Association of neutrophil percentage-to-albumin ratio with all-cause and respiratory disease-related mortality in US adults with asthma: results from NHANES 1999-2018

Association of neutrophil percentage-to-albumin ratio with all-cause and respiratory disease-related mortality in US adults with asthma: results from 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 Association of neutrophil percentage-to-albumin ratio with all-cause and respiratory disease-related mortality in US adults with asthma: results from NHANES 1999-2018 Dan Wang, Rui Su, Ruina Li, Xiaofeng Li, Hui Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5650982/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jul, 2025 Read the published version in BMC Pulmonary Medicine → Version 1 posted 12 You are reading this latest preprint version Abstract Background: Systemic inflammation and immune dysregulation may be associated with asthma onset and progression. Neutrophil percentage-to-albumin ratio (NPAR) is a novel marker of systemic inflammation. We aimed to investigate the association of NPAR with all-cause and respiratory disease-related mortality in adults with asthma through NHANES 1999-2018. Methods: NPAR was calculated by neutrophil percentage (% of total white blood cell count) × 100/serum albumin (g/dl). Asthma was diagnosed by standardized questionnaire. Mortality was obtained by prospective matching with the National Death Index. Multivariate Cox proportional hazards regression analysis was used to explore these associations. Results: A total of 5042 adults with asthma were included. After a median follow-up duration of 104 months, 697 participants died, of which 101 were respiratory disease-related deaths. In fully adjusted models, NPAR was significantly and positively associated with both all-cause and respiratory disease-related mortality (hazard ratios [HR] of 1.126 and 1.194, respectively, both p < 0.05). Compared to Q1, NPAR at Q4 was associated with significantly increased all-cause and respiratory disease-related mortality (all-cause: HR 2.112, p < 0.0001; respiratory disease-related: HR 2.595, p = 0.013). NPAR was nonlinearly associated with all-cause mortality, with an inflection point of 13.76. This association was significant only after the inflection point. The effect of NPAR on all-cause mortality was more pronounced in those <60 years of age. Conclusions: Higher NPAR was associated with increased all-cause and respiratory disease mortality in US adults with asthma. Further exploration of the prognostic potential and underlying mechanisms of NPAR is needed. Clinical trial number Not applicable. systemic inflammation asthma neutrophil albumin mortality Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Asthma is a chronic inflammatory airway disease with marked heterogeneity and complex pathophysiologic manifestations[ 1 ]. It is usually characterized by variable expiratory airflow limitation and airway hyperresponsiveness, and consists of respiratory symptoms such as wheezing, shortness of breath, chest tightness, and cough that change and worsen over time[ 2 ]. While the age-standardized prevalence and mortality rates for asthma have declined over the past 30 years, the disease burden associated with asthma continues to rise[ 3 ]. In 2019, there were approximately 262 million cases of asthma worldwide, representing the most common chronic respiratory disease[ 4 , 5 ]. Asthma affects more than 25 million children and adults in the U.S., and about 8.0% of U.S. adults currently have asthma[ 6 , 7 ]. The consequences of asthma exacerbation can be fatal. In the U.S., an estimated 25,000 to 50,000 asthma patients are admitted to respiratory intensive care units each year[ 8 ]. A meta-analysis showed that asthma is associated with an increased risk of cardiovascular disease (CVD) and all-cause mortality[ 9 ]. Exploring modifiable risk factors associated with the development or prognosis of asthma has important clinical relevance. Inflammation plays an important role in the pathogenesis and progression of asthma. The pathogenesis of asthma involves the immune system and includes the interaction of multiple inflammatory cells, inflammatory mediators, and cytokines leading to airway inflammation[ 10 ]. The role of systemic inflammation in asthma progression remains largely unknown[ 11 ]. Blood neutrophils as common immune cells are suggested to be involved in the immunologic mechanisms of asthma, especially in type 2 low asthma[ 12 ]. Accumulation of neutrophils and release of neutrophil extracellular traps may lead to chronic airway inflammation associated with worsening asthma[ 13 ]. In addition, multiple cytokines such as interleukin-17 inflammasome activation may play a role in severe neutrophilic asthma[ 14 ]. Clinical studies have demonstrated that elevated blood neutrophils are associated with unique inflammatory patterns and clinical features in adult asthma[ 15 ]. In addition, albumin, an important marker of immunity and nutrition, may be involved in asthma development. Observational studies have shown that serum albumin is associated with blood eosinophil counts and mortality in adults with asthma[ 16 , 17 ]. Recently, a novel neutrophil- and albumin-based marker of systemic inflammation has been proposed, the neutrophil percentage-to-albumin ratio (NPAR)[ 18 ]. Compared to individual inflammatory markers, NPAR combines blood neutrophils and albumin and may provide a more complete picture of systemic inflammation levels. Accumulating clinical evidence suggests that NPAR is associated with prognosis in a variety of populations, including the general population[ 19 ], chronic obstructive pulmonary disease (COPD)[ 20 ], CVD[ 21 ], sepsis[ 22 ], and critically ill populations[ 23 ]. However, the association of NPAR with prognosis in adult asthma populations remains unknown. Elucidating this association could help reveal the potential value of NPAR as a novel systemic inflammatory marker in mortality prediction and risk stratification in adult asthma populations. In this study, we used population-level data from the National Health and Nutrition Examination Survey (NHANES) to explore the association of NPAR with all-cause and respiratory disease-related mortality in adult asthma participants. We aimed to comprehensively explore whether NPAR has potential prognostic value in the adult asthma population and provide new insights for future clinical prognostic marker studies. 2. METHODS Study design and population NHANES is a nationally representative, population-based, continuous cross-sectional survey conducted by the National Center for Health Statistics (NCHS) for the purpose of assessing the health and nutritional status of noninstitutionalized populations in the U.S. The database encompasses a comprehensive array of questionnaires, physical examinations, and laboratory tests. A stratified multistage probability sampling design with specific weights was employed to guarantee a representative sample. Approval was granted by the NCHS Ethics Review Committee for all survey cycles and written informed consent prior to participation was obtained from all subjects. The study population selection process was presented in Fig. 1 . A total of 13,773 asthma participants from NHANES 1999–2018 were included. We sequentially excluded participants who were < 20 years of age (n = 6480), missing NPAR data (n = 788), missing survival data (n = 11), and missing significant covariates (n = 1452). A total of 5042 eligible asthma participants were included in the analysis. Calculation of NPAR Based on previous studies, the formula for calculating NPAR is neutrophil percentage (of total white blood cell count) (%) × 100 /serum albumin concentration (g/dl)[ 24 ]. Neutrophil percentages were obtained according to the parameters of complete blood count (CBC). CBC was obtained based on the Beckman Coulter counting and sieving method combined with an automated dilution and mixing device for sample processing and a single-beam photometer for hemoglobin determination. Serum albumin was obtained according to the standard biochemical test i.e. dye bromocresol purple method. Diagnosis of asthma We assessed participants' asthma status as previously described in the relevant literature. In the NHANES Medical Conditions Questionnaire, participants were asked “Has a doctor or other health professional ever told you that you have asthma?”. A positive response to this question indicated the presence of asthma[ 25 ]. Mortality data Baseline asthma participants from NHANES 1999–2018 were followed until December 31, 2019. Mortality data were obtained by prospectively matching death records from the National Death Index database. Respiratory disease mortality information was derived from the International Classification of Diseases, 10th Revision codes including J09-J18 and J40-J47[ 26 ]. Covariates Several important covariates were included in the study to correct for potential confounding effects. These covariates included age (in years), sex (male or female), race/ethnicity (non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic, or other race), marital status (non-single or single), household poverty income ratio (PIR), education level (less than high school, high school, or greater than high school education), smoking (never/former/current), alcohol consumption (never/former/mild/moderate/heavy), physical activity (no/moderate/vigorous), body mass index (BMI), total dietary energy intake (kcal/d), diabetes (yes or no), and hypertension (yes or no). Demographic characteristics, smoking, alcohol consumption, and physical activity were self-reported from standardized questionnaires. Those who smoked no more than 100 cigarettes in their lifetime were considered never smokers, those who smoked at least 100 cigarettes but do not smoke now were former smokers, and those who still smoke now were current smokers[ 27 ]. Alcohol consumption was categorized as never drinkers (< 12 drinks in a lifetime), former drinkers (≥ 12 drinks in a lifetime but no alcohol in the last year), and current drinkers (subdivided into mild, moderate, and heavy by alcohol consumption criteria)[ 28 ]. Intensity of physical activity participation was assessed based on self-report on the Physical Activity Questionnaire[ 29 ]. BMI was calculated as weight (kg) divided by the square of height (m) as measured at the mobile examination center. Total energy intake was derived from dietary interview data[ 30 ]. Diabetes was assessed by self-reported history of diabetes or meeting the American Diabetes Association's criteria for laboratory parameters[ 31 ]. Hypertension was evaluated through self-report, the measurement of blood pressure at ≥ 140/90 mmHg, or the use of anti-hypertensive medications[ 32 ]. Statistical analysis In view of the complexity of the NHANES study design, all statistical analyses were appropriately weighted in accordance with the NHANES analysis guidelines. In the baseline analysis, the asthmatic participants were grouped according to NPAR quartiles. Continuous variables were analyzed using a weighted one-way analysis of variance (ANOVA) and expressed as the mean ± standard error. Categorical variables were evaluated using a weighted chi-square test and reported as the number (percentage). To examine the impact of NPAR on survival in individuals with asthma, we employed Kaplan-Meier (KM) survival analysis. We constructed multivariate Cox proportional hazards regression models for exploring the association of NPAR with all-cause and respiratory disease-related mortality in the asthma population. The crude model did not adjust for any covariates; Model 1 partially adjusted for age, sex, race/ethnicity, education level, PIR, and marital status; and Model 2 continued to adjust for smoking, alcohol consumption, physical activity, BMI, total energy intake, diabetes, and hypertension. Restricted cubic spline (RCS) models were employed to investigate whether these associations were nonlinear and to elucidate differences in associations before and after the inflection point through threshold effects analysis. To ascertain whether the observed associations between NPAR and all-cause mortality in participants with asthma remained consistent within defined subgroups, stratified analyses were conducted. A statistically significant result from the interaction analysis indicated that the variable was a potential effect modifier. Finally, we performed a sensitivity analysis. To exclude the effect of short-term follow-up, we excluded asthmatic participants with a follow-up length of less than two years to verify the reliability of the findings. The data were analyzed employing the R software (version 4.2.3), and results were deemed statistically significant when the p-value was less than 0.05. 