Variation of all-cause mortality with Fat-free Mass Index（FFMI）and Fat Mass Index（FMI）in individuals with Asthma: results from the NHANES database prospective cohort study

Variation of all-cause mortality with Fat-free Mass Index（FFMI）and Fat Mass Index（FMI）in individuals with Asthma: results from the NHANES database prospective cohort study | 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 Variation of all-cause mortality with Fat-free Mass Index（FFMI）and Fat Mass Index（FMI）in individuals with Asthma: results from the NHANES database prospective cohort study Jing Chen, Zihe Cheng, Yang Yao, Shengyu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4499532/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background The relationship between FFMI, FMI, and mortality in patients with asthma remains unknown. This study aimed to examine the associations between FFMI and FMI and all-cause mortality in a cohort of American adults diagnosed with asthma. Methods This study included 15200 adults from NHANES. To assess mortality, we linked participant records to the National Death Index. Survival differences across quintiles of FFMI and FMI were explored using Kaplan-Meier plots and log-rank tests. The study investigated the nonlinear relationships between FFMI, FMI, and mortality. Additionally, stratified analyses were undertaken to pinpoint subgroups of individuals who exhibited an elevated risk of mortality. Results After a median follow-up, 13360 were presumed alive and 1840 had deceased. Kaplan-Meier plots revealed significant differences in all-cause mortality in patients with asthma among the FFMI quintiles and FMI quintiles. Specifically, individuals in the lowest FFMI quintile (Q1, 10.4-16.0) exhibited a markedly increased risk of all-cause mortality (HR: 4.63; 95% CI: 1.59,13.5; p < 0.01). Similarly, increased all-cause mortality risks were observed in the upper three quintiles of FMI-Q3 (4.8-6.1) with HR: 2.9 (1.20,7.00, p <0.05), Q4 (6.2-8.3) with HR: 3.37 (1.41,8.03, p <0.01), and Q5 (8.4-22.8) with HR: 4.6 (1.31,16.2, p <0.05). Moreover, the risk of all-cause mortality increased with increasing FMI and decreasing FFMI ( p for non-linearity<0.001 in both cases) respectively. Subgroup analyses further elucidated these associations across different categories. Conclusions All-cause mortality is decreased in patients with asthma when FMI is less than 5.4 kg/m 2 , and increased when FFMI is less than 18.6 kg/m 2 . FFMI FMI Asthma All-cause mortality NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Asthma, a chronic inflammatory respiratory disorder, impacts over 300 million people globally, establishing it as the most prevalent long-term respiratory disease[ 1 ]. It arises from intricate interactions between genetic predispositions and environmental influences, leading to symptoms such as wheezing, coughing, chest tightness, and episodic shortness of breath[ 2 ]. Asthma's heterogeneity is exemplified by its variable airway obstruction and bronchial hyperresponsiveness, contributing significantly to its morbidity and mortality rates. Economically, the burden is staggering; in the United States alone, the direct medical costs, coupled with indirect expenses from mortality and lost productivity, exceed $ 80 billion annually[ 3 ]. Current research highlights the critical role of body composition in respiratory health, demonstrating that higher muscle mass correlates with improved pulmonary function, evidenced by normal values in Forced Expiratory Volume in one second (FEV 1 ) and Forced Vital Capacity (FVC)[ 4 ]. Variations in body composition are also linked to different respiratory diseases; for instance, obesity and increased adiposity are frequently associated with asthma and chronic bronchitis, while rapid gains in fat mass (FM) have been identified as potential risk factors for the development of airway hyperresponsiveness (AHR) in adults[ 5 , 6 ]. Chronic obstructive pulmonary disease (COPD), on the other hand, is commonly associated with underweight and reduced lean body mass [ 7 ]. Additionally, elevated total and abdominal fat indices in adolescents are associated with impaired lung function, whereas greater FFM during childhood and adolescence correlates with enhanced function of small airways[ 8 ]. Increased fat mass is prevalent in individuals with treatment-resistant asthma and correlates strongly with diminished control of asthma symptoms, particularly among male patients. Research suggests that targeted weight loss interventions can enhance pulmonary function in those afflicted with refractory asthma[ 9 ]. Moreover, obesity may induce respiratory dysfunction due to an imbalance between the growth of lung parenchyma and airway size. This discrepancy can result in seemingly normal FEV 1 and FVC measures, yet an abnormal FEV 1 /FVC ratio, a diagnostic marker of airflow obstruction, heightening asthma risk. Furthermore, adipose tissue can aggravate airway inflammation by secreting pro-inflammatory cytokines, complicating the pathophysiological landscape of asthma[ 10 ]. Elevated body mass index (BMI) is well-documented as a significant risk factor for asthma and chronic bronchitis in adults[ 11 ]. Targeted interventions, such as weight loss and sustained weight management, are effective in mitigating asthma symptoms[ 11 ]. Obesity also contributes to reduced tidal volume and functional residual capacity, impairing pulmonary function[ 12 ]. Both asthma and obesity are characterized by systemic inflammation, with the tumor necrosis factor α (TNF-α) pathway playing a crucial role in the pathophysiology of both conditions. Dysregulation of this pathway can exacerbate when both diseases coexist[ 13 ]. Previous research has extensively explored the relationship between physical indicators and health outcomes such as cardiovascular disease, COPD, and population-wide mortality. While similar studies in asthma have primarily examined its prevalence and morbidity, the association between FFMI and FMI with all-cause mortality in asthmatic patients remains less understood. Our investigation seeks to fill this knowledge gap by analyzing the correlation between FMI and FFMI with all-cause mortality among asthma patients using data from the National Health and Nutrition Examination Survey (NHANES) database. 2 Methods 2.1 Participants This study leveraged three datasets-Demographics Data, Examination Data, and Questionnaire Data-rom the NHANES covering the periods 1999–2006 and 2011–2018. A total of 15 200 adults were selected for analysis, with strict exclusion criteria applied to enhance data reliability: individuals lacking complete information on critical physical indicators, baseline data, or mortality records were systematically excluded (Fig. 1 ). Additionally, pregnant women were not included in the dual-energy X-ray absorptiometry (DXA) assessments. The study focused on adults aged 20 to 85 years. Mortality status was determined through the NHANES public-use linked mortality file, updated as of December 31, 2018, and cross-referenced with the National Center for Health Statistics (NCHS) and the National Death Index (NDI) via a probability matching algorithm, ensuring accurate mortality tracking. 2.2 Assessment NHANES staff used questionnaires during home interviews to collect data on smoking status, alcohol consumption, physical activity levels, and medical history, including the presence of COPD, chronic bronchitis, emphysema, asthma, hypertension, and diabetes. In addition to these health variables, demographic data such as age, gender, race, poverty-income ratio (PIR), and education level were meticulously collected to support nuanced analysis. At the mobile physical examination centers, the study further assessed anthropometric and compositional measurements of the participants, including height, weight, total fat mass, lean mass which encompasses bone mineral content (BMC), and BMI. The NHANES DXA examination offers nationally representative data on body composition, encompassing both bone and soft tissue. The total body and regional values include total mass (g), BMC (g), fat mass (g), and lean mass (g) including BMC (g). FFMI was derived by dividing FFM (kg) by the square of height (m 2 ), based on the analogy of BMI and the high correlation between FFM and height. DXA scanning assessed body composition, including lean mass, fat mass, and bone mineral content in patients. Body mass index (BMI, kg/m 2 ) is calculated as weight divided by height squared, while fat mass index (FMI, kg/m 2 ) is calculated as fat mass divided by height squared. The Fat-free mass index (FFMI, kg/m 2 ) is calculated by dividing the sum of total lean mass and bone mineral content by the square of height [ 9 , 14 , 15 ]. Gender is classified as male or female. Age is classified as under 40 years old, 40–65 years old, or over 65 years old. Race is classified as non-Hispanic whites, non-Hispanic blacks, Hispanics (excluding Mexican Americans), Mexican Americans, or other races. Education level is classified into five categories: Less Than 9th Grade, 9-11th Grade, High School Graduate or General Educational Development (GED), Some College or Associate's Degree, and College Graduate or higher. The PIR is categorized as follows: ≤130%, > 130–350% (reference group), and > 350% (calculated by dividing household income by the poverty threshold). Physical activity is measured in Metabolic equivalent (MET) minutes per week and classified as low (< 600 minutes per week), moderate (600-8,000 minutes per week), or high (≥ 8,000 minutes per week). MET represents the oxygen consumption required to maintain resting metabolism and is a common indicator used to express the relative energy metabolism level during various activities. BMI is calculated by dividing weight in kilograms by height in meters squared (kg/m²). The categories are as follows: ≤25 (reference group), > 25–30, and > 30. Respondents were classified as former smokers if they had smoked at least 100 cigarettes in their lifetime and had quit smoking by the time they answered the questionnaire. Smoking status was categorized into three groups: never-smokers (reference group), former smokers, and current smokers. Participants were divided into four categories based on their alcohol consumption: Non-drinker (reference group), 1-<5 drinks/month, 5-<10 drinks/month, and ≥ 10 drinks/month. The average number of alcoholic beverages consumed per month in the past 12 months was calculated based on these questions. The study categorized diabetes into three groups: 'yes', 'borderline', and 'no'. Hypertension was categorized into two groups: 'yes' and 'no', based on the participants' questionnaire data. Each covariate was divided into a reference group and other groups. When analyzing, all other groups are compared to this reference group to estimate the relative odds ratio. 2.3 Statistical analysis To explore the association between physical indices (FFMI and FMI) and mortality among patients with asthma, this study employed Kaplan-Meier plots and log-rank tests to examine survival differences across quintiles of FFMI and FMI. We developed a Cox proportional hazards regression model, adjusting for multiple covariates including sex, age, race, smoking status, alcohol consumption, PIR, education level, physical activity, and the presence of hypertension and diabetes. Further, restricted cubic spline analysis was utilized to investigate nonlinear relationships between FFMI, FMI, and mortality. When a nonlinear association was detected, a recursive algorithm determined the inflection point, and threshold effect analysis was subsequently employed for validation. Interaction and subgroup analyses were conducted to scrutinize relationships within various categories. The χ2 test was applied to categorical variables, while a linear regression model was used for continuous variables. Categorical data were presented as frequencies and percentages, and continuous variables were expressed as means and standard deviations. Statistical analyses were performed using R version 4.2.2, with significance determined by a two-tailed p-value. 