Associations of Pan-immune-inflammation Value with Asthma and Mortality in Adults: A Cross Sectional Analysis of the NHANES 1999–2018

Associations of Pan-immune-inflammation Value with Asthma and Mortality in Adults: A Cross Sectional Analysis of the NHANES 1999–2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations of Pan-immune-inflammation Value with Asthma and Mortality in Adults: A Cross Sectional Analysis of the NHANES 1999–2018 Guangji Cao, Chun Xu, Quanqing Liu, Li Liu, Hao Wen, Weicheng Lin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8807916/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background The pan-immune-inflammation value (PIV), calculated as (neutrophil count × platelet count × monocyte count) / lymphocyte count, has been linked to outcomes in various diseases, but its role in asthma remains unclear. We aimed to assess the associations of PIV with asthma prevalence and all-cause mortality in a nationally representative cohort. Methods Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 1999–2018. Logistic regression models was used to examine the association between PIV and asthma prevalence, and Cox proportional hazards models were applied to evaluate hazard ratios (HRs) for mortality in asthmatic participants. Restricted cubic splines assessed nonlinear associations, and Kaplan–Meier curves compared survival across PIV tertiles. Results A total of 38,395 participants were included, of whom 5,220 reported physician-diagnosed asthma. According to the multivariable-adjusted models, PIV was not significantly associated with asthma prevalence. However, among 5,212 asthmatic individuals with follow-up mortality data, higher PIV levels were associated with increased all-cause mortality. Compared to the lowest PIV tertile, the highest tertile showed a significantly greater risk of death (adjusted HR = 1.39; 95% CI: 1.15–1.68). A J-shaped dose–response relationship between PIV and mortality risk was observed. Kaplan–Meier curves confirmed the increased cumulative mortality in the highest PIV tertile. Conclusions Our study demonstrated that higher PIV levels were associated with an increased all-cause mortality among individuals with asthma, although no significant association was observed with asthma prevalence. Specifically, the association between PIV and all-cause mortality in asthma is non-linear, characterized by a J-shaped curve with a distinct threshold. Consequently, a higher PIV level may identify a high-risk inflammatory state among asthmatic patients, for whom intensified monitoring and management of systemic comorbidities could be beneficial. However, further well-designed prospective studies are warranted to validate and expand these findings. Pan-immune-inflammation Value Asthma Prevalence Mortality NHANES Figures Figure 1 Figure 2 Figure 3 1. Introduction Asthma is a common chronic inflammatory disease of the airways characterized by recurrent episodes of wheezing, chest tightness, shortness of breath, and coughing, accompanied by airway hyperresponsiveness and reversible airflow limitation.[ 1 ] Globally, asthma affects approximately 334 million people, and its prevalence continues to rise. The disease is responsible for an estimated 400,000 deaths annually, posing a substantial burden on global health systems.[ 2 , 3 ] In addition to direct healthcare costs—amounting to hundreds of dollars per patient each year—the indirect costs, such as productivity loss, further amplify its economic impact.[ 4 ] The pan-immune-inflammation value (PIV) is a newly identified composite biomarker that reflects systemic immune and inflammatory status by integrating peripheral blood counts of neutrophils (NE), platelets (PLT), monocytes (MONO), and lymphocytes (LY).[ 5 ] PIV has shown promising prognostic value in various malignancies[ 6 – 8 ] and has also been significantly associated with all-cause mortality in immune-mediated conditions like rheumatoid arthritis.[ 9 ] NE are known to be recruited into the lungs of patients with allergic asthma, particularly during symptomatic episodes, indicating a potentially proinflammatory and deleterious role in asthma pathogenesis.[ 10 ] PLTs contribute to asthma progression by releasing mediators such as ATP, histamine, and platelet-activating factor, which exacerbate airway inflammation.[ 11 ] MONO expressing C–C motif chemokine receptor 5 can migrate to sites of inflammation and release high levels of proinflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β), further influencing airway inflammation in asthma.[ 12 ] In addition, LYs secrete lymphokines such as granulocyte/macrophage colony-stimulating factor and IL-5, which promote eosinophil survival and adhesion.[ 13 ] Therefore, exploring the relationship between PIV and asthma may provide novel insights into the heterogeneity of asthma pathogenesis and offer a new perspective for disease severity assessment and individualized treatment strategies. Although research on PIV has advanced in various cancers and certain inflammatory diseases, large-scale population-based evidence regarding its association with asthma remains lacking. To address this gap, our study utilized data from the NHANES spanning 1999–2018 to systematically investigate, for the first time, the associations between PIV and both asthma prevalence and all-cause mortality in adults with asthma. These findings may provide epidemiological evidence supporting the involvement of systemic immune-inflammation in asthma and help generate hypotheses for future mechanistic and prognostic studies. 2. Materials and Methods 2.1 Study population The data used in this study were obtained from the NHANES conducted between 1999 and 2018. NHANES is a nationwide cross-sectional study administered by the National Center for Health Statistics (NCHS), which operates under the Centers for Disease Control and Prevention in the United States. The primary objective of NHANES is to systematically assess the nutritional status and health conditions of children and adults in the U.S. population.[ 14 ] NHANES is a publicly accessible database, with the exception of certain restricted-use data. For this study, all data were obtained from publicly available portions of NHANES and were used in accordance with relevant data use regulations. Additionally, all personal information of participants was anonymized to protect their privacy and rights. Written informed consent was obtained from all participants. The study sample was selected based on the following criteria: (1) exclusion of participants under 18 years of age; (2) exclusion of individuals with missing data on blood test results, asthma status, or mortality information; (3) exclusion of pregnant participants; and (4) exclusion of participants with missing data on relevant covariates. 2.2 Assessment of PIV PIV was calculated using complete blood count data according to the following formula: PIV = (neutrophil count × platelet count × monocyte count) / lymphocyte count. All cell counts were derived from automated hematology analyzers and expressed as ×10³/µL. 2.3 Assessment of asthma Asthma was determined using a self-reported questionnaire in NHANES. The participants were asked, “Has a doctor or other health professional ever told you that you have asthma?” Those who answered “Yes” were considered to have physician-diagnosed asthma. 2.4 Assessment of mortality Mortality data were obtained by linkage with the National Death Index. The NHANES Linked Mortality File provides follow-up information on all-cause mortality through December 31, 2019. All-cause mortality was defined as death from any cause occurring between the NHANES interview date and the end of follow-up. Follow-up time was calculated from the date of the interview to the date of death or the censoring date (December 31, 2019), whichever came first. 2.5 Assessment of covariates Covariates included demographic and health-related variables collected through questionnaires and laboratory tests: age (years), sex (male or female), race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, or Other), marital status (married, widowed, divorced, separated, never married, or living with partner), educational attainment (below high school, high school, or above high school), body mass index (BMI, kg/m²), and poverty income ratio (PIR), which is the ratio of family income to the poverty threshold. Smoking status was classified as never smoker (smoked fewer than 100 cigarettes in a lifetime), former smoker (smoked ≥ 100 cigarettes but currently not smoking), and current smoker (smoked ≥ 100 cigarettes and currently smoking).[ 15 ] Alcohol use was determined by asking participants, “Had at least 12 alcohol drinks/1 year?”. Those who answered “Yes” were classified as drinkers, and those who answered “No” as nondrinkers.[ 16 ] Hypertension: Participants were asked in the questionnaire whether they had ever been diagnosed with hypertension by a doctor or other health professional. If the answer was “Yes,” the participant was classified as having hypertension. Diabetes: Participants were asked whether they had ever been diagnosed with diabetes by a doctor or other health professional, and based on the answers, they were categorized as “Yes,” “No,” or “Borderline.” 2.6 Statistical analysis Normally distributed continuous variables were described as means ± standard errors (SEs), and continuous variables without a normal distribution were presented as medians (interquartile range [IQR]). Categorical variables were presented as numbers (percentages). Continuous variables are compared using Student’s t-test (normal distribution) or the Mann-Whitney U test (non-normal distribution). Categorical variables are compared using the chi-square test. Participants were categorized into three groups according to their PIV levels, ranging from the lowest (Tertile 1, T1) to the highest (Tertile 3, T3). In line with common practice for initial investigations of novel biomarkers in large epidemiological cohorts, tertile categorization provides a balanced approach to visualize risk gradients while maintaining sufficient sample size in each stratum for stable estimates. Three models were constructed to assess the association: Crude adjusted for none. Model 1 was adjusted for age, sex, and race. Model 2 was further adjusted for age, sex, race, body mass index, poverty income ratio, education level, marital status, smoking status, drinking status, blood pressure, and diabetes status. A multiple logistic regression model was used to determine the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of the association between PIV and the prevalence of asthma. Multiple COX regression was implemented to calculate adjusted hazard ratios (HRs) and 95% CIs in relation to all-cause mortality of participants with asthma. Schoenfeld residuals were used to verify the proportional hazards assumption. No significant violations were observed for the model overall ( P > 0.05). To explore the dose–response curves between PIV and mortality in asthma patients, restricted cubic spline(RCS) regression analysis was performed. The knots were placed at each exposure variable’s 10th, 50th and 90th percentiles. The Kaplan–Meier method was used to estimate cumulative all-cause mortality, and survival curves were generated for participants grouped into tertiles based on the PIV. Differences in survival between groups were compared using the log-rank test. We also performed stratified analyses in various subgroups. All the statistical analyses were performed using EmpowerStats software ( www.empowerstats.com ) and R software (version 4.4.1; www.r-project.org ). A two-sided P value < 0.05 was considered statistically significant. 