3. RESULTS Baseline characteristics A total of 5042 asthmatic participants were enrolled with a mean age of 45.019 years. The quartile distributions of NPAR were Q1 (< 12.1), Q2 (12.1–13.8), Q3 (13.8–15.5), and Q4 (≥ 15.5). As NPAR quartiles increased, asthma participants were older, had lower PIR and energy intake, higher BMI, and were more likely to be female, non-Hispanic White, former alcohol drinkers, have no/moderate physical activity participation, and have diabetes and hypertension (Table 1 ) . Table 1 Baseline analysis of participants with asthma according to NPAR quartiles. Variables Total (n = 5042) Q1 (n = 1263) Q2 (n = 1258) Q3 (n = 1266) Q4 (n = 1255) P-value Age 45.019 ± 0.324 41.127 ± 0.557 44.084 ± 0.553 46.298 ± 0.608 48.572 ± 0.540 < 0.0001 PIR 2.903 ± 0.045 2.926 ± 0.068 2.954 ± 0.066 2.989 ± 0.074 2.733 ± 0.071 0.038 Energy intake, kcal/day 2215.272 ± 20.682 2313.888 ± 38.275 2262.134 ± 38.224 2174.268 ± 38.262 2109.860 ± 40.843 < 0.001 BMI 30.020 ± 0.166 27.741 ± 0.203 29.147 ± 0.260 30.322 ± 0.247 32.923 ± 0.385 < 0.0001 Sex < 0.0001 male 2170(41.683) 677(53.003) 562(45.928) 504(38.539) 427(29.112) female 2872(58.317) 586(46.997) 696(54.072) 762(61.461) 828(70.888) Race/ethnicity < 0.0001 Mexican American 507(4.585) 94(3.525) 155(5.374) 129(4.751) 129(4.639) Non-Hispanic Black 1155(11.486) 423(17.286) 252(9.588) 230(9.108) 250(10.200) Non-Hispanic White 2555(72.495) 541(67.119) 651(73.626) 689(75.589) 674(73.404) Other Hispanic 422(5.310) 86(4.777) 98(5.603) 124(5.407) 114(5.431) Other Race 403(6.124) 119(7.292) 102(5.810) 94(5.145) 88(6.326) Marital Status 0.052 non-single 2746(59.415) 702(61.017) 697(60.268) 705(61.026) 642(55.164) single 2296(40.585) 561(38.983) 561(39.732) 561(38.974) 613(44.836) Education 0.147 high school 2825(63.275) 746(64.374) 725(65.123) 712(63.887) 642(59.545) Smoking 0.051 never 2483(49.847) 635(50.469) 663(53.001) 629(50.095) 556(45.604) former 1328(26.140) 323(24.966) 312(25.684) 335(27.323) 358(26.543) now 1231(24.012) 305(24.566) 283(21.315) 302(22.582) 341(27.853) Drinking < 0.0001 never 584(9.066) 134(8.303) 153(9.712) 157(9.687) 140(8.484) former 906(15.044) 181(10.786) 212(15.171) 204(12.875) 309(21.531) mild 1672(35.588) 450(36.065) 420(35.948) 421(37.421) 381(32.758) moderate 834(17.890) 218(19.128) 201(17.910) 229(19.393) 186(15.009) heavy 1046(22.412) 280(25.719) 272(21.259) 255(20.623) 239(22.217) Physical activity < 0.0001 no 2569(46.859) 603(43.136) 625(46.238) 662(48.403) 679(49.620) moderate 1230(26.161) 272(23.158) 330(26.515) 301(25.762) 327(29.243) vigorous 1243(26.981) 388(33.706) 303(27.247) 303(25.835) 249(21.137) Diabetes < 0.0001 No 4043(85.527) 1102(91.534) 1026(87.031) 1006(84.315) 909(79.163) Yes 999(14.473) 161(8.466) 232(12.969) 260(15.685) 346(20.837) Hypertension < 0.0001 No 2727(60.120) 764(66.652) 721(64.289) 669(58.356) 573(50.988) Yes 2315(39.880) 499(33.348) 537(35.711) 597(41.644) 682(49.012) Continuous variables were analyzed using a weighted one-way analysis of variance (ANOVA) and expressed as the mean ± standard error. Categorical variables were evaluated using a weighted chi-square test and reported as the number (percentage). Association of NPAR with all-cause and respiratory disease mortality in adults with asthma KM survival analysis demonstrated that higher NPAR quartiles were associated with significantly lower all-cause and respiratory disease-related survival (log-rank p < 0.001 for both) ( Fig. 2 A and 2 B). A median follow-up period of 104 months (interquartile range: 59–157 months) was observed, during which time 697 participants with asthma died. Of these deaths, 101 were attributed to respiratory disease. In both crude and partially adjusted models, we observed that NPAR (continuous and categorical variables) was significantly associated with all-cause mortality in the adult asthma population. In model 2, NPAR remained positively associated with the risk of all-cause mortality in adults with asthma (hazard ratio [HR] and 95% confidence interval [CI] 1.126 (1.082,1.171), p < 0.0001). Compared with Q1, NPAR at Q4 was associated with a significantly increased risk of all-cause mortality (HR 2.112, p < 0.0001; p for trend < 0.0001) (Table 2 ). Similarly, NPAR was positively associated with the risk of respiratory disease-related mortality (HR 1.194, 95% CI 1.091–1.308, p < 0.001). Compared to Q1, NPAR at Q4 was associated with significantly increased respiratory disease mortality (HR 2.595, p = 0.013; p for trend = 0.002) (Table 3 ). Table 2 Association of NPAR with all-cause mortality in adults with asthma. Crude Model HR (95%CI) P-value Model 1 HR (95%CI) P-value Model 2 HR (95%CI) P-value NPAR 1.240(1.188,1.294) < 0.0001 1.151(1.107,1.196) < 0.0001 1.126(1.082,1.171) < 0.0001 NPAR Q1 ref ref ref ref ref ref Q2 1.603(1.140,2.254) 0.007 1.448(1.035,2.026) 0.031 1.365(0.981,1.901) 0.065 Q3 1.708(1.216,2.398) 0.002 1.404(1.006,1.960) 0.046 1.337(0.962,1.859) 0.084 Q4 3.776(2.906,4.906) < 0.0001 2.480(1.883,3.266) < 0.0001 2.112(1.600,2.788) < 0.0001 P for trend < 0.0001 < 0.0001 < 0.0001 The crude model did not adjust for any covariates; Model 1 partially adjusted for age, sex, race/ethnicity, education level, PIR, and marital status; and Model 2 continued to adjust for smoking, alcohol consumption, physical activity, BMI, total energy intake, diabetes, and hypertension. Table 3 Association of NPAR with respiratory disease-related mortality in adults with asthma. Crude Model HR (95%CI) P-value Model 1 HR (95%CI) P-value Model 2 HR (95%CI) P-value NPAR 1.334(1.217,1.463) < 0.0001 1.221(1.110, 1.342) < 0.0001 1.194(1.091, 1.308) < 0.001 NPAR Q1 ref ref ref ref ref ref Q2 1.222(0.493, 3.030) 0.665 1.212(0.490, 2.997) 0.677 0.996(0.407, 2.438) 0.993 Q3 1.848(0.827, 4.127) 0.134 1.483(0.666, 3.303) 0.335 1.322(0.584, 2.993) 0.503 Q4 5.156(2.480,10.721) < 0.0001 3.283(1.514, 7.116) 0.003 2.595(1.219, 5.525) 0.013 P for trend < 0.0001 0.001 0.002 The crude model did not adjust for any covariates; Model 1 partially adjusted for age, sex, race/ethnicity, education level, PIR, and marital status; and Model 2 continued to adjust for smoking, alcohol consumption, physical activity, BMI, total energy intake, diabetes, and hypertension. RCS modeling and threshold effects analysis The results of the RCS analysis indicated a nonlinear association between NPAR and all-cause mortality in the adult asthma population, with an inflection point of 13.76 and a p-value for nonlinearity of 0.0223. Conversely, a linear association was observed between NPAR and respiratory disease-related mortality, with a p-value of 0.4633 (Figs. 3 A and 3 B). Threshold effect analysis revealed that the association between NPAR and all-cause mortality was significant only after the inflection point (HR 1.192, 95% CI 1.118–1.271, p < 0.0001), while it was not associated before the inflection point (p = 0.264) (Table 4 ). Table 4 Threshold effects analysis of the association between NPAR and all-cause mortality in adults with asthma. HR (95%CI) p NPAR <13.76 1.078(0.945,1.229) 0.264 ≥13.76 1.192(1.118,1.271) < 0.0001 Stratified analysis Interaction analyses indicated that age was a significant effect modifier of the association between NPAR and all-cause mortality (p for interaction = 0.014), and this association was more pronounced in those 0.05) (Table 5 ). The association between NPAR and respiratory disease-related mortality remained stable across subgroups (p for interaction all > 0.05) (Table 6 ). Table 5 Stratified analysis of the association between NPAR and all-cause mortality in the adult asthma population. Character HR (95% CI) p p for interaction Age 0.014 < 60 1.189(1.106,1.278) < 0.0001 ≥60 1.093(1.045,1.143) < 0.0001 Sex 0.311 male 1.136(1.074,1.202) < 0.0001 female 1.126(1.069,1.186) < 0.0001 Race 0.994 Mexican American 1.177(1.017, 1.363) 0.029 Non-Hispanic Black 1.161(1.087,1.240) < 0.0001 Non-Hispanic White 1.122(1.068,1.178) < 0.0001 Other Hispanic 1.204(0.991, 1.465) 0.062 Other Race 1.035(0.900, 1.190) 0.629 Marital Status 0.795 non-single 1.109(1.053,1.168) < 0.0001 single 1.148(1.088,1.211) < 0.0001 Education 0.844 <high school 1.125(1.022,1.238) 0.016 high school 1.164(1.104,1.229) high school 1.092(1.033,1.154) 0.002 PIR 0.448 <1 1.191(1.109,1.279) < 0.0001 1–3 1.132(1.068,1.200) 3 1.061(0.989, 1.138) 0.1 Smoking 0.248 never 1.171(1.100,1.246) < 0.0001 former 1.083(1.023,1.148) 0.006 now 1.138(1.051, 1.233) 0.001 Drinking 0.204 never 1.211(1.070, 1.370) 0.002 former 1.137(1.078,1.199) < 0.0001 mild 1.054(0.984,1.128) 0.133 moderate 1.264(1.082, 1.478) 0.003 heavy 1.127(1.004, 1.265) 0.043 Physical activity 0.406 no 1.140(1.082,1.201) < 0.0001 moderate 1.119(1.047, 1.197) < 0.001 vigorous 1.067(0.969, 1.175) 0.19 BMI 0.99 <25 1.119(1.040,1.203) 0.003 25–30 1.112(1.039,1.189) 0.002 ≥30 1.127(1.064,1.193) < 0.0001 Diabetes 0.482 No 1.118(1.070,1.168) < 0.0001 Yes 1.137(1.063,1.215) < 0.001 Hypertension 0.926 No 1.112(1.021,1.211) 0.015 Yes 1.132(1.086,1.181) < 0.0001 Energy intake 0.088 T1 1.164(1.105,1.226) < 0.0001 T2 1.076(1.012,1.143) 0.019 T3 1.073(0.981, 1.173) 0.125 Table 6 Stratified analysis of the association between NPAR and respiratory disease-related mortality in the adult asthma population. Character HR (95% CI) p p for interaction Age 0.836 < 60 1.201(0.958, 1.505) 0.112 ≥60 1.205(1.093, 1.327) < 0.001 Sex 0.051 male 1.400(1.201, 1.632) < 0.0001 female 1.148(1.014, 1.300) 0.029 Race 0.695 Mexican American 0.995(0.829,1.195) 0.958 Non-Hispanic Black 1.246(0.977,1.588) 0.076 Non-Hispanic White 1.327(1.195,1.474) < 0.0001 Other Hispanic 1.687(0.994,2.862) 0.053 Other Race 1.482(1.086,2.023) 0.013 Marital Status 0.878 non-single 1.161(1.022, 1.320) 0.022 single 1.263(1.073, 1.486) 0.005 Education 0.917 high school 1.227(1.034, 1.456) 0.019 PIR 0.843 <1 1.292(1.040, 1.604) 0.021 1–3 1.263(1.130, 1.412) 3 1.078(0.856, 1.359) 0.522 Smoking 0.359 never 1.336(1.043, 1.711) 0.022 former 1.192(1.055, 1.347) 0.005 now 1.187(0.957, 1.472) 0.12 Drinking 0.375 never 1.904(1.194, 3.036) 0.007 former 1.293(1.119, 1.493) < 0.001 mild 1.171(1.010, 1.357) 0.037 moderate 0.813(0.553, 1.195) 0.291 heavy 1.212(0.829, 1.772) 0.321 Physical activity 0.597 no 1.253(1.112, 1.412) < 0.001 moderate 1.124(0.946, 1.336) 0.183 vigorous 1.080(0.714, 1.634) 0.716 BMI 0.347 <25 1.115(0.961, 1.292) 0.151 25–30 1.213(1.034, 1.424) 0.018 ≥30 1.311(1.116, 1.541) < 0.001 Diabetes 0.882 No 1.194(1.063, 1.341) 0.003 Yes 1.197(1.033, 1.386) 0.017 Hypertension 0.231 No 0.980(0.748, 1.283) 0.883 Yes 1.246(1.132, 1.371) < 0.0001 Energy intake 0.742 T1 1.234(1.094, 1.392) < 0.001 T2 1.144(0.978, 1.338) 0.092 T3 1.280(1.067, 1.535) 0.008 Sensitivity analysis Excluding participants with asthma who died in the last two years did not significantly change the results. NPAR remained significantly positively associated with all-cause mortality (HR 1.099, p < 0.0001; Q4 compared to Q1: HR = 1.868, p < 0.0001) ( Supplementary Table S1 ). Similarly, the association between NPAR and respiratory disease mortality remained significant (HR 1.183, p < 0.01) ( Supplementary Table S2 ). 