3 Results 3.1 Study participants After a median follow-up for 184 months, 13,360 were presumed alive, while 1,840 had passed away in the 15,200 participants by December 31, 2018.FMI was divided into 5 groups according to quintiles: Q1:0.9–3.4, Q2:3.5–4.7, Q3:4.8–6.1, Q4:6.2–8.3, Q5:8.4–22.8; FFMI was divided into 5 groups according to quintiles: Q1:10.4–16.0, Q2:16.1–17.8, Q3:17.9–19.5, Q4:19.6–21.5, Q5:21.6–38.5, and the baseline map was obtained according to the survival status (Table 1 ). In addition to race and FFMI, differences in FMI, gender, age, smoking status, poverty-to-income ratio, education, drinking status, physical activity, hypertension coexistence, and diabetes coexistence were statistically significant ( p < 0.05) between the survivor and mortality groups. Among the racial variables, non-Hispanic whites were disproportionately represented. In the age group, patients aged 65 years or older accounted for a smaller proportion of the overall population. Table 1 Baseline characteristics of participants by survival status (n = 15200) Characteristic survival, N = 13360 (92%) death, N = 1840 (8.2%) p -value Sex 0.021 female 6,544 (49%) 1,100 (61%) male 6,816 (51%) 740 (39%) Age(years) < 0.001 ≤40 years 4,657 (36%) 855 (57%) 40-≤65 years 8,183 (62%) 90 (4.6%) ≥65 years 520 (2.4%) 895 (39%) Race 0.4 Non-Hispanic White 7,382 (76%) 1,056 (72%) Non-Hispanic Black 3,090 (11%) 450 (13%) Mexican American 1,513 (4.1%) 167 (2.1%) Other/multiracial 742 (4.7%) 65 (4.4%) Other Hispanic 633 (4.8%) 102 (8.3%) Smoking status < 0.001 Never smoker 7,806(58.4%) 683(37.1%) Former smoker 2,873(21.5%) 638(34.7%) Current smoker 2,674(20.0%) 519(28.2%) Poverty income ratio (PIR) 0.004 ≤1.3 3,944 (28%) 617 (32%) 1.3-≤3.5 2,992 (18%) 662 (33%) ≥3.5 5,697 (54%) 420 (36%) Education < 0.001 Less Than 9th Grade 384 (1.5%) 327 (12%) 9-11th Grade 1,495 (8.3%) 304 (13%) High School Grad/GED 2,422 (19%) 380 (20%) Some College or AA degree 4,870 (36%) 563 (38%) College Graduate or above 4,183 (35%) 266 (17%) Drinking status 0.012 Non-drinker 1,810 (13%) 193 (11%) 1-≤5 drinks/month 6,064 (53%) 696 (43%) 5-≤10 drinks/month 1,615 (16%) 87 (8.3%) ≥10 drinks/month 1,962 (18%) 384 (37%) Physical activity 0.004 normal physical activity 4,033(30.2%) 404(22.0%) low physical activity 9,002(67.4%) 1,429(77.7%) High physical activity 325(2.4%) 7(0.4%) Hypertension 2,850 (19%) 1,080 (56%) < 0.001 Diabetes < 0.001 No 12,628 (96%) 1,306 (77%) Borderline 171 (0.8%) 100 (5.4%) Yes 560 (3.1%) 434 (18%) BMI (kg/m 2 ) 0.003 Underweight (< 18.5) 244 (2.4%) 21 (0.9%) Normal (18.5-<25) 4,471 (38%) 403 (21%) Overweight (25 -<30) 4,134 (30%) 713 (41%) Obese (≥ 30) 4,511 (30%) 703 (37%) < 0.001 Height, mean (SD) (cm) 1.71 (1.63, 1.78) 1.65 (1.59, 1.72) < 0.001 BMI,mean (SD) (kg/m 2 ) 26 (23, 31) 29 (26, 33) < 0.001 FMI, mean (SD) (kg/m 2 ) 4.88 (3.41, 6.88) 6.60 (4.90, 8.55) < 0.001 FFMI, mean (SD) (kg/m 2 ) 18.5 (16.3, 20.8) 18.2 (15.6, 20.4) 0.2 FMI (kg/m 2 ) < 0.001 Q1(0.9–3.4) 2,615 (21%) 115 (5.1%) Q2(3.5–4.7) 2,550 (21%) 202 (9.7%) Q3(4.8–6.1) 2,635 (20%) 351 (21%) Q4(6.2–8.3) 2,621 (19%) 541 (30%) Q5(8.4–22.8) 2,939 (19%) 631 (35%) FFMI (kg/m 2 ) 0.4 Q1(10.4–16.0) 2,183 (19%) 489 (29%) Q2(16.1–17.8) 2,606 (20%) 330 (16%) Q3(17.9–19.5) 2,840 (20%) 398 (19%) Q4(19.6–21.5) 2,785 (20%) 349 (21%) Q5(21.6–38.5) 2,946 (20%) 274 (15%) 3.2 All-cause mortality The relationship between FMI, FFMI, and all-cause mortality is depicted in Fig. 2 . The Kaplan-Meier survival curves indicated statistically significant differences in survival probabilities across different FMI and FFMI groups, as confirmed by the log-rank test ( p < 0.001). Notably, survival outcomes worsened with increasing FMI values. Conversely, a decrease in FFMI was generally associated with poorer survival outcomes. To explore the relationship between FMI and all-cause mortality among asthma patients, we designated the Q1(0.9–3.4) FMI group as the reference category. Model 1, adjusted for continuous FFMI variables, indicated increased all-cause mortality for the Q3 (4.8–6.1), Q4 (6.2–8.3), and Q5 (8.4–22.8) FMI groups with HRs of 4.48 (95% CI: 1.82,11.0, p < 0.001), 6.4 (95% CI: 2.85,14.3, p < 0.001), and 8.96 (95% CI: 4.32,18.6, p < 0.001) respectively, with a statistically significant trend (p for trend < 0.001). Model 2, further adjusted for age, sex, race, and smoking status, showed similar trends, with HRs of 2.39 (95% CI: 1.07,5.36, p < 0.01), 3.31 (95% CI: 1.61,6.79, p < 0.01), and 3.95 (95% CI: 1.62,9.63, p < 0.01) for Q3, Q4, and Q5 respectively ( p for trend < 0.001).Model 3 included adjustments for a comprehensive set of variables including poverty-to-income ratio, education, alcohol consumption status, physical activity, diabetes co-existence, and hypertension co-existence. The HRs in this model were 2.9 (95% CI: 1.20,7.00, p < 0.05), 3.37 (95% CI: 1.41,8.03, p < 0.01), and 4.6 (95% CI: 1.31,16.2, p < 0.05) for Q3, Q4, and Q5 respectively, maintaining a significant trend ( p for trend = 0.008). Additionally, we investigated the impact of FFMI on mortality by setting the Q5(21.6–38.5) FFMI group as the reference. Model 1, adjusted for FMI continuity variables, revealed that the Q1(10.4–16.0) group of FFMI had an increased risk of mortality with an HR of 4.16 (95% CI: 1.82,9.49, p < 0.001) ( p for trend = 0.017). Model 2 adjusted for FMI continuity variables, age, gender, race, and smoking status. The Q1(10.4–16.0) group of FFMI increased the all-cause mortality of asthma patients (HR:3.32, 95%CI: 1.44,7.62), p < 0.01, p for trend = 0.021; Model 3 adjusted for FMI continuity variables, sex, age, race, poverty-to-income ratio, education, smoking status, alcohol consumption status, physical activity, diabetes, and hypertension. The Q1(10.4–16.0) group of FFMI increased all-cause mortality in patients with asthma (HR:4.63,95% CI: 1.59,13.5), p < 0.01, p for trend = 0.016) (Table 2 ). Table 2 Hazard ratio (95% CI) for all-cause mortality according to different levels of FMI, FFMI Characteristic Model 1 Model 2 Model 3 FMI(kg/m 2 ) Q1(0.9–3.4) Reference Reference Reference Q2(3.5–4.7) 1.92(0.86, 4.28) 1.24(0.60, 2.54) 0.84(0.34, 2.05) Q3(4.8–6.1) 4.48(1.82, 11.0) *** 2.39(1.07, 5.36) ** 2.9(1.20, 7.00) * Q4(6.2–8.3) 6.4(2.85, 14.3) *** 3.31(1.61, 6.79) ** 3.37(1.41, 8.03) ** Q5(8.4–22.8) 8.96(4.32, 18.6) *** 3.95(1.62, 9.63) ** 4.6(1.31, 16.2) * p value for trend < 0.001 < 0.001 0.008 FFMI (kg/m 2 ) Q1(10.4–16.0) 4.16(1.82, 9.49) *** 3.32(1.44, 7.62) ** 4.63(1.59, 13.5) ** Q2(16.1–17.8) 1.82(0.81, 4.08) 1.46(0.66, 3.20) 1.68(0.70, 4.03) Q3(17.9–19.5) 2.38(1.15, 4.89) 1.61(0.80, 3.25) 2.04(0.86, 4.83) Q4(19.6–21.5) 2.31(0.84, 6.33) 1.51(0.42, 5.41) 2.05(0.36, 11.7) Q5(21.6–38.5) Reference Reference Reference p value for trend 0.017 0.021 0.016 CI = Confidence interval, HR = Hazard ratio. * p < 0.05, ** p < 0.01, *** p < 0.001 The relationship between FMI and FFMI with all-cause mortality among asthmatic patients was explored using restricted cubic spline analysis (Fig. 3 ). This analysis highlighted a statistically significant non-linear relationship (P < 0.0001), with spline knots positioned at the 1st, 50th, and 90th percentiles for FMI, and the 5th, 35th, 65th, and 95th percentiles for FFMI. The reference points were established at 5.4 kg/m² for FMI and 18.6 kg/m² for FFMI. Results from the analysis indicated distinct patterns in the association between body composition and mortality risk. Figure 3 A illustrates that the minimum and maximum mortality risks for FMI were observed at 1.3 kg/m² and 21.7 kg/m², respectively. At the reference FMI of 5.4 kg/m², the HR was set at 1. Below this reference, the risk ratio was less than 1, signifying a reduced mortality risk. Conversely, above this reference value, the risk ratio significantly exceeded 1, suggesting an increased mortality risk associated with higher FMI. This trend of increasing risk ratio with rising FMI was statistically significant. Similarly, Fig. 3 B depicts the mortality risk associated with FFMI. The highest and lowest mortality risks occurred at FFMI levels of 11.9 kg/m² and 34.7 kg/m², respectively. At the reference FFMI of 18.6 kg/m², the HR was 1. Mortality risk was significantly higher below this reference value and decreased below 1 with increasing FFMI, indicating a protective effect of higher FFMI against mortality in asthmatic patients. Threshold effect analysis identified critical inflection points for FFMI and FMI, influencing all-cause mortality among patients (Table 3 ). The analysis determined that the inflection point for FFMI is 18.6 kg/m 2 . Below this threshold, each two-fold increase in FFMI was associated with a 29% reduction in the risk of all-cause mortality (HR = 0.71, 95% CI: 0.56, 0.89, p = 0.004). Conversely, the inflection point for FMI was established at 5.4 kg/m 2 . Below this threshold, each two-fold increase in FMI corresponded to an 84% increase in the risk of all-cause mortality (HR = 1.84, 95% CI: 1.08,3.15, p = 0.025). The log-likelihood ratio test further substantiated these findings, indicating a significant statistical difference between the two models ( p < 0.001). Table 3 Threshold effect analysis of FFMI and FMI on mortality All-cause mortality HR (95%CI) for FFMI, p -value All-cause mortality HR (95%CI) for FMI, p -value Total One line 0.92(0.80, 1.05), 0.2 1.18(1.06, 1.32), 0.004 Fitting by two-piecewise Cox proportional risk model Inflection value 18.6 5.4 0.9 1.11(0.92, 1.35), 0.3 P for Log-likelihood ratiolikelihood ratiolikelihood < 0.001 < 0.001 CI = Confidence interval, HR = Hazard ratio, FFMI and FMI fit the Cox regression model. Analysis was adjusted for gender, age, race, poverty to income ratio, education level, drinking status, smoking status, physical activity, hypertension co-existence, diabetes co-existence and FMI or FFMI. The lowest quintile for FFMI (Q1: 10.4–16.0) was associated with an increased risk of all-cause mortality. Similarly, higher quintiles of FMI (Q3: 4.8–6.1, Q4: 6.2–8.3, and Q5: 8.4–22.8) were linked to elevated mortality rates. Additionally, specific demographic and behavioral factors were found to exacerbate mortality risks. Male gender, smoking, high alcohol consumption (more than 10 drinks per day), and co-existing diabetes significantly increased the risk of death among these patients(Fig. 4 ). 3.3 Stratified analyses There is no interaction between continuous variable FFMI and other covariables (Fig. 5 ). Continuous variable FMI has interaction with three covariables: gender ( p for interaction = 0.009), poverty index ( p for interaction = 0.003) and education level (p for interaction = 0.014). With the increase of FMI, there was a significant increase in mortality in males, middle-income groups and High School Grad/GED educated groups, with hazard ratios of 1.56, (95% CI: 1.23, 1.97, p < 0.001), 1.32, (95% CI: 1.11, 1.57, p = 0.002) and 1.63, (95% CI: 1.18, 2.27, p = 0.003), respectively (Fig. 6 ). The association between FFMI, FMI, and cause-specific mortality in patients with asthma is shown in Table S1 . The stratified analysis of the relationship between quintile grouping variables FFMI, FMI and mortality of asthmatic patients is shown in Table S2, Table S3 and Table S4. 