression was transformed into a continuous variable based on the PHQ-9 score for linear regression analysis. The results showed that the F-statistic was 10.79 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This indicates that the research results can reject the null hypothesis (i.e., all regression coefficients are equal to zero), and the modulatory variable model is statistically significant.\u003c/p\u003e \u003cp\u003eThe regression results (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed a significant positive correlation between segmented neutrophil count and depression level (β\u0026thinsp;=\u0026thinsp;0.1083, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This means that, while keeping the physical activity level constant, as the neutrophil count increases, the depression score rises. Conversely, when controlling for segmented neutrophil count, there is a negative correlation between physical activity level and depression level, but this is not statistically significant (β=-0.1644, P\u0026thinsp;=\u0026thinsp;0.28).\u003c/p\u003e \u003cp\u003eTherefore, to explore the specific relationship between the three variables, physical activity level was treated as a modulating variable. The research results (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that there is an interactive effect between physical activity level and neutrophils on depression, and the modulatory effect is statistically significant (β=-0.1691, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, as the physical activity level transitions from none to some, the positive correlation between segmented neutrophil count and depression level weakens.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.2211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear Regression Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ(Regression Coefficient)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%Cl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.0659,0.1507]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[-0.4659,0.1371]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil Count * Physical Activity Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[-0.2591,-0.0791]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eDependent Variable: Depression Score\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThe prevalence of depression in the study population was found to be 28.5%. Notably, specific demographics such as females, middle-aged to elderly individuals (47\u0026ndash;61 years old), individuals with a BMI greater than 32, and married individuals exhibited a higher incidence of depression. These findings are consistent with previous studies\u003csup\u003e[27\u0026ndash;28]\u003c/sup\u003e. Biologically, fluctuations in female hormone levels\u003csup\u003e[29]\u003c/sup\u003eand gender differences in brain structures\u003csup\u003e[30\u0026ndash;31]\u003c/sup\u003emay elevate the risk of depression. Some research suggests that females, due to their smaller hippocampal spatial structure, are more susceptible to stress. Additionally, factors such as obesity, age, and marital status have been linked to depression, likely influenced by lifestyle choices and psychological stressors\u003csup\u003e[32\u0026ndash;35]\u003c/sup\u003e. These insights underscore the importance for researchers and mental health professionals to prioritize depression prevention and treatment in these specific populations, advocating for healthier lifestyles to mitigate depression risks.\u003c/p\u003e \u003cp\u003eFurthermore, our study identified a significant positive correlation between neutrophil counts and depression, suggesting that an increase in these counts can aid in identifying patients with inflammation-associated depression. This aligns with prior research\u003csup\u003e[15]\u003c/sup\u003e.The underlying biological mechanism supports the \"inflammation-induced depression\" theory\u003csup\u003e[36\u0026ndash;37,39]\u003c/sup\u003e. Chronic inflammatory states can potentially impact brain functions through various pathways, including neurotransmitter metabolism, neuroplasticity, and neuroendocrine functions, leading to the onset of depression[40]. Neutrophils, a category of white blood cells, serve as primary markers for the body's response to infections\u003csup\u003e[41]\u003c/sup\u003e. An excessive increase in their numbers might reflect heightened inflammation, subsequently affecting neurobiological mechanisms in the brain. Studies have confirmed\u003csup\u003e[42]\u003c/sup\u003ethat neutrophils can activate glial cells in the brain, such as astrocytes and microglia, leading to the release of a plethora of cytokines, like TNF-α and IL-1β. These cytokines, crossing the blood-brain barrier, intensify the inflammatory response, impacting neurotransmitter synthesis, release, and neuronal growth and survival, thereby inducing depression\u003csup\u003e[36\u0026ndash;39]\u003c/sup\u003e. From a clinical perspective, while the diagnosis of depression primarily relies on symptom assessment, the use of biological markers like neutrophils can offer a more precise diagnostic foundation.