4. DISCUSSION The findings of this study, based on data from NHANES 1999–2018, indicated that NPAR was positively associated with the risk of mortality from all causes and respiratory diseases among adult participants with asthma in U.S. Higher NPAR was associated with a significantly increased risk of mortality in the asthma population. The association between NPAR and all-cause mortality was present only when NPAR was > 13.76 and this association was more significant in those < 60 years of age. These findings were not affected by the exclusion of participants with short follow-up. To the best of our knowledge, our research contributes to the existing literature by exploring, for the first time, the association between NPAR and all-cause and respiratory disease-related mortality in adults with asthma in a national population-based longitudinal cohort study. NPAR as an emerging marker of systemic inflammation has been shown to be associated with prognosis in a variety of diseases. In a retrospective cohort study, Cui et al. proposed for the first time the calculation of NPAR in combination with neutrophil percentage and serum albumin and demonstrated that NPAR was independently associated with and had good predictive sensitivity for in-hospital mortality in patients with ST-segment elevation myocardial infarction[ 18 ]. Similarly, numerous studies have employed the NHANES database to demonstrate that NPAR is linked to an increased risk of mortality across diverse populations. Using data from NHANES 2005–2016, Wu et al. demonstrated that NPAR was positively associated with the risk of all-cause mortality in heart failure survivors (HR 1.08, 95% CI 1.05–1.12), however its predictive value for mortality as a marker alone was relatively low (area under the curve [AUC] 0.61)[ 33 ]. Data from NHANES 1999–2018 showed that NPAR was positively associated with all-cause (HR 1.14, 95% CI 1.08–1.20), CVD-related (HR 1.15, 95% CI 1.07–1.24), and respiratory disease-related mortality (HR was 1.16, 95% CI 1.08–1.23) risks in the COPD population, while NPAR had the optimal predictive value compared to other common inflammatory markers (AUC = 0.808)[ 20 ]. Liu et al. suggested that higher NPAR was associated with an increased risk of all-cause (HR 1.46, 95% CI 1.33–1.61) and CVD-related mortality (HR 1.54, 95% CI 1.32–1.80) in the general population, with nonlinear associations[ 19 ]. Another cohort study from NHANES 1999–2010 demonstrated that NPAR in the highest tertile (compared with the lowest tertile) was significantly associated with increased all-cause and CVD mortality in hypertensive adults (both HR 1.63)[ 34 ]. Similarly, Jing et al. indicated that higher NPAR was associated with increased all-cause (HR 1.37, 95% CI 1.22–1.52) and diabetes-related mortality (HR 1.58, 95% CI 1.26–1.97) in people with diabetes[ 35 ]. Consistently, another recently published NHANES-related study showed that NPAR was positively associated with all-cause and CVD mortality in a diabetic population, with each unit increase associated with a 14% and 12% increased risk, respectively[ 36 ]. In addition to NHANES, numerous cohort studies from other data sources have revealed the prognostic value of NPAR. A single-center retrospective cohort study that included 1,966 peritoneal dialysis patients demonstrated that participants in the highest tertile of NPAR were associated with a significant increase in all-cause and CVD mortality, however the predictive power was relatively low[ 37 ]. A substantial body of research has demonstrated a correlation between higher NPAR and elevated in-hospital mortality rates among critically ill patients in the intensive care unit, including those presenting with myocardial infarction[ 23 ], sepsis[ 22 ], acute kidney injury[ 38 ], coronary artery disease[ 39 ], and stroke[ 40 ]. Collectively, these lines of clinical research evidence consistently demonstrate the potential prognostic value of NPAR for a wide range of diseases and hold promise as a superior systemic marker of inflammation to comprise prognostic models. Our study provides new insights into the predictive value of NPAR for mortality in a community-dwelling adult asthma population. NPAR was shown to be independently associated with both all-cause and respiratory disease-related mortality in adults with asthma, suggesting that NPAR may serve as a potential marker to predict mortality, including respiratory disease-related death. In particular, higher NPAR (> 13.76) needs to be of concern in the adult asthma population, as threshold effects analyses showed that the association between NPAR and all-cause mortality was significant after this point. Interestingly, stratified analyses showed a more significant effect of NPAR on all-cause mortality in the asthma population < 60 years of age. There is evidence that older adults with asthma have higher complication rates and mortality[ 41 , 42 ], suggesting that specific immune responses and comorbidities in older populations may partially explain this association. Therefore, special attention needs to be paid to the prognostic value of elevated NPAR in nonelderly adults with asthma. Several other CBC-based systemic inflammatory markers have also been implicated in mortality in adult asthmatic populations. A few other systemic inflammatory markers based on blood leukocytes have also been implicated in mortality in adult population with asthma. Leveraging NHANES 1999–2018, Fu et al. indicated that advanced lung cancer inflammation index (based on BMI, albumin, and neutrophil-lymphocyte ratio [NLR]) was inversely associated with both all-cause and respiratory disease-related mortality in the adult asthma population (HRs in the highest quartile were 0.45 and 0.31, respectively)[ 43 ]. Another cohort study from NHANES 1999–2018 suggested that NLR, monocyte-lymphocyte ratio, systemic inflammatory response index (SIRI), and systemic immune-inflammation index were all positively associated with the risk of all-cause and respiratory disease-associated mortality in an adult asthma population[ 44 ]. Consistently, Xu et al. showed that higher SIRI was associated with an increased risk of all-cause mortality in the asthma population (HR in the highest tertile 2.08, p for trend < 0.001)[ 45 ]. Our study provides further clinical evidence for the association of NPAR as a novel systemic inflammatory marker with adult asthma populations, consistently demonstrating the potential clinical prognostic value of CBC-derived systemic inflammatory markers. The biological mechanisms underlying the association of NPAR with mortality in asthma populations remain unclear. Higher NPAR represents increased levels of systemic inflammation, and there is already evidence that systemic inflammation and neutrophils may be associated with more comorbidities and poorer treatment response in asthma populations[ 11 , 46 ]. Neutrophil-associated inflammation is associated with increased asthma severity[ 47 ]. Neutrophils produce neutrophil extracellular traps, chemokines, cytokines, and a variety of enzymes involved in the development and progression of asthma[ 44 ]. Albumin may be indirectly involved in immunomodulation by influencing immune cell function and the transport of inflammatory mediators[ 48 ]. However, the pathophysiologic role of serum albumin in asthma remains little studied. Our study has some significant advantages. It is a nationally representative, population-based study, making the findings potentially generalizable. The nature of prospective cohort studies reduces study bias. The findings persisted among participants with longer follow-up durations, demonstrating stability. However, our study has limitations. The diagnosis of asthma was based on self-report and may have been influenced by recall bias. This was an observational study and therefore causal associations could not be obtained. We did not have access to the specific medication history and treatment response of the asthma population to assess the confounding influence of these important factors. This study was conducted only in a community-dwelling adult asthma population, and the generalizability of these findings to patients in hospitals and respiratory intensive care units needs to be further explored. Finally, because NHANES only collected data on U.S. participants, whether these findings can be generalized to other countries needs to be validated in the future. 5. CONCLUSIONS NPAR was independently and positively associated with all-cause and respiratory disease-related mortality in the adult asthma population in the U.S. The association between NPAR and all-cause mortality was present when NPAR was > 13.76 and was not significant before the inflection point. Age influenced the association of NPAR with all-cause mortality in adults with asthma, and this association was more significant in those < 60 years of age. These findings suggest that NPAR may be an independent prognostic factor in the adult asthma population. Future exploration of the potential of NPAR as a prognostic marker and exploration of the mechanisms involved are warranted. Declarations Acknowledgments Not applicable. Author contributions : DW : Conceptualization, methodology, and writing – original draft. RS : Data curation, writing – review & editing, and visualization. RL: Formal analysis, and visualization. XL : Data analysis, data interpretation HZ : Conceptualization, methodology, supervision, writing – review & editing, and funding acquisition. Funding This research was supported by Shanxi Provincial Department of Science and Technology Regional Cooperation Project (No. 202204041101031). Data availability This study analyzed publicly available datasets and can be found at https://www.cdc.gov/nchs/nhanes/. Ethics approval and consent to participate t : All protocols were approved by the NCHS Ethics Review Board, and participants have provided written informed consent. Consent for publication All authors agree to publish this work Competing interests The authors declare that they have no competing interests. Author details 1 Department of Pulmonary and Critical Care Medicine, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China; 2 The First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi 030001, China; 3 Department of General Medicine, Linfen City People’s Hospital, Linfen, Shanxi 041000, China; Clinical trial number Not applicable. References Armeftis C, Gratziou C, Siafakas N, Katsaounou P, Pana ZD, Bakakos P. An update on asthma diagnosis. J Asthma. 2023;60(12):2104-10. doi: 10.1080/02770903.2023.2228911. Mims JW. 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The neutrophil percentage-to-albumin ratio is associated with all-cause mortality in patients with chronic heart failure. BMC Cardiovasc Disord. 2023;23(1):568. doi: 10.1186/s12872-023-03472-9. Hu C, He Y, Li J, Zhang C, Hu Q, Li W, et al. Association between neutrophil percentage-to-albumin ratio and 28-day mortality in Chinese patients with sepsis. J Int Med Res. 2023;51(6):3000605231178512. doi: 10.1177/03000605231178512. Lin Y, Lin Y, Yue J, Zou Q. The neutrophil percentage-to-albumin ratio is associated with all-cause mortality in critically ill patients with acute myocardial infarction. BMC Cardiovasc Disord. 2022;22(1):115. doi: 10.1186/s12872-022-02559-z. Liu CF, Chien LW. Predictive Role of Neutrophil-Percentage-to-Albumin Ratio (NPAR) in Nonalcoholic Fatty Liver Disease and Advanced Liver Fibrosis in Nondiabetic US Adults: Evidence from NHANES 2017-2018. Nutrients. 2023;15(8). doi: 10.3390/nu15081892. You J, He Y, Xu M, Qian M. Association between the C-reactive protein to albumin ratio with asthma and mortality in adult: a population-based study. Sci Rep. 2024;14(1):20573. doi: 10.1038/s41598-024-71754-z. Liu M, Zhang Z, Zhou C, Ye Z, He P, Zhang Y, et al. Predicted fat mass and lean mass in relation to all-cause and cause-specific mortality. J Cachexia Sarcopenia Muscle. 2022;13(2):1064-75. doi: 10.1002/jcsm.12921. Zhu S, Ji L, He Z, Zhang W, Tong Y, Luo J, et al. Association of smoking and osteoarthritis in US (NHANES 1999-2018). Sci Rep. 2023;13(1):3911. doi: 10.1038/s41598-023-30644-6. Jiang M, Tang X, Wang P, Yang L, Du R. Association between daily alcohol consumption and serum alpha klotho levels among U.S. adults over 40 years old: a cross-sectional study. BMC Public Health. 2023;23(1):1901. doi: 10.1186/s12889-023-16830-1. Liu W, Yang X, Zhan T, Huang M, Tian X, Tian X, et al. Weight-adjusted waist index is positively and linearly associated with all-cause and cardiovascular mortality in metabolic dysfunction-associated steatotic liver disease: findings from NHANES 1999-2018. Front Endocrinol (Lausanne). 2024;15:1457869. doi: 10.3389/fendo.2024.1457869. Liu Q, Guo J, Hu L, Veronese N, Smith L, Yang L, et al. Association between Intake of Energy and Macronutrients and Memory Impairment Severity in US Older Adults, National Health and Nutrition Examination Survey 2011-2014. Nutrients. 2020;12(11). doi: 10.3390/nu12113559. Sun X, Zhang Q, Kadier K, Hu P, Liu X, Liu J, et al. Association between diabetes status and breast cancer in US adults: findings from the US National Health and Nutrition Examination Survey. Front Endocrinol (Lausanne). 2023;14:1059303. doi: 10.3389/fendo.2023.1059303. Cao Y, Li P, Zhang Y, Qiu M, Li J, Ma S, et al. Association of systemic immune inflammatory index with all-cause and cause-specific mortality in hypertensive individuals: Results from NHANES. Front Immunol. 