4 Discussion The exclusive reliance on BMI as a measure of health in patients with asthma, without consideration of specific body composition details such as fat distribution or muscle mass, may obscure critical associations between these elements and an increased risk of mortality [ 16 ]. For patients with asthma, BMI alone provides an incomplete picture of health status and prognostic. It is imperative, therefore, to integrate other physical indices such as FMI and FFMI to furnish a more comprehensive assessment of patient condition and prognosis. Our research elucidates a significant association between these physical indices and mortality risk in asthma patients, offering valuable perspectives on how to enhance survival outcomes. Our research highlights a significant increase in mortality risk among asthma patients associated with elevated FMI, a finding that underscores the complex interplay between obesity and asthma. The linkage between increased FMI and asthma mortality reflects broader epidemiological observations where obesity exacerbates both the prevalence and severity of asthma due to a variety of mechanisms, including mechanical factors, systemic inflammation, and comorbidities [ 17 , 18 ]. Obesity induces systemic inflammation, contributing to metabolic syndrome, insulin resistance, and heightened levels of C-reactive protein (CRP) [ 19 ]. In patients with severe refractory asthma (SRA), which is characterized by chronic airway inflammation often with neutrophil activation. This inflammatory cascade can impair respiratory function and lead to airflow obstruction by altering the FEV 1 /FVC ratio [ 20 ].The management of asthma in obese patients thus presents unique challenges, including increased exacerbations and diminished responsiveness to conventional corticosteroid treatment. Recent research has delineated obesity-related non-T2 inflammatory pathways such as mechanical stress, hyperinsulinemia, and metabolic disturbances as pivotal in exacerbating asthma symptoms and therapeutic resistance [ 14 ].Promisingly, interventions aimed at mitigating obesity's impact-through weight management, dietary modifications, and targeted therapies-have shown potential in improving outcomes for asthmatic patients. Dietary approaches like high-fat diets (HFD) have been implicated in increasing inflammation and oxidative stress, whereas agents like pentamidine-liked bisindole may activate anti-inflammatory pathways, such as AMPK, offering therapeutic relief in obesity-related asthma[ 15 ]. Moreover, novel pharmacological treatments like CPX (carnosine-phenylalanine-boron) and FFA4 activators are being explored for their efficacy in treating obesity-aggravated asthma, particularly by preventing airway remodeling and fibrosis [ 21 , 22 ]. Our research indicates that a low FFMI significantly elevates all-cause mortality in patients with asthma, corroborating existing literature that associates higher FFMI with improved respiratory function, as evidenced by normal FEV 1 and FVC values, and fewer respiratory symptoms [ 4 ]. Notably, low muscle mass is commonly observed in patients with moderate to severe asthma and is particularly prevalent in those suffering from SRA. These patients often present with more pronounced airway obstruction and reduced functional motor capacity, which contribute to poorer clinical outcomes and compromised functional status [ 6 , 23 ].Furthermore, muscle-reducing obesity has been identified as a significant predictor of asthma, emphasizing the role of muscle mass in respiratory health. The negative impact of reduced muscle mass extends to greater airway obstruction and decreased functional motor ability, while diminished muscle strength correlates with poorer asthma control and reduced quality of life [ 24 ].A longitudinal study from Germany highlighted that patients with uncontrolled asthma experienced notable declines in muscle mass over a two-year period, despite maintaining a stable BMI[ 25 ]. Such findings align with broader research, which implicates systemic inflammation as a mediator of muscle dysfunction in various conditions [ 26 ]. Markers such as CRP, TNF-α, and IL-6 have been particularly noted for their involvement in the development of muscle dysfunction in both acute and chronic diseases[ 27 ].The positive impact of comprehensive multidisciplinary pulmonary rehabilitation has been documented, demonstrating reductions in emergency admissions and hospitalizations for asthma attacks over one year. Additionally, these programs have shown improvements in dyspnea, quality of life, exercise capacity, and psychosocial status among overweight and obese patients [ 28 ].Collectively, our findings and the corroborative evidence from the literature underscore the imperative to consider FFMI as a critical factor in the prognosis and management of asthma. Our study establishes a significant association between both FMI and FFMI with all-cause mortality among patients with asthma, emphasizing the critical role of body composition in influencing health outcomes in this population. These findings illuminate potential pathways for targeted interventions and underscore the necessity for personalized healthcare strategies that consider body composition as a key factor. The utilization of a large national cohort has allowed us to explore the relationships between FFMI, FMI, and survival status comprehensively. The detailed individual records and extensive follow-up period averaging fourteen years lend considerable credibility to our findings. However, our study is not without limitations. The reliance on questionnaire data for disease status introduces potential recall bias, which may affect the accuracy of the reported health information. Additionally, the absence of lung function data precludes a precise assessment of asthma severity within our cohort, which is critical for a deeper understanding of the disease impact on mortality. Looking ahead, future research could benefit from a multicenter study or meta-analysis to broaden the understanding of these associations across different populations and healthcare settings. Moreover, our analysis did not account for dietary factors, which are increasingly recognized as influential in asthma management. Subsequent studies should aim to identify optimal dietary patterns for individuals with asthma and investigate the interplay between diet, nutritional factors, and asthma outcomes. Such research would further refine our approach to managing asthma, potentially leading to more effective and tailored treatment protocols. Abbreviations AHR airway hyperresponsiveness BMC bone mineral content BMI body mass index CI confidence interval COPD chronic obstructive pulmonary disease CRP C-reactive protein DXA dual-energy X-ray absorptiometry FEV 1 forced expiratory volume in one second FFMI fat-free mass index FM fat mass FMI fat mass index FVC forced vital capacity GED General Educational Development GED General Educational Development HFD high-fat diets HR hazard ratio MET metabolic equivalent NCHS National Center for Health Statistics NDI national death index NHANES National Health and Nutrition Examination Survey PIR poverty-income ratio RCS restricted cubic splines SRA severe refractory asthma TNF-α tumor necrosis factor α Declarations Ethics Approval and Consent to Participate No individual participated to this study. Consent for Publication No individual participated to this study. Availability of data and materials Researchers can download the datasets free of charge from the following website: https://www.cdc.gov/nchs/nhanes/index.htm;https://www.cdc.gov/nchs/data-linkage/mortality-public.htm Competing interests None to declare Funding Foundation and Transformation of Prevention and Treatment of acute Lung Injury, Xi 'an Medical University (Shaanxi Jiaohan [2024] No. 21); Acute Lung Injury Prevention and Treatment Foundation and Transformation Innovation Team (Shaanxi Jiaohan [2023] No. 997) Author Contributions W.S. and Y.Y. were responsible for the study concept and design. C.J. extracted and processed the data inputs. C.J. wrote the first draft of the paper. C.Z. contributed to writing subsequent versions of the manuscript, critically reviewed the methods and results, and approved the final version of the manuscript. C.Z. managed the project. All authors provided intellectual inputs into aspects of this study. C.J. and Y.Y. accessed and verified the data. W.S. had final responsibility for the decision to submit for publication. Acknowledgements The author s would like to thank the Xi’an Medical University for assistance with access to funding bodies. References Soriano JB, Abajobir AA, Abate KH, Abera SF, Agrawal A, Ahmed MB, et al. Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Respiratory Med. 2017;5(9):691–706. Mims JW. Asthma: definitions and pathophysiology. Int Forum Allergy Rhinology. 2015;5:S1. Shah R, Yang Y. Health and Economic Burden of Obesity in Elderly Individuals with Asthma in the United States. Popul Health Manage. 2015;18(3):186–91. Schiffers C, Faner R, Ofenheimer A, Sunanta O, Puchhammer P, Mraz T, et al. Supranormal lung function: Prevalence, associated factors and clinical manifestations across the lifespan. Respirology. 2023;28(10):942–53. Shim J-S, Kim S-S, Lee S-H, Kim M-H, Cho Y-J, Park H-W. Fat mass index and airway hyperresponsiveness in Korean adults. Postgrad Med. 2023;135(5):480–5. Visser E, de Jong K, van Zutphen T, Kerstjens HAM, ten Brinke A. Muscle Function in Moderate to Severe Asthma: Association With Clinical Outcomes and Inflammatory Markers. J Allergy Clin Immunology: Pract. 2023;11(5):1439–e473. Trompeter G, Grigsby MR, Miele CH, Wise RA, Gilman RH, Miranda JJ, et al. Patterns of Body Composition Relating to Chronic Respiratory Diseases Among Adults in Four Resource-Poor Settings in Peru. Lung. 2018;196(3):277–84. Wu T, Santos S, Quezada-Pinedo HG, Vernooij MW, Jaddoe VWV, Klein S, et al. Body composition and respiratory outcomes in children: a population-based prospective cohort study. Thorax. 2024;79(5):448–56. Bafadhel M, Singapuri A, Terry S, Hargadon B, Monteiro W, Green RH, et al. Body Mass and Fat Mass in Refractory Asthma: An Observational 1 Year Follow-Up Study. J Allergy. 2010;2010:1–9. Sharma V, Cowan DC. Obesity, Inflammation, and Severe Asthma: an Update. Curr Allergy Asthma Rep. 2021;21(12). Hsu Y-E, Chen S-C, Geng J-H, Wu D-W, Wu P-Y, Huang J-C. Obesity-Related Indices Are Associated with Longitudinal Changes in Lung Function: A Large Taiwanese Population Follow-Up Study. Nutrients. 2021;13(11). McLachlan CR, Poulton R, Car G, Cowan J, Filsell S, Greene JM, et al. Adiposity, asthma, and airway inflammation. J Allergy Clin Immunol. 2007;119(3):634–9. Sapey E, Wood AM, Ahmad A, Stockley RA. Tumor Necrosis Factor–α rs361525 Polymorphism Is Associated with Increased Local Production and Downstream Inflammation in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2010;182(2):192–9. Shailesh H, Bhat AA, Janahi IA. Obesity-Associated Non-T2 Mechanisms in Obese Asthmatic Individuals. Biomedicines. 2023;11(10). Zhang L, Liang D, Liu L, Liu L. Plumbagin alleviates obesity-related asthma: Targeting inflammation, oxidative stress, and the AMPK pathway. Immun Inflamm Dis. 2023;11(9). Wang X, Liang Q, Li Z, Li F. Body Composition and COPD: A New Perspective. Int J Chronic Obstr Pulm Dis. 2023;18:79–97. Rabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, Calverley P, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2007;176(6):532–55. Shore SA. Obesity and asthma: Possible mechanisms. J Allergy Clin Immunol. 2008;121(5):1087–93. Chandalia M, Abate N. Metabolic complications of obesity: inflated or inflamed? J Diabetes Complicat. 2007;21(2):128–36. Ray A, Kolls JK. Neutrophilic Inflammation in Asthma and Association with Disease Severity. Trends Immunol. 2017;38(12):942–54. Han H, Choi YJ, Hong H, Kim CY, Byun MK, Cho JH et al. Effects of chitinase-1 inhibitor in obesity-induced and -aggravated asthma in a murine model. Life Sci. 2023;334. Son S-E, Koh J-M, Im D-S. Free fatty acid receptor 4 (FFA4) activation attenuates obese asthma by suppressing adiposity and resolving metaflammation. Biomed Pharmacother. 2024;174. Reiss I, Minas M, Papaioannou AI, Tsaroucha A, Daniil Z, Hatzoglou C et al. Body Composition in Severe Refractory Asthma: Comparison with COPD Patients and Healthy Smokers. PLoS ONE. 2010;5(10). Xiao J, Cain A, Purcell SA, Ormsbee MJ, Contreras RJ, Kim J-S, et al. Sarcopenic obesity and health outcomes in patients seeking weight loss treatment. Clin Nutr ESPEN. 