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Abstract

Background:

Composite nutritional and immune indices (NIIs) have been associated with chronic lung diseases (CLDs). However, systematic research on their associations with different CLD subtypes and potential threshold patterns remains limited.

Objectives:

To examine cross-sectional associations between multiple NII and CLD subtypes, and to explore potential linear/nonlinear relationships and threshold ranges using a representative dataset.

Design:

Cross-sectional, population-based study using National Health and Nutrition Examination Survey (NHANES) data.

Methods:

Data were obtained from the US NHANES from 2007 to 2012. Participants aged 18–79 years with complete blood and baseline data were included. Survey-weighted regression models were used to assess associations between NIIs and CLDs. Restricted cubic spline (RCS) regression models were employed to explore linear/nonlinear relationships and potential threshold ranges.

Results:

The study included 5837 participants (mean age 49.82 ± 16.15). Regression analyses revealed significant associations between NIIs and CLDs across different ranges. RCS analysis identified thresholds for each NII: prognostic nutritional index (PNI) levels > 46.04 showed a significant inverse association with emphysema and chronic bronchitis. Elevated platelet-to-lymphocyte ratio (PLR) (>113.57) and MLR (>0.14) were positively associated with these conditions. systemic immune-inflammation index (SII) (>429.43) and neutrophil-to-lymphocyte ratio (NLR) (>0.99) were associated with a higher prevalence of asthma, emphysema, and chronic bronchitis.

Conclusion:

In this cross-sectional population-based study, NIIs were associated with different CLD subtypes, with evidence of potential threshold patterns. These findings may help inform future epidemiological studies and hypothesis generation, while causal inference and clinical application require further longitudinal validation.

Plain language summary

Nutritional and immune indices in chronic lung disease: NHANES 2007-2012

Chronic lung diseases (CLDs) like asthma, emphysema, and chronic bronchitis are major health issues. In this study, we explored the association between nutritional and immune indices (NIIs) and CLDs. These indices measure a person’s nutrition and immune function. We analyzed data from the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2012, focusing on adults aged 18-79. We examined how different NIIs related to CLDs and identified key threshold values that could predict disease risk. Our results showed that higher levels of prognostic nutritional index (PNI) were linked to a lower risk of emphysema and chronic bronchitis. In contrast, higher levels of platelet-to-lymphocyte ratio (PLR) and MLR were associated with a higher risk of these diseases. Additionally, elevated levels of systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR) were connected to a greater risk of asthma, emphysema, and chronic bronchitis. More importantly, we clarified specific values and constructed a new predictive framework. This study provides a useful framework for predicting CLD risk based on simple measurements. It can help doctors detect these diseases early and improve prevention and treatment strategies for patients.

Keywords

asthma chronic obstructive inflammation National Health and Nutrition Examination Survey pulmonary disease

Introduction

Chronic lung diseases (CLDs), encompassing asthma, interstitial lung disease, and chronic obstructive pulmonary disease (COPD, including chronic bronchitis and emphysema), are highly prevalent respiratory disorders that significantly reduce life expectancy and impair quality of life.¹ Due to their insidious onset and multifactorial etiology, including genetic predisposition, environmental exposures (e.g., smoking and air pollution), immune-inflammatory dysregulation, oxidative stress, and nutritional status, early screening (e.g., pulmonary function assessment and inflammatory biomarker monitoring) and timely risk interventions (e.g., smoking cessation and antioxidant therapy) are essential strategies for mitigating CLDs incidence, particularly in high-risk populations such as long-term smokers and individuals with occupational exposures. Emerging evidence suggests that chronic activation of immune-inflammatory pathways, oxidative stress-related injury, and metabolic dysregulation contribute substantially to CLD pathogenesis and progression.^2,3 Investigating the immune-inflammatory regulatory mechanisms underlying CLDs is therefore critical, not only for advancing the understanding of disease pathophysiology but also for informing the development of targeted therapeutic strategies (e.g., anti-inflammatory interventions and nutritional modulation). These efforts may ultimately enable more precise prevention and management of CLDs.^4,5

