Introduction

Chronic obstructive pulmonary disease (COPD) is an increasingly serious global health issue, imposing a huge burden on countries around the world. According to the World Health Organization (WHO), as of 2019, COPD has become the third-leading cause of death globally.1 Approximately 4.7% of the global population is affected by COPD, In the United States alone, approximately 15.5 million adults have been diagnosed with COPD, with the total economic burden estimated at $101 billion in 2020.2 In Asia, the figure is as high as 6.2%.3 COPD is a chronic progressive disease characterized by partially reversible airflow limitation.4 Smoking is widely recognized as the main risk factor,5 but identifying and managing other potential risk factors is a crucial for reducing the health and economic burden of COPD patients.

In recent years, increasing evidence suggests that systemic immune-inflammation index (SII) and dietary quality (DQ) play important roles in the development of COPD.6,7 SII is a comprehensive index that measures systemic immune-inflammatory responses in the body. Initially developed as a prognostic indicator for adverse outcomes in cancer patients, SII is calculated as: platelet count × neutrophil count / lymphocyte count.8 Studies have shown that SII can objectively reflect the balance between inflammation and immunity in cancer patients.9,10 Moreover, SII has been extended to various diseases, including coronary heart disease,11 rheumatoid arthritis,12 and liver fibrosis,13 and is used to track treatment responses. Recent studies have indicated that SII levels are positively correlated with COPD prevalence.14 Higher SII levels are associated with a higher prevalence of COPD, and COPD patients with higher SII levels have an increased risk of all-cause mortality.14 In addition, studies have shown that NLR, PLR, and SII levels are elevated in frequent AECOPD patients, serving as cost-effective markers for exacerbation frequency and severity.15 Therefore, elevated SII levels may be a key risk factor for poor prognosis in COPD patients.

Additionally, dietary quality is closely related to COPD. Numerous studies have emphasized the importance of specific dietary patterns. Increasing epidemiological evidence suggests that high-quality diets are associated with better lung function and a lower risk of COPD.16 Diets rich in fruits, vegetables, whole grains, and fish have been found to reduce the risk of COPD.17,18 However, many studies focus on isolated nutrients rather than overall dietary quality. Therefore, the Healthy Eating Index-2015 (HEI-2015), a widely used measure of overall dietary quality, is introduced here. HEI-2015 aligns with the Dietary Guidelines for Americans.19,20 Higher HEI-2015 scores indicate healthier and more balanced diets. Current research has shown significant correlations between HEI and various disease risks, including obesity,21 depression,22 and diabetes.23 However, the relationship between HEI-2015 and COPD, particularly in middle-aged and elderly populations, remains insufficiently studied.

Notably, previous studies have primarily focused on the individual effects of systemic immune-inflammation index and dietary quality on COPD, with few studies examining the potential interaction between these two factors. Therefore, this study plans to comprehensively analyze data from the National Health and Nutrition Examination Survey (NHANES) to explore how systemic immune-inflammation index and dietary quality jointly influence the development of COPD in middle-aged and elderly adults. The goal is to identify and quantify the potential benefits of systemic immune-inflammation index levels and dietary quality in preventing or managing COPD, thereby improving patient health outcomes and reducing disease burden.

Methods Source of Data and Study Population

NHANES is a series of complex, multi-stage, probabilistic sampling surveys of the United States. NHANES data is released on a 2-year cycle. We analysed data from 2007–2018, which included the population aged 40 years and over.This database is collected and maintained by the National Center for Health Statistics (NCHS), which is under the Centers for Disease Control and Prevention (CDC) of the United States. NHANES aims to assess the health and nutritional status of adults and children in the United States.24 It collects and analyzes data representative of the non-institutionalized US population by using a complex multi-stage. These datasets are aimed to research purposes, and NCHS provides authorization for researchers to use the data. NHANES participants first finish a household interview and are then invited to a mobile examination center (MEC) for comprehensive health examinations, including physical examinations, professional measurements, and laboratory tests. Therefore, the judgements for participants in NHANES database are comprehensive, reliable, and multidimensional, equivalent to population-level assessments.25

In our study, we selected 10,898 participants aged 40 and above from six consecutive NHANES cycles from 2007 to 2018. Exclusion criteria were as follows: participants without HEI-2015 data; participants without SII data; participants without COPD data; missing covariates; and missing survival status (Figure 1).