2018;23:79–83. Abdo M, Waschki B, Kirsten A-M, Trinkmann F, Biller H, Herzmann C, et al. Persistent Uncontrolled Asthma: Long-Term Impact on Physical Activity and Body Composition. J Asthma Allergy. 2021;14:229–40. Peters MC, McGrath KW, Hawkins GA, Hastie AT, Levy BD, Israel E, et al. Plasma interleukin-6 concentrations, metabolic dysfunction, and asthma severity: a cross-sectional analysis of two cohorts. Lancet Respiratory Med. 2016;4(7):574–84. Tuttle CSL, Thang LAN, Maier AB. Markers of inflammation and their association with muscle strength and mass: A systematic review and meta-analysis. Ageing Res Rev. 2020;64. Candemir I, Ergun P, Kaymaz D. Efficacy of a multidisciplinary pulmonary rehabilitation outpatient program on exacerbations in overweight and obese patients with asthma. Wiener klinische Wochenschrift. 2017;129(19–20):655–64. Additional Declarations No competing interests reported. Supplementary Files 4Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Aug, 2024 Reviews received at journal 05 Aug, 2024 Reviews received at journal 24 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers agreed at journal 16 Jun, 2024 Reviewers invited by journal 16 Jun, 2024 Editor assigned by journal 04 Jun, 2024 Submission checks completed at journal 02 Jun, 2024 First submitted to journal 29 May, 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-4499532","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313578543,"identity":"e3f50428-f05b-41e6-92ad-9ef8b6792e48","order_by":0,"name":"Jing Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi'an Medical University, Xi’an Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Chen","suffix":""},{"id":313578544,"identity":"4c451c88-7947-4548-aeae-3b9cc3268965","order_by":1,"name":"Zihe Cheng","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi'an Medical University, Xi’an Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zihe","middleName":"","lastName":"Cheng","suffix":""},{"id":313578545,"identity":"98ad60a1-ad86-4038-89d7-11c997478850","order_by":2,"name":"Yang Yao","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi'an Medical University, Xi’an Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yao","suffix":""},{"id":313578546,"identity":"94cf722a-da3b-42d8-adf2-6c8ebce1157a","order_by":3,"name":"Shengyu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYLACxgYI9QBIJJCkhdkAqgXKJ0ILmwRRWgyOnz388ucOmzz5iByzap4KuzwG6ebjD/BqOZOXZs17Jq3Y8EaO2W2eM8nFDDLHEvHaYnYgx8yYse1w4sYZQC28bcyJDRI5hvi1nH9jZviz7T9YSzHvv3qglvyP+LXcyDF+wNt2IHG+RI4ZM2/DYZAt+L1vf+MNUGVbcuIGnmfFknOOHU9sk0gznIFPi2R/jvHHn212ifPbkzd+eFNTndgvkfzgAz4tDNDoYDC4kMDAxAPiElAOAsxgM+X7DzAw/iBC+SgYBaNgFIw8AADW+VK1F999dwAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Xi'an Medical University, Xi’an Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shengyu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-30 00:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4499532/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4499532/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58744690,"identity":"a431b860-0e42-4854-9271-8a085e2de624","added_by":"auto","created_at":"2024-06-20 14:46:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109582,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study participants\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4499532/v1/9ba8687c0ff627f51974d180.png"},{"id":58745321,"identity":"3f8cb5ed-c554-40b1-9b3e-fa94f42341c9","added_by":"auto","created_at":"2024-06-20 14:54:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":214927,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves for all-cause according to FMI quintiles(2A) and FFMI quintiles(2B),Log-rank test revealed statistically significant differences in survival outcomes across FMI and FFMI.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4499532/v1/ca31385a1cc1c5bb148752ee.png"},{"id":58744686,"identity":"1bebcc77-d10d-4cc2-8970-303e179031bf","added_by":"auto","created_at":"2024-06-20 14:46:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":180916,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curves of relations between FMI(3A) and FFMI(3B) with all-cause mortality with asthma. Analysis was adjusted for gender, age, race, poverty to income ratio, education level, drinking status, smoking status, physical activity, hypertension co-existence, diabetes co-existence, and FMI or FFMI. The red line and the shaded area symbolize the hazard ratios and corresponding 95% confidence intervals, respectively.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4499532/v1/d950cc779531d6237fd31651.png"},{"id":58745322,"identity":"ac82cc06-a75e-4bef-9887-9a39b0529216","added_by":"auto","created_at":"2024-06-20 14:54:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":113693,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of Cox regression analysis for all-cause mortality in patients with asthma.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4499532/v1/c601745de01eb7d8f93265ef.png"},{"id":58744688,"identity":"09e58cf9-904a-47a1-bae6-1c9162a4ed72","added_by":"auto","created_at":"2024-06-20 14:46:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":141599,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of subgroup analysis for variation of all-cause mortality with FFMI in individuals with asthma.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4499532/v1/79b28a6544b9d708dd7e6534.png"},{"id":58744685,"identity":"edbca280-8b3a-4c7f-9c05-02621c6f3403","added_by":"auto","created_at":"2024-06-20 14:46:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":137394,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of subgroup analysis for variation of all-cause mortality with FMI in individuals with asthma.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4499532/v1/a18d759b7d152dd8b7354567.png"},{"id":58745891,"identity":"a4f14f97-7b54-4d7f-abff-63aa487966cb","added_by":"auto","created_at":"2024-06-20 15:02:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1479056,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4499532/v1/1dff1215-86a2-4264-9e3d-3cb3428f58ca.pdf"},{"id":58744683,"identity":"3e27e43d-5660-4dba-8cfe-1f0a6dc29b7d","added_by":"auto","created_at":"2024-06-20 14:46:35","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":19743,"visible":true,"origin":"","legend":"","description":"","filename":"4Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4499532/v1/679b451989d7958afb843e21.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Variation of all-cause mortality with Fat-free Mass Index（FFMI）and Fat Mass Index（FMI）in individuals with Asthma: results from the NHANES database prospective cohort study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAsthma, a chronic inflammatory respiratory disorder, impacts over 300\u0026nbsp;million people globally, establishing it as the most prevalent long-term respiratory disease[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It arises from intricate interactions between genetic predispositions and environmental influences, leading to symptoms such as wheezing, coughing, chest tightness, and episodic shortness of breath[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Asthma's heterogeneity is exemplified by its variable airway obstruction and bronchial hyperresponsiveness, contributing significantly to its morbidity and mortality rates. Economically, the burden is staggering; in the United States alone, the direct medical costs, coupled with indirect expenses from mortality and lost productivity, exceed \u003cspan\u003e$\u003c/span\u003e80\u0026nbsp;billion annually[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent research highlights the critical role of body composition in respiratory health, demonstrating that higher muscle mass correlates with improved pulmonary function, evidenced by normal values in Forced Expiratory Volume in one second (FEV\u003csub\u003e1\u003c/sub\u003e) and Forced Vital Capacity (FVC)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Variations in body composition are also linked to different respiratory diseases; for instance, obesity and increased adiposity are frequently associated with asthma and chronic bronchitis, while rapid gains in fat mass (FM) have been identified as potential risk factors for the development of airway hyperresponsiveness (AHR) in adults[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Chronic obstructive pulmonary disease (COPD), on the other hand, is commonly associated with underweight and reduced lean body mass [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, elevated total and abdominal fat indices in adolescents are associated with impaired lung function, whereas greater FFM during childhood and adolescence correlates with enhanced function of small airways[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIncreased fat mass is prevalent in individuals with treatment-resistant asthma and correlates strongly with diminished control of asthma symptoms, particularly among male patients. Research suggests that targeted weight loss interventions can enhance pulmonary function in those afflicted with refractory asthma[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, obesity may induce respiratory dysfunction due to an imbalance between the growth of lung parenchyma and airway size. This discrepancy can result in seemingly normal FEV\u003csub\u003e1\u003c/sub\u003e and FVC measures, yet an abnormal FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio, a diagnostic marker of airflow obstruction, heightening asthma risk. Furthermore, adipose tissue can aggravate airway inflammation by secreting pro-inflammatory cytokines, complicating the pathophysiological landscape of asthma[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eElevated body mass index (BMI) is well-documented as a significant risk factor for asthma and chronic bronchitis in adults[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Targeted interventions, such as weight loss and sustained weight management, are effective in mitigating asthma symptoms[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Obesity also contributes to reduced tidal volume and functional residual capacity, impairing pulmonary function[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Both asthma and obesity are characterized by systemic inflammation, with the tumor necrosis factor α (TNF-α) pathway playing a crucial role in the pathophysiology of both conditions. Dysregulation of this pathway can exacerbate when both diseases coexist[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious research has extensively explored the relationship between physical indicators and health outcomes such as cardiovascular disease, COPD, and population-wide mortality. While similar studies in asthma have primarily examined its prevalence and morbidity, the association between FFMI and FMI with all-cause mortality in asthmatic patients remains less understood. Our investigation seeks to fill this knowledge gap by analyzing the correlation between FMI and FFMI with all-cause mortality among asthma patients using data from the National Health and Nutrition Examination Survey (NHANES) database.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eThis study leveraged three datasets-Demographics Data, Examination Data, and Questionnaire Data-rom the NHANES covering the periods 1999\u0026ndash;2006 and 2011\u0026ndash;2018. A total of 15 200 adults were selected for analysis, with strict exclusion criteria applied to enhance data reliability: individuals lacking complete information on critical physical indicators, baseline data, or mortality records were systematically excluded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, pregnant women were not included in the dual-energy X-ray absorptiometry (DXA) assessments. The study focused on adults aged 20 to 85 years. Mortality status was determined through the NHANES public-use linked mortality file, updated as of December 31, 2018, and cross-referenced with the National Center for Health Statistics (NCHS) and the National Death Index (NDI) via a probability matching algorithm, ensuring accurate mortality tracking.