Recent clinical studies have identified composite blood-based nutritional immunological indices (NIIs), such as the prognostic nutritional index (PNI), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and monocyte-to-lymphocyte ratio (MLR), as potential biomarkers associated with the presence or prevalence of CLDs.^6
–8 However, several limitations constrain their clinical utility in the identification and risk stratification of CLDs. First, population heterogeneity and the lack of standardized CLD diagnostic criteria across studies have led to inconsistencies in threshold values for the same NIIs when predicting the same CLD subtypes. Second, the threshold values of the same NIIs vary across different CLD subtypes. However, a standardized set of NII thresholds for assessing CLD risk has not yet been established, posing a significant barrier to their clinical implementation. Therefore, large-scale cohort studies are warranted to refine and validate the associations and discriminative ability of these biomarkers across various CLD subtypes and to establish robust, standardized reference thresholds, thereby enhancing their clinical applicability.

To address this critical gap, the present study leveraged data from the National Health and Nutrition Examination Survey (NHANES) to identify individuals with different CLD subtypes. Regression models were employed to assess linear/nonlinear associations between PNI, SII, NLR, PLR, MLR, and the presence of CLDs. Subsequently, restricted cubic spline (RCS) models were applied to flexibly model nonlinear relationships and identify optimal cutoff values for each NII-CLD pair. This study aims to establish a unified multi-index threshold framework to aid in the identification and risk stratification of individuals with potential CLDs, providing a potential auxiliary tool for clinical assessment. We emphasize that, given the cross‑sectional design of this study, the observed associations do not imply causality and cannot support their use for prospective risk prediction. The findings require validation in longitudinal cohort studies.

Materials and methods

The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.⁹

Study population

The National Health and Nutrition Examination Survey (NHANES) ( https://www.cdc.gov/nchs/nhanes/index.htm ), conducted by the Centers for Disease Control and Prevention, is a population-based cross-sectional survey integrating questionnaires, physical examinations, and laboratory assessments. The study population was derived from participants of the NHANES 2007–2012. Eligible participants were restricted to adults aged 20–79 who completed both the interview and examination components. Key inclusion criteria required the availability of valid spirometry data for defining chronic lung disease (asthma, emphysema, chronic bronchitis by self-reported physician diagnosis) and complete laboratory data for the specified serum nutritional biomarkers and immune/inflammatory markers. Participants who were pregnant, had a history of active malignancy or other severe systemic diseases, or presented with extreme outliers in biomarker values were excluded. Additionally, individuals with missing or technically unacceptable spirometry data were not included in the final analytic sample. A flowchart detailing participant selection is provided in Figure 1, resulting in a final analytic sample of 5837 participants for this study. Additionally, it should be noted that no prior statistical power calculations were performed.

Figure 1.

Flowchart of the study design. The participant selection process for NHANES 2007–2012.

Assessment of nutritional and immune indices

Five nutritional and immune indices (NIIs) were derived from NHANES laboratory data. Blood parameters were obtained following written informed consent, and serum albumin concentrations were measured by the bromocresol purple method. PNI = albumin (g/L) + 5 × lymphocyte count (1000 cells/μL). SII = platelet count (1000 cells/μL) × neutrophil count (1000 cells/μL) / lymphocyte count (1000 cells/μL). PLR = platelet count (1000 cells/μL) / lymphocyte count (1000 cells/μL). NLR = neutrophil count (1000 cells/μL)/ lymphocyte count (1000 cells/μL). MLR = monocyte count (1000 cells/μL) / lymphocyte count (1000 cells/μL).^10
–12

Variables and outcomes

In this study, demographic, socioeconomic, and clinical variables include age, gender, race/ethnicity, education level, marital status, poverty-income ratio, body mass index (BMI), health insurance coverage, smoking status, sedentary activity, and chronic lung diseases (asthma, emphysema, chronic bronchitis). Participants were stratified by age into those under 65 years (<65) and those 65 years or older (⩾65). Race/ethnicity categories comprised Mexican American, Other Hispanic, non-Hispanic white, non-Hispanic black, and Other Races. Educational levels were divided into three tiers: less than high school completion, high school graduation, and greater than high school. Marital status was classified as married/living with partner, widowed/divorced/separated, or never married. Smoking status was categorized as daily, occasional, or never smoked. The primary outcomes were the thresholds characterizing the associations between NIIs and the presence of CLDs, including asthma, emphysema, and chronic bronchitis.