Figure 1 Flowchart of participants selection from NHANES 2007–2018.

Definition of HEI-2015

Dietary intake data were collected through two 24-hour recall interviews, which were conducted by professionally trained dietary interviewers.24 The first interview was conducted face-to-face, and the second was conducted by phone 3 to 10 days after the initial interview. All participants were told to recall and report the types and quantities of foods and beverages consumed in the past 24 hours, and the average of these two reports was used to estimate dietary intake.26 The energy and nutrient intake of all food items were calculated using the Food and Nutrient Database for Dietary Studies.27 The dietary data from NHANES database used to construct the Healthy Eating Index-2015 (HEI-2015). HEI-2015 is a tool for evaluating an individual’s diet quality, and this score reflects compliance with the 2015–2020 Dietary Guidelines for Americans (DGA).28 HEI-2015 is not based on absolute intake amounts but on energy density per 1,000 kcal (Supplementary Table S1). It includes 13 components, among which 9 are adequacy components that encourage sufficient intake: total fruits, whole fruits, total vegetables, greens and beans, total protein foods, seafood and plant proteins (each 0–5 points), whole grains, dairy, and fatty acids (each 0–10 points). The higher the intake of these components, the higher the score. The remaining 4 components are moderation components, with recommended intake limits: sodium, refined grains, added sugars, and saturated fats (each 0–10 points). Lower intake of these components results in higher scores.19 The HEI-2015 score was calculated using SAS code29 The theoretical range for HEI-2015 scores is 0–100, with higher scores indicating better overall dietary quality. Participants with an average HEI score equal to or above the 60th percentile were considered to have a healthy diet (adhering to dietary guidelines or consuming healthy foods); otherwise, they were considered to have an unhealthy diet.30

Definition of SII

Whole blood samples were collected from eligible participants at the NHANES mobile examination center. The development and calculation of the SII have been reported in previous studies.8 SII includes platelet count, neutrophil count, and lymphocyte count, calculated using the formula: SII = platelet count×neutrophil count/lymphocyte count. Laboratory data from the 5-part differential complete blood count were assessed using the Beckman Coulter DxH 800 instrument, which quantifies neutrophils, lymphocytes, platelets, and monocytes, expressed as 1,000 cells/μL. Complete blood count parameters were measured and sized according to Beckman Coulter methods. SII was log10-transformed to ensure normal distribution.

Diagnostic Criteria of COPD

In the NHANES questionnaire (Medical Conditions dataset), physician-diagnosed COPD was determined by a positive response to any of the following questions: 1) After the participant inhaled a β2-adrenergic bronchodilator drug, the ratio of forced expiratory volume in 1 second to forced vital capacity (FEV₁/FVC) was < 0.70; 2) Participants who were told by a doctor or other health professional that they had emphysema or chronic bronchitis. This method of identifying COPD patients has been successfully implemented in many previous studies using NHANES data.31,32 Participants who answered “yes” to any of these questions were coded as having COPD.