2023;14:1087345. doi: 10.3389/fimmu.2023.1087345. Wu CC, Wu CH, Lee CH, Cheng CI. Association between neutrophil percentage-to-albumin ratio (NPAR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and long-term mortality in community-dwelling adults with heart failure: evidence from US NHANES 2005-2016. BMC Cardiovasc Disord. 2023;23(1):312. doi: 10.1186/s12872-023-03316-6. Liu Z, Dong L, Shen G, Sun Y, Liu Y, Mei J, et al. Associations of neutrophil-percentage-to-albumin ratio level with all-cause mortality and cardiovascular disease-cause mortality among patients with hypertension: evidence from NHANES 1999-2010. Front Cardiovasc Med. 2024;11:1397422. doi: 10.3389/fcvm.2024.1397422. Jing Y, Tian B, Deng W, Ren Z, Xu X, Zhang D, et al. The Neutrophil Percentage-to-Albumin Ratio as a Biomarker for All-Cause and Diabetes-Cause Mortality Among Diabetes Patients: Evidence From the NHANES 1988-2018. J Clin Lab Anal. 2024;38(21):e25110. doi: 10.1002/jcla.25110. Li X, Gu Z, Gao J. Elevated neutrophil percentage-to-albumin ratio predicts increased all-cause and cardiovascular mortality among individuals with diabetes. Sci Rep. 2024;14(1):27870. doi: 10.1038/s41598-024-79355-6. Yu Y, Zhong Z, Yang W, Yu J, Li J, Guo X, et al. Neutrophil Percentage-to-Albumin Ratio and Risk of Mortality in Patients on Peritoneal Dialysis. J Inflamm Res. 2023;16:6271-81. doi: 10.2147/jir.S437256. Wang B, Li D, Cheng B, Ying B, Gong Y. The Neutrophil Percentage-to-Albumin Ratio Is Associated with All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury. Biomed Res Int. 2020;2020:5687672. doi: 10.1155/2020/5687672. Sun T, Shen H, Guo Q, Yang J, Zhai G, Zhang J, et al. Association between Neutrophil Percentage-to-Albumin Ratio and All-Cause Mortality in Critically Ill Patients with Coronary Artery Disease. Biomed Res Int. 2020;2020:8137576. doi: 10.1155/2020/8137576. Chen Z, Xie D, Li Y, Dai Z, Xiang S, Chen Z, et al. Neutrophil Albumin Ratio is Associated with All-Cause Mortality in Stroke Patients: A Retrospective Database Study. Int J Gen Med. 2022;15:1-9. doi: 10.2147/ijgm.S323114. Baptist AP, Busse PJ. Asthma Over the Age of 65: All's Well That Ends Well. J Allergy Clin Immunol Pract. 2018;6(3):764-73. doi: 10.1016/j.jaip.2018.02.007. Engelkes M, de Ridder MA, Svensson E, Berencsi K, Prieto-Alhambra D, Lapi F, et al. Multinational cohort study of mortality in patients with asthma and severe asthma. Respir Med. 2020;165:105919. doi: 10.1016/j.rmed.2020.105919. Fu J, Zou Y, Luo L, Zhang J, Wang X, Zhang D. Associations of advanced lung cancer inflammation index with all-cause and respiratory disease mortality in adults with asthma: NHANES, 1999-2018. Sci Rep. 2024;14(1):29693. doi: 10.1038/s41598-024-80983-1. 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. doi: 10.3389/fimmu.2023.1205687. Xu F, Jiang H, Li F, Wen Y, Jiang P, Chen F, et al. Association between the systemic inflammation response index and mortality in the asthma population. Front Med (Lausanne). 2024;11:1446364. doi: 10.3389/fmed.2024.1446364. Nabe T. Steroid-Resistant Asthma and Neutrophils. Biol Pharm Bull. 2020;43(1):31-5. doi: 10.1248/bpb.b19-00095. Ray A, Kolls JK. Neutrophilic Inflammation in Asthma and Association with Disease Severity. Trends Immunol. 2017;38(12):942-54. doi: 10.1016/j.it.2017.07.003. Pompili E, Zaccherini G, Baldassarre M, Iannone G, Caraceni P. Albumin administration in internal medicine: A journey between effectiveness and futility. Eur J Intern Med. 2023;117:28-37. doi: 10.1016/j.ejim.2023.07.003. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Jul, 2025 Read the published version in BMC Pulmonary Medicine → Version 1 posted Editorial decision: Revision requested 06 Mar, 2025 Reviews received at journal 25 Feb, 2025 Reviews received at journal 23 Feb, 2025 Reviews received at journal 23 Feb, 2025 Reviewers agreed at journal 22 Feb, 2025 Reviewers agreed at journal 18 Feb, 2025 Reviewers agreed at journal 17 Feb, 2025 Reviewers invited by journal 10 Feb, 2025 Editor invited by journal 23 Dec, 2024 Editor assigned by journal 20 Dec, 2024 Submission checks completed at journal 20 Dec, 2024 First submitted to journal 16 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5650982","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392891218,"identity":"dcea5a6c-4fef-4b73-b41a-0ee526c1b0af","order_by":0,"name":"Dan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYDCCA8wHHyQY2MjxMwMZRGphSzb4UJFmLNkOZBCphUdNcMaZQ4kG53nMBIjSwXcjh42Zt+1AgvFhBjMGhhqbaIJaJG/kHnvM23Ynz+wwQ9oDhmNpuQ2EtBjczks35m17VgzUctyAseEwMVpyzKR52w4nbm5mbJMgWovkjDOHEzcwM7MRp0Xy/jNIIEscZmM2SCDGL3xnDkOjsv/8xwcfamwIa0EFCaQpHwWjYBSMglGACwAAcS1GTg0iV24AAAAASUVORK5CYII=","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dan","middleName":"","lastName":"Wang","suffix":""},{"id":392891219,"identity":"8adcdb99-b99d-4d77-97e9-0c199acaca52","order_by":1,"name":"Rui Su","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Su","suffix":""},{"id":392891220,"identity":"71b53dec-3aa0-43ce-9d5c-1f893e52fd88","order_by":2,"name":"Ruina Li","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruina","middleName":"","lastName":"Li","suffix":""},{"id":392891221,"identity":"023f7444-5b26-4e43-9ed2-284a42583038","order_by":3,"name":"Xiaofeng Li","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Li","suffix":""},{"id":392891222,"identity":"2d81648c-bbbd-4711-ac66-49f165d5ce47","order_by":4,"name":"Hui Zhao","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-12-16 06:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5650982/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5650982/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-025-03786-8","type":"published","date":"2025-07-03T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72364495,"identity":"d54f0a8b-f300-40af-bc97-3707462a8454","added_by":"auto","created_at":"2024-12-26 06:26:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30713,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study population selection, NHANES 1999-2018.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5650982/v1/f459b0a77674b2c74b1fe010.png"},{"id":72363584,"identity":"4766c663-4021-4799-b792-9d917000f4ba","added_by":"auto","created_at":"2024-12-26 06:18:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101188,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival function of NPAR and all-cause and respiratory disease-related survival in the adult asthma population.\u003cstrong\u003e A\u003c/strong\u003e: all-cause survival; \u003cstrong\u003eB\u003c/strong\u003e: respiratory disease-related survival.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5650982/v1/b7cc0ca4ab31da7b03d032ea.png"},{"id":72363583,"identity":"c4bbaaec-dd38-4524-aa96-650b54f0e9f3","added_by":"auto","created_at":"2024-12-26 06:18:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87170,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of the association of NPAR and all-cause and respiratory disease-related in the adult asthma population.\u003cstrong\u003e A\u003c/strong\u003e: all-cause mortality; \u003cstrong\u003eB\u003c/strong\u003e: respiratory disease-related mortality.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5650982/v1/8be97b3c590444b631e1b505.png"},{"id":86179130,"identity":"26604fc4-7adc-47b8-a832-402cb3c73f84","added_by":"auto","created_at":"2025-07-07 16:16:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1676462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5650982/v1/695af837-7902-4017-bffb-24fa14a5a543.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of neutrophil percentage-to-albumin ratio with all-cause and respiratory disease-related mortality in US adults with asthma: results from NHANES 1999-2018","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAsthma is a chronic inflammatory airway disease with marked heterogeneity and complex pathophysiologic manifestations[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is usually characterized by variable expiratory airflow limitation and airway hyperresponsiveness, and consists of respiratory symptoms such as wheezing, shortness of breath, chest tightness, and cough that change and worsen over time[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While the age-standardized prevalence and mortality rates for asthma have declined over the past 30 years, the disease burden associated with asthma continues to rise[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In 2019, there were approximately 262\u0026nbsp;million cases of asthma worldwide, representing the most common chronic respiratory disease[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Asthma affects more than 25\u0026nbsp;million children and adults in the U.S., and about 8.0% of U.S. adults currently have asthma[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The consequences of asthma exacerbation can be fatal. In the U.S., an estimated 25,000 to 50,000 asthma patients are admitted to respiratory intensive care units each year[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A meta-analysis showed that asthma is associated with an increased risk of cardiovascular disease (CVD) and all-cause mortality[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Exploring modifiable risk factors associated with the development or prognosis of asthma has important clinical relevance.\u003c/p\u003e \u003cp\u003eInflammation plays an important role in the pathogenesis and progression of asthma. The pathogenesis of asthma involves the immune system and includes the interaction of multiple inflammatory cells, inflammatory mediators, and cytokines leading to airway inflammation[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The role of systemic inflammation in asthma progression remains largely unknown[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Blood neutrophils as common immune cells are suggested to be involved in the immunologic mechanisms of asthma, especially in type 2 low asthma[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Accumulation of neutrophils and release of neutrophil extracellular traps may lead to chronic airway inflammation associated with worsening asthma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, multiple cytokines such as interleukin-17 inflammasome activation may play a role in severe neutrophilic asthma[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Clinical studies have demonstrated that elevated blood neutrophils are associated with unique inflammatory patterns and clinical features in adult asthma[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, albumin, an important marker of immunity and nutrition, may be involved in asthma development. Observational studies have shown that serum albumin is associated with blood eosinophil counts and mortality in adults with asthma[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, a novel neutrophil- and albumin-based marker of systemic inflammation has been proposed, the neutrophil percentage-to-albumin ratio (NPAR)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Compared to individual inflammatory markers, NPAR combines blood neutrophils and albumin and may provide a more complete picture of systemic inflammation levels. Accumulating clinical evidence suggests that NPAR is associated with prognosis in a variety of populations, including the general population[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], chronic obstructive pulmonary disease (COPD)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], CVD[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], sepsis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and critically ill populations[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the association of NPAR with prognosis in adult asthma populations remains unknown. Elucidating this association could help reveal the potential value of NPAR as a novel systemic inflammatory marker in mortality prediction and risk stratification in adult asthma populations.\u003c/p\u003e \u003cp\u003eIn this study, we used population-level data from the National Health and Nutrition Examination Survey (NHANES) to explore the association of NPAR with all-cause and respiratory disease-related mortality in adult asthma participants. We aimed to comprehensively explore whether NPAR has potential prognostic value in the adult asthma population and provide new insights for future clinical prognostic marker studies.