3. Results 3.1 Characteristics of study participants Based on NHANES data from 1999 to 2018, a total of 101,316 participants were initially identified. According to the study design, we excluded individuals aged < 18 years (n = 42,112), those with missing values for PIV-related indices (n = 5,914), those lacking asthma assessment data (n = 52), and pregnant participants (n = 1,426). We further excluded individuals with missing covariate information (n = 13,417), resulting in a final analytical sample of 38,395 adults. Among them, 5,220 self-reported a physician diagnosis of asthma, while 33,175 were classified as non-asthmatic. After excluding eight asthma participants who lacked mortality follow-up data, 5,212 individuals with asthma were included in the survival analysis (Fig. 1 ). Among the 38,395 participants, 13.60% (n = 5,220) reported a history of asthma (Table 1 ). Compared with individuals without asthma, those with asthma were generally younger, more likely to be female, had higher BMI, lower PIR, and were less likely to be married. Significant differences were also observed in race, educational attainment, smoking status, and the prevalence of hypertension and diabetes (all P < 0.001). Although there was no significant difference in alcohol consumption between the two groups (P = 0.385), the asthma group showed significantly higher levels in most hematological parameters, including white blood cell (WBC) count, NE count, PLT count, and PIV (all P < 0.001). Table 1 Baseline characteristics of individuals in NHANES 1999–2018. Characteristics Total Asthma P-value No Yes N 38395 33175 5220 Age 49.95 ± 17.87 50.32 ± 17.88 47.59 ± 17.63 < 0.001 Gender < 0.001 Female 19083 (49.70%) 16101 (48.53%) 2982 (57.13%) Male 19312 (50.30%) 17074 (51.47%) 2238 (42.87%) Race < 0.001 Mexican American 6501 (16.93%) 5981 (18.03%) 520 (9.96%) Other Hispanic 2996 (7.80%) 2553 (7.70%) 443 (8.49%) Non-Hispanic White 18102 (47.15%) 15476 (46.65%) 2626 (50.31%) Non-Hispanic Black 7680 (20.00%) 6470 (19.50%) 1210 (23.18%) Other race 3116 (8.12%) 2695 (8.12%) 421 (8.07%) BMI, kg/m 2 29.03 ± 6.82 28.80 ± 6.57 30.51 ± 8.05 < 0.001 PIR 2.58 ± 1.62 2.61 ± 1.62 2.44 ± 1.64 < 0.001 Education level < 0.001 Below high school 9706 (25.28%) 8546 (25.76%) 1160 (22.22%) High school 8913 (23.21%) 7768 (23.42%) 1145 (21.93%) Above high school 19776 (51.51%) 16861 (50.82%) 2915 (55.84%) Marital status < 0.001 Married 20465 (53.30%) 18059 (54.44%) 2406 (46.09%) Widowed 3203 (8.34%) 2799 (8.44%) 404 (7.74%) Divorced 4157 (10.83%) 3446 (10.39%) 711 (13.62%) Separated 1235 (3.22%) 1041 (3.14%) 194 (3.72%) Never married 6505 (16.94%) 5423 (16.35%) 1082 (20.73%) Living with partner 2830 (7.37%) 2407 (7.26%) 423 (8.10%) Smoking < 0.001 Current smoker 8240 (21.46%) 6948 (20.94%) 1292 (24.75%) Former smoker 9804 (25.53%) 8425 (25.40%) 1379 (26.42%) Never smoker 20351 (53.00%) 17802 (53.66%) 2549 (48.83%) Drinking status 0.385 Nondrinker 13009 (33.88%) 11268 (33.97%) 1741 (33.35%) Drinker 25386 (66.12%) 21907 (66.03%) 3479 (66.65%) Blood pressure < 0.001 No 24798 (64.59%) 21716 (65.46%) 3082 (59.04%) Yes 13597 (35.41%) 11459 (34.54%) 2138 (40.96%) Diabetes < 0.001 Yes 4621 (12.04%) 3843 (11.58%) 778 (14.90%) No 32982 (85.90%) 28666 (86.41%) 4316 (82.68%) Borderline 792 (2.06%) 666 (2.01%) 126 (2.41%) WBC, 10 3 /µL 6.90 (5.70–8.40) 6.90 (5.70–8.30) 7.10 (5.90–8.60) < 0.001 LY, 10 3 /µL 2.00 (1.60–2.50) 2.00 (1.60–2.50) 2.10 (1.70–2.60) < 0.001 MONO, 10 3 /µL 0.50 (0.40–0.70) 0.50 (0.40–0.70) 0.50 (0.40–0.70) 0.007 NE, 10 3 /µL 4.00 (3.10–5.10) 4.00 (3.10–5.10) 4.10 (3.20–5.30) < 0.001 E, 10 3 /µL 0.20 (0.10–0.30) 0.20 (0.10–0.20) 0.20 (0.10–0.30) < 0.001 B, 10 3 /µL 0.00 (0.00-0.10) 0.00 (0.00-0.10) 0.00 (0.00-0.10) < 0.001 PLT, 10 3 /µL 245.00 (207.00-289.00) 244.00 (207.00-288.00) 250.00 (211.00-297.00) < 0.001 PIV, 10 3 /µL 250.80 (163.10-387.17) 249.20 (162.15-383.98) 260.23 (168.31-407.74) < 0.001 BMI, body mass index; PIR, poverty income ratio; WBC, white blood cell; LY, lymphocyte; MONO, monocyte; NE, neutrophil; E, eosinophil; B, basophil; PLT, platelet; PIV, pan-immune-inflammation value. Normally distributed continuous variables are described as means ± SEs, and continuous variables without a normal distribution are presented as medians [interquartile ranges]. Categorical variables are presented as numbers (percentages). During a median follow-up period of 103 months (IQR: 59–156), a total of 737 asthma patients (14.14%) died from all causes (Supplementary Table S1 ). Compared to survivors, deceased participants were generally older, had lower PIR, lower educational levels, and were more likely to be unmarried and have comorbidities such as hypertension and diabetes. They were also more likely to be former smokers and Non-Hispanic Whites. Regarding hematologic indicators, the death group had elevated levels of WBC, NE, MONO count, and PIV, along with lower LY count and slightly lower PLT levels (all P < 0.01). 3.2 Associations between pan immune inflammation value and the prevalence of asthma As shown in Table 2 , using T1 group as the reference, the odds ratio for asthma prevalence increased significantly in the higher PIV groups under both the crude model and Model 1. However, after further adjustment for PIR, education level, marital status, smoking, drinking, hypertension, diabetes, and BMI in Model 2, the association was weakened and no longer statistically significant (T3: OR = 1.06, 95% CI: 0.98–1.14). Table 2 OR (95% CIs) of the prevalence of asthma according to tertiles of pan-immune-inflammation value in NHANES 1999–2018. OR (95% CIs) T1 ( 331.43) P for trend Crude 1.00 [Reference] 1.08 (1.01, 1.16) 1.14 (1.06, 1.22) 0.00025 Model 1 1.00 [Reference] 1.11 (1.03, 1.20) 1.20 (1.11, 1.29) 0.000002 Model 2 1.00 [Reference] 1.05 (0.98, 1.14) 1.06 (0.98, 1.14) 0.153291 OR, odds Ratio; CI, confidence interval. Crude adjusted for none. Model 1 adjusted for age, gender, and race. Model 2 adjusted for age, sex, race, body mass index, poverty income ratio, education level, marital status, smoking, drinking status, blood pressure, and diabetes. 3.3 Associations between pan immune inflammation and all-cause mortality As shown in Table 3 , in the crude model, the risk of all-cause mortality in T3 group was significantly higher than that in T1 group. In Model 1, T3 group still showed a significantly elevated risk. In Model 2, T3 group remained significantly associated with increased mortality risk (T3: HR = 1.39, 95% CI: 1.15–1.68). Table 3 HRs (95% CIs) of all-cause mortality according to tertiles of pan-immune-inflammation value with asthma in NHANES 1999–2018. HRs (95% CIs) T1 ( 331.43) P for trend Crude 1.00 [Reference] 1.13 (0.93, 1.38) 1.99 (1.66, 2.38) < 0.0001 Model 1 1.00 [Reference] 1.09 (0.89, 1.34) 1.64 (1.36, 1.97) < 0.0001 Model 2 1.00 [Reference] 0.98 (0.80, 1.21) 1.39 (1.15, 1.68) 0.0001 HR, hazard ratio; CI, confidence interval. Crude adjusted for none. Model 1 adjusted for age, gender, and race. Model 2 adjusted for age, sex, race, body mass index, poverty income ratio, education level, marital status, smoking, drinking status, blood pressure, and diabetes. As shown in Fig. 2 , a nonlinear relationship was observed between PIV and all-cause mortality among individuals with asthma ( P = 0.000341). The curve revealed a J-shaped association, with an inflection point at a PIV value of 202.37. To validate the robustness of the findings, we conducted a sensitivity analysis by excluding participants with follow-up duration less than two years and re-estimated the Cox model. As shown in Supplementary Table S2, the significant association between PIV and all-cause mortality remained. 3.4 Kaplan–Meier curves for all-cause mortality according to pan immune inflammation tertiles in individuals with asthma We performed Kaplan–Meier survival analysis for overall mortality across the three PIV tertile groups (Fig. 3 ). The curves showed that individuals in the T3 group had significantly lower cumulative survival compared to those in the T1 and T2 groups ( P < 0.0001). The risk of all-cause mortality increased progressively with higher PIV tertiles. 3.5 Stratified analysis We further conducted stratified analysis to evaluate the associations between PIV tertiles and all-cause mortality among individuals with asthma (Supplementary Table S3). A significant association between higher PIV (T3 group) and increased mortality risk was observed among participants aged ≥ 60 years, females, Non-Hispanic Whites, those with PIR ≤ 1.0, never smokers, and individuals with diabetes. In addition, the association between PIV and mortality was particularly pronounced among those who were widowed, divorced, or separated. 4. Discussion This study is the first to systematically investigate the associations between PIV and both asthma prevalence and all-cause mortality in an adult population. Based on a cross-sectional analysis of 38,395 adults from the NHANES database spanning 1999 to 2018, our results indicated that, after adjusting for multiple potential confounders, there was no statistically significant association between PIV levels and asthma prevalence. However, among 5,212 individuals diagnosed with asthma, higher PIV levels were significantly associated with an increased risk of all-cause mortality. These findings suggest that while PIV may have limited value in asthma diagnosis, it shows a potential prognostic association, highlighting its role as a factor associated with mortality risk in asthma patients. Asthma is a heterogeneous group of diseases characterized by chronic airway inflammation, where multiple inflammatory cells collectively contribute to airway remodelling.[ 17 ] Recently, composite systemic inflammatory markers have attracted attention for their potential prognostic value in asthma. Previous studies have reported associations between asthma prognosis and biomarkers such as platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and systemic immune-inflammation index (SII).[ 18 ] Elevated SII and SIRI levels in asthma patients significantly increase the incidence of stroke, especially in individuals with concurrent obesity and dyslipidemia.[ 19 ] NLR has been linked to the severity of asthma attacks and hospitalization likelihood in children.[ 20 ] Furthermore, combinations of biomarkers including NLR, NLR-alanine aminotransferase ratio, and NLR-albumin ratio can distinguish children with worsening asthma from healthy controls.