Association of pan-immune inflammation value and lung health in adults

Abstract

Background

Lung health is intricately linked with inflammation. The pan-immune-inflammation value (PIV) emerges as a promising biomarker, offering reflection into systemic inflammatory states and assisting in the prognosis of diverse diseases. This research aims to explore the associations between PIV and respiratory symptoms, respiratory diseases and lung function.

Methods

The study was a cross-sectional population study from the National Health and Nutrition Examination Survey (NHANES). Restricted cubic spline (RCS) models were conducted to explore the relationships between PIV and respiratory health outcomes, while weighted linear regression models and weighted logistic regression models were the ones used for regression analysis. Trend tests probed the evolving relationship among PIV quartiles and outcomes. The study incorporated subgroup analysis and interaction tests to examine associations within specific subpopulations.

Results

From the cohort of 6,263 participants, a distinct negative correlation was identified between PIV and lung health. Subsequent to confounding factors, a 100-unit increment in PIV was linked to a 2% increase in the incidence of cough and phlegm (OR, 95% CI: 1.02, 1.00 to 1.05; 1.02, 1.00 to 1.04). Additionally, higher PIV was associated with reductions in FEV1 (MD, 95% CI: -5.37, -9.10 to -1.64) and FVC (MD, 95% CI: -5.75, -10.34 to -1.15). Categorizing PIV into quartiles revealed an ascending trend: A significantly higher risk of cough/phlegm/wheeze was found in participants in the second/third/fourth PIV quartile compared to those in the first PIV quartile (all p for trend < 0.05). Moreover, lung function indicators (FEV1, FEV1%, FVC, FVC%, FEV1/FVC) declined significantly in the fourth quartile (all p for trend < 0.05). Besides, a nonlinear relationship between PIV and outcomes was evident. Subgroup analysis revealed variations in these associations stratified by gender, age, smoking and drinking status, as well as certain disease history.

Conclusions

The study highlighted the potential connections between PIV and respiratory symptoms, respiratory diseases and lung function. Monitoring PIV level could provide valuable insights into the inflammatory status and may inform clinical approaches for managing respiratory health.

Background

Lungs are essential respiratory organs, principally involved in gas exchange. When exposed to endogenous or exogenous stimuli, the network of specialized immune cells will sense and initiate protective inflammatory responses to protect lungs from damage [1]. Inflammation plays a pivotal role in the onset and progression of chronic respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), and interstitial lung diseases [2,3,4]. According to the world health organization, respiratory diseases are leading causes of death worldwide [5]. Consequently, it is imperative that we prioritize monitoring lung health. It is an urgent requirement in clinical practice for early detection and routine screening with simple, cost-effective, and easily accessible appliances.

Inflammatory state can be assessed through various biochemical or haematological markers measured in routine blood examinations or as ratios derived from these measurements. Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), regarded as systemic inflammatory responses markers, have been evaluated in the prediction, diagnosis and prognosis of several respiratory disorders, including COPD, lung cancer and interstitial lung disease [6,7,8,9]. Recent researches have underlined that the pan-immune inflammation value (PIV) is a novel indicator of systemic inflammation status, synthesizing peripheral neutrophil, platelet, monocyte and lymphocyte [10, 11]. These four types of blood cells represent different inflammatory and immune pathways within the body, offering a more comprehensive reflection of the body inflammatory status. Currently, PIV has been extensively studied in the researches of cancer [10, 12, 13], stroke [11], myocardial infarction [14] and aortic dissection [15].

Available literature seldom addresses the association between PIV and lung health. Therefore, this study endeavors to elucidate the potential relationship between PIV and respiratory symptoms, respiratory diseases, lung function. Besides, we hope to contribute valuable information that may enhance understanding of the role of systemic inflammation in respiratory conditions and ultimately improve clinical practices related to lung health management.

Methods

Study population and exclusion criteria

Our study was conducted as a cross-sectional population study utilizing data from The National Health and Nutrition Examination Survey (NHANES). Since only three survey cycles (2007–2008, 2009–2010 and 2011–2012) contained information on the spirometry, we collected 30,442 participants' data within this timeframe. Participants were excluded if (1) they were aged < 20 years old; (2) they had missing information regarding respiratory diseases (asthma, emphysema and chronic bronchitis) and symptoms (cough, phlegm, wheeze and exertional dyspnea); (3) they were lacking of spirometry data, or the quality of the spirometry was not up to scratch; (4) they had incomplete blood examination data. (5) they were pregnant individuals.