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cp\u003e \u003cb\u003eStudy design and population\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNHANES is a nationally representative, population-based, continuous cross-sectional survey conducted by the National Center for Health Statistics (NCHS) for the purpose of assessing the health and nutritional status of noninstitutionalized populations in the U.S. The database encompasses a comprehensive array of questionnaires, physical examinations, and laboratory tests. A stratified multistage probability sampling design with specific weights was employed to guarantee a representative sample. Approval was granted by the NCHS Ethics Review Committee for all survey cycles and written informed consent prior to participation was obtained from all subjects.\u003c/p\u003e \u003cp\u003eThe study population selection process was presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 13,773 asthma participants from NHANES 1999\u0026ndash;2018 were included. We sequentially excluded participants who were \u0026lt;\u0026thinsp;20 years of age (n\u0026thinsp;=\u0026thinsp;6480), missing NPAR data (n\u0026thinsp;=\u0026thinsp;788), missing survival data (n\u0026thinsp;=\u0026thinsp;11), and missing significant covariates (n\u0026thinsp;=\u0026thinsp;1452). A total of 5042 eligible asthma participants were included in the analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCalculation of NPAR\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on previous studies, the formula for calculating NPAR is neutrophil percentage (of total white blood cell count) (%) \u0026times; 100 /serum albumin concentration (g/dl)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Neutrophil percentages were obtained according to the parameters of complete blood count (CBC). CBC was obtained based on the Beckman Coulter counting and sieving method combined with an automated dilution and mixing device for sample processing and a single-beam photometer for hemoglobin determination. Serum albumin was obtained according to the standard biochemical test i.e. dye bromocresol purple method.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiagnosis of asthma\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe assessed participants' asthma status as previously described in the relevant literature. In the NHANES Medical Conditions Questionnaire, participants were asked \u0026ldquo;Has a doctor or other health professional ever told you that you have asthma?\u0026rdquo;. A positive response to this question indicated the presence of asthma[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eMortality data\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBaseline asthma participants from NHANES 1999\u0026ndash;2018 were followed until December 31, 2019. Mortality data were obtained by prospectively matching death records from the National Death Index database. Respiratory disease mortality information was derived from the International Classification of Diseases, 10th Revision codes including J09-J18 and J40-J47[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eCovariates\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeveral important covariates were included in the study to correct for potential confounding effects. These covariates included age (in years), sex (male or female), race/ethnicity (non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic, or other race), marital status (non-single or single), household poverty income ratio (PIR), education level (less than high school, high school, or greater than high school education), smoking (never/former/current), alcohol consumption (never/former/mild/moderate/heavy), physical activity (no/moderate/vigorous), body mass index (BMI), total dietary energy intake (kcal/d), diabetes (yes or no), and hypertension (yes or no). Demographic characteristics, smoking, alcohol consumption, and physical activity were self-reported from standardized questionnaires. Those who smoked no more than 100 cigarettes in their lifetime were considered never smokers, those who smoked at least 100 cigarettes but do not smoke now were former smokers, and those who still smoke now were current smokers[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Alcohol consumption was categorized as never drinkers (\u0026lt;\u0026thinsp;12 drinks in a lifetime), former drinkers (\u0026ge;\u0026thinsp;12 drinks in a lifetime but no alcohol in the last year), and current drinkers (subdivided into mild, moderate, and heavy by alcohol consumption criteria)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Intensity of physical activity participation was assessed based on self-report on the Physical Activity Questionnaire[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. BMI was calculated as weight (kg) divided by the square of height (m) as measured at the mobile examination center. Total energy intake was derived from dietary interview data[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Diabetes was assessed by self-reported history of diabetes or meeting the American Diabetes Association's criteria for laboratory parameters[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Hypertension was evaluated through self-report, the measurement of blood pressure at \u0026ge;\u0026thinsp;140/90 mmHg, or the use of anti-hypertensive medications[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003e In view of the complexity of the NHANES study design, all statistical analyses were appropriately weighted in accordance with the NHANES analysis guidelines. In the baseline analysis, the asthmatic participants were grouped according to NPAR quartiles. Continuous variables were analyzed using a weighted one-way analysis of variance (ANOVA) and expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error. Categorical variables were evaluated using a weighted chi-square test and reported as the number (percentage). To examine the impact of NPAR on survival in individuals with asthma, we employed Kaplan-Meier (KM) survival analysis. We constructed multivariate Cox proportional hazards regression models for exploring the association of NPAR with all-cause and respiratory disease-related mortality in the asthma population. The crude model did not adjust for any covariates; Model 1 partially adjusted for age, sex, race/ethnicity, education level, PIR, and marital status; and Model 2 continued to adjust for smoking, alcohol consumption, physical activity, BMI, total energy intake, diabetes, and hypertension. Restricted cubic spline (RCS) models were employed to investigate whether these associations were nonlinear and to elucidate differences in associations before and after the inflection point through threshold effects analysis. To ascertain whether the observed associations between NPAR and all-cause mortality in participants with asthma remained consistent within defined subgroups, stratified analyses were conducted. A statistically significant result from the interaction analysis indicated that the variable was a potential effect modifier. Finally, we performed a sensitivity analysis. To exclude the effect of short-term follow-up, we excluded asthmatic participants with a follow-up length of less than two years to verify the reliability of the findings. The data were analyzed employing the R software (version 4.2.3), and results were deemed statistically significant when the p-value was less than 0.05.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 5042 asthmatic participants were enrolled with a mean age of 45.019 years. The quartile distributions of NPAR were Q1 (\u0026lt;\u0026thinsp;12.1), Q2 (12.1\u0026ndash;13.8), Q3 (13.8\u0026ndash;15.5), and Q4 (\u0026ge;\u0026thinsp;15.5). As NPAR quartiles increased, asthma participants were older, had lower PIR and energy intake, higher BMI, and were more likely to be female, non-Hispanic White, former alcohol drinkers, have no/moderate physical activity participation, and have diabetes and hypertension (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline analysis of participants with asthma according to NPAR quartiles.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;5042)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1 (n\u0026thinsp;=\u0026thinsp;1263)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2 (n\u0026thinsp;=\u0026thinsp;1258)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3 (n\u0026thinsp;=\u0026thinsp;1266)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4 (n\u0026thinsp;=\u0026thinsp;1255)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.019\u0026thinsp;\u0026plusmn;\u0026thinsp;0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.127\u0026thinsp;\u0026plusmn;\u0026thinsp;0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.084\u0026thinsp;\u0026plusmn;\u0026thinsp;0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.298\u0026thinsp;\u0026plusmn;\u0026thinsp;0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.572\u0026thinsp;\u0026plusmn;\u0026thinsp;0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.903\u0026thinsp;\u0026plusmn;\u0026thinsp;0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.926\u0026thinsp;\u0026plusmn;\u0026thinsp;0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.954\u0026thinsp;\u0026plusmn;\u0026thinsp;0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.989\u0026thinsp;\u0026plusmn;\u0026thinsp;0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.733\u0026thinsp;\u0026plusmn;\u0026thinsp;0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnergy intake, kcal/day\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2215.272\u0026thinsp;\u0026plusmn;\u0026thinsp;20.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2313.888\u0026thinsp;\u0026plusmn;\u0026thinsp;38.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2262.134\u0026thinsp;\u0026plusmn;\u0026thinsp;38.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2174.268\u0026thinsp;\u0026plusmn;\u0026thinsp;38.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2109.860\u0026thinsp;\u0026plusmn;\u0026thinsp;40.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.741\u0026thinsp;\u0026plusmn;\u0026thinsp;0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.147\u0026thinsp;\u0026plusmn;\u0026thinsp;0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.322\u0026thinsp;\u0026plusmn;\u0026thinsp;0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.923\u0026thinsp;\u0026plusmn;\u0026thinsp;0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2170(41.683)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e677(53.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e562(45.928)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e504(38.539)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e427(29.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2872(58.317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e586(46.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e696(54.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e762(61.461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e828(70.888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e507(4.585)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(3.525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155(5.374)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129(4.751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129(4.639)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1155(11.486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e423(17.286)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252(9.588)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230(9.108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250(10.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2555(72.495)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e541(67.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e651(73.626)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e689(75.589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e674(73.404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422(5.310)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(4.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98(5.603)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124(5.407)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114(5.