[ 21 ] Despite widespread application of these inflammatory markers in asthma research, the diagnostic and prognostic value of PIV in asthma remains unclear. Current research on PIV primarily focuses on inflammatory diseases and malignancies, showing promising prospects especially in tumor immune microenvironment evaluation and all-cause mortality risk assessment. Studies have indicated that elevated baseline PIV is significantly associated with increased risks of all-cause mortality, cardiovascular mortality, and infection-related mortality in patients undergoing peritoneal dialysis.[ 22 ] PIV is recognized as an important risk factor for all-cause mortality in antineutrophil cytoplasmic antibody-associated vasculitis.[ 23 ] It serves as a reliable marker of immune microenvironment response in tumor-infiltrating LY of rectal cancer.[ 24 ] Moreover, PIV has potential correlations with pathological complete response to neoadjuvant therapy in cancers such as breast cancer,[ 7 ] non-small cell lung cancer,[ 25 ] and esophageal squamous cell carcinoma.[ 26 ] PIV is calculated by integrating peripheral blood NE, MONO, PLT, and LY counts, reflecting both inflammatory and immune status. Its advantage lies in comprehensively assessing the impact of multiple inflammatory cell types on disease, offering greater systemic insight compared to single markers. Considering the involvement of diverse immune cells in asthma pathogenesis, PIV is promising as a marker for systemic inflammation evaluation in asthma. Asthma, characterized by diverse clinical phenotypes, involves different T-cell subsets at various disease stages.[ 27 ] Current evidence emphasizes the central role of allergen-specific T helper 2 (Th2) cells in allergic asthma pathogenesis.[ 28 ] Th2 cells produce cytokines such as IL-4, IL-5, and IL-13 that mediate Type 2-driven asthma, driving eosinophilic airway infiltration, mast cell and basophil activation, and release of inflammatory mediators, thus participating in the pathophysiology of allergic asthma. Consequently, asthma endotypes based on high and low Th2 cell activity have been widely discussed.[ 29 , 30 ] PLTs contribute to airway remodeling and hyperresponsiveness by upregulating receptors such as CD40L and RANKL, releasing cytokines including IL-33 and Dickkopf-1, interacting with dendritic cells, and forming complexes with eosinophils and NE to recruit inflammatory cells and induce adaptive immune responses.[ 11 , 31 ] NE participate in neutrophilic asthma via multiple mechanisms including thymic stromal lymphopoietin/T-helper 17 pathways, bacterial colonization/microbiome alterations, and NE extracellular traps.[ 32 ] Submucosal MONO may contribute to asthma development by producing leptin.[ 33 ] IL-33-induced mitochondrial autophagy promotes differentiation of M2 macrophages in MONO lines, a characteristic of severe asthma.[ 34 ] The components constituting PIV partially reflect the chronic inflammatory burden of the organism, thus better representing disease progression and long-term prognosis rather than short-term diagnostic events. This might explain why PIV was not associated with asthma prevalence in our analysis, but showed a significant association with mortality, suggesting its potential utility as a prognostic marker that requires prospective validation. RCS regression analysis revealed a significant nonlinear association between PIV and all-cause mortality in asthma patients. The relationship exhibited a J-shaped curve, with mortality risk being relatively stable at lower to moderate PIV levels and increasing sharply beyond an inflection point (visualized at a PIV of approximately 202.37). Notably, the lowest mortality risk was not observed in the lowest PIV group, but rather mortality risk was most stable in the moderate PIV range. These findings suggest that in this chronic inflammatory disease, a moderate immune and inflammatory response is crucial for maintaining immune homeostasis. Both excessively low and high PIV values may indicate immune dysfunction. Kaplan–Meier survival analysis further supported the significant association between PIV and mortality risk in asthma, highlighting its potential as an indicator of systemic inflammation related to long-term prognosis, which warrants further prospective confirmation. However, given the retrospective, cross-sectional design of NHANES, potential survivorship bias cannot be excluded. Asthma patients with lower PIV who survive longer may not fully represent the entire spectrum of asthma outcomes. Therefore, future prospective longitudinal studies are warranted to validate these findings and mitigate potential survivorship bias. Our study found a significantly higher prevalence of asthma among females compared to males, consistent with previous research.[ 35 ] Women are more susceptible to asthma, potentially related to hormonal fluctuations and immune response characteristics. Estrogen and progesterone levels vary across the menstrual cycle, peaking during the late follicular and mid-luteal phases; studies have observed decreased forced expiratory volume in one second and forced vital capacity as well as increased asthma-related healthcare utilization during the luteal phase.[ 36 – 38 ] During menopause, substantial hormonal fluctuations occur, with declines in lung function and increased asthma symptoms observed in menopausal women (cessation of menstruation for at least 6 months) compared to premenopausal women.[ 39 ] Estrogen and progesterone can influence immune response characteristics by acting on the pulmonary mononuclear phagocyte system, contributing to sex differences in asthma.[ 40 ] These findings highlight the importance of considering sex-related differences in inflammatory burden in clinical management. Our study has several strengths. First, PIV, as a novel composite inflammatory marker, offers a new perspective for asthma research. By integrating changes in multiple blood cell counts, PIV provides a more comprehensive reflection of systemic inflammation and immune status, which is crucial for understanding inflammatory mechanisms of asthma. Moreover, PIV calculation is based on routine blood tests that are readily available and cost-effective in clinical practice, enhancing its feasibility. However, several limitations exist. First, the optimal cut-off values of PIV remain to be established, necessitating further research to define its specific clinical application in asthma. Second, the sample consisted solely of U.S. adults, which limits the applicability of these findings to children or populations in other regions. Third, this is a retrospective observational study and thus cannot establish causality. Although confounding variables were adjusted for in this research, other potential or unmeasured confounders, such as asthma severity, medication use, inflammatory phenotype data and level of control, remain difficult to eliminate. Fourth, clinical application of PIV is constrained by variations in detection methods and lack of standardization, possibly leading to interlaboratory discrepancies. Fifth, some of the basic characteristics in this study were obtained through questionnaires or face-to-face interviews, making recall bias inevitable. Given these limitations, future research with larger, multi-center, high-quality studies are warranted to further elucidate the role of PIV in asthma pathogenesis and validate its clinical utility. 5. Conclusions Our study demonstrated that higher PIV levels were associated with an increased all-cause mortality among individuals with asthma, although no significant association was observed with asthma prevalence. Specifically, the association between PIV and all-cause mortality in asthma is non-linear, characterized by a J-shaped curve with a distinct threshold. Consequently, a higher PIV level may identify a high-risk inflammatory state among asthmatic patients, for whom intensified monitoring and management of systemic comorbidities could be beneficial. However, further well-designed prospective studies are warranted to validate and expand these findings. Abbreviations PIV Pan-immune-inflammation value NHANES National Health and Nutrition Examination Survey HRs Hazard ratios TNF-α Tumor necrosis factor - α IL Interleukin BMI Body mass index PIR Poverty income ratio ORs Odds ratios CIs Confidence intervals RCS Restricted cubic spline WBC White blood cell NE Neutrophil PLT Platelet MONO Monocyte LY Lymphocyte PLR Platelet-to-lymphocyte ratio NLR Neutrophil-to-lymphocyte ratio SIRI Systemic inflammation response index SII Systemic immune-inflammation index Th2 T helper 2 Declarations Authorship contribution statement G.C : Conceptualization, Data curation, Methodology, Formal analysis, Visualization, Writing–original draft. C.X, Q.L : Conceptualization, Methodology, Visualization, Writing–original draft. L.L : Supervision, Validation, Writing– review & editing. H.W, W.L : Visualization, Writing–original draft. W.L, D.C : Supervision, Validation, Writing– review & editing. All authors contributed to the article and approved the submitted version. Ethics approval and consent to participate All participants signed a written informed consent, and the NHANES study was approved by the NCHS and Research Ethics Review Board. Personal information was removed from the public data to ensure confidentiality. Consent for publication Not applicable. Data availability The survey data are publicly available on the internet for data users and researchers throughout the world ( www.cdc.gov/nchs/nhanes/). Funding This study was supported by Jiangxi Provincial R&D Investment Incentive Project for Research and Development Institutions in Shangrao City (2025D031); Shangrao Municipal Scientific and Technological Plan Guiding Project in the Medical and Health Field (20252CZDX30). Competing interests The authors declare that they have no competing interests. Acknowledgements Special appreciation should be given to the NHANES team and its participants. Clinical Trial Registration Not applicable. Author details 1 Department of Clinical Medicine, the First Clinical School of Guangzhou Medical University, Guangzhou, 510180, China 2 Department of Clinical Laboratory, Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Laboratory, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, China 3 Department of Medical Technology, Jiangxi Medical College, No. 399 Zhimin Avenue, Xinzhou District, Shangrao, Jiangxi Province, 334000, People’s Republic of China 4 Department of Clinical laboratory, Shangrao Central Hospital (The First Affiliated Hospital of Jiangxi Medical College, Shangrao Ophthalmic Hospital), No. 101 East Fenghuang Avenue, Xinzhou District, Shangrao, Jiangxi Province, 334000, People’s Republic of China 5 Department of Clinical laboratory, ShangRao People’s Hospital, No. 169 North Qingfeng Road, Xinzhou District, Shangrao City, Jiangxi Province, 334000, People’s Republic of China References Tian T, Xie M, Sun G. 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Front Immunol. 2022;13:846055. 10.3389/fimmu.2022.846055 . Mostafa R, Al-Diwany O, Hammad R, Hamed DH. The role of monocytes and natural killers' immunophenotypic subsets in bronchial asthma in children. J Asthma. 2023;60(2):244–54. 