Covariates

Data on age, gender, race, poverty-income ratio (PIR), marital status, and educational level were collected by trained NHANES interviewers using the Household and Sample Person Demographic Questionnaire and the Computer - Assisted Personal Interviewing (CAPI) system (Rummit Corp., New York, USA). Data were weighted according to NHANES protocols. Body mass index (BMI) was calculated based on NHANES examination measurements as weight (kg) divided by height (m) squared. Hypertension was defined as an Yes response to at least one of the following questions: “Have you ever been told by a doctor or other health professional that you have hypertension, also called high blood pressure?” or “Have you ever been told to take prescription medication for your (high blood pressure)?” or an average systolic blood pressure (SBP) ≥140 mmHg or diastolic blood pressure (DBP) ≥90 mmHg over three consecutive measurements. Diabetes mellitus (DM) was identified based on an Yes response to at least one of the following questions: “Are you now taking insulin?” or “Has a doctor ever told you that you have diabetes?” or “Are you now taking medication to lower your blood sugar?” or laboratory data showing glycated hemoglobin (HbA1c) ≥6.5%, fasting blood glucose ≥126 mg/dL, or oral glucose tolerance test (OGTT) blood glucose ≥200 mg/dL.33 Alcohol consumption was categorized as never drinker (no alcohol consumption in the past 12 months and fewer than 12 drinks in a lifetime), current drinker (alcohol consumption in the past 12 months), and former drinker (no alcohol consumption in the past 12 months but more than 12 drinks in a lifetime). Smoking status was categorized into three groups: never smoker (never smoked or smoked fewer than 100 cigarettes and quit), former smoker (smoked more than 100 cigarettes but currently does not smoke), and current smoker (smoked more than 100 cigarettes and answered “yes” to “Do you now smoke cigarettes?”).34 Cardiovascular disease history was defined based on a Yes response to questions about physician-diagnosed congestive heart failure, myocardial infarction, angina, coronary heart disease, or stroke. Laboratory data, including platelet, neutrophil, white blood cell, monocyte, lymphocyte, and red blood cell counts, were obtained from NHANES laboratory data files.

Statistical Analysis

This study data is weighted using NHANES dietary interview sample weights to account for the complex survey design. We used the dietary day one sample weight set (WTDRD 1) for weighted analysis. Summarize baseline characteristics using descriptive statistics, where Continuous variables are presented as means ± SD and categorical variables as percentages; between-group comparisons used weighted χ²-tests. Multivariable logistic regression was performed in three sequential models: Model 1, unadjusted; Model 2, adjusted for age, sex, and race; and Model 3, further adjusted for education, poverty-to-income ratio, marital status, BMI, hypertension, and cardiovascular disease, with results reported as ORs (95% CIs). Non-linear relationships between log10-transformed SII and HEI-2015 with COPD were examined using four-knot restricted cubic splines, and ROC curves were used to compare the AUCs across the three models. Interaction effects were quantified with RERI, API, and the synergy index (S), and subgroup analyses were stratified by age, sex, race, and other covariates. All-cause mortality among COPD patients was assessed with Kaplan–Meier curves and Cox regression. All tests were two-sided, with P < 0.05 considered significant, and analyses were conducted in R Studio (v4.3.1).

Results Baseline Characteristics of the Population

This study data from NHANES were derived from the NHANES database (2007–2018), including 10,898 participants aged 40 and above. Participants were grouped based on the presence or absence of COPD, and detailed study characteristics are presented in Table 1. Among the participants, 1,764 were diagnosed with COPD, including 940 males (53.3%) and 824 females (46.7%). Compared to the non-COPD group, COPD participants were more likely to be older, non-Hispanic white, have higher education levels, be current or former smokers, be current or former alcohol and have hypertension. Significant differences were also observed between the two groups in terms of PIR, dietary quality, cardiovascular disease, and diabetes (all p <0.05). Overall, except for sex, monocyte count, lymphocyte count, and red blood cell count, significant differences in baseline characteristics were observed between the non-COPD and COPD groups. The grouping by different lifestyle patterns and gender is detailed in Supplementary Tables S2 and S3.