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e403(6.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119(7.292)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102(5.810)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(5.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88(6.326)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2746(59.415)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e702(61.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e697(60.268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e705(61.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e642(55.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2296(40.585)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e561(38.983)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e561(39.732)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e561(38.974)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e613(44.836)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e395(3.920)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(3.779)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(4.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102(3.882)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110(3.924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1822(32.805)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e428(31.847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e439(30.789)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e452(32.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e503(36.531)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2825(63.275)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e746(64.374)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e725(65.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e712(63.887)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e642(59.545)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2483(49.847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e635(50.469)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e663(53.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e629(50.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e556(45.604)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eformer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1328(26.140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323(24.966)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312(25.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e335(27.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e358(26.543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1231(24.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305(24.566)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e283(21.315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e302(22.582)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e341(27.853)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e584(9.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134(8.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153(9.712)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157(9.687)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140(8.484)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eformer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e906(15.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181(10.786)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212(15.171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204(12.875)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e309(21.531)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1672(35.588)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450(36.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e420(35.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e421(37.421)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381(32.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e834(17.890)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218(19.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201(17.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229(19.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186(15.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eheavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1046(22.412)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e280(25.719)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272(21.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255(20.623)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239(22.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2569(46.859)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e603(43.136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e625(46.238)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e662(48.403)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e679(49.620)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1230(26.161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272(23.158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330(26.515)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301(25.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327(29.243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1243(26.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e388(33.706)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e303(27.247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e303(25.835)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249(21.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4043(85.527)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1102(91.534)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1026(87.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1006(84.315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e909(79.163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e999(14.473)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161(8.466)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232(12.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260(15.685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346(20.837)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2727(60.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e764(66.652)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e721(64.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e669(58.356)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e573(50.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2315(39.880)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e499(33.348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e537(35.711)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e597(41.644)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e682(49.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables were analyzed using a weighted one-way analysis of variance (ANOVA) and expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error. Categorical variables were evaluated using a weighted chi-square test and reported as the number (percentage).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of NPAR with all-cause and respiratory disease mortality in adults with asthma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKM survival analysis demonstrated that higher NPAR quartiles were associated with significantly lower all-cause and respiratory disease-related survival (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both) \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). A median follow-up period of 104 months (interquartile range: 59\u0026ndash;157 months) was observed, during which time 697 participants with asthma died. Of these deaths, 101 were attributed to respiratory disease. In both crude and partially adjusted models, we observed that NPAR (continuous and categorical variables) was significantly associated with all-cause mortality in the adult asthma population. In model 2, NPAR remained positively associated with the risk of all-cause mortality in adults with asthma (hazard ratio [HR] and 95% confidence interval [CI] 1.126 (1.082,1.171), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Compared with Q1, NPAR at Q4 was associated with a significantly increased risk of all-cause mortality (HR 2.112, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, NPAR was positively associated with the risk of respiratory disease-related mortality (HR 1.194, 95% CI 1.091\u0026ndash;1.308, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared to Q1, NPAR at Q4 was associated with significantly increased respiratory disease mortality (HR 2.595, p\u0026thinsp;=\u0026thinsp;0.013; p for trend\u0026thinsp;=\u0026thinsp;0.002) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation of NPAR with all-cause mortality in adults with asthma.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude Model\u003c/p\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPAR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.240(1.188,1.294)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.151(1.107,1.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.126(1.082,1.171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPAR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.603(1.140,2.254)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.448(1.035,2.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.365(0.981,1.901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.708(1.216,2.398)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.404(1.006,1.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.337(0.962,1.859)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.776(2.906,4.906)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.480(1.883,3.266)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.112(1.600,2.788)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe crude model did not adjust for any covariates; Model 1 partially adjusted for age, sex, race/ethnicity, education level, PIR, and marital status; and Model 2 continued to adjust for smoking, alcohol consumption, physical activity, BMI, total energy intake, diabetes, and hypertension.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation of NPAR with respiratory disease-related mortality in adults with asthma.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude Model\u003c/p\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPAR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.334(1.217,1.463)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.221(1.110, 1.342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.194(1.091, 1.308)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPAR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.222(0.493, 3.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.212(0.490, 2.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996(0.407, 2.438)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.848(0.827, 4.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.483(0.666, 3.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.322(0.584, 2.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.156(2.480,10.721)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.283(1.514, 7.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.595(1.219, 5.525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe crude model did not adjust for any covariates; Model 1 partially adjusted for age, sex, race/ethnicity, education level, PIR, and marital status; and Model 2 continued to adjust for smoking, alcohol consumption, physical activity, BMI, total energy intake, diabetes, and hypertension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRCS modeling and threshold effects analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the RCS analysis indicated a nonlinear association between NPAR and all-cause mortality in the adult asthma population, with an inflection point of 13.76 and a p-value for nonlinearity of 0.0223. Conversely, a linear association was observed between NPAR and respiratory disease-related mortality, with a p-value of 0.4633 (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Threshold effect analysis revealed that the association between NPAR and all-cause mortality was significant only after the inflection point (HR 1.192, 95% CI 1.118\u0026ndash;1.271, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while it was not associated before the inflection point (p\u0026thinsp;=\u0026thinsp;0.264) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThreshold effects analysis of the association between NPAR and all-cause mortality in adults with asthma.