Association of Life’s Crucial 9 with asthma in U.S. adults: a cross-sectional study from National Health and Nutrition Examination Survey (NHANES)

Introduction

Asthma constitutes a prevalent chronic disorder characterized by sustained airway inflammation, posing a significant global health burden (1,2). Its prevalence continues to rise globally, as shown by the Global Burden of Disease Study (3). Asthma has a complex etiology that includes lifestyle variables, environmental exposures, and genetic predisposition (4). There is growing evidence that cardiovascular disease (CVD) and asthma are significantly correlated (5-7). Smoking, exposure to environmental toxins, obesity, sedentary lifestyles, and chronic inflammation are risk factors for both illnesses (8-10). Accordingly, efforts to promote better cardiovascular health (CVH) may be able to successfully forestall the onset of asthma.

For the purpose of enhancing heart health and minimizing the likelihood of CVD, the American Heart Association developed Life’s Simple 7 in 2010 (11). Recognizing the importance of sleep for CVH, the American Heart Association added sleep to Life’s Simple 7, creating Life’s Essential 8 (LE8) (12). Depression is a prevalent and debilitating mental disorder that significantly affects an individual’s emotional, cognitive, and physical well-being. More than 300 million individuals worldwide experience depression, making it a major contributor to impairment rates (13). Extensive research has robustly established depression as an independent risk factor for CVD (14-16). Recently, a new perspective has shown how important psychological health is for warding off CVD, and a new framework, Life’s Crucial 9 (LC9), has been suggested, which builds on the current LE8 by including psychological health (17). Recent research has examined LC9’s potential as a predictor of cardiovascular mortality, infertility, and overactive bladder (18-20). Nonetheless, no studies have investigated whether LC9 is associated with asthma incidence. Several lines of evidence support the rationale for investigating LC9 in relation to asthma. Previous studies have demonstrated that higher LE8 scores are associated with a lower likelihood of asthma, suggesting that overall CVH profiles may be linked to respiratory outcomes (21,22). LC9 builds on the LE8 framework by incorporating psychological health, which has been shown to have a bidirectional relationship with asthma (23). Furthermore, many individual components of LC9—such as diet quality, obesity, sleep, smoking, and glucose metabolism—have already been implicated as important factors in asthma onset or control (24-27). Therefore, LC9 provides a comprehensive construct that integrates multiple health dimensions relevant to asthma, making it a suitable index for examining their cross-sectional association in a nationally representative U.S. population.

This study explored the relationship between LC9 and the prevalence of asthma in adults using data from National Health and Nutrition Examination Survey (NHANES). We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-839/rc).

Methods

Selection of participants

By employing a randomized sampling method, NHANES ensures that its biennial survey encompasses a diverse cross-section of the population, spanning different age groups, genders, ethnic backgrounds, and socioeconomic statuses. This comprehensive study delves into an extensive spectrum of health-related subjects, ranging from persistent illnesses and dietary patterns to environmental influences and beyond. It employs a multifaceted approach, incorporating face-to-face interviews, clinical assessments, and advanced laboratory diagnostics (28). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Between 2005 and 2018, a total of 70,190 participants successfully completed the NHANES study. However, to refine our analysis, we applied several exclusion criteria: individuals under 20 years old, pregnant participants, those with ambiguous asthma diagnoses, and those missing LC9 data were omitted. After implementing these exclusions, our final dataset comprised 25,675 eligible individuals, ensuring a more precise and reliable foundation for our study (Figure 1).

Figure 1 Diagram outlining the participant selection process for the cross-sectional study based on NHANES data from 2005 to 2018. LC9, Life’s Crucial 9; NHANES, National Health and Nutrition Examination Survey.

Definition and criteria of LC9

LC9 encompasses a comprehensive framework of nine essential health parameters, divided into two primary domains: lifestyle behaviors and health indicators. The behavioral domain includes dietary patterns, engagement in physical activities, nicotine exposure, and sleep quality. On the other hand, the health indicators consist of body mass index (BMI), blood pressure, blood sugar, lipid assessment, and psychological health. The assessment of psychological health was derived from the Patient Health Questionnaire-9 (PHQ-9), a widely recognized and validated tool designed for systematically screening and evaluating depressive symptoms through a structured questionnaire format (29). Dietary assessment was conducted using the Healthy Eating Index-2015 (HEI-2015), a standardized metric for evaluating overall diet quality (30). To analyze the 13 individual components of HEI-2015, we leveraged data from the NHANES database, specifically utilizing information obtained from the initial 24-hour dietary recall interview, which captures total nutrient intake within a single day (Table S1). A comprehensive breakdown of the scoring methodology for each LC9 indicator is presented in Table S2, offering an in-depth explanation of the calculation criteria and assessment framework. Each indicator was scored 0–100. LE8 score was the mean of the eight non-psychological components; LC9 score was the mean of all nine. Participants were grouped into tertiles by LC9 score.