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment\u003c/h2\u003e \u003cp\u003eNHANES staff used questionnaires during home interviews to collect data on smoking status, alcohol consumption, physical activity levels, and medical history, including the presence of COPD, chronic bronchitis, emphysema, asthma, hypertension, and diabetes. In addition to these health variables, demographic data such as age, gender, race, poverty-income ratio (PIR), and education level were meticulously collected to support nuanced analysis. At the mobile physical examination centers, the study further assessed anthropometric and compositional measurements of the participants, including height, weight, total fat mass, lean mass which encompasses bone mineral content (BMC), and BMI.\u003c/p\u003e \u003cp\u003eThe NHANES DXA examination offers nationally representative data on body composition, encompassing both bone and soft tissue. The total body and regional values include total mass (g), BMC (g), fat mass (g), and lean mass (g) including BMC (g). FFMI was derived by dividing FFM (kg) by the square of height (m\u003csup\u003e2\u003c/sup\u003e), based on the analogy of BMI and the high correlation between FFM and height. DXA scanning assessed body composition, including lean mass, fat mass, and bone mineral content in patients. Body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e) is calculated as weight divided by height squared, while fat mass index (FMI, kg/m\u003csup\u003e2\u003c/sup\u003e) is calculated as fat mass divided by height squared. The Fat-free mass index (FFMI, kg/m\u003csup\u003e2\u003c/sup\u003e) is calculated by dividing the sum of total lean mass and bone mineral content by the square of height [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGender is classified as male or female. Age is classified as under 40 years old, 40\u0026ndash;65 years old, or over 65 years old. Race is classified as non-Hispanic whites, non-Hispanic blacks, Hispanics (excluding Mexican Americans), Mexican Americans, or other races. Education level is classified into five categories: Less Than 9th Grade, 9-11th Grade, High School Graduate or General Educational Development (GED), Some College or Associate's Degree, and College Graduate or higher. The PIR is categorized as follows: \u0026le;130%, \u0026gt;\u0026thinsp;130\u0026ndash;350% (reference group), and \u0026gt;\u0026thinsp;350% (calculated by dividing household income by the poverty threshold).\u003c/p\u003e \u003cp\u003ePhysical activity is measured in Metabolic equivalent (MET) minutes per week and classified as low (\u0026lt;\u0026thinsp;600 minutes per week), moderate (600-8,000 minutes per week), or high (\u0026ge;\u0026thinsp;8,000 minutes per week). MET represents the oxygen consumption required to maintain resting metabolism and is a common indicator used to express the relative energy metabolism level during various activities. BMI is calculated by dividing weight in kilograms by height in meters squared (kg/m\u0026sup2;). The categories are as follows: \u0026le;25 (reference group), \u0026gt;\u0026thinsp;25\u0026ndash;30, and \u0026gt;\u0026thinsp;30.\u003c/p\u003e \u003cp\u003eRespondents were classified as former smokers if they had smoked at least 100 cigarettes in their lifetime and had quit smoking by the time they answered the questionnaire. Smoking status was categorized into three groups: never-smokers (reference group), former smokers, and current smokers. Participants were divided into four categories based on their alcohol consumption: Non-drinker (reference group), 1-\u0026lt;5 drinks/month, 5-\u0026lt;10 drinks/month, and \u0026ge;\u0026thinsp;10 drinks/month. The average number of alcoholic beverages consumed per month in the past 12 months was calculated based on these questions.\u003c/p\u003e \u003cp\u003eThe study categorized diabetes into three groups: 'yes', 'borderline', and 'no'. Hypertension was categorized into two groups: 'yes' and 'no', based on the participants' questionnaire data. Each covariate was divided into a reference group and other groups. When analyzing, all other groups are compared to this reference group to estimate the relative odds ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eTo explore the association between physical indices (FFMI and FMI) and mortality among patients with asthma, this study employed Kaplan-Meier plots and log-rank tests to examine survival differences across quintiles of FFMI and FMI. We developed a Cox proportional hazards regression model, adjusting for multiple covariates including sex, age, race, smoking status, alcohol consumption, PIR, education level, physical activity, and the presence of hypertension and diabetes. Further, restricted cubic spline analysis was utilized to investigate nonlinear relationships between FFMI, FMI, and mortality. When a nonlinear association was detected, a recursive algorithm determined the inflection point, and threshold effect analysis was subsequently employed for validation. Interaction and subgroup analyses were conducted to scrutinize relationships within various categories. The χ2 test was applied to categorical variables, while a linear regression model was used for continuous variables. Categorical data were presented as frequencies and percentages, and continuous variables were expressed as means and standard deviations. Statistical analyses were performed using R version 4.2.2, with significance determined by a two-tailed p-value.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study participants\u003c/h2\u003e \u003cp\u003eAfter a median follow-up for 184 months, 13,360 were presumed alive, while 1,840 had passed away in the 15,200 participants by December 31, 2018.FMI was divided into 5 groups according to quintiles: Q1:0.9\u0026ndash;3.4, Q2:3.5\u0026ndash;4.7, Q3:4.8\u0026ndash;6.1, Q4:6.2\u0026ndash;8.3, Q5:8.4\u0026ndash;22.8; FFMI was divided into 5 groups according to quintiles: Q1:10.4\u0026ndash;16.0, Q2:16.1\u0026ndash;17.8, Q3:17.9\u0026ndash;19.5, Q4:19.6\u0026ndash;21.5, Q5:21.6\u0026ndash;38.5, and the baseline map was obtained according to the survival status (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition to race and FFMI, differences in FMI, gender, age, smoking status, poverty-to-income ratio, education, drinking status, physical activity, hypertension coexistence, and diabetes coexistence were statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the survivor and mortality groups. Among the racial variables, non-Hispanic whites were disproportionately represented. In the age group, patients aged 65 years or older accounted for a smaller proportion of the overall population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants by survival status (n\u0026thinsp;=\u0026thinsp;15200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esurvival, N\u0026thinsp;=\u0026thinsp;13360 (92%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edeath, N\u0026thinsp;=\u0026thinsp;1840 (8.2%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,544 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,100 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,816 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e740 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,657 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e855 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40-\u0026le;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,183 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e520 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e895 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,382 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,056 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,090 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e450 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,513 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/multiracial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e742 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e633 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,806(58.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e683(37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,873(21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e638(34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,674(20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e519(28.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty income ratio (PIR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,944 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e617 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3-\u0026le;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,992 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e662 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,697 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e420 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess Than 9th Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e327 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,495 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e304 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School Grad/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,422 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome College or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,870 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e563 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege Graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,183 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,810 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-\u0026le;5 drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,064 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e696 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-\u0026le;10 drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,615 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;10 drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,962 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e384 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enormal physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,033(30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e404(22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,002(67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,429(77.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e325(2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,850 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,080 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,628 (96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,306 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorderline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e560 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e434 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (18.5-\u0026lt;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,471 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e403 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (25 -\u0026lt;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,134 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e713 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026ge;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,511 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e703 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, mean (SD) (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.71 (1.63, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65 (1.59, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI,mean (SD) (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (23, 31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (26, 33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMI, mean (SD) (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.88 (3.41, 6.