Definition of PIV

The Beckman Coulter MAXM instrument was utilized to perform the complete blood count on blood specimens. PIV was calculated by neutrophil number (1000 cells/uL) × platelet number (1000 cells/uL) × monocyte number (1000 cells/uL) /lymphocyte number (1000 cells/uL). PIV100 was defined as PIV divided by 100.

Definition of outcomes

Respiratory symptoms were gathered by the NHANES Household Questionnaire Interview, which was one part of the health examination surveys in NHANES. The questionnaire poses the following questions: 1) “Do you usually cough on most days for 3 consecutive months or more during the year?”; 2) “Do you bring up phlegm on most days for 3 consecutive months or more during the year?”; 3) “In the past 12 months have you had wheezing or whistling in your chest?”; 4) “In the past 12 months, has your chest sounded wheezy during or after exercise or physical activity?” [16].

Respiratory diseases were diagnosed based on self-reported medical history, the question “Has a doctor or other health professional ever told you that you had asthma?” identified whether participants were the presence of asthma; the question “Has a doctor or other health professional ever told you that you had emphysema?” ascertained whether participants were the presence of emphysema; the question “Has a doctor or other health professional ever told you that you had chronic bronchitis?” indicated whether participants were the presence of chronic bronchitis [16].

Spirometry was a routinely used clinical lung function test. The Ohio 822/827 dry-rolling seal volume spirometers were employed in this study. The spirometry procedures adhered to the guidelines set forth by American Thoracic Society (ATS), ensuring that the quality of the spirometry met a grade of B or higher [17]. The forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) were measured, and the corresponding predicted values were calculated using the Hankinson Eq. [18]. The obstructive spirometry pattern was defined as FEV1/FVC < 0.70; the restrictive spirometry pattern was identified by FEV1/FVC ≥ 0.70 and percent- predicted FVC < 80%.

Assessment of covariates

The covariates included age, gender, race, education level, marital status, smoke, drink, poverty-to-income ratio (PIR), hypertension, diabetes mellitus, coronary heart disease (CAD), liver problem, arthritis, thyroid problem and cancer. The race was categorized as non-Hispanic white, non-Hispanic black and Mexican American. Education level was classified into less than 9th grade, 9-11th grade (including 12th grade with no diploma), high school grade/general equivalent diploma, some college or associate degree, and college graduate or above. Married, widowed, divorced, separated, never married, living with partner were incorporate into marital status. Participants who consumed at least 12 drinks of any type of alcoholic beverage in a year were characterized as drinkers. Those who reported smoking less than 100 cigarettes during their lifetime were categorized as never smokers, while current smokers were defined as individuals who had smoked more than 100 cigarettes and were still smoking at the time of the study. Former smokers were classified as individuals who had smoked more than 100 cigarettes in their lifetime but had since quit smoking. In addition, disease history (hypertension, diabetes mellitus, CAD, liver problem, arthritis, thyroid problem and cancer) was based on previous medical records provided by healthcare professionals or physicians.

Statistical analysis

Data distribution of all continuous variables was assessed using the Shapiro–Wilk test. They were nonnormally distributed and manifested as median and interquartile range. All categorical data was expressed as number and percentage. Kruskal–Wallis or Fisher's exact test was performed to evaluate statistical differences among different groups of PIV.

In order to evaluate the independent effect of PIV on lung health, weighted linear models were employed on the analysis of continuous variable outcomes and weighted logistic regressions were utilized to analyze categorical variable outcomes. The result of weighted linear models was summarized as mean difference (MD), 95% confidence interval (CI) and that of weighted logistic regressions was odds ratio (OR), 95%CI. Weighted multinomial logistic regression was conducted to assess the relative risk ratio (RRR) between PIV and spirometry patterns (obstructive and restrictive), with a normal model as the reference group. Model I was conducted for univariable regression. Model II was adjusted for age, gender and race. Model III was adjusted for age, gender, race, education level, marital status, smoke, drink, PIR, hypertension, diabetes mellitus, CAD, liver problem, arthritis, thyroid problem and cancer. Restricted cubic spline (RCS) models were conducted to further explore potential dose–response relationships between PIV and lung health, which were adjusted for age, gender, race, education level, marital status, smoke, drink, PIR and disease history. Subgroup analysis was conducted to estimate the effect of gender on association between PIV and lung health, and we got the p value for interaction.