Table 1 Basic Characteristics of Participants (n = 10898) in the NHANES 2007–2018

Relationship Between SII, Dietary Quality, and COPD

To further explore the relationship between dietary quality, SII, and COPD, Table 2 presents three weighted logistic regression models. From the three models, it can be seen that log-SII is positively associated with COPD, while HEI is negatively associated with COPD. Model 1 is a crude model, patients with high-scoring HEI-2015 have a 0.96-fold lower odds of COPD than patients with low-scoring HEI-2015 (OR: 0.96, 95% CI: 0.94 to 0.98).However, patients with high log-SII have a 1.04-fold higher odds of COPD compared to those in the low log-SII (OR = 1.04, 95% CI: 1.02 to 1.06). Model 2 adjusted for demographic characteristics (ie, sex, age group, race). Additionally, Model 3 further adjusted for education, poverty-to-income ratio, marital status, BMI, hypertension history, and cardiovascular disease history. In both Model 2 and Model 3, additional covariate adjustments did not materially attenuate the associations of SII and HEI-2015 with COPD. Moreover, Model 3 demonstrated that a higher HEI-2015 score remained significantly associated with decreased COPD prevalence (OR: 0.97; 95% CI: 0.96 to 0.99). Conversely, higher log-SII showed a significant association with increased COPD risk (OR: 1.03; 95% CI: 1.01 to 1.05). Besides, in the adjusted model, age,race,PIR,cardiovascular were significantly correlated with COPD statues.

Table 2 Association Between SII and Dietary Quality and Prevalence of COPD Among 10,898 Study Participants

To further investigate potential nonlinear associations between SII and HEI and COPD risk after adjusting for age and sex, restricted cubic spline (RCS) analyses were performed. As illustrated in Figure 2, the relationship between Log-SII and COPD risk demonstrated significant nonlinear characteristics, with a P-nonlinear value of 0.006 and P-overall < 0.001, indicating a statistically significant nonlinear correlation.Besides,it reveals an inverted L-shaped dose-response pattern between Log-SII and COPD risk, with an identified inflection point at 2.887 (P-nonlinear = 0.006). In contrast, no significant nonlinear relationship was observed between HEI and COPD risk, as evidenced by a nonsignificant P-nonlinear value of 0.786 (Figure 3).

Figure 2 RCS analysis of the association between Log-SII and COPD.The association was adjusted for age, sex. p<0.05 was considered statistically significant.

Abbreviations: SII, Systemic Immune-Inflammation Index; RCS, restricted cubic spline.

Figure 3 RCS analysis of the association between HEI and COPD. p<0.05 was considered statistically significant.

Abbreviations: HEI_ALL, Healthy Eating Index_ALL; RCS, restricted cubic spline.

Additionally, we employed ROC curves to conduct a comparative analysis of Model 1, Model 2, and Model 3 (Figure 4). Compared to Model 1 (AUC = 0.56) and Model 2 (AUC = 0.65), Model 3 (AUC = 0.68) demonstrated a higher predictive value for COPD prevalence.

Figure 4 ROC curve analysis. ROC curves for predicting COPD risk by SII and HEI. Model 1: no covariates were adjusted. Model 2 age, sex, and race were adjusted Model 3 sex, and race,education level, poverty, marital status, BMI, cardiovascular disease, and hypertension were adjusted. p < 0.05 was considered statistically significant.

Abbreviations: 95% CI, 95% confidence interval; OR, odds ratio.

Interaction Analysis of SII and Dietary Quality Levels on Outcomes

Next, we further investigated the joint effects of HEI-2015 and SII under different conditions to analyze the interaction between these two variables. Details are shown in Table 3 and Table 4. The risk of COPD in individuals with high log-SII and low HEI-2015 was 1.28-fold higher (OR:1.28,95% CI: 1.11 to 1.49) than in those with low log-SII and low HEI-2015. In contrast, individuals with low log-SII and high HEI-2015 had a 0.81-fold lower risk of COPD (OR:0.81,95% CI: 0.70 to 0.93) compared to those with low log-SII and low HEI-2015. Thus, individuals with low inflammation index and high healthy eating index had a lower risk of COPD. Furthermore, when log-SII increased from low to high, the risk increased (OR from 1 to 1.28) if HEI-2015 was low, but the risk change was minimal (OR from 1 to 1.04) if HEI-2015 was high. This suggests that a high level of healthy diet can improve the impact of inflammation on COPD risk. Additionally, the table shows that in individuals with high healthy eating index, high inflammation index was associated with a 29% increased risk of COPD (OR: 1.29; 95% CI: 1.11 to 1.52). Conversely, in individuals with high inflammation index, high healthy eating index was associated with a 19% reduced risk of COPD (OR: 0.81; 95% CI: 0.68 to 0.96).