10.1080/02770903.2022.2043361 . Kay AB. Lymphocytes in asthma. Respir Med. 1991;85(2):87–90. 10.1016/s0954-6111(06)80283-0 . Zipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and operations, 1999–2010. Vital Health Stat. 2013;1(56):1–37. Qiu Z, Chen X, Geng T, Wan Z, Lu Q, Li L, Zhu K, Zhang X, Liu Y, Lin X, Chen L, Shan Z, Liu L, Pan A, Liu G. Associations of Serum Carotenoids With Risk of Cardiovascular Mortality Among Individuals With Type 2 Diabetes: Results From NHANES. Diabetes Care. 2022;45(6):1453–61. 10.2337/dc21-2371 . Lin Y, Lin X, Ren C, Song L, Gu C. Association of pan-immune inflammation value and lung health in adults. BMC Pulm Med. 2025;25(1):18. 10.1186/s12890-025-03493-4 . Mims JW. Asthma: definitions and pathophysiology. Int Forum Allergy Rhinol, 2015. 5 Suppl 1: pp. S2-6. 10.1002/alr.21609 Ke J, Qiu F, Fan W, Wei S. Associations of complete blood cell count-derived inflammatory biomarkers with asthma and mortality in adults: a population-based study. Front Immunol. 2023;14:1205687. 10.3389/fimmu.2023.1205687 . Cheng W, Bu X, Xu C, Wen G, Kong F, Pan H, Yang S, Chen S. Higher systemic immune-inflammation index and systemic inflammation response index levels are associated with stroke prevalence in the asthmatic population: a cross-sectional analysis of the NHANES 1999–2018. Front Immunol. 2023;14:1191130. 10.3389/fimmu.2023.1191130 . Arwas N, Shvartzman SU, Goldbart A, Bari R, Hazan I, Horev A. Golan Tripto, Elevated Neutrophil-to-Lymphocyte Ratio Is Associated with Severe Asthma Exacerbation in Children . J Clin Med. 2023;12(9). 10.3390/jcm12093312 . Pan R, Ren Y, Li Q, Zhu X, Zhang J, Cui Y, Yin H. Neutrophil-lymphocyte ratios in blood to distinguish children with asthma exacerbation from healthy subjects. Int J Immunopathol Pharmacol. 2023;37:3946320221149849. 10.1177/03946320221149849 . Zhang F, Li L, Wu X, Wen Y, Zhan X, Peng F, Wang X, Zhou Q, Feng X. Pan-immune-inflammation value is associated with poor prognosis in patients undergoing peritoneal dialysis. Ren Fail. 2023;45(1):2158103. 10.1080/0886022x.2022.2158103 . Lee LE, Ahn SS, Pyo JY, Song JJ, Park YB, Lee SW. Pan-immune-inflammation value at diagnosis independently predicts all-cause mortality in patients with antineutrophil cytoplasmic antibody-associated vasculitis. Clin Exp Rheumatol. 2021;39(2):88–93. 10.55563/clinexprheumatol/m46d0v . Wang Q, Zhong W, Xiao Y, Lin G, Lu J, Xu L, Zhang G, Liu A, Du J, Wu B. Pan-immune-inflammation value predicts immunotherapy response and reflects local antitumor immune response in rectal cancer. Cancer Sci. 2025;116(2):350–66. 10.1111/cas.16400 . Zhai WY, Duan FF, Lin YB, Lin YB, Zhao ZR, Wang JY, Rao BY, Zheng L, Long H. Pan-Immune-Inflammatory Value in Patients with Non-Small-Cell Lung Cancer Undergoing Neoadjuvant Immunochemotherapy. J Inflamm Res. 2023;16:3329–39. 10.2147/jir.S418276 . Feng J, Wang L, Yang X, Chen Q, Cheng X. Pretreatment Pan-Immune-Inflammation Value (PIV) in Predicting Therapeutic Response and Clinical Outcomes of Neoadjuvant Immunochemotherapy for Esophageal Squamous Cell Carcinoma. Ann Surg Oncol. 2024;31(1):272–83. 10.1245/s10434-023-14430-2 . Cosmi L, Liotta F, Maggi E, Romagnani S, Annunziato F. Th17 cells: new players in asthma pathogenesis. Allergy, 2011. 66(8): pp. 989 – 98. 10.1111/j.1398-9995.2011.02576.x Leόn B. T Cells in Allergic Asthma: Key Players Beyond the Th2 Pathway. Curr Allergy Asthma Rep. 2017;17(7):43. 10.1007/s11882-017-0714-1 . Boonpiyathad T, Sözener ZC, Satitsuksanoa P, Akdis CA. Immunologic mechanisms in asthma. Semin Immunol. 2019;46:101333DOI. 10.1016/j.smim.2019.101333 . Robinson D, Humbert M, Buhl R, Cruz AA, Inoue H, Korom S, Hanania NA, Nair P. Revisiting Type 2-high and Type 2-low airway inflammation in asthma: current knowledge and therapeutic implications. Clin Exp Allergy, 2017. 47(2): pp. 161–175. 10.1111/cea.12880 Xie X, Wang R, Li J, Luo L, Wen D, Zhong Y, Zhao C. Fluorocarbon chain end-capped poly(carbonate urethane)s as biomaterials: blood compatibility and chemical stability assessments. J Biomed Mater Res B Appl Biomater. 2009;89(1):223–41. 10.1002/jbm.b.31212 . Yamasaki A, Okazaki R, Harada T. Neutrophils Asthma Diagnostics (Basel). 2022;12(5). 10.3390/diagnostics12051175 . Watanabe K, Suzukawa M, Kawauchi-Watanabe S, Igarashi S, Asari I, Imoto S, Tashimo H, Fukami T, Hebisawa A, Tohma S, Nagase T, Ohta K. Leptin-producing monocytes in the airway submucosa may contribute to asthma pathogenesis. Respir Investig. 2023;61(1):5–15. 10.1016/j.resinv.2022.09.005 . Lin YC, Lin YC, Tsai ML, Tsai YG, Kuo CH, Hung CH. IL-33 regulates M1/M2 chemokine expression via mitochondrial redox-related mitophagy in human monocytes. Chem Biol Interact. 2022;359:109915. 10.1016/j.cbi.2022.109915 . Chowdhury NU, Guntur VP, Newcomb DC, Wechsler ME. Sex and gender in asthma. Eur Respir Rev. 2021;30(162). 10.1183/16000617.0067-2021 . Farha S, Asosingh K, Laskowski D, Hammel J, Dweik RA, Wiedemann HP, Erzurum SC. Effects of the menstrual cycle on lung function variables in women with asthma. Am J Respir Crit Care Med. 2009;180(4):304–10. 10.1164/rccm.200904-0497OC . Rao CK, Moore CG, Bleecker E, Busse WW, Calhoun W, Castro M, Chung KF, Erzurum SC, Israel E, Curran-Everett D, Wenzel SE. Characteristics of perimenstrual asthma and its relation to asthma severity and control: data from the Severe Asthma Research Program. Chest. 2013;143(4):984–92. 10.1378/chest.12-0973 . Tan KS, McFarlane LC, Lipworth BJ. Loss of normal cyclical beta 2 adrenoceptor regulation and increased premenstrual responsiveness to adenosine monophosphate in stable female asthmatic patients. Thorax. 1997;52(7):608–11. 10.1136/thx.52.7.608 . Real FG, Svanes C, Omenaas ER, Antò JM, Plana E, Jarvis D, Janson C, Neukirch F, Zemp E, Dratva J, Wjst M, Svanes K, Leynaert B, Sunyer J. Lung function, respiratory symptoms, and the menopausal transition. J Allergy Clin Immunol. 2008;121(1):72–e803. 10.1016/j.jaci.2007.08.057 . Chiarella SE, Cardet JC, Prakash YS. Sex, Cells, and Asthma. Mayo Clin Proc. 2021;96(7):1955–69. 10.1016/j.mayocp.2020.12.007 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers invited by journal 09 Mar, 2026 Editor invited by journal 09 Feb, 2026 Editor assigned by journal 07 Feb, 2026 Submission checks completed at journal 07 Feb, 2026 First submitted to journal 06 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8807916","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588513927,"identity":"7add2734-448d-48cc-a2b9-0b4e23b8165c","order_by":0,"name":"Guangji Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACAwbGxgcfKmzk+JmZDz4gUgtzs+GMM2nGku1syQZEamFvE+ZtO5S44TyPmQBRWszZD7YxzmA7YGx8mMGMgaHGJpqgFsuexLYHH3juyJkdZkh7wHAsLbeBoMNuMLYbzpB4ZgzUctyAseEwUVrapHkMDidubmZskyBBS8LhxA3MzGzEaQH6BRjIB9KMJQ6zMRskEOMXc/bjDx98/AeMyv7zHx98qLEhrAUVJJCmfBSMglEwCkYBLgAAjGVDQwDUR+MAAAAASUVORK5CYII=","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guangji","middleName":"","lastName":"Cao","suffix":""},{"id":588513928,"identity":"42d577e1-9d8c-4722-8a08-93733c0a0714","order_by":1,"name":"Chun Xu","email":"","orcid":"","institution":"Jiangxi Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chun","middleName":"","lastName":"Xu","suffix":""},{"id":588513929,"identity":"e045d09d-6fb6-40c9-8683-47de7d372a90","order_by":2,"name":"Quanqing 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13:56:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8807916/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8807916/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102397463,"identity":"2e67d500-4613-428e-aa08-42844df0cec0","added_by":"auto","created_at":"2026-02-11 10:17:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":289777,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study participants.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8807916/v1/2526c921888326ed94a749c2.png"},{"id":102398239,"identity":"6966f42c-12fe-4fec-acb4-0a8d6be80e43","added_by":"auto","created_at":"2026-02-11 10:21:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128357,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analyses the association of pan-immune-inflammation value with all-cause mortality in individuals with asthma.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8807916/v1/5a421e6227dc864a5efc8531.png"},{"id":102397453,"identity":"8895e89e-5b0a-4052-85b8-684c0faea8f9","added_by":"auto","created_at":"2026-02-11 10:17:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139857,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival analysis plot for all-cause mortality with pan-immune-inflammation value categories.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8807916/v1/434189391d5fb7ee2a5c5f73.png"},{"id":102399292,"identity":"5c05d1bd-87f8-49bf-9f05-cf6a15e10eb4","added_by":"auto","created_at":"2026-02-11 10:33:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1433623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8807916/v1/00e190c3-dcb6-4fd8-8dc3-a1947e57b3f5.pdf"},{"id":102339654,"identity":"5c45f653-b6bd-4424-a0f2-9f35377a1320","added_by":"auto","created_at":"2026-02-10 16:33:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":44090,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8807916/v1/c51fb913d1b01dfc82fd2c09.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations of Pan-immune-inflammation Value with Asthma and Mortality in Adults: A Cross Sectional Analysis of the NHANES 1999–2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAsthma is a common chronic inflammatory disease of the airways characterized by recurrent episodes of wheezing, chest tightness, shortness of breath, and coughing, accompanied by airway hyperresponsiveness and reversible airflow limitation.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Globally, asthma affects approximately 334\u0026nbsp;million people, and its prevalence continues to rise. The disease is responsible for an estimated 400,000 deaths annually, posing a substantial burden on global health systems.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] In addition to direct healthcare costs\u0026mdash;amounting to hundreds of dollars per patient each year\u0026mdash;the indirect costs, such as productivity loss, further amplify its economic impact.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe pan-immune-inflammation value (PIV) is a newly identified composite biomarker that reflects systemic immune and inflammatory status by integrating peripheral blood counts of neutrophils (NE), platelets (PLT), monocytes (MONO), and lymphocytes (LY).[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] PIV has shown promising prognostic value in various malignancies[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and has also been significantly associated with all-cause mortality in immune-mediated conditions like rheumatoid arthritis.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] NE are known to be recruited into the lungs of patients with allergic asthma, particularly during symptomatic episodes, indicating a potentially proinflammatory and deleterious role in asthma pathogenesis.