Statistical analyses were performed using R software version 4.3.0; p value of < 0.05 was deemed statistically significant.

Results

Baseline characteristics

Finally, a total of 6,263 participants were recruited in our study (Supplementary Fig. 1). The baseline characteristics of individuals by quartiles of the PIV were displayed in Table 1, and Supplementary Table 1 showed the weighted baseline information. The participants had a median age of 65, most of them were non-Hispanic white and relatively well-educated. However, a majority of individuals were addicted to tobacco and alcohol, and combined with certain primary diseases. For the overall population, there was a definite prevalence of respiratory symptoms. 12.33% of participants reported a history of asthma. There was 20.1% of the participants being obstructive respiratory dysfunction detected by spirometry. And a minority of participants (8.53%) had a restrictive pattern on spirometry.

Table 1 Baseline characteristics of participants by quartiles of pan-immune inflammation value

Compared with the first PIV quartile, there were higher prevalences of cough, phlegm and wheeze in the fourth PIV quartile. With the increase of PIV value, the incidence of respiratory symptoms was gradually rising. There was no difference in the presence of asthma among these four groups, but the incidence of emphysema and chronic bronchitis increased progressively. FEV1, FEV1%, FVC% and FEV1/FVC were lower in the fourth PIV quartile when compared with the first PIV quartile. Interestingly, FVC had a growing tendency from the first PIV quartile to the third PIV quartile, but showed a small decline in the last group. And the fourth PIV group was the one with the highest proportion of obstructive and restrictive breathing patterns.

Association between the PIV and study outcomes

The correlation between PIV and all outcomes was shown in Supplementary Table 2. In term of respiratory symptoms, PIV was positively associated with cough and whether the covariates were adjusted or not. In brief, the prevalence of cough and phlegm increased by 2% as every 100-units increase in PIV. But PIV seemingly had no significant association with wheeze and exertional dyspnea. With respect to respiratory diseases, there was no obvious relationship between PIV and these self-reported lung diseases. In the aspect of lung function measures, only the negative correlations of PIV to FEV1% (MD, 95% CI: −0.23, −0.35 to −0.11), FVC% (MD, 95% CI: −0.16, −0.26 to −0.05) and FEV1/FVC (MD, 95% CI: −0.12, −0.18 to −0.06) were observed when there was no variable to adjust. After adjusting for age, gender and race, PIV was negatively related to all lung function measure indicators. After further adjustment for more variables, FEV1 (MD, 95% CI: −5.37, −9.10 to −1.64) and FVC (MD, 95% CI: −5.75, −10.34 to −1.15) remained correlated with PIV. And PIV were associated with obstructive respiratory model potentially to some extent (RRR, 95% CI: Model I: 1.06, 1.03 to 1.09; Model II: 1.03, 1.01 to 1.06; Model III: 1.02, 1.00 to 1.04).

Association of quartiles of PIV on study outcomes

In order to further explore the robust relationships between PIV and study outcomes, PIV was divided the into quartiles (Table 2). An SD increase in PIV100 was related with an increase in the risk of the incidence of cough (OR, 95% CI: Model I: 1.20, 1.07 to 1.34; Model II: 1.12, 1.03 to 1.25; Model III: 1.08, 1.01 to 1.10) and phlegm (OR, 95% CI: Model I: 1.16, 1.05 to 1.31; Model II: 1.11, 1.02 to 1.25; Model III: 1.07, 1.00 to 1.17). Consistent with the quartile results, the odds of cough and phlegm showed an increasing trend from first quartile to forth quartile. The presence of wheeze increased progressively as well, but this trend was not observed in every SD increase of PIV. In the respect of diagnosis of respiratory diseases, higher PIV value was a significant risk factor for emphysema and chronic bronchitis; however, this prediction did not seem to be remarkable when participants' demographic background, life style and medical history was adjusted.