Statistical analysis

The statistical analysis protocol was structured as follows: First, baseline characteristics were described using means (SD) for continuous variables and frequencies (%) for categorical variables. Second, logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs)for the association between NIIs and CLDs, adjusted for age, gender, race/ethnicity, education level, marital status, poverty-income ratio, BMI, health insurance coverage, smoking status and sedentary behavior. Third, stratified analyses were conducted across subgroups defined by age (<65 and ⩾ 65 years), gender (male and female), smoking status (daily, sometimes, never), and race/ethnicity (Mexican American, Other Hispanic, non-Hispanic white, non-Hispanic black, other race) to delineate association patterns within specific populations. Fourth, stratified analysis evaluated NII-CLDs relationships by quartiles (Q1, Q2, Q3, and Q4 at 25%, 50%, and 75% nodes). Fifth, RCS regression models were used to examine potential nonlinear relationships between NIIs and CLDs after adjusting for confounders. Finally, the thresholds linking NIIs to CLD subtypes were identified and summarized.

This study used NHANES survey weights, and data were analyzed using SPSS (version 26.0) and the “rms” & “plotRCS” package in R (version 4.0.3). Graphical representations depicted ORs as solid lines with shaded 95% CIs. Statistical significance was set at p < 0.05 (two-tailed).

Results

Characteristics of the study population

The characteristics of the study population are shown in Table 1. The baseline demographic characteristics, lifestyle factors, and chronic diseases of the study population are presented in detail. A total of 5837 participants (56.20% male, 43.8% female, 74.73% non-Hispanic white, 9.38% non-Hispanic black, 6.50% Mexican American, 9.39% other ethnicity) from the 2007 to 2012 NHANES databases were analyzed. The mean (SD) ages of the participants were 49.83 (16.15) years. Among them, 15.68% had a history of asthma, 3.52% had emphysema, and 7.40% had chronic bronchitis. The mean (SD) values for the indices were as follows: PNI = 53.5057 (5.5518), SII = 557.5274 (346.6152), PLR = 124.2449 (51.5870), NLR = 2.2360 (1.1866), and MLR = 0.2716 (0.1174). Supplemental Tables 1–5 show the characteristics of participants in different NIIs groups.

Table 1.

The baseline characteristics of participants included in this study (NHANES 2007–2012).

Characteristic	Overall (N = 5837)^a
Age	49.83 (16.15)
Gender
Male	3209 (56.20%)
Female	2628 (43.80%)
Race/ethnicity
Mexican American	756 (6.50%)
Other Hispanic	510 (4.38%)
Non-Hispanic white	3074 (74.73%)
Non-Hispanic black	1155 (9.38%)
Other Race	342 (5.01%)
Education
Less than high school	1713 (21.51%)
High school graduate	1478 (25.02%)
Greater than high school	2646 (53.47%)
Marital status
Married/Living with partner	3645 (66.29%)
Widowed/Divorced/Separated	1457 (21.42%)
Never married	735 (12.29%)
Poverty-income ratio	2.35 (1.60)
BMI	29.06 (6.73)
Health insurance coverage
Yes	4328 (77.64%)
No	1509 (22.36%)
Smoking status
Every day	2361 (39.25%)
Some day	462 (7.26%)
Not at all	3014 (53.49%)
Sedentary activity	333.17 (197.51)
Chronic lung diseases
Asthma	925 (15.68%)
Emphysema	240 (3.52%)
Chronic bronchitis	472 (7.40%)
PNI	53.5057 (5.5518)
SII	557.5274 (346.6152)
PLR	124.2449 (51.5870)
NLR	2.2360 (1.1866)
MLR	0.2716 (0.1174)

Mean (SD) for continuous; n (%) for categorical.