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNPAR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;13.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.078(0.945,1.229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;13.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.192(1.118,1.271)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eStratified analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInteraction analyses indicated that age was a significant effect modifier of the association between NPAR and all-cause mortality (p for interaction\u0026thinsp;=\u0026thinsp;0.014), and this association was more pronounced in those\u0026thinsp;\u0026lt;\u0026thinsp;60 years of age (HR 1.189 compared with 1.093). The remaining variables did not significantly affect this association (p for interaction all \u0026gt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The association between NPAR and respiratory disease-related mortality remained stable across subgroups (p for interaction all \u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStratified analysis of the association between NPAR and all-cause mortality in the adult asthma population.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep for interaction\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.189(1.106,1.278)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.093(1.045,1.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.136(1.074,1.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.126(1.069,1.186)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.177(1.017, 1.363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.161(1.087,1.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.122(1.068,1.178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.204(0.991, 1.465)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.035(0.900, 1.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.109(1.053,1.168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.148(1.088,1.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.125(1.022,1.238)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.164(1.104,1.229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.092(1.033,1.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.191(1.109,1.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.132(1.068,1.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.061(0.989, 1.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.171(1.100,1.246)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eformer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.083(1.023,1.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.138(1.051, 1.233)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.211(1.070, 1.370)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eformer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.137(1.078,1.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.054(0.984,1.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.264(1.082, 1.478)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eheavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.127(1.004, 1.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.140(1.082,1.201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.119(1.047, 1.197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.067(0.969, 1.175)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.119(1.040,1.203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.112(1.039,1.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.127(1.064,1.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.118(1.070,1.168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.137(1.063,1.215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.112(1.021,1.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.132(1.086,1.181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnergy intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.164(1.105,1.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.076(1.012,1.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.073(0.981, 1.173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStratified analysis of the association between NPAR and respiratory disease-related mortality in the adult asthma population.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep for interaction\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.201(0.958, 1.505)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.205(1.093, 1.327)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.400(1.201, 1.632)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.148(1.014, 1.300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.995(0.829,1.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.246(0.977,1.588)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.327(1.195,1.474)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.687(0.994,2.862)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.482(1.086,2.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.161(1.022, 1.320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.263(1.073, 1.486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.208(1.068, 1.366)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.232(1.085, 1.399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.227(1.034, 1.456)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.292(1.040, 1.604)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.263(1.130, 1.412)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.078(0.856, 1.359)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.336(1.043, 1.711)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eformer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.192(1.055, 1.347)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.187(0.957, 1.472)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.904(1.194, 3.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eformer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.293(1.119, 1.493)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.171(1.010, 1.357)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.813(0.553, 1.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eheavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.212(0.829, 1.772)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.253(1.112, 1.412)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.124(0.946, 1.336)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.080(0.714, 1.634)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.115(0.961, 1.292)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.213(1.034, 1.424)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.311(1.116, 1.541)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.194(1.063, 1.341)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.197(1.033, 1.386)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.980(0.748, 1.283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.246(1.132, 1.371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnergy intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.234(1.094, 1.392)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.144(0.978, 1.338)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.280(1.067, 1.535)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExcluding participants with asthma who died in the last two years did not significantly change the results. NPAR remained significantly positively associated with all-cause mortality (HR 1.099, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Q4 compared to Q1: HR\u0026thinsp;=\u0026thinsp;1.868, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Similarly, the association between NPAR and respiratory disease mortality remained significant (HR 1.183, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (\u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e).\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe findings of this study, based on data from NHANES 1999\u0026ndash;2018, indicated that NPAR was positively associated with the risk of mortality from all causes and respiratory diseases among adult participants with asthma in U.S. Higher NPAR was associated with a significantly increased risk of mortality in the asthma population. The association between NPAR and all-cause mortality was present only when NPAR was \u0026gt;\u0026thinsp;13.76 and this association was more significant in those\u0026thinsp;\u0026lt;\u0026thinsp;60 years of age. These findings were not affected by the exclusion of participants with short follow-up.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, our research contributes to the existing literature by exploring, for the first time, the association between NPAR and all-cause and respiratory disease-related mortality in adults with asthma in a national population-based longitudinal cohort study. NPAR as an emerging marker of systemic inflammation has been shown to be associated with prognosis in a variety of diseases. In a retrospective cohort study, Cui et al. proposed for the first time the calculation of NPAR in combination with neutrophil percentage and serum albumin and demonstrated that NPAR was independently associated with and had good predictive sensitivity for in-hospital mortality in patients with ST-segment elevation myocardial infarction[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Similarly, numerous studies have employed the NHANES database to demonstrate that NPAR is linked to an increased risk of mortality across diverse populations. Using data from NHANES 2005\u0026ndash;2016, Wu et al. demonstrated that NPAR was positively associated with the risk of all-cause mortality in heart failure survivors (HR 1.08, 95% CI 1.05\u0026ndash;1.12), however its predictive value for mortality as a marker alone was relatively low (area under the curve [AUC] 0.61)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Data from NHANES 1999\u0026ndash;2018 showed that NPAR was positively associated with all-cause (HR 1.14, 95% CI 1.08\u0026ndash;1.20), CVD-related (HR 1.15, 95% CI 1.07\u0026ndash;1.24), and respiratory disease-related mortality (HR was 1.16, 95% CI 1.08\u0026ndash;1.23) risks in the COPD population, while NPAR had the optimal predictive value compared to other common inflammatory markers (AUC\u0026thinsp;=\u0026thinsp;0.808)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Liu et al. suggested that higher NPAR was associated with an increased risk of all-cause (HR 1.46, 95% CI 1.33\u0026ndash;1.61) and CVD-related mortality (HR 1.54, 95% CI 1.32\u0026ndash;1.80) in the general population, with nonlinear associations[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Another cohort study from NHANES 1999\u0026ndash;2010 demonstrated that NPAR in the highest tertile (compared with the lowest tertile) was significantly associated with increased all-cause and CVD mortality in hypertensive adults (both HR 1.63)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similarly, Jing et al. indicated that higher NPAR was associated with increased all-cause (HR 1.37, 95% CI 1.22\u0026ndash;1.52) and diabetes-related mortality (HR 1.58, 95% CI 1.26\u0026ndash;1.97) in people with diabetes[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Consistently, another recently published NHANES-related study showed that NPAR was positively associated with all-cause and CVD mortality in a diabetic population, with each unit increase associated with a 14% and 12% increased risk, respectively[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition to NHANES, numerous cohort studies from other data sources have revealed the prognostic value of NPAR. A single-center retrospective cohort study that included 1,966 peritoneal dialysis patients demonstrated that participants in the highest tertile of NPAR were associated with a significant increase in all-cause and CVD mortality, however the predictive power was relatively low[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A substantial body of research has demonstrated a correlation between higher NPAR and elevated in-hospital mortality rates among critically ill patients in the intensive care unit, including those presenting with myocardial infarction[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], sepsis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], acute kidney injury[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], coronary artery disease[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and stroke[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Collectively, these lines of clinical research evidence consistently demonstrate the potential prognostic value of NPAR for a wide range of diseases and hold promise as a superior systemic marker of inflammation to comprise prognostic models. Our study provides new insights into the predictive value of NPAR for mortality in a community-dwelling adult asthma population. NPAR was shown to be independently associated with both all-cause and respiratory disease-related mortality in adults with asthma, suggesting that NPAR may serve as a potential marker to predict mortality, including respiratory disease-related death. In particular, higher NPAR (\u0026gt;\u0026thinsp;13.76) needs to be of concern in the adult asthma population, as threshold effects analyses showed that the association between NPAR and all-cause mortality was significant after this point. Interestingly, stratified analyses showed a more significant effect of NPAR on all-cause mortality in the asthma population\u0026thinsp;\u0026lt;\u0026thinsp;60 years of age. There is evidence that older adults with asthma have higher complication rates and mortality[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], suggesting that specific immune responses and comorbidities in older populations may partially explain this association. Therefore, special attention needs to be paid to the prognostic value of elevated NPAR in nonelderly adults with asthma.