Table 3 The Interaction of the HEI_2015 and Log_SII on the Prevalence of COPD

Table 4 Additive Interaction Between the HEI_2015 and Log_SII

To further investigate potential interactions between HEI-2015 and SII, interaction index-synergy index (S), relative excess risk of interaction (RERI), and attributable proportion of interaction (API)-were calculated (Table 4). Both RERI and API were −0.05, suggesting a potential negative correlation; however, their 95% confidence intervals (CIs) included 0, indicating that the interaction effects of these metrics were non-significant. The synergy index (S) was 0.48 (95% CI: 0.01 to 18.57), with a wide confidence interval spanning from 0.01 to 18.57, reflecting unstable effect estimates and statistical non-significance. These results demonstrate no significant interaction between HEI-2015 and SII, suggesting that both factors independently influence COPD risk.

Subgroup Analysis

Subgroup analyses stratified by age group, sex, race, marital status, education level, PIR, smoking, alcohol consumption, hypertension, cardiovascular disease, and diabetes were conducted to explore the relationship between lifestyle factors and COPD. Significant associations were observed in the 40–60 age group and among individuals with PIR ≥1 (P < 0.05). Notably, males with high HEI-2015 scores and low SII levels had reduced COPD risk (OR: 0.70, 95% CI: 0.54–0.91), as did non-Hispanic White individuals (OR: 0.66, 95% CI: 0.52–0.82) and those with hypertension (OR: 0.65, 95% CI: 0.50–0.86). Interaction analysis revealed significant effect modifications only for sex and race (P < 0.05) (Table 5).

Table 5 Subgroup Analyses of the Association Between Log-SII and HEI-2015 and COPD

Different Lifestyle Groups and COPD All-Cause Mortality

The joint relationship between HEI-2015, SII, and COPD mortality risk is presented in Table 6. Compared to individuals with low log-SII and low HEI-2015, those with high log-SII and high HEI-2015 had a 36% increased risk of all-cause mortality (HR: 1.36, 95% CI: 1.10–1.68). In contrast, combinations of high log-SII with low HEI-2015 or low SII with high HEI-2015 showed no significant association with COPD mortality. After further adjusting the age, gender, education and PIR, the results remained similar (Supplementary Tables S4 and S5). Kaplan-Meier survival curves (Figure 5) demonstrated a gradual decline in survival probability over time across all groups. The survival curves differed significantly among the four groups (P < 0.0001), indicating that distinct combinations of SII and dietary quality profoundly impacted survival outcomes in COPD patients. Specifically, the combination of low SII and high HEI-2015 was associated with higher survival probabilities at most time points, suggesting that healthier dietary habits and lower systemic inflammation levels may improve survival. Conversely, the combination of high SII and low HEI-2015 correlated with poorer survival outcomes, likely reflecting the detrimental effects of chronic inflammation and poor dietary quality.

Table 6 Joint Relationship Between HEI-2105 and SII and Mortality

Figure 5 The Kaplan-Meier curves display the long-term all-cause mortality by different lifestyle group. factor(HEI_SII)=Low SII&Low HEl-2015: factor(HEI_SII)=High SIl&High HEI-2015: factor(HEI_SII)=High SII&Low HEl-2015: factor(HEI_SII)=Low SII&High HEl-2015: .

Abbreviations: SII, Systemic Immune-Inflammation Index; HEI-2015: Healthy Eating Index-2015.