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] PLTs contribute to asthma progression by releasing mediators such as ATP, histamine, and platelet-activating factor, which exacerbate airway inflammation.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] MONO expressing C\u0026ndash;C motif chemokine receptor 5 can migrate to sites of inflammation and release high levels of proinflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β), further influencing airway inflammation in asthma.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] In addition, LYs secrete lymphokines such as granulocyte/macrophage colony-stimulating factor and IL-5, which promote eosinophil survival and adhesion.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Therefore, exploring the relationship between PIV and asthma may provide novel insights into the heterogeneity of asthma pathogenesis and offer a new perspective for disease severity assessment and individualized treatment strategies.\u003c/p\u003e \u003cp\u003eAlthough research on PIV has advanced in various cancers and certain inflammatory diseases, large-scale population-based evidence regarding its association with asthma remains lacking. To address this gap, our study utilized data from the NHANES spanning 1999\u0026ndash;2018 to systematically investigate, for the first time, the associations between PIV and both asthma prevalence and all-cause mortality in adults with asthma. These findings may provide epidemiological evidence supporting the involvement of systemic immune-inflammation in asthma and help generate hypotheses for future mechanistic and prognostic studies.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThe data used in this study were obtained from the NHANES conducted between 1999 and 2018. NHANES is a nationwide cross-sectional study administered by the National Center for Health Statistics (NCHS), which operates under the Centers for Disease Control and Prevention in the United States. The primary objective of NHANES is to systematically assess the nutritional status and health conditions of children and adults in the U.S. population.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] NHANES is a publicly accessible database, with the exception of certain restricted-use data. For this study, all data were obtained from publicly available portions of NHANES and were used in accordance with relevant data use regulations. Additionally, all personal information of participants was anonymized to protect their privacy and rights. Written informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003eThe study sample was selected based on the following criteria: (1) exclusion of participants under 18 years of age; (2) exclusion of individuals with missing data on blood test results, asthma status, or mortality information; (3) exclusion of pregnant participants; and (4) exclusion of participants with missing data on relevant covariates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment of PIV\u003c/h2\u003e \u003cp\u003ePIV was calculated using complete blood count data according to the following formula: PIV = (neutrophil count \u0026times; platelet count \u0026times; monocyte count) / lymphocyte count. All cell counts were derived from automated hematology analyzers and expressed as \u0026times;10\u0026sup3;/\u0026micro;L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of asthma\u003c/h2\u003e \u003cp\u003eAsthma was determined using a self-reported questionnaire in NHANES. The participants were asked, \u0026ldquo;Has a doctor or other health professional ever told you that you have asthma?\u0026rdquo; Those who answered \u0026ldquo;Yes\u0026rdquo; were considered to have physician-diagnosed asthma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Assessment of mortality\u003c/h2\u003e \u003cp\u003eMortality data were obtained by linkage with the National Death Index. The NHANES Linked Mortality File provides follow-up information on all-cause mortality through December 31, 2019. All-cause mortality was defined as death from any cause occurring between the NHANES interview date and the end of follow-up. Follow-up time was calculated from the date of the interview to the date of death or the censoring date (December 31, 2019), whichever came first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Assessment of covariates\u003c/h2\u003e \u003cp\u003eCovariates included demographic and health-related variables collected through questionnaires and laboratory tests: age (years), sex (male or female), race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, or Other), marital status (married, widowed, divorced, separated, never married, or living with partner), educational attainment (below high school, high school, or above high school), body mass index (BMI, kg/m\u0026sup2;), and poverty income ratio (PIR), which is the ratio of family income to the poverty threshold. Smoking status was classified as never smoker (smoked fewer than 100 cigarettes in a lifetime), former smoker (smoked\u0026thinsp;\u0026ge;\u0026thinsp;100 cigarettes but currently not smoking), and current smoker (smoked\u0026thinsp;\u0026ge;\u0026thinsp;100 cigarettes and currently smoking).[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Alcohol use was determined by asking participants, \u0026ldquo;Had at least 12 alcohol drinks/1 year?\u0026rdquo;. Those who answered \u0026ldquo;Yes\u0026rdquo; were classified as drinkers, and those who answered \u0026ldquo;No\u0026rdquo; as nondrinkers.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Hypertension: Participants were asked in the questionnaire whether they had ever been diagnosed with hypertension by a doctor or other health professional. If the answer was \u0026ldquo;Yes,\u0026rdquo; the participant was classified as having hypertension. Diabetes: Participants were asked whether they had ever been diagnosed with diabetes by a doctor or other health professional, and based on the answers, they were categorized as \u0026ldquo;Yes,\u0026rdquo; \u0026ldquo;No,\u0026rdquo; or \u0026ldquo;Borderline.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eNormally distributed continuous variables were described as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard errors (SEs), and continuous variables without a normal distribution were presented as medians (interquartile range [IQR]). Categorical variables were presented as numbers (percentages). Continuous variables are compared using Student\u0026rsquo;s t-test (normal distribution) or the Mann-Whitney U test (non-normal distribution). Categorical variables are compared using the chi-square test. Participants were categorized into three groups according to their PIV levels, ranging from the lowest (Tertile 1, T1) to the highest (Tertile 3, T3). In line with common practice for initial investigations of novel biomarkers in large epidemiological cohorts, tertile categorization provides a balanced approach to visualize risk gradients while maintaining sufficient sample size in each stratum for stable estimates. Three models were constructed to assess the association: Crude adjusted for none. Model 1 was adjusted for age, sex, and race. Model 2 was further adjusted for age, sex, race, body mass index, poverty income ratio, education level, marital status, smoking status, drinking status, blood pressure, and diabetes status.\u003c/p\u003e \u003cp\u003eA multiple logistic regression model was used to determine the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of the association between PIV and the prevalence of asthma. Multiple COX regression was implemented to calculate adjusted hazard ratios (HRs) and 95% CIs in relation to all-cause mortality of participants with asthma. Schoenfeld residuals were used to verify the proportional hazards assumption. No significant violations were observed for the model overall (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). To explore the dose\u0026ndash;response curves between PIV and mortality in asthma patients, restricted cubic spline(RCS) regression analysis was performed. The knots were placed at each exposure variable\u0026rsquo;s 10th, 50th and 90th percentiles. The Kaplan\u0026ndash;Meier method was used to estimate cumulative all-cause mortality, and survival curves were generated for participants grouped into tertiles based on the PIV. Differences in survival between groups were compared using the log-rank test. We also performed stratified analyses in various subgroups.\u003c/p\u003e \u003cp\u003eAll the statistical analyses were performed using EmpowerStats software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.empowerstats.com\" target=\"_blank\"\u003ewww.empowerstats.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and R software (version 4.4.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.empowerstats.com\" target=\"_blank\"\u003ewww.r-project.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of study participants\u003c/h2\u003e \u003cp\u003eBased on NHANES data from 1999 to 2018, a total of 101,316 participants were initially identified. According to the study design, we excluded individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years (n\u0026thinsp;=\u0026thinsp;42,112), those with missing values for PIV-related indices (n\u0026thinsp;=\u0026thinsp;5,914), those lacking asthma assessment data (n\u0026thinsp;=\u0026thinsp;52), and pregnant participants (n\u0026thinsp;=\u0026thinsp;1,426). We further excluded individuals with missing covariate information (n\u0026thinsp;=\u0026thinsp;13,417), resulting in a final analytical sample of 38,395 adults. Among them, 5,220 self-reported a physician diagnosis of asthma, while 33,175 were classified as non-asthmatic. After excluding eight asthma participants who lacked mortality follow-up data, 5,212 individuals with asthma were included in the survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the 38,395 participants, 13.60% (n\u0026thinsp;=\u0026thinsp;5,220) reported a history of asthma (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared with individuals without asthma, those with asthma were generally younger, more likely to be female, had higher BMI, lower PIR, and were less likely to be married. Significant differences were also observed in race, educational attainment, smoking status, and the prevalence of hypertension and diabetes (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although there was no significant difference in alcohol consumption between the two groups (P\u0026thinsp;=\u0026thinsp;0.385), the asthma group showed significantly higher levels in most hematological parameters, including white blood cell (WBC) count, NE count, PLT count, and PIV (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of individuals in NHANES 1999\u0026ndash;2018.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.95\u0026thinsp;\u0026plusmn;\u0026thinsp;17.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.32\u0026thinsp;\u0026plusmn;\u0026thinsp;17.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.59\u0026thinsp;\u0026plusmn;\u0026thinsp;17.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19083 (49.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16101 (48.