Table 2 Association of quartiles of pan-immune inflammation value on study outcomes

PIV had a distinctly negative association with lung function indicators and spirometry patterns inferred by spirometry. The MD values of FEV1, FVC and FEV1/FVC from second quartile group to fourth quartile group were significantly higher than that of the first quartile group as the reference, and all p for trend was less than 0.05. Due to confounding factors, the definite correlation between PIV and FEV1 and FVC was not showed. However, the associations between independent variables and those outcomes were highlighted after adjusting for confounding variables. Furthermore, highest PIV levels were independently associated with obstructive and restrictive respiratory model (quartile 4 vs. quartile 1: RRR, 95% CI: 1.45, 1.17 to 1.80; 1.52, 1.15 to 2.03).

Linear relation of PIV and study outcomes

Figure 1 and Fig. 2 showed the RCS models of PIV and outcomes. The relationships between PIV and a majority of lung health-related outcomes were non-linear. With the gradual increase of PIV value, the OR of cough, phlegm and wheeze increased accordingly and then leveled off; unexpectedly, this trend was similar to the variation of the RRR value of obstructive and restrictive spirometry pattern. Nevertheless, the MD values of lung function indicators decreased and then leveled off.

Fig. 1

The RCS curve of the association between PIV and respiratory diseases and symptoms. a cough; b phlegm; c wheeze; d exertional dyspnea; e asthma; f emphysema; g chronic bronchitis. RCS regression was adjusted for age, gender, race, education level, marital status, smoke, drink, PIR, hypertension, diabetes mellitus, CAD, liver problem, arthritis, thyroid problem and cancer. RCS, restricted cubic spline; PIV, pan-immune inflammation value; OR, odds ratio; CI, confidence interval

Fig. 2

The RCS curve of the association between PIV and lung function. a FEV1; b FEV1%; c FVC; d FVC%; e FEV1/FVC; f obstructive spirometry pattern; g restrictive spirometry pattern. RCS regression was adjusted for age, gender, race, education level, marital status, smoke, drink, PIR, hypertension, diabetes mellitus, CAD, liver problem, arthritis, thyroid problem and cancer. RCS, restricted cubic spline; PIV, pan-immune inflammation value; MD, mean difference; RRR, relative risk ratio; CI, confidence interval; FEV1, forced expiratory volume 1st second; FEV1%, percent-predicted forced expiratory volume 1st second; FVC, forced vital capacity; FVC%, percent-predicted forced vital capacity

Subgroup analysis of PIV on study outcomes

Table 3 showed the associations of PIV with outcomes in male and female individuals. Based on the results, there were significant sex differences in the association of PIV with lung health-related indicators. However, gender had no influence on the association between PIV and exertional dyspnea, emphysema, chronic bronchitis, FEV1/FVC and obstructive spirometry pattern (p > 0.05).

Table 3 Subgroup analysis of pan-immune inflammation value on study outcomes by gender

According to Supplementary Table 3–6, significant differences in lung function and spirometry patterns were observed among subgroups with different smoking and drinking status, while certain indicators of lung function were diverse in two age subgroups (p < 0.05). In addition, drinking status had a remarkable influence on respiratory symptoms, such as cough, phlegm and wheeze. However, there was no difference in BMI subgroups.

Supplementary Table 7–13 showed that hypertension and cancer significantly affected the association between PIV and respiratory symptoms (P for interaction < 0.05). And the protective effect of PIV against chronic bronchitis in participants with liver problem was observed. Among indicators of lung function, there were significant interactions between PIV levels and other potential confounders except for liver problem. In participants without hypertension, with thyroid disease and with cancer, PIV had a hazardous effect on obstructive respiratory model. Besides, a statistically positive association was only observed in individuals with cancer on restrictive spirometry pattern.

Discussion

To investigate the association between PIV and lung health, we performed a cross-sectional analysis involving 6,263 participants from the NHANES study. Lung health encompassed the prevalence of respiratory symptoms and respiratory diseases, and lung function measurement. Our study identified that PIV was a risk factor for respiratory signs, spirometry and respiratory patterns. In brief, higher PIV was associated with poorer pulmonary outcomes. However, it is noteworthy that PIV did not show a clear association with the diagnosis of respiratory diseases. In addition, there was sexual dimorphism among these associations. PIV appears to be a promising biomarker for predicting lung health, especially among female individuals.