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.60 (4.90, 8.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFMI, mean (SD) (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.5 (16.3, 20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.2 (15.6, 20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1(0.9\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,615 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2(3.5\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,550 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3(4.8\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,635 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4(6.2\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,621 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e541 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5(8.4\u0026ndash;22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,939 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e631 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1(10.4\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,183 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e489 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2(16.1\u0026ndash;17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,606 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3(17.9\u0026ndash;19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,840 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e398 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4(19.6\u0026ndash;21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,785 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5(21.6\u0026ndash;38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,946 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 All-cause mortality\u003c/h2\u003e \u003cp\u003eThe relationship between FMI, FFMI, and all-cause mortality is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The Kaplan-Meier survival curves indicated statistically significant differences in survival probabilities across different FMI and FFMI groups, as confirmed by the log-rank test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, survival outcomes worsened with increasing FMI values. Conversely, a decrease in FFMI was generally associated with poorer survival outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore the relationship between FMI and all-cause mortality among asthma patients, we designated the Q1(0.9\u0026ndash;3.4) FMI group as the reference category. Model 1, adjusted for continuous FFMI variables, indicated increased all-cause mortality for the Q3 (4.8\u0026ndash;6.1), Q4 (6.2\u0026ndash;8.3), and Q5 (8.4\u0026ndash;22.8) FMI groups with HRs of 4.48 (95% CI: 1.82,11.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 6.4 (95% CI: 2.85,14.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 8.96 (95% CI: 4.32,18.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) respectively, with a statistically significant trend (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Model 2, further adjusted for age, sex, race, and smoking status, showed similar trends, with HRs of 2.39 (95% CI: 1.07,5.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), 3.31 (95% CI: 1.61,6.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and 3.95 (95% CI: 1.62,9.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for Q3, Q4, and Q5 respectively (\u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001).Model 3 included adjustments for a comprehensive set of variables including poverty-to-income ratio, education, alcohol consumption status, physical activity, diabetes co-existence, and hypertension co-existence. The HRs in this model were 2.9 (95% CI: 1.20,7.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 3.37 (95% CI: 1.41,8.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and 4.6 (95% CI: 1.31,16.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for Q3, Q4, and Q5 respectively, maintaining a significant trend (\u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.008). Additionally, we investigated the impact of FFMI on mortality by setting the Q5(21.6\u0026ndash;38.5) FFMI group as the reference. Model 1, adjusted for FMI continuity variables, revealed that the Q1(10.4\u0026ndash;16.0) group of FFMI had an increased risk of mortality with an HR of 4.16 (95% CI: 1.82,9.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.017). Model 2 adjusted for FMI continuity variables, age, gender, race, and smoking status. The Q1(10.4\u0026ndash;16.0) group of FFMI increased the all-cause mortality of asthma patients (HR:3.32, 95%CI: 1.44,7.62), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.021; Model 3 adjusted for FMI continuity variables, sex, age, race, poverty-to-income ratio, education, smoking status, alcohol consumption status, physical activity, diabetes, and hypertension. The Q1(10.4\u0026ndash;16.0) group of FFMI increased all-cause mortality in patients with asthma (HR:4.63,95% CI: 1.59,13.5), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.016) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratio (95% CI) for all-cause mortality according to different levels of FMI, FFMI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1(0.9\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2(3.5\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.92(0.86, 4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24(0.60, 2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84(0.34, 2.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3(4.8\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.48(1.82, 11.0) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39(1.07, 5.36) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9(1.20, 7.00) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4(6.2\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.4(2.85, 14.3) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31(1.61, 6.79) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.37(1.41, 8.03) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5(8.4\u0026ndash;22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.96(4.32, 18.6) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.95(1.62, 9.63) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6(1.31, 16.2) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1(10.4\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.16(1.82, 9.49) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.32(1.44, 7.62) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.63(1.59, 13.5) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2(16.1\u0026ndash;17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.82(0.81, 4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46(0.66, 3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68(0.70, 4.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3(17.9\u0026ndash;19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38(1.15, 4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61(0.80, 3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.04(0.86, 4.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4(19.6\u0026ndash;21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31(0.84, 6.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51(0.42, 5.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.05(0.36, 11.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5(21.6\u0026ndash;38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCI\u0026thinsp;=\u0026thinsp;Confidence interval, HR\u0026thinsp;=\u0026thinsp;Hazard ratio. *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe relationship between FMI and FFMI with all-cause mortality among asthmatic patients was explored using restricted cubic spline analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This analysis highlighted a statistically significant non-linear relationship (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with spline knots positioned at the 1st, 50th, and 90th percentiles for FMI, and the 5th, 35th, 65th, and 95th percentiles for FFMI. The reference points were established at 5.4 kg/m\u0026sup2; for FMI and 18.6 kg/m\u0026sup2; for FFMI. Results from the analysis indicated distinct patterns in the association between body composition and mortality risk. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA illustrates that the minimum and maximum mortality risks for FMI were observed at 1.3 kg/m\u0026sup2; and 21.7 kg/m\u0026sup2;, respectively. At the reference FMI of 5.4 kg/m\u0026sup2;, the HR was set at 1. Below this reference, the risk ratio was less than 1, signifying a reduced mortality risk. Conversely, above this reference value, the risk ratio significantly exceeded 1, suggesting an increased mortality risk associated with higher FMI. This trend of increasing risk ratio with rising FMI was statistically significant. Similarly, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB depicts the mortality risk associated with FFMI. The highest and lowest mortality risks occurred at FFMI levels of 11.9 kg/m\u0026sup2; and 34.7 kg/m\u0026sup2;, respectively. At the reference FFMI of 18.6 kg/m\u0026sup2;, the HR was 1. Mortality risk was significantly higher below this reference value and decreased below 1 with increasing FFMI, indicating a protective effect of higher FFMI against mortality in asthmatic patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThreshold effect analysis identified critical inflection points for FFMI and FMI, influencing all-cause mortality among patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The analysis determined that the inflection point for FFMI is 18.6 kg/m\u003csup\u003e2\u003c/sup\u003e. Below this threshold, each two-fold increase in FFMI was associated with a 29% reduction in the risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;0.71, 95% CI: 0.56, 0.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Conversely, the inflection point for FMI was established at 5.4 kg/m\u003csup\u003e2\u003c/sup\u003e. Below this threshold, each two-fold increase in FMI corresponded to an 84% increase in the risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.84, 95% CI: 1.08,3.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025). The log-likelihood ratio test further substantiated these findings, indicating a significant statistical difference between the two models (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis of FFMI and FMI on mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll-cause mortality HR (95%CI) for FFMI, \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll-cause mortality HR (95%CI) for FMI, \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92(0.80, 1.05), 0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18(1.06, 1.32), 0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFitting by two-piecewise Cox proportional risk model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;Inflection value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71(0.56, 0.89), 0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84(1.08, 3.15), 0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;Inflection value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01(0.84, 1.21), \u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11(0.92, 1.35), 0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for Log-likelihood ratiolikelihood ratiolikelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCI\u0026thinsp;=\u0026thinsp;Confidence interval, HR\u0026thinsp;=\u0026thinsp;Hazard ratio, FFMI and FMI fit the Cox regression model. Analysis was adjusted for gender, age, race, poverty to income ratio, education level, drinking status, smoking status, physical activity, hypertension co-existence, diabetes co-existence and FMI or FFMI.