\u003c/p\u003e \u003cp\u003eInterestingly, our study revealed that the level of physical activity (PA) did not have a direct influence on the incidence rate of depression (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, when combined with neutrophil counts, PA exhibited a significant modulatory effect, effectively mitigating the impact of neutrophils on depression. This might be attributed to physical activity modulating the quantity and activity of neutrophils by influencing immune functions. In an \"anti-inflammatory\" state, physical activity, by increasing the production of anti-inflammatory cytokines (e.g., IL-4 and IL-10), suppresses the activation of neutrophils and the production of inflammatory cytokines\u003csup\u003e[43]\u003c/sup\u003e. Moreover, from a neurotransmitter and hormonal perspective, physical activity can modulate serotonin and dopamine levels in the brain\u003csup\u003e[44]\u003c/sup\u003e, neurotransmitters believed to regulate mood and suppress inflammation\u003csup\u003e[45]\u003c/sup\u003e, providing both analgesic and euphoric effects. Additionally, physical activity can enhance the secretion of hormones like cortisol and growth hormone, which inhibit the activation of neutrophils and the production of inflammatory cytokines. From an antioxidative mechanism standpoint, physical activity can bolster the body's antioxidative capacity, such as enhancing the activity of superoxide dismutase and hydrogen peroxide enzymes\u003csup\u003e[46]\u003c/sup\u003e, aiding in neutralizing free radicals, thereby reducing cellular damage and inflammation. In terms of neuroplasticity, physical activity can effectively enhance neuroplasticity\u003csup\u003e[47\u0026ndash;49]\u003c/sup\u003e, promoting neuronal growth, survival, and the formation and repair of synapses through the elevation of neurotrophic factors like brain-derived neurotrophic factor (BDNF). In summary, engaging in physical activity can, through various biological mechanisms\u0026mdash;including immune function modulation, neurotransmitter and hormonal influences, antioxidative effects, and enhanced neuroplasticity\u0026mdash;suppress the activation of neutrophils and the production of inflammatory cytokines, thereby reducing the risk of depression.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study suggest a positive correlation between neutrophil counts and depression in adults. An excessive increase in neutrophil counts and percentages emerges as one of the risk factors contributing to depression. Furthermore, the study identifies the level of physical activity as a significant modulatory factor, attenuating the impact of neutrophils on depression. Consequently, future diagnostic approaches for depression should emphasize monitoring inflammatory cells like neutrophils, inhibiting the production of inflammatory cytokines, enhancing the psychological well-being of high-risk populations, and preventing the onset of mental disorders.\u003c/p\u003e \u003cp\u003e \u003cb\u003e6. Limitations and Future Directions\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe primary foundation of this study is a cross-sectional analysis based on the NHANES database, making it challenging to discern causal relationships between the variables.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn analyzing data related to physical activity, the Global Physical Activity Questionnaire (GPAQ) did not categorize activity into low, medium, or high intensities. As a result, this study could not delve into the specific effects of varying activity intensities on mitigating the influence of neutrophils on depression.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe PHQ-9, utilized in this study, is a self-reported depression scale and not a clinical diagnosis, potentially introducing a bias risk.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eFuture research should focus on larger sample sizes to further elucidate the intricate relationships between physical activity levels, neutrophils, and depression. There is a pressing need to delve deeper into the efficacy of neutrophils in predicting and identifying depression. Incorporating the five essential elements of exercise into longitudinal studies could pave the way for crafting precise strategies for depression prevention and management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll NHANES data for this study are publicly available and can be found here: https://wwwn.cdc.gov/nchs/nhanes.The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMr. Z.G.: data collect and manuscript editing；Mrs. Z.X.: data analysis and original draft writing；Mr. P.W.: data analysis；Mrs. S.L.: data analysis；Mrs. X.X: data collect；\u0026nbsp;Mr. X.W.: review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Shanghai Key Lab of Human Performance (Shanghai University of sport) (NO. 11DZ2261100).