Association between NIIs and chronic lung diseases

The results of the logistic regression analyses for the associations between NIIs and CLDs are presented in Figure 2. Within specific ranges, lower PNI values were associated with an increased prevalence of emphysema (OR = 0.931, 95% CI 0.907–0.957, p < 0.001) and chronic bronchitis (OR = 0.945, 95% CI 0.926–0.963, p < 0.001). Conversely, elevated SII was associated with an increased prevalence of asthma (OR = 1.000, 95% CI 1.000–1.000, p = 0.003), emphysema (OR = 1.001, 95% CI 1.001–1.001, p < 0.001) and chronic bronchitis (OR = 1.001, 95% CI 1.000–1.001, p < 0.001).

Figure 2.

The association between NIIs and chronic lung diseases. Association of (a) PNI; (b) SII; (c) PLR; (d) NLR; (e) MLR with asthma, emphysema, and chronic bronchitis.

Similar to SII, elevated PLR was also associated with a higher prevalence of emphysema (OR = 1.005, 95% CI 1.003–1.007, p < 0.001) and chronic bronchitis (OR = 1.003, 95% CI 1.001–1.004, p = 0.002). Meanwhile, elevated NLR values were associated with increased prevalence of asthma (OR = 1.076, 95% CI 1.019–1.136, p = 0.009), emphysema (OR = 1.311, 95% CI 1.212–1.419, p < 0.001) and chronic bronchitis (OR = 1.186, 95% CI 1.111–1.267, p < 0.001). Additionally, elevated MLR was significantly associated with an increased prevalence of emphysema (OR = 9.205, 95% CI 3.896–21.745, p < 0.001) and chronic bronchitis (OR = 3.035, 95% CI 1.500–6.140, p = 0.002).

Subsequently, NIIs were categorized into four groups based on quartiles in NHANES, and the associations between the four NIIs groups and the prevalence of CLDs were evaluated. The results are shown in Supplemental Table 6. In addition, RCS models were applied to assess the dose-response relationships between NIIs and CLDs, with NIIs modeled as continuous variables (Figure 3). We found a nonlinear, positive association between SII and asthma (p = 0.044 for nonlinear), SII and emphysema (p = 0.024 for nonlinear), and NLR and emphysema (p = 0.047 for nonlinear). Besides, there was a nonlinear, negative association between PNI and emphysema (p = 0.014 for nonlinear). These results demonstrated a dose-dependent association between NIIs and CLDs. Beyond a critical threshold of NII values, statistically significant correlations with the risk of CLD onset were observed. To quantify these threshold effects, RCS models were used to calculate the optimal inflection point. The threshold values for PNI, SII, PLR, NLR and MLR were 46.0427, 429.4277, 113.5733, 0.9938, and 0.1371, respectively.

Figure 3.

Shapes of the relationship between chronic lung disease and NIIs. The relationship between asthma and (a) PNI; (b) SII; (c) PLR; (d) NLR; (e) MLR. The relationship between emphysema and (f) PNI; (g) SII; (h) PLR; (i) NLR; (j) MLR. The relationship between chronic bronchitis and (k) PNI; (l) SII; (m) PLR; (n) NLR; (o) MLR.

The results demonstrated that beyond the inflection point of the NIIs, the PNI exhibited a significant negative association with chronic bronchitis (OR = 0.954, 95% CI: 0.931–0.977, p < 0.001) and emphysema (OR = 0.960, 95% CI: 0.928–0.993, p = 0.018), indicating its protective role against CLDs. Conversely, PLR and MLR showed significant positive associations with both conditions, with elevated PLR and MLR values serving as high-risk factors for chronic bronchitis(OR = 1.004, 95% CI: 1.002–1.006, p < 0.001) (OR = 3.068, 95% CI: 1.467–6.415, p = 0.003)and emphysema(OR = 1.005, 95% CI: 1.003–1.007, p < 0.001) (OR = 8.645, 95% CI: 3.551–21.044, p < 0.001). Additionally, SII and NLR were positively associated with the prevalence of asthma(OR = 1.000, 95% CI: 1.000–1.001, p = 0.024) (OR = 1.076, 95% CI: 1.017–1.138, p = 0.011), emphysema(OR = 1.001, 95% CI: 1.000–1.001, p < 0.001) (OR = 1.296, 95% CI: 1.195–1.405, p < 0.001), and chronic bronchitis(OR = 1.000, 95% CI: 1.000–1.001, p < 0.001) (OR = 1.187, 95% CI: 1.110–1.270, p < 0.001). Higher SII and NLR values were associated with increased susceptibility to CLDs (Figure 4).