Discussion

This study, based on NHANES data, highlights significant associations between systemic immune-inflammation index, dietary quality, and COPD in middle-aged and elderly adults. In this cross-sectional analysis of 10,898 participants, elevatedlogSII levels were associated with increased COPD risk, while higher HEI-2015 scores were linked to reduced risk in unadjusted models. And then the SII might exhibit a nonlinear dose-response relationship with COPD risk. Furthermore, the ROC curve analysis indicated that, compared to Model 1 and Model 2, Model 3 exhibited a higher predictive value for COPD prevalence. These associations remained robust after adjusting for covariates, aligning with prior evidence underscoring the roles of inflammation and dietary quality in COPD pathogenesis.35,36 Notably, while the combination of high log-SII and low HEI-2015 markedly elevated COPD risk, interaction indices (S, RERI, API) were statistically non-significant, supporting the notion that SII and dietary quality act as independent risk factors. Subgroup analyses further revealed that specific populations, such as non-Hispanic White individuals and hypertensive patients, benefited most from the protective effects of low SII combined with high HEI-2015. Survival analyses reinforced these findings, demonstrating that coexisting high systemic inflammation and poor dietary habits significantly increased mortality risk in COPD patients.

Our study further elucidates the crucial roles of inflammation and diet quality in the pathogenesis of COPD. COPD is a leading cause of morbidity and mortality globally. Nevertheless, the precise pathological and physiological mechanisms underlying the relationship between the SII and COPD remain elusive. From a biological standpoint, inflammation is one of the central factors in the development of COPD. As an indicator of the systemic inflammatory state, a high SII value reflects an intensification of the body’s inflammatory response. An increasing body of research has demonstrated that inflammation and immune stress are associated with the risk and progression of COPD.37,38 Stimuli such as cigarette smoke, bacteria, and viruses can trigger neutrophilic inflammation by promoting airway epithelial cells to release neutrophil mediators. Neutrophils are key inflammatory cells in the pathogenesis of COPD,39 being particularly active during the acute exacerbation of COPD (AECOPD). They participate in pathogen clearance and release inflammatory mediators.40,41 The inflammatory response damages lung tissue and disrupts the normal regulation of lung function, thereby further driving the progression of COPD. Additionally, previous studies have indicated that inflammatory markers have certain value in the treatment of COPD7,42,43. Meanwhile, HEI-2015 assesses the overall quality of an individual’s diet. Existing research has shown that dietary factors play a significant role in ameliorating inflammation, oxidative stress, and regulating immunity and metabolism.16,44 Previous reports have confirmed an inverse relationship between a healthy diet pattern and the incidence of COPD.45–47 Besides,a case-control study in Iranian adults reported an inverse association between adherence to a low-carbohydrate diet (LCD) and the odds of COPD.48 Similarly, Another study showed that a significant inverse association between adherence to the HEI-2010 recommendations and the odds of developing COPD in a Middle Eastern population.49 Our findings, similar to those of these studies, suggest that adherence to the HEI-2015 recommendations reduces the risk of developing COPD and adverse outcomes, and therefore recommends that people with COPD consume more fruits, vegetables, and nuts while limiting their intake of sodium, refined grains, added sugars, and saturated fats.Thus, it can be inferred that a healthy dietary intake may be protective against COPD and its symptoms.50