Diagnosis of asthma

Asthma status was determined through in-person interviews, during which participants were asked two key questions: (I) “Has a doctor or healthcare professional ever diagnosed you with asthma?” and (II) “Do you currently have asthma?” Individuals who responded affirmatively to both questions were classified as having asthma, while those who answered “No” to either were categorized as non-asthmatic. While self-report may be subject to recall bias or misclassification, this approach is widely used in large-scale epidemiological studies, including NHANES, and has been shown to provide reasonably valid prevalence estimates (31,32).

Covariates

Demographic and socioeconomic data were collected using a structured questionnaire, covering key variables such as age, gender, race, educational attainment, poverty-income ratio (PIR), marital status, and family history of asthma. Age groups were classified into three distinct categories: 20–39, 40–59, and 60 years or older. Educational background was segmented based on years of schooling into three levels: less than high school, high school graduate or equivalent, and college education or higher. PIR was divided into three financial strata: below 1.3, between 1.3 and 3.5, and above 3.5. Marital status was dichotomized based on partnership status, grouping individuals as either married/living with a partner or single, which included widowed, divorced, separated, or never married individuals.

Statistical analysis

R software (v 4.3.1) was used for data analysis and interpretation. This study is a cross-sectional analysis based on data from the NHANES, which uses a complex, multistage, stratified probability sampling design to select participants representative of the U.S. civilian. Specifically, the 2-year MEC weights (WTMEC2YR) were adjusted to account for the seven combined survey cycles from 2005 to 2018 by dividing each weight by seven, in accordance with NHANES analytic guidelines. The analysis employed weighted percentages (%) alongside observed frequencies to characterize categorical variables, with group comparisons conducted through a weighted Chi-squared statistical approach. Continuous variables were summarized as mean [95% confidence interval (CI)] and compared using weighted t-tests.

In order to assess the reliability of the correlation between LC9 and asthma, we used weighted logistic regression models that divided LC9 into tertiles and computed P values for linear trends. Crude model was univariate analysis; Model 1 was adjusted for age (continuous), gender and race; Model 2 expanded upon Model 1 by include additional adjustments for factors such as educational level, PIR, marital status and family history of asthma. To achieve more detailed characterization of the relationship, we utilized restricted cubic splines (RCS) functions within logistic regression models, which allowed exploration of potential dose-response patterns. In addition, to gain deeper insights into how LC9 scores relate to asthma across various subpopulations and to detect any potential differences among them, we carried out comprehensive subgroup analyses and performed interaction tests to assess potential variations. To rigorously test the robustness of our findings, we performed a sensitivity analysis. In this reassessment, we deliberately excluded individuals with a history of CVD to minimize potential confounding influences and ensure a clearer understanding of the true relationship between LC9 and asthma risk. Odds ratios (ORs) were computed to quantify the association corresponding to each 10-point increment in LC9. Statistical significance was defined as a two-sided P value <0.05.

Results

Baseline characteristics

This study analyzed data from 25,675 participants, whose average age was 48.1 years (95% CI: 47.6–48.6). The gender distribution showed a slight female predominance, with 51.4% women and 48.6% men. A total of 2,167 individuals were identified as having asthma, corresponding to a prevalence of 8.4%. Relative to the non-asthma group, individuals in the asthma group were more likely to be female, had a lower representation of Mexican Americans, faced greater economic hardship, were more frequently living alone, and had a higher occurrence of familial asthma history (P<0.05). Furthermore, the LC9 scores were lower in the asthma group compared to the non-asthma group (P<0.05). Among the nine components of LC9, all exhibited lower values in the asthma group, except for the Blood Lipids score (Table 1).

Associations of LC9 scores with adult asthma

Weighted logistic regression analysis indicated that, after controlling for all covariates, each 10-point increase in LC9 was associated with lower odds of prevalent asthma (OR =0.79; 95% CI: 0.75–0.83; P<0.001), highlighting a potential protective effect of higher LC9 scores against asthma. Following the tertile-based classification of LC9 scores, weighted logistic regression analysis of the fully adjusted model demonstrated a significant reduction in asthma prevalence for those in the highest LC9 score group compared to the lowest (OR =0.54, 95% CI: 0.46–0.63, P<0.001) (Table 2). The RCS analysis revealed a complex nonlinear association between LC9 scores and