Figure 4.

Shapes of the relationship between NIIs and chronic lung diseases and its thresholds. (a) The thresholds for SII and chronic lung diseases were 199.0442 and 429.4277. (b) The threshold for NLR and chronic lung diseases was 0.9938. (c) The threshold of PNI with emphysema and chronic bronchitis was 46.0427. (d) The thresholds of PLR with emphysema and chronic bronchitis were 65.7903 and 113.5733. (e) The threshold of MLR with emphysema and chronic bronchitis was 0.1371. (f) Baseline table of threshold analyses for NIIs and chronic lung diseases.

Subgroup analysis

In further analyses, we conducted subgroup analyses of the cohort by age group, sex, race and ethnicity, and smoking status. The results are presented in Supplemental Tables 7–11. Our analysis revealed that MLR exhibited a significant age-specific association with CLD risk, demonstrating a robust correlation exclusively among individuals aged > 65 years. In contrast, PNI, SII, NLR, and PLR demonstrated age-independent associations with CLD susceptibility across all age subgroups.

In sex- and ethnicity-stratified analyses, NIIs demonstrated statistically significant associations with CLD risk across all subgroups, including males, females, and diverse ethnic populations. These findings underscore the robustness of NIIs as a trans-demographic predictor of CLD susceptibility. However, subgroup analyses stratified by smoking status revealed a significant modifying effect: NIIs were strongly associated with CLD risk in never-smokers, whereas this association was attenuated and lost statistical significance among current smokers.

Discussion

Emerging evidence has revealed that individual chronic inflammation status and immune-nutritional profiles, including micronutrient levels, inflammatory biomarkers, and immune cell ratios, play a critical role in the pathogenesis and progression of CLDs.^13
–16 Multiple hematologic nutritional and immune indices (NIIs), such as PNI, SII, NLR, PLR, and MLR, have been reported to be associated with the presence of CLDs. However, their clinical utility remains limited by inconsistent reference ranges, population heterogeneity, confounding factors, and complex calculation methods. To address these challenges, the present study used large-scale, nationally representative cross-sectional data from NHANES to systematically examine the associations between NIIs and CLDs. Given the cross-sectional design, the directionality of these associations cannot be determined. Our analysis revealed robust associations between multiple NIIs and the presence of CLDs (including asthma, emphysema, and chronic bronchitis), with specific thresholds and nonlinear/linear relationships (see Supplemental Table 12 for participant distribution by CLD subtype). As NHANES relies on self-reported diagnoses, emphysema and chronic bronchitis were analyzed as separate categories, although they are clinically recognized as components of COPD. The absence of spirometry or imaging data limits the ability to construct a physiologically defined composite COPD diagnosis, which should be considered when interpreting these findings.