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2982 (57.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19312 (50.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17074 (51.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2238 (42.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6501 (16.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5981 (18.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e520 (9.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2996 (7.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2553 (7.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443 (8.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18102 (47.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15476 (46.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2626 (50.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7680 (20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6470 (19.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1210 (23.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3116 (8.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2695 (8.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e421 (8.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.03\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.80\u0026thinsp;\u0026plusmn;\u0026thinsp;6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.51\u0026thinsp;\u0026plusmn;\u0026thinsp;8.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9706 (25.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8546 (25.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1160 (22.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8913 (23.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7768 (23.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1145 (21.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19776 (51.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16861 (50.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2915 (55.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20465 (53.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18059 (54.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2406 (46.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3203 (8.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2799 (8.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e404 (7.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4157 (10.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3446 (10.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e711 (13.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1235 (3.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1041 (3.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194 (3.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6505 (16.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5423 (16.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1082 (20.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2830 (7.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2407 (7.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e423 (8.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8240 (21.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6948 (20.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1292 (24.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9804 (25.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8425 (25.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1379 (26.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20351 (53.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17802 (53.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2549 (48.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNondrinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13009 (33.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11268 (33.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1741 (33.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25386 (66.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21907 (66.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3479 (66.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24798 (64.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21716 (65.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3082 (59.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13597 (35.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11459 (34.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2138 (40.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4621 (12.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3843 (11.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e778 (14.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32982 (85.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28666 (86.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4316 (82.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorderline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e792 (2.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e666 (2.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (2.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.90 (5.70\u0026ndash;8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.90 (5.70\u0026ndash;8.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.10 (5.90\u0026ndash;8.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLY, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00 (1.60\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 (1.60\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.10 (1.70\u0026ndash;2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMONO, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50 (0.40\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50 (0.40\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.40\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNE, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00 (3.10\u0026ndash;5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00 (3.10\u0026ndash;5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.10 (3.20\u0026ndash;5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20 (0.10\u0026ndash;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20 (0.10\u0026ndash;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20 (0.10\u0026ndash;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00-0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (0.00-0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (0.00-0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245.00 (207.00-289.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244.00 (207.00-288.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250.00 (211.00-297.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIV, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250.80 (163.10-387.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249.20 (162.15-383.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e260.23 (168.31-407.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI, body mass index; PIR, poverty income ratio; WBC, white blood cell; LY, lymphocyte; MONO, monocyte; NE, neutrophil; E, eosinophil; B, basophil; PLT, platelet; PIV, pan-immune-inflammation value. Normally distributed continuous variables are described as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SEs, and continuous variables without a normal distribution are presented as medians [interquartile ranges]. Categorical variables are presented as numbers (percentages).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring a median follow-up period of 103 months (IQR: 59\u0026ndash;156), a total of 737 asthma patients (14.14%) died from all causes (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Compared to survivors, deceased participants were generally older, had lower PIR, lower educational levels, and were more likely to be unmarried and have comorbidities such as hypertension and diabetes. They were also more likely to be former smokers and Non-Hispanic Whites. Regarding hematologic indicators, the death group had elevated levels of WBC, NE, MONO count, and PIV, along with lower LY count and slightly lower PLT levels (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Associations between pan immune inflammation value and the prevalence of asthma\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, using T1 group as the reference, the odds ratio for asthma prevalence increased significantly in the higher PIV groups under both the crude model and Model 1. However, after further adjustment for PIR, education level, marital status, smoking, drinking, hypertension, diabetes, and BMI in Model 2, the association was weakened and no longer statistically significant (T3: OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 0.98\u0026ndash;1.14).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOR (95% CIs) of the prevalence of asthma according to tertiles of pan-immune-inflammation value in NHANES 1999\u0026ndash;2018.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR (95% CIs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1 (\u0026lt;\u0026thinsp;190.89)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2 (190.89-331.43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT3 (\u0026gt;\u0026thinsp;331.43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08 (1.01, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14 (1.06, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11 (1.03, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20 (1.11, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.98, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.98, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.153291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOR, odds Ratio; CI, confidence interval. Crude adjusted for none. Model 1 adjusted for age, gender, and race. Model 2 adjusted for age, sex, race, body mass index, poverty income ratio, education level, marital status, smoking, drinking status, blood pressure, and diabetes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Associations between pan immune inflammation and all-cause mortality\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, in the crude model, the risk of all-cause mortality in T3 group was significantly higher than that in T1 group. In Model 1, T3 group still showed a significantly elevated risk. In Model 2, T3 group remained significantly associated with increased mortality risk (T3: HR\u0026thinsp;=\u0026thinsp;1.39, 95% CI: 1.15\u0026ndash;1.68).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHRs (95% CIs) of all-cause mortality according to tertiles of pan-immune-inflammation value with asthma in NHANES 1999\u0026ndash;2018.