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe lowest quintile for FFMI (Q1: 10.4\u0026ndash;16.0) was associated with an increased risk of all-cause mortality. Similarly, higher quintiles of FMI (Q3: 4.8\u0026ndash;6.1, Q4: 6.2\u0026ndash;8.3, and Q5: 8.4\u0026ndash;22.8) were linked to elevated mortality rates. Additionally, specific demographic and behavioral factors were found to exacerbate mortality risks. Male gender, smoking, high alcohol consumption (more than 10 drinks per day), and co-existing diabetes significantly increased the risk of death among these patients(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Stratified analyses\u003c/h2\u003e \u003cp\u003eThere is no interaction between continuous variable FFMI and other covariables (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Continuous variable FMI has interaction with three covariables: gender (\u003cem\u003ep\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.009), poverty index (\u003cem\u003ep\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.003) and education level (p for interaction\u0026thinsp;=\u0026thinsp;0.014). With the increase of FMI, there was a significant increase in mortality in males, middle-income groups and High School Grad/GED educated groups, with hazard ratios of 1.56, (95% CI: 1.23, 1.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 1.32, (95% CI: 1.11, 1.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and 1.63, (95% CI: 1.18, 2.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe association between FFMI, FMI, and cause-specific mortality in patients with asthma is shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The stratified analysis of the relationship between quintile grouping variables FFMI, FMI and mortality of asthmatic patients is shown in Table S2, Table S3 and Table S4.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe exclusive reliance on BMI as a measure of health in patients with asthma, without consideration of specific body composition details such as fat distribution or muscle mass, may obscure critical associations between these elements and an increased risk of mortality [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For patients with asthma, BMI alone provides an incomplete picture of health status and prognostic. It is imperative, therefore, to integrate other physical indices such as FMI and FFMI to furnish a more comprehensive assessment of patient condition and prognosis. Our research elucidates a significant association between these physical indices and mortality risk in asthma patients, offering valuable perspectives on how to enhance survival outcomes.\u003c/p\u003e \u003cp\u003eOur research highlights a significant increase in mortality risk among asthma patients associated with elevated FMI, a finding that underscores the complex interplay between obesity and asthma. The linkage between increased FMI and asthma mortality reflects broader epidemiological observations where obesity exacerbates both the prevalence and severity of asthma due to a variety of mechanisms, including mechanical factors, systemic inflammation, and comorbidities [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Obesity induces systemic inflammation, contributing to metabolic syndrome, insulin resistance, and heightened levels of C-reactive protein (CRP) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In patients with severe refractory asthma (SRA), which is characterized by chronic airway inflammation often with neutrophil activation. This inflammatory cascade can impair respiratory function and lead to airflow obstruction by altering the FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].The management of asthma in obese patients thus presents unique challenges, including increased exacerbations and diminished responsiveness to conventional corticosteroid treatment. Recent research has delineated obesity-related non-T2 inflammatory pathways such as mechanical stress, hyperinsulinemia, and metabolic disturbances as pivotal in exacerbating asthma symptoms and therapeutic resistance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].Promisingly, interventions aimed at mitigating obesity's impact-through weight management, dietary modifications, and targeted therapies-have shown potential in improving outcomes for asthmatic patients. Dietary approaches like high-fat diets (HFD) have been implicated in increasing inflammation and oxidative stress, whereas agents like pentamidine-liked bisindole may activate anti-inflammatory pathways, such as AMPK, offering therapeutic relief in obesity-related asthma[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, novel pharmacological treatments like CPX (carnosine-phenylalanine-boron) and FFA4 activators are being explored for their efficacy in treating obesity-aggravated asthma, particularly by preventing airway remodeling and fibrosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur research indicates that a low FFMI significantly elevates all-cause mortality in patients with asthma, corroborating existing literature that associates higher FFMI with improved respiratory function, as evidenced by normal FEV\u003csub\u003e1\u003c/sub\u003e and FVC values, and fewer respiratory symptoms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Notably, low muscle mass is commonly observed in patients with moderate to severe asthma and is particularly prevalent in those suffering from SRA. These patients often present with more pronounced airway obstruction and reduced functional motor capacity, which contribute to poorer clinical outcomes and compromised functional status [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].Furthermore, muscle-reducing obesity has been identified as a significant predictor of asthma, emphasizing the role of muscle mass in respiratory health. The negative impact of reduced muscle mass extends to greater airway obstruction and decreased functional motor ability, while diminished muscle strength correlates with poorer asthma control and reduced quality of life [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].A longitudinal study from Germany highlighted that patients with uncontrolled asthma experienced notable declines in muscle mass over a two-year period, despite maintaining a stable BMI[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Such findings align with broader research, which implicates systemic inflammation as a mediator of muscle dysfunction in various conditions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Markers such as CRP, TNF-α, and IL-6 have been particularly noted for their involvement in the development of muscle dysfunction in both acute and chronic diseases[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].The positive impact of comprehensive multidisciplinary pulmonary rehabilitation has been documented, demonstrating reductions in emergency admissions and hospitalizations for asthma attacks over one year. Additionally, these programs have shown improvements in dyspnea, quality of life, exercise capacity, and psychosocial status among overweight and obese patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].Collectively, our findings and the corroborative evidence from the literature underscore the imperative to consider FFMI as a critical factor in the prognosis and management of asthma.\u003c/p\u003e \u003cp\u003eOur study establishes a significant association between both FMI and FFMI with all-cause mortality among patients with asthma, emphasizing the critical role of body composition in influencing health outcomes in this population. These findings illuminate potential pathways for targeted interventions and underscore the necessity for personalized healthcare strategies that consider body composition as a key factor. The utilization of a large national cohort has allowed us to explore the relationships between FFMI, FMI, and survival status comprehensively. The detailed individual records and extensive follow-up period averaging fourteen years lend considerable credibility to our findings. However, our study is not without limitations. The reliance on questionnaire data for disease status introduces potential recall bias, which may affect the accuracy of the reported health information. Additionally, the absence of lung function data precludes a precise assessment of asthma severity within our cohort, which is critical for a deeper understanding of the disease impact on mortality. Looking ahead, future research could benefit from a multicenter study or meta-analysis to broaden the understanding of these associations across different populations and healthcare settings. Moreover, our analysis did not account for dietary factors, which are increasingly recognized as influential in asthma management. Subsequent studies should aim to identify optimal dietary patterns for individuals with asthma and investigate the interplay between diet, nutritional factors, and asthma outcomes. Such research would further refine our approach to managing asthma, potentially leading to more effective and tailored treatment protocols.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAHR\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; airway hyperresponsiveness\u003c/p\u003e\n\u003cp\u003eBMC\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; bone mineral content\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; body mass index\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; confidence interval\u003c/p\u003e\n\u003cp\u003eCOPD\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eCRP\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; C-reactive protein\u003c/p\u003e\n\u003cp\u003eDXA\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; dual-energy X-ray absorptiometry\u003c/p\u003e\n\u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; forced expiratory volume in one second\u003c/p\u003e\n\u003cp\u003eFFMI\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; fat-free mass index\u003c/p\u003e\n\u003cp\u003eFM\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; fat mass\u003c/p\u003e\n\u003cp\u003eFMI\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; fat mass index\u003c/p\u003e\n\u003cp\u003eFVC\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; forced vital capacity\u003c/p\u003e\n\u003cp\u003eGED\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; General Educational Development\u003c/p\u003e\n\u003cp\u003eGED\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; General Educational Development\u003c/p\u003e\n\u003cp\u003eHFD\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; high-fat diets\u003c/p\u003e\n\u003cp\u003eHR\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; hazard ratio\u003c/p\u003e\n\u003cp\u003eMET\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; metabolic equivalent\u003c/p\u003e\n\u003cp\u003eNCHS\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; National Center for Health Statistics\u003c/p\u003e\n\u003cp\u003eNDI\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; national death index\u003c/p\u003e\n\u003cp\u003eNHANES\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003ePIR\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; poverty-income ratio\u003c/p\u003e\n\u003cp\u003eRCS\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; restricted cubic splines\u003c/p\u003e\n\u003cp\u003eSRA\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; severe refractory asthma\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; tumor necrosis factor \u0026alpha;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate \u003c/strong\u003eNo individual participated to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication \u003c/strong\u003eNo individual participated to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e Researchers can download the datasets free of charge from the following website: https://www.cdc.gov/nchs/nhanes/index.htm;https://www.cdc.gov/nchs/data-linkage/mortality-public.htm\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e None to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e Foundation and Transformation of Prevention and Treatment of acute Lung Injury, Xi 'an Medical University (Shaanxi Jiaohan [2024] No. 21); Acute Lung Injury Prevention and Treatment Foundation and Transformation Innovation Team (Shaanxi Jiaohan [2023] No. 997)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions \u003c/strong\u003eW.S. and Y.Y. were responsible for the study concept and design. C.J. extracted and \u003cstrong\u003eprocessed\u003c/strong\u003e the data inputs. C.J. wrote the first draft of the paper. C.Z. contributed to writing subsequent versions of the manuscript, critically reviewed the methods and results, and approved the final version of the manuscript. C.Z. managed the project. All authors provided intellectual inputs into aspects of this study.