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consented to the publication of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflicts and interests relevant to the content of this review.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n\u003cli\u003eHerrman H, Patel V, Kieling C,et al.,2022.Time for united action on depression: a Lancet-World Psychiatric Association Commission[J].Lancet, 399(10328):957-1022.\u003c/li\u003e\n\u003cli\u003eLiu Q, He H, Yang J,et al.,2020.Changes in the global burden of depression from 1990 to 2017: findings from the global burden of disease study[J].J. 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Effect of Physical Exercise and Acute Escitalopram on the Excitability of Brain Monoamine Neurons: In Vivo Electrophysiological Study in Rats[J].International Journal of Neuropsychopharmacology,2017(7):585-592.\u003c/li\u003e\n\u003cli\u003eMagdalena W, Jadwiga S, Marcin M, et al.,2018. Anaerobic Exercise-Induced Activation of Antioxidant Enzymes in the Blood of Women and Men[J].Frontiers in Physiology,2018,9:1006.\u003c/li\u003e\n\u003cli\u003eLi X, Luo P,Wang Q,et al.,2016.Hurdle Aerobic Exercise Induces Neuroplasticity and Angiogenesis in Hippocampus of Mice[J].Neural Plasticity,2016:1-9.\u003c/li\u003e\n\u003cli\u003eValero J, Mastrella G, Neiva I, et al.,2019. Neural Stem Cells and Adult Neurogenesis: The Role of Inflammation[J]. Frontiers in Aging Neuroscience,11:137.\u003c/li\u003e\n\u003cli\u003eEsposito G, Scuderi C, Valenza M, et al.,2011. Cannabidiol Reduces A\u0026beta;-Induced Neuroinflammation and Promotes Hippocampal Neurogenesis through PPAR\u0026gamma; Involvement[J].PLoS ONE,6(12):e28668.\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Physical activity, Neutrophils, Depression, Moderation model","lastPublishedDoi":"10.21203/rs.3.rs-3850398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3850398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: To analyze the relationship between neutrophil count and depression based on the NHANES database and to explore the moderating effect of physical activity on the relationship between neutrophils and depression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Cross-sectional data from the NHANES database were extracted, including demographic information, neutrophil count, scores from the PHQ-9 depression self-assessment scale, and scores from the GPAQ (Global Physical Activity Questionnaire). Pearson correlation, binary logistic regression, restricted cubic spline models, and linear regression equation models were used to analyze the association among physical activity, neutrophil count, and depression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: There was a positive correlation between neutrophil count and depression, with an OR[95%CI] of 1.12[1.011,1.24]. The effect of physical activity in moderating the impact of neutrophils on depression was statistically significant (β=-0.1691, P\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Identifying depression in adults can be informed by monitoring neutrophil counts, which may be related to the \"inflammation-induced depression\" theory. Physical activity, as an effective moderator, significantly reduces the impact of neutrophils on depression, offering potential value in future prevention and intervention strategies for depression.\u003c/p\u003e","manuscriptTitle":"The Moderating Effect of Physical Activity on the Relationship Between Neutrophils and Depression: A Cross-sectional Analysis Based on the NHANES Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-19 21:26:50","doi":"10.21203/rs.3.rs-3850398/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-01T04:25:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-22T00:33:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ea737c5e-f977-47de-a327-504c02018363","date":"2024-03-11T13:10:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9f0ea0f1-8e2d-41a9-940b-c1837cb1e558","date":"2024-02-26T18:35:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-16T23:11:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-16T23:08:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-17T17:17:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-17T13:38:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-10T13:22:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7daa8c3-fc1d-4d79-8f2d-e1e0b8419125","owner":[],"postedDate":"January 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":28217113,"name":"Biological sciences/Cell biology"},{"id":28217114,"name":"Biological sciences/Immunology"},{"id":28217115,"name":"Biological sciences/Psychology"},{"id":28217116,"name":"Health sciences/Diseases"},{"id":28217117,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-05-29T03:55:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-19 21:26:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3850398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3850398","identity":"rs-3850398","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}