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRs (95% CIs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1 (\u0026lt;\u0026thinsp;190.89)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2 (190.89-331.43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT3 (\u0026gt;\u0026thinsp;331.43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.93, 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.99 (1.66, 2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.89, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.64 (1.36, 1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.80, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.39 (1.15, 1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eHR, hazard ratio; CI, confidence interval. Crude adjusted for none. Model 1 adjusted for age, gender, and race. Model 2 adjusted for age, sex, race, body mass index, poverty income ratio, education level, marital status, smoking, drinking status, blood pressure, and diabetes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, a nonlinear relationship was observed between PIV and all-cause mortality among individuals with asthma (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000341). The curve revealed a J-shaped association, with an inflection point at a PIV value of 202.37. To validate the robustness of the findings, we conducted a sensitivity analysis by excluding participants with follow-up duration less than two years and re-estimated the Cox model. As shown in Supplementary Table S2, the significant association between PIV and all-cause mortality remained.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Kaplan\u0026ndash;Meier curves for all-cause mortality according to pan immune inflammation tertiles in individuals with asthma\u003c/h2\u003e \u003cp\u003eWe performed Kaplan\u0026ndash;Meier survival analysis for overall mortality across the three PIV tertile groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The curves showed that individuals in the T3 group had significantly lower cumulative survival compared to those in the T1 and T2 groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The risk of all-cause mortality increased progressively with higher PIV tertiles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Stratified analysis\u003c/h2\u003e \u003cp\u003eWe further conducted stratified analysis to evaluate the associations between PIV tertiles and all-cause mortality among individuals with asthma (Supplementary Table S3). A significant association between higher PIV (T3 group) and increased mortality risk was observed among participants aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, females, Non-Hispanic Whites, those with PIR\u0026thinsp;\u0026le;\u0026thinsp;1.0, never smokers, and individuals with diabetes. In addition, the association between PIV and mortality was particularly pronounced among those who were widowed, divorced, or separated.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is the first to systematically investigate the associations between PIV and both asthma prevalence and all-cause mortality in an adult population. Based on a cross-sectional analysis of 38,395 adults from the NHANES database spanning 1999 to 2018, our results indicated that, after adjusting for multiple potential confounders, there was no statistically significant association between PIV levels and asthma prevalence. However, among 5,212 individuals diagnosed with asthma, higher PIV levels were significantly associated with an increased risk of all-cause mortality. These findings suggest that while PIV may have limited value in asthma diagnosis, it shows a potential prognostic association, highlighting its role as a factor associated with mortality risk in asthma patients.\u003c/p\u003e \u003cp\u003eAsthma is a heterogeneous group of diseases characterized by chronic airway inflammation, where multiple inflammatory cells collectively contribute to airway remodelling.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Recently, composite systemic inflammatory markers have attracted attention for their potential prognostic value in asthma. Previous studies have reported associations between asthma prognosis and biomarkers such as platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and systemic immune-inflammation index (SII).[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Elevated SII and SIRI levels in asthma patients significantly increase the incidence of stroke, especially in individuals with concurrent obesity and dyslipidemia.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] NLR has been linked to the severity of asthma attacks and hospitalization likelihood in children.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Furthermore, combinations of biomarkers including NLR, NLR-alanine aminotransferase ratio, and NLR-albumin ratio can distinguish children with worsening asthma from healthy controls.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eDespite widespread application of these inflammatory markers in asthma research, the diagnostic and prognostic value of PIV in asthma remains unclear. Current research on PIV primarily focuses on inflammatory diseases and malignancies, showing promising prospects especially in tumor immune microenvironment evaluation and all-cause mortality risk assessment. Studies have indicated that elevated baseline PIV is significantly associated with increased risks of all-cause mortality, cardiovascular mortality, and infection-related mortality in patients undergoing peritoneal dialysis.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] PIV is recognized as an important risk factor for all-cause mortality in antineutrophil cytoplasmic antibody-associated vasculitis.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] It serves as a reliable marker of immune microenvironment response in tumor-infiltrating LY of rectal cancer.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Moreover, PIV has potential correlations with pathological complete response to neoadjuvant therapy in cancers such as breast cancer,[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] non-small cell lung cancer,[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and esophageal squamous cell carcinoma.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003cp\u003ePIV is calculated by integrating peripheral blood NE, MONO, PLT, and LY counts, reflecting both inflammatory and immune status. Its advantage lies in comprehensively assessing the impact of multiple inflammatory cell types on disease, offering greater systemic insight compared to single markers. Considering the involvement of diverse immune cells in asthma pathogenesis, PIV is promising as a marker for systemic inflammation evaluation in asthma. Asthma, characterized by diverse clinical phenotypes, involves different T-cell subsets at various disease stages.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Current evidence emphasizes the central role of allergen-specific T helper 2 (Th2) cells in allergic asthma pathogenesis.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Th2 cells produce cytokines such as IL-4, IL-5, and IL-13 that mediate Type 2-driven asthma, driving eosinophilic airway infiltration, mast cell and basophil activation, and release of inflammatory mediators, thus participating in the pathophysiology of allergic asthma. Consequently, asthma endotypes based on high and low Th2 cell activity have been widely discussed.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] PLTs contribute to airway remodeling and hyperresponsiveness by upregulating receptors such as CD40L and RANKL, releasing cytokines including IL-33 and Dickkopf-1, interacting with dendritic cells, and forming complexes with eosinophils and NE to recruit inflammatory cells and induce adaptive immune responses.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] NE participate in neutrophilic asthma via multiple mechanisms including thymic stromal lymphopoietin/T-helper 17 pathways, bacterial colonization/microbiome alterations, and NE extracellular traps.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] Submucosal MONO may contribute to asthma development by producing leptin.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] IL-33-induced mitochondrial autophagy promotes differentiation of M2 macrophages in MONO lines, a characteristic of severe asthma.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] The components constituting PIV partially reflect the chronic inflammatory burden of the organism, thus better representing disease progression and long-term prognosis rather than short-term diagnostic events. This might explain why PIV was not associated with asthma prevalence in our analysis, but showed a significant association with mortality, suggesting its potential utility as a prognostic marker that requires prospective validation.\u003c/p\u003e \u003cp\u003eRCS regression analysis revealed a significant nonlinear association between PIV and all-cause mortality in asthma patients. The relationship exhibited a J-shaped curve, with mortality risk being relatively stable at lower to moderate PIV levels and increasing sharply beyond an inflection point (visualized at a PIV of approximately 202.37). Notably, the lowest mortality risk was not observed in the lowest PIV group, but rather mortality risk was most stable in the moderate PIV range. These findings suggest that in this chronic inflammatory disease, a moderate immune and inflammatory response is crucial for maintaining immune homeostasis. Both excessively low and high PIV values may indicate immune dysfunction. Kaplan\u0026ndash;Meier survival analysis further supported the significant association between PIV and mortality risk in asthma, highlighting its potential as an indicator of systemic inflammation related to long-term prognosis, which warrants further prospective confirmation. However, given the retrospective, cross-sectional design of NHANES, potential survivorship bias cannot be excluded. Asthma patients with lower PIV who survive longer may not fully represent the entire spectrum of asthma outcomes. Therefore, future prospective longitudinal studies are warranted to validate these findings and mitigate potential survivorship bias.\u003c/p\u003e \u003cp\u003eOur study found a significantly higher prevalence of asthma among females compared to males, consistent with previous research.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Women are more susceptible to asthma, potentially related to hormonal fluctuations and immune response characteristics. Estrogen and progesterone levels vary across the menstrual cycle, peaking during the late follicular and mid-luteal phases; studies have observed decreased forced expiratory volume in one second and forced vital capacity as well as increased asthma-related healthcare utilization during the luteal phase.[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] During menopause, substantial hormonal fluctuations occur, with declines in lung function and increased asthma symptoms observed in menopausal women (cessation of menstruation for at least 6 months) compared to premenopausal women.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] Estrogen and progesterone can influence immune response characteristics by acting on the pulmonary mononuclear phagocyte system, contributing to sex differences in asthma.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] These findings highlight the importance of considering sex-related differences in inflammatory burden in clinical management.