\u003c/p\u003e \u003cp\u003eSeveral other CBC-based systemic inflammatory markers have also been implicated in mortality in adult asthmatic populations. A few other systemic inflammatory markers based on blood leukocytes have also been implicated in mortality in adult population with asthma. Leveraging NHANES 1999\u0026ndash;2018, Fu et al. indicated that advanced lung cancer inflammation index (based on BMI, albumin, and neutrophil-lymphocyte ratio [NLR]) was inversely associated with both all-cause and respiratory disease-related mortality in the adult asthma population (HRs in the highest quartile were 0.45 and 0.31, respectively)[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Another cohort study from NHANES 1999\u0026ndash;2018 suggested that NLR, monocyte-lymphocyte ratio, systemic inflammatory response index (SIRI), and systemic immune-inflammation index were all positively associated with the risk of all-cause and respiratory disease-associated mortality in an adult asthma population[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Consistently, Xu et al. showed that higher SIRI was associated with an increased risk of all-cause mortality in the asthma population (HR in the highest tertile 2.08, p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001)[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Our study provides further clinical evidence for the association of NPAR as a novel systemic inflammatory marker with adult asthma populations, consistently demonstrating the potential clinical prognostic value of CBC-derived systemic inflammatory markers.\u003c/p\u003e \u003cp\u003eThe biological mechanisms underlying the association of NPAR with mortality in asthma populations remain unclear. Higher NPAR represents increased levels of systemic inflammation, and there is already evidence that systemic inflammation and neutrophils may be associated with more comorbidities and poorer treatment response in asthma populations[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Neutrophil-associated inflammation is associated with increased asthma severity[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Neutrophils produce neutrophil extracellular traps, chemokines, cytokines, and a variety of enzymes involved in the development and progression of asthma[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Albumin may be indirectly involved in immunomodulation by influencing immune cell function and the transport of inflammatory mediators[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, the pathophysiologic role of serum albumin in asthma remains little studied.\u003c/p\u003e \u003cp\u003eOur study has some significant advantages. It is a nationally representative, population-based study, making the findings potentially generalizable. The nature of prospective cohort studies reduces study bias. The findings persisted among participants with longer follow-up durations, demonstrating stability. However, our study has limitations. The diagnosis of asthma was based on self-report and may have been influenced by recall bias. This was an observational study and therefore causal associations could not be obtained. We did not have access to the specific medication history and treatment response of the asthma population to assess the confounding influence of these important factors. This study was conducted only in a community-dwelling adult asthma population, and the generalizability of these findings to patients in hospitals and respiratory intensive care units needs to be further explored. Finally, because NHANES only collected data on U.S. participants, whether these findings can be generalized to other countries needs to be validated in the future.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eNPAR was independently and positively associated with all-cause and respiratory disease-related mortality in the adult asthma population in the U.S. The association between NPAR and all-cause mortality was present when NPAR was \u0026gt;\u0026thinsp;13.76 and was not significant before the inflection point. Age influenced the association of NPAR with all-cause mortality in adults with asthma, and this association was more significant in those\u0026thinsp;\u0026lt;\u0026thinsp;60 years of age. These findings suggest that NPAR may be an independent prognostic factor in the adult asthma population. Future exploration of the potential of NPAR as a prognostic marker and exploration of the mechanisms involved are warranted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDW\u003c/strong\u003e: Conceptualization, methodology, and writing \u0026ndash; original draft.\u003cbr\u003e\u003cstrong\u003eRS\u003c/strong\u003e: Data curation, writing \u0026ndash; review \u0026amp; editing, and visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRL:\u003c/strong\u003e Formal analysis, and visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXL\u003c/strong\u003e: Data analysis, data interpretation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHZ\u003c/strong\u003e: Conceptualization, methodology, supervision, writing \u0026ndash; review \u0026amp; editing, and funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Shanxi Provincial Department of Science and Technology Regional Cooperation Project (No. 202204041101031).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed publicly available datasets and can be found at https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e: All protocols were approved by the NCHS Ethics Review Board, and participants have provided written informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree to publish this work\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\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026nbsp;1\u003c/sup\u003eDepartment of Pulmonary and Critical Care Medicine, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China; \u003csup\u003e2\u0026nbsp;\u003c/sup\u003eThe First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi 030001, China; \u003csup\u003e3\u003c/sup\u003eDepartment of General Medicine, Linfen City People\u0026rsquo;s Hospital, Linfen, Shanxi\u0026nbsp;041000, China;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArmeftis C, Gratziou C, Siafakas N, Katsaounou P, Pana ZD, Bakakos P. 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Eur J Intern Med. 2023;117:28-37. doi: 10.1016/j.ejim.2023.07.003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"systemic inflammation, asthma, neutrophil, albumin, mortality","lastPublishedDoi":"10.21203/rs.3.rs-5650982/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5650982/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Systemic inflammation and immune dysregulation may be associated with asthma onset and progression. Neutrophil percentage-to-albumin ratio (NPAR) is a novel marker of systemic inflammation. We aimed to investigate the association of NPAR with all-cause and respiratory disease-related mortality in adults with asthma through NHANES 1999-2018.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eNPAR was calculated by neutrophil percentage (% of total white blood cell count) × 100/serum albumin (g/dl). Asthma was diagnosed by standardized questionnaire. Mortality was obtained by prospective matching with the National Death Index. Multivariate Cox proportional hazards regression analysis was used to explore these associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eA total of 5042 adults with asthma were included. After a median follow-up duration of 104 months, 697 participants died, of which 101 were respiratory disease-related deaths. In fully adjusted models, NPAR was significantly and positively associated with both all-cause and respiratory disease-related mortality (hazard ratios [HR] of 1.126 and 1.194, respectively, both p \u0026lt; 0.05). Compared to Q1, NPAR at Q4 was associated with significantly increased all-cause and respiratory disease-related mortality (all-cause: HR 2.112, p \u0026lt; 0.0001; respiratory disease-related: HR 2.595, p = 0.013). NPAR was nonlinearly associated with all-cause mortality, with an inflection point of 13.76. This association was significant only after the inflection point. The effect of NPAR on all-cause mortality was more pronounced in those \u0026lt;60 years of age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eHigher NPAR was associated with increased all-cause and respiratory disease mortality in US adults with asthma. Further exploration of the prognostic potential and underlying mechanisms of NPAR is needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number \u003c/strong\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Association of neutrophil percentage-to-albumin ratio with all-cause and respiratory disease-related mortality in US adults with asthma: results from NHANES 1999-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-26 06:17:56","doi":"10.21203/rs.3.rs-5650982/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-06T06:50:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-26T03:53:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-23T06:28:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-23T05:00:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91727144673881799636652935348242006754","date":"2025-02-22T05:31:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98965272808904962616824151809491515687","date":"2025-02-18T09:55:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299077107932998956280103145822140563817","date":"2025-02-18T02:33:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-10T10:03:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-12-23T09:33:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-20T14:14:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-20T14:13:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2024-12-16T06:22:22+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":"43bf19b1-946b-4318-95c0-93845b8d4191","owner":[],"postedDate":"December 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:05:57+00:00","versionOfRecord":{"articleIdentity":"rs-5650982","link":"https://doi.org/10.1186/s12890-025-03786-8","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2025-07-03 15:57:20","publishedOnDateReadable":"July 3rd, 2025"},"versionCreatedAt":"2024-12-26 06:17:56","video":"","vorDoi":"10.1186/s12890-025-03786-8","vorDoiUrl":"https://doi.org/10.1186/s12890-025-03786-8","workflowStages":[]},"version":"v1","identity":"rs-5650982","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5650982","identity":"rs-5650982","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}