Inflammation plays a profound influence on the structure and function of the lungs, and each type of cell performs its function to reflect immune system status. Neutrophils are phagocytic immune cells which patrol the blood vessels and become activated in response to inflammatory substances. While recruiting to the site of injury, neutrophils release neutrophil extracellular trap and an array of proteases to maintain lung homeostasis [1, 19]. Upon recruitment to the site of injury, monocyte-derived macrophages differentiate to dampen inflammation and stimulate the release of danger signals during inflammatory conditions [1, 20]. Lymphocytes patrolling the lung via the lymph, are integral to the immune surveillance and response. Specialized subsets of T cells are mobilized to the lung following encounter with antigens, working collaboratively with the local dendritic cell network, thereby ensuring efficient triggering of both innate and memory responses [20]. Platelets are increasingly being understood as active participants in the immune response. They recruit and localize in the lung tissues and interact with various immune cells, influencing their functions during pulmonary inflammation [21].

All the time, systemic inflammatory indices to predict the occurrence and progression of respiratory diseases holds significant practical importance. These indices are not only cost-effective but also widely accessible. Chuang Cai et al. [22] found that NLR, PLR and Monocyte-to-Lymphocyte Ratio (MLR) could function as biomarkers for diagnosis and assessment of acute exacerbations among COPD patients. Junhua Ke et al. [23] demonstrated that the prevalence of asthma was found to be positively associated with NLR, PLR, MLR, systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI). A large systematic review and meta-analysis highlighted that the SII on admission was significantly associated with severe disease and mortality in patients with coronavirus disease 2019 (COVID-19) [24].

PIV, a new systemic immune-inflammatory biomarker, is composed of neutrophil, monocyte, lymphocyte, and platelet and firstly reported by Giovanni Fucà in metastatic colorectal cancer [25]. Since all pro-inflammatory cells in the blood count are counted in the formula, PIV has a robust biologic rationale as a biomarker and might potentially result in better risk stratification than other immune-inflammatory biomarkers.

In our study, we indicated that PIV was found to be positively associated with prevalence of cough, phlegm and wheeze. Notably, the incidence of respiratory symptoms in the highest PIV quartile group was distinctly greater than that in the lowest PIV quartile group. Moreover, we highlighted that there was an obvious correlation between PIV and lung function indicators. Given that the test methods for complete blood count are economical and practical, PIV would permit it possible to facilitate a rapid evaluation of lung health risks. Especially, this utility is especially valuable in economically underdeveloped regions where healthcare resources may be limited, and in circumstances where patients may not be able to undergo traditional lung function assessments.

Sexual dimorphism was shown in the subgroup analysis of the study. Our project certified that the efficacy of PIV to monitor lung health was more potent in women. Possible reasons may contribute to this condition. Firstly, studies have shown that sex hormones can affect airway tone and inflammation, and exert effects on different lung cell types [26]. Secondly, women tend to have smaller airways than men, which can affect airflow and the vulnerability to certain airway diseases [27]. Besides, lifestyle behaviors – including smoking patterns, occupational hazards and social factors – can contribute to variations in airway disease prevalence and severity between sexes [28].

There are some limitations in our study. Firstly, due to the cross-sectional nature of the data, it is hard to establish a causal relationship between PIV and lung health. Therefore, more prospectively designed studies are necessary to validate the effectiveness of PIV. Secondly, due to database limitations, the data of participants after 2012 is lacking. And several participants were excluded because of contrary to the inclusion criteria, which may have affected the results. Thirdly, information on respiratory symptoms and respiratory illness was presented as self-reported by the participants, as they are subject to over- reporting or under-reporting. Fourthly, the long-term dynamic variation of PIV cannot be accurately measured and collected in the present study. Finally, the covariates included in this study were incomplete, and some unmeasured confounding factors were not mentioned. Variables for medication use were not considered due to data limitations.

Conclusions

The study highlighted the potential connections between PIV and respiratory symptoms, respiratory diseases and lung function. Monitoring PIV level could provide valuable insights into the inflammatory status and may inform clinical approaches for managing respiratory health.