\u003c/p\u003e \u003cp\u003eOur study has several strengths. First, PIV, as a novel composite inflammatory marker, offers a new perspective for asthma research. By integrating changes in multiple blood cell counts, PIV provides a more comprehensive reflection of systemic inflammation and immune status, which is crucial for understanding inflammatory mechanisms of asthma. Moreover, PIV calculation is based on routine blood tests that are readily available and cost-effective in clinical practice, enhancing its feasibility. However, several limitations exist. First, the optimal cut-off values of PIV remain to be established, necessitating further research to define its specific clinical application in asthma. Second, the sample consisted solely of U.S. adults, which limits the applicability of these findings to children or populations in other regions. Third, this is a retrospective observational study and thus cannot establish causality. Although confounding variables were adjusted for in this research, other potential or unmeasured confounders, such as asthma severity, medication use, inflammatory phenotype data and level of control, remain difficult to eliminate. Fourth, clinical application of PIV is constrained by variations in detection methods and lack of standardization, possibly leading to interlaboratory discrepancies. Fifth, some of the basic characteristics in this study were obtained through questionnaires or face-to-face interviews, making recall bias inevitable. Given these limitations, future research with larger, multi-center, high-quality studies are warranted to further elucidate the role of PIV in asthma pathogenesis and validate its clinical utility.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur study demonstrated that higher PIV levels were associated with an increased all-cause mortality among individuals with asthma, although no significant association was observed with asthma prevalence. Specifically, the association between PIV and all-cause mortality in asthma is non-linear, characterized by a J-shaped curve with a distinct threshold. Consequently, a higher PIV level may identify a high-risk inflammatory state among asthmatic patients, for whom intensified monitoring and management of systemic comorbidities could be beneficial. However, further well-designed prospective studies are warranted to validate and expand these findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePIV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Pan-immune-inflammation value\u003c/p\u003e\n\u003cp\u003eNHANES \u0026nbsp; National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eHRs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hazard ratios\u003c/p\u003e\n\u003cp\u003eTNF-α\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003eTumor necrosis factor\u003c/strong\u003e-\u003c/strong\u003eα\u003c/p\u003e\n\u003cp\u003eIL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interleukin\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body mass index\u003c/p\u003e\n\u003cp\u003ePIR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Poverty income ratio\u003c/p\u003e\n\u003cp\u003eORs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Odds ratios\u003c/p\u003e\n\u003cp\u003eCIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Confidence intervals\u003c/p\u003e\n\u003cp\u003eRCS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Restricted cubic spline\u003c/p\u003e\n\u003cp\u003eWBC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;White blood cell\u003c/p\u003e\n\u003cp\u003eNE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Neutrophil\u003c/p\u003e\n\u003cp\u003ePLT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Platelet\u003c/p\u003e\n\u003cp\u003eMONO \u0026nbsp; \u0026nbsp; \u0026nbsp;Monocyte\u003c/p\u003e\n\u003cp\u003eLY \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lymphocyte\u003c/p\u003e\n\u003cp\u003ePLR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Platelet-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eNLR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Neutrophil-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eSIRI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Systemic inflammation response index\u003c/p\u003e\n\u003cp\u003eSII \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Systemic immune-inflammation index\u003c/p\u003e\n\u003cp\u003eTh2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;T helper 2\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG.C\u003c/strong\u003e: Conceptualization, Data curation, Methodology, Formal analysis, Visualization, Writing\u0026ndash;original draft.\u003cstrong\u003e\u0026nbsp;C.X, Q.L\u003c/strong\u003e: Conceptualization, Methodology, Visualization, Writing\u0026ndash;original draft. \u003cstrong\u003eL.L\u003c/strong\u003e: Supervision, Validation, Writing\u0026ndash; review \u0026amp; editing. \u003cstrong\u003eH.W, W.L\u003c/strong\u003e: Visualization, Writing\u0026ndash;original draft. \u003cstrong\u003eW.L, D.C\u003c/strong\u003e: Supervision, Validation, Writing\u0026ndash; review \u0026amp; editing. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants signed a written informed consent, and the NHANES study was approved by the NCHS and Research Ethics Review Board. Personal information was removed from the public data to ensure confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey data are publicly available on the internet for data users and researchers throughout the world ( www.cdc.gov/nchs/nhanes/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Jiangxi Provincial R\u0026amp;D Investment Incentive Project for Research and Development Institutions in Shangrao City (2025D031); Shangrao Municipal Scientific and Technological Plan Guiding Project in the Medical and Health Field (20252CZDX30).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial appreciation should be given to the NHANES team and its participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Department of Clinical Medicine, the First Clinical School of Guangzhou Medical University, Guangzhou, 510180, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Department of Clinical Laboratory, Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Laboratory, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Department of Medical Technology, Jiangxi Medical College, No. 399 Zhimin Avenue, Xinzhou District, Shangrao, Jiangxi Province, 334000, People\u0026rsquo;s Republic of China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003e Department of Clinical laboratory, Shangrao Central Hospital (The First Affiliated Hospital of Jiangxi Medical College, Shangrao Ophthalmic Hospital), No. 101 East Fenghuang Avenue, Xinzhou District, Shangrao, Jiangxi Province, 334000, People\u0026rsquo;s Republic of China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003e Department of Clinical laboratory, ShangRao People\u0026rsquo;s Hospital, No. 169 North Qingfeng Road, Xinzhou District, Shangrao City, Jiangxi Province, 334000, People\u0026rsquo;s Republic of China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTian T, Xie M, Sun G. 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Mayo Clin Proc. 2021;96(7):1955\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mayocp.2020.12.007\u003c/span\u003e\u003cspan address=\"10.1016/j.mayocp.2020.12.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pan-immune-inflammation Value, Asthma, Prevalence, Mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-8807916/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8807916/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe pan-immune-inflammation value (PIV), calculated as (neutrophil count \u0026times; platelet count \u0026times; monocyte count) / lymphocyte count, has been linked to outcomes in various diseases, but its role in asthma remains unclear. We aimed to assess the associations of PIV with asthma prevalence and all-cause mortality in a nationally representative cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were obtained from the National Health and Nutrition Examination Survey (NHANES) 1999\u0026ndash;2018. Logistic regression models was used to examine the association between PIV and asthma prevalence, and Cox proportional hazards models were applied to evaluate hazard ratios (HRs) for mortality in asthmatic participants. Restricted cubic splines assessed nonlinear associations, and Kaplan\u0026ndash;Meier curves compared survival across PIV tertiles.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 38,395 participants were included, of whom 5,220 reported physician-diagnosed asthma. According to the multivariable-adjusted models, PIV was not significantly associated with asthma prevalence. However, among 5,212 asthmatic individuals with follow-up mortality data, higher PIV levels were associated with increased all-cause mortality. Compared to the lowest PIV tertile, the highest tertile showed a significantly greater risk of death (adjusted HR\u0026thinsp;=\u0026thinsp;1.39; 95% CI: 1.15\u0026ndash;1.68). A J-shaped dose\u0026ndash;response relationship between PIV and mortality risk was observed. Kaplan\u0026ndash;Meier curves confirmed the increased cumulative mortality in the highest PIV tertile.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study demonstrated that higher PIV levels were associated with an increased all-cause mortality among individuals with asthma, although no significant association was observed with asthma prevalence. Specifically, the association between PIV and all-cause mortality in asthma is non-linear, characterized by a J-shaped curve with a distinct threshold. Consequently, a higher PIV level may identify a high-risk inflammatory state among asthmatic patients, for whom intensified monitoring and management of systemic comorbidities could be beneficial. However, further well-designed prospective studies are warranted to validate and expand these findings.\u003c/p\u003e","manuscriptTitle":"Associations of Pan-immune-inflammation Value with Asthma and Mortality in Adults: A Cross Sectional Analysis of the NHANES 1999–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 16:33:31","doi":"10.21203/rs.3.rs-8807916/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-10T12:25:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54877773287676189896616651602008327067","date":"2026-03-10T11:45:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-09T06:23:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-09T18:41:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-07T14:15:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-07T14:15:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-02-06T13:29:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67e3bb1c-4f2b-4c9e-83e6-eaa0686fcbab","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T06:38:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 16:33:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8807916","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8807916","identity":"rs-8807916","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}