SII, as a comprehensive inflammatory indicator, was first developed to predict the prognosis of patients after radical resection of liver cancer.8 By incorporating the counts of neutrophils, lymphocytes, and platelets, SII can reflect the body’s systemic inflammatory state more comprehensively. Compared with single inflammatory markers, it has a stronger ability to predict disease risks.51 Multiple studies have shown that SII is associated with acute exacerbations in patients with stable-phase COPD and the all-cause mortality of COPD patients.14,52 In addition, HEI-2015 quantifies diet quality by evaluating an individual’s dietary pattern. It can reflect the nutritional intake status more effectively than single components of a healthy diet. Our study further highlights the advantages of SII and diet quality in assessing the risk of COPD and their potential application value. Although our study has observed that the combination of a high log-SII and a low HEI-2015 significantly increases the risk of COPD, the results of the interaction analysis suggest that SII and HEI-2015 may influence the risk of COPD as independent factors, each affecting the likelihood of COPD occurrence. Moreover, the results of the subgroup analysis indicate that in specific populations, such as non-Hispanic whites and individuals with hypertension, the combination of a low systemic inflammation index and a high healthy-diet-quality index is associated with a lower risk of COPD. This may provide a new perspective for the individualized prevention of COPD. More importantly, the survival curve analysis further strengthens this relationship, demonstrating a significant increase in the mortality of COPD patients in the context of co-existing chronic inflammation and poor dietary habits.However, NHANES provides representative data for the US population, and our results should consider the potential impact when applied to other populations, such as Asian populations with lower BMI thresholds.

It is worth noting that the selection of the NHANES database provides a solid data foundation and broad representativeness. Second, this is the first study to integrate SII and the HEI-2015 to examine their associations with COPD risk. By exploring the interplay between these two critical health indicators at a systemic level, this research offers a dual perspective on COPD pathogenesis, combining insights from inflammatory responses and dietary patterns. A “inflammation-nutrition” dual-path intervention framework was established for clinical practice, and individualized dietary intervention strategies were better specified for COPD patients. Rigorous adjustment for confounding factors further enhances the reliability and representativeness of the findings. Additionally, the interaction analysis provides evidence supporting the independent effects of SII and HEI-2015, while subgroup analyses offer valuable guidance for developing personalized prevention and treatment strategies. Our study provides a new perspective for further mechanism exploration in the future, and at the same time, the integration of anti-inflammatory and dietary to establish the first “diet-inflammation” quantitative efficacy evaluation system will promote the update of clinical nutrition guidelines.

However, this study is not without limitations. It should be acknowledged that our analysis did not adjust for air pollution exposure—a known risk factor for COPD exacerbation and pulmonary function decline. Additionally, direct pulmonary function measurements were unavailable in NHANES, limiting our capacity to assess airflow limitation severity.Besides, Diagnosis of COPD relies on self-reported questionnaire data in NHANES and may be subject to recall bias; In particular, changes in the definition of COPD during the study may affect the uniformity of case identification. Survival analyses did not account for competitive risk and may overestimate COPD-specific mortality; At the same time, unadjusted for survivorship bias may lead to an underestimation of true risk.

Conclusions

In conclusion, higher SII levels and lower HEI-2015 scores were associated with increased COPD prevalence and mortality after statistical adjustment for potential confounders. Patients with COPD with elevated SIIs and suboptimal dietary quality are at higher risk of adverse outcomes. Although our study was unable to establish causal relationships, these associations highlighted the potential importance of inflammation and dietary quality in the management of COPD, and further statistical adjustments for potential confounders, taking into account non-significant variables, may help refine these associations and better understand their clinical relevance. In addition, the clinical significance of the results of this study is to suggest that clinicians should pay attention to the monitoring of inflammation level and the improvement of dietary quality in the management of COPD patients. From a public health perspective, this highlights the need for a combination of inflammatory management and nutritional interventions to reduce COPD morbidity and mortality in COPD prevention and management strategies.

Ethics Approval and Informed Consent

This study used the publicly available NHANES database, which was collected with patient informed consent and ethics approval from its source institution. Since this study only involves secondary analysis of existing and de-identified data, it meets the exemption conditions stipulated in Article 32 (1) and/or (2) of the “Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects” issued by the National Health Commission on February 18, 2023, and has been approved by the First Affiliated Hospital of Nanjing Medical University.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Acknowledgments

All the authors involved in this study thank all the staff of the NHANES research team for their work.

Funding

This study was supported by grants from the National Natural Science Foundation of China (No. 82171576, No. 82471605) and the Natural Science Foundation of Jiangsu Province (No. BK20211377).

Disclosure

The authors report no conflicts of interest in this work.

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