\u0026nbsp; C.J. and Y.Y. accessed and verified the data. W.S. had final responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e The author\u003cstrong\u003es\u003c/strong\u003e would like to thank the Xi\u0026rsquo;an Medical University for assistance with access to funding bodies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSoriano JB, Abajobir AA, Abate KH, Abera SF, Agrawal A, Ahmed MB, et al. Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990\u0026ndash;2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Respiratory Med. 2017;5(9):691\u0026ndash;706.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMims JW. Asthma: definitions and pathophysiology. Int Forum Allergy Rhinology. 2015;5:S1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah R, Yang Y. Health and Economic Burden of Obesity in Elderly Individuals with Asthma in the United States. Popul Health Manage. 2015;18(3):186\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchiffers C, Faner R, Ofenheimer A, Sunanta O, Puchhammer P, Mraz T, et al. Supranormal lung function: Prevalence, associated factors and clinical manifestations across the lifespan. Respirology. 2023;28(10):942\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShim J-S, Kim S-S, Lee S-H, Kim M-H, Cho Y-J, Park H-W. Fat mass index and airway hyperresponsiveness in Korean adults. Postgrad Med. 2023;135(5):480\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisser E, de Jong K, van Zutphen T, Kerstjens HAM, ten Brinke A. Muscle Function in Moderate to Severe Asthma: Association With Clinical Outcomes and Inflammatory Markers. J Allergy Clin Immunology: Pract. 2023;11(5):1439\u0026ndash;e473.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrompeter G, Grigsby MR, Miele CH, Wise RA, Gilman RH, Miranda JJ, et al. Patterns of Body Composition Relating to Chronic Respiratory Diseases Among Adults in Four Resource-Poor Settings in Peru. Lung. 2018;196(3):277\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu T, Santos S, Quezada-Pinedo HG, Vernooij MW, Jaddoe VWV, Klein S, et al. Body composition and respiratory outcomes in children: a population-based prospective cohort study. Thorax. 2024;79(5):448\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBafadhel M, Singapuri A, Terry S, Hargadon B, Monteiro W, Green RH, et al. Body Mass and Fat Mass in Refractory Asthma: An Observational 1 Year Follow-Up Study. J Allergy. 2010;2010:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma V, Cowan DC. Obesity, Inflammation, and Severe Asthma: an Update. Curr Allergy Asthma Rep. 2021;21(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu Y-E, Chen S-C, Geng J-H, Wu D-W, Wu P-Y, Huang J-C. Obesity-Related Indices Are Associated with Longitudinal Changes in Lung Function: A Large Taiwanese Population Follow-Up Study. Nutrients. 2021;13(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLachlan CR, Poulton R, Car G, Cowan J, Filsell S, Greene JM, et al. Adiposity, asthma, and airway inflammation. J Allergy Clin Immunol. 2007;119(3):634\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSapey E, Wood AM, Ahmad A, Stockley RA. Tumor Necrosis Factor\u0026ndash;α rs361525 Polymorphism Is Associated with Increased Local Production and Downstream Inflammation in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2010;182(2):192\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShailesh H, Bhat AA, Janahi IA. Obesity-Associated Non-T2 Mechanisms in Obese Asthmatic Individuals. Biomedicines. 2023;11(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Liang D, Liu L, Liu L. Plumbagin alleviates obesity-related asthma: Targeting inflammation, oxidative stress, and the AMPK pathway. Immun Inflamm Dis. 2023;11(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Liang Q, Li Z, Li F. Body Composition and COPD: A New Perspective. Int J Chronic Obstr Pulm Dis. 2023;18:79\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, Calverley P, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2007;176(6):532\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShore SA. Obesity and asthma: Possible mechanisms. J Allergy Clin Immunol. 2008;121(5):1087\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandalia M, Abate N. Metabolic complications of obesity: inflated or inflamed? J Diabetes Complicat. 2007;21(2):128\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRay A, Kolls JK. Neutrophilic Inflammation in Asthma and Association with Disease Severity. Trends Immunol. 2017;38(12):942\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan H, Choi YJ, Hong H, Kim CY, Byun MK, Cho JH et al. Effects of chitinase-1 inhibitor in obesity-induced and -aggravated asthma in a murine model. Life Sci. 2023;334.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSon S-E, Koh J-M, Im D-S. Free fatty acid receptor 4 (FFA4) activation attenuates obese asthma by suppressing adiposity and resolving metaflammation. Biomed Pharmacother. 2024;174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReiss I, Minas M, Papaioannou AI, Tsaroucha A, Daniil Z, Hatzoglou C et al. Body Composition in Severe Refractory Asthma: Comparison with COPD Patients and Healthy Smokers. PLoS ONE. 2010;5(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao J, Cain A, Purcell SA, Ormsbee MJ, Contreras RJ, Kim J-S, et al. Sarcopenic obesity and health outcomes in patients seeking weight loss treatment. Clin Nutr ESPEN. 2018;23:79\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdo M, Waschki B, Kirsten A-M, Trinkmann F, Biller H, Herzmann C, et al. Persistent Uncontrolled Asthma: Long-Term Impact on Physical Activity and Body Composition. J Asthma Allergy. 2021;14:229\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters MC, McGrath KW, Hawkins GA, Hastie AT, Levy BD, Israel E, et al. Plasma interleukin-6 concentrations, metabolic dysfunction, and asthma severity: a cross-sectional analysis of two cohorts. Lancet Respiratory Med. 2016;4(7):574\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuttle CSL, Thang LAN, Maier AB. Markers of inflammation and their association with muscle strength and mass: A systematic review and meta-analysis. Ageing Res Rev. 2020;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCandemir I, Ergun P, Kaymaz D. Efficacy of a multidisciplinary pulmonary rehabilitation outpatient program on exacerbations in overweight and obese patients with asthma. Wiener klinische Wochenschrift. 2017;129(19\u0026ndash;20):655\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\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":"journal-of-epidemiology-and-global-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Epidemiology and Global Health](https://www.springer.com/journal/44197)","snPcode":"44197","submissionUrl":"https://submission.nature.com/new-submission/44197/3","title":"Journal of Epidemiology and Global Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"FFMI, FMI, Asthma, All-cause mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-4499532/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4499532/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e The relationship between FFMI, FMI, and mortality in patients with asthma remains unknown. This study aimed to examine the associations between FFMI and FMI and all-cause mortality in a cohort of American adults diagnosed with asthma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e This study included 15200 adults from NHANES. To assess mortality, we linked participant records to the National Death Index. Survival differences across quintiles of FFMI and FMI were explored using Kaplan-Meier plots and log-rank tests. The study investigated the nonlinear relationships between FFMI, FMI, and mortality. Additionally, stratified analyses were undertaken to pinpoint subgroups of individuals who exhibited an elevated risk of mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e After a median follow-up, 13360 were presumed alive and 1840 had deceased. Kaplan-Meier plots revealed significant differences in all-cause mortality in patients with asthma among the FFMI quintiles and FMI quintiles. Specifically, individuals in the lowest FFMI quintile (Q1, 10.4-16.0) exhibited a markedly increased risk of all-cause mortality (HR: 4.63; 95% CI: 1.59,13.5; \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01). Similarly, increased all-cause mortality risks were observed in the upper three quintiles of FMI-Q3 (4.8-6.1) with HR: 2.9 (1.20,7.00, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05), Q4 (6.2-8.3) with HR: 3.37 (1.41,8.03, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01), and Q5 (8.4-22.8) with HR: 4.6 (1.31,16.2, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). Moreover, the risk of all-cause mortality increased with increasing FMI and decreasing FFMI (\u003cem\u003ep\u003c/em\u003e for non-linearity\u0026lt;0.001 in both cases) respectively. Subgroup analyses further elucidated these associations across different categories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e All-cause mortality is decreased in patients with asthma when FMI is less than 5.4 kg/m\u003csup\u003e2\u003c/sup\u003e, and increased when FFMI is less than 18.6 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e","manuscriptTitle":"Variation of all-cause mortality with Fat-free Mass Index（FFMI）and Fat Mass Index（FMI）in individuals with Asthma: results from the NHANES database prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 14:46:27","doi":"10.21203/rs.3.rs-4499532/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-06T10:05:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-05T19:04:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-25T01:11:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269111414863890678623215030158035958050","date":"2024-07-22T08:20:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174131038397908268467491568164653148080","date":"2024-07-16T19:31:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335971005437044568374085336513489372634","date":"2024-07-15T16:52:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197150379473490396592587959549876379954","date":"2024-06-16T13:20:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-16T11:19:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-04T11:32:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-03T00:47:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Epidemiology and Global Health","date":"2024-05-30T00:31:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-epidemiology-and-global-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Epidemiology and Global Health](https://www.springer.com/journal/44197)","snPcode":"44197","submissionUrl":"https://submission.nature.com/new-submission/44197/3","title":"Journal of Epidemiology and Global Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ec18f8e-1ab7-4e25-8fad-09f420cd35aa","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-21T12:21:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-20 14:46:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4499532","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4499532","identity":"rs-4499532","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}