RCS curve analysis further demonstrated that when individual NIIs reach or fall below specific inflection points, their associations with CLDs stabilize. Importantly, these RCS-derived thresholds should be regarded as statistical reference points reflecting the association patterns observed in the study population-level, rather than definitive clinical decision thresholds. While such threshold-based patterns may simplify risk stratification by categorizing individuals into groups with a different likelihood of having CLDs, their translation into routine clinical practice requires validation in independent longitudinal and clinical cohorts. Nonetheless, by refining association-based stratification strategies and proposing interpretable reference ranges, this study provides a methodological foundation for improving the consistency and transparency of NII utilization in CLD assessment. Current research on NIIs predominantly focuses on developing composite index strategies based on individual NIIs to stratify risk for or assess specific CLD subtypes. For instance, Liu et al.¹⁷ demonstrated that combining NLR, PLR, and CRP significantly improves the accuracy of predicting acute exacerbations of chronic obstructive pulmonary disease (AECOPD) compared to individual biomarkers. Similarly, Lin et al.¹⁸ reported enhanced AECOPD risk stratification by integrating the PNI with inflammatory and nutritional markers (e.g., eosinophils, platelets, CRP, and low-density lipoprotein cholesterol). The study conducted by Iva Hlapcˇić et al.¹⁹ also demonstrated that inflammation-based indices associated with leukocyte subsets including NLR, derived NLR (dNLR), MLR, basophil-to-lymphocyte ratio, basophil-to-monocyte ratio, and monocyte/granulocyte-to-lymphocyte ratio (M/GLR) could exhibit strong predictive value for COPD risk and serve as reliable indicators for assessing disease severity. While these findings underscore the potential of NIIs in CLD risk prediction, several challenges hinder their clinical translation. First, heterogeneity in baseline population characteristics and outcome definitions across studies leads to inconsistent cutoff values and predictive performance for NIIs. Second, NIIs are susceptible to short-term inflammatory fluctuations (e.g., upper respiratory infections) and nutritional interventions,^20,21 yet most studies rely on single-timepoint measurements, limiting insights into dynamic biomarker trajectories and context-specific applicability. Third, NIIs often involve complex multi-parameter calculations, posing challenges for rapid clinical assessment. Given that clinicians prefer straightforward, dichotomous risk classifications (e.g., “high-risk” vs “low-risk”), current clinical guidelines (including GOLD, GINA, or ATS/ERS) primarily recommend easily accessible, unidimensional indicators, such as pulmonary function metrics (e.g., FEV1%) and clinical history indicators (e.g., exacerbation history), for risk stratification.^6,22,23 In this context, NIIs may serve as supplementary markers reflecting systemic immune-nutritional status rather than stand-alone diagnostic or prognostic tools. Consequently, simplifying their classification and interpreting them cautiously remains essential for practical clinical application.

Our research team posits that, as NIIs are calculated from integrated immune, nutritional, and inflammatory parameters, they inherently hold potential for stratifying risk for a range of diseases .^13,24 While different CLD subtypes have distinct pathophysiological mechanisms, they share common immune-inflammatory drivers such as chronic inflammation and airway remodeling, suggesting that individual NIIs can be used to predict the risk of multiple CLDs.^25,26 Through RCS modeling, our study identified relatively stable association cutoff values for individual NIIs across multiple CLDs. Furthermore, each NII can serve as an independent stratification marker or be combined with others for a more comprehensive risk assessment. Each index may function as an independent marker or be combined with others to support multidimensional risk stratification. However, these findings should be interpreted as association-based stratification tools, and not as evidence of predictive causality. Although our findings require further validation in large-scale clinical cohorts, this study provides a novel conceptual framework for optimizing the clinical application of NIIs. By establishing clinically interpretable thresholds and refining implementation strategies, our approach lays the groundwork for integrating NIIs into routine practice for CLD risk assessment and management. Furthermore, based on the threshold values identified in this study, an automated clinical decision support system could be developed to provide real-time alerts for CLDs high-risk patients.

Additionally, subgroup analyses revealed important demographic and behavioral modifiers. The age-specific association of MLR with chronic lung diseases in older adults (>65 years) aligns with immune-senescence theories, wherein age-related shifts in monocyte-lymphocyte homeostasis may amplify inflammatory lung injury.²⁷ Similarly, the attenuation of NII-disease associations in current smokers suggests that smoking-induced inflammation overshadows the incremental predictive value of NII in this subgroup.²⁸ Previous studies have reported that in healthy smokers, the number of smoking-induced inflammatory cells returns to normal levels only after 5 years of smoking cessation. Whether the NII-based assessment framework remains applicable to this subset of former smokers still requires further investigation in future research.^29,30 Notably, some disease-specific subgroups-particularly emphysema-had relatively small sample sizes, which may have resulted in less stable effect estimates. Therefore, findings from these strata should be interpreted with caution.

Strengths and limitations

Our study demonstrates several key strengths. First, the utilization of a large, well-characterized cohort with rigorous covariate adjustment and advanced modeling techniques (e.g., RCS for nonlinearity analysis) enhances the internal validity and generalizability of our findings. Second, by stratifying COPD patients into two distinct subtypes (emphysema and chronic bronchitis), we achieved greater specificity in defining clinical outcomes, thereby reducing phenotypic heterogeneity. Finally, and most critically, this study pioneers the optimization of NIIs for clinical risk assessment of CLDs. We streamlined the NII-based risk stratification framework by establishing simplified threshold criteria, which facilitate large-scale screening of high-risk populations and enable the timely implementation of early preventive interventions.

However, this study has some limitations. First, the cross-sectional design of this study cannot determine the causal relationship or temporal sequence between NIIs and CLDs. Second, NHANES relies on self-reported diagnoses and cannot distinguish disease severity or clinical phenotypes, which may introduce misclassification bias and potentially attenuate the observed associations. As a fixed, publicly available survey dataset is utilized, no prior test performance calculations were conducted. Third, the single-timepoint measurement of NII limits insight into dynamic changes in inflammation-nutrition status over time. Fourth, since it lacks data on other typical chronic lung diseases from the NHANES database such as interstitial pneumonia, the small sample size may compromise the stability of the assessment. Fifth, the absence of an independent external validation cohort prevents confirmation of the robustness and broader applicability of the study's conclusions. In brief, further large-scale studies are still needed to refine and validate the NII-based CLD risk stratification strategy proposed in this study.

Conclusion

In conclusion, by systematically evaluating critical NII thresholds across CLD subtypes, this study establishes an optimized and streamlined NII-based strategy for predicting CLD risk. Rather than establishing causal or predictive claims, the findings support the potential role of NIIs as complementary markers for disease risk stratification. With further longitudinal validation, this streamlined framework may contribute to early identification and preventive strategies among individuals at elevated risk of CLDs.

Supplemental Material

sj-docx-1-tar-10.1177_17534666261432503 – Supplemental material for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012

Supplemental material, sj-docx-1-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

Supplemental Material

sj-tif-10-tar-10.1177_17534666261432503 – Supplemental material for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012

Supplemental material, sj-tif-10-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

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Supplemental material, sj-tif-11-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

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Supplemental material, sj-tif-12-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

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sj-tif-13-tar-10.1177_17534666261432503 – Supplemental material for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012

Supplemental material, sj-tif-13-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

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sj-tif-2-tar-10.1177_17534666261432503 – Supplemental material for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012

Supplemental material, sj-tif-2-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

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sj-tif-3-tar-10.1177_17534666261432503 – Supplemental material for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012

Supplemental material, sj-tif-3-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

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Supplemental material, sj-tif-4-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

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Supplemental material, sj-tif-5-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

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Supplemental material, sj-tif-6-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

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Supplemental material, sj-tif-7-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

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Supplemental material, sj-tif-8-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

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sj-tif-9-tar-10.1177_17534666261432503 – Supplemental material for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012

Supplemental material, sj-tif-9-tar-10.1177_17534666261432503 for Optimizing clinical strategies for nutritional and immune indices prediction of chronic lung diseases: a cross-sectional study from NHANES 2007–2012 by Song He, Cong Chen, Jianqi Hao, Yueli Shu, Cheng Yu, Xiaojun Liu, Xiaoqing Wu, Nanzhi Luo, Wenjing Zhou and Zhengyu Zha in Therapeutic Advances in Respiratory Disease

Footnotes

Acknowledgements

The data used in this study were obtained from the National Health and Nutrition Examination Survey (NHANES). The authors would like to express our gratitude to the dedicated staff, researchers, and participants of NHANES for their valuable contributions to this national health surveillance program.

Declarations

Supplemental material

Supplemental material for this article is available online.

Artificial intelligence

ChatGPT (GPT-3.5 Turbo, official website access) was used solely for language editing. All text was reviewed sentence-by-sentence, and the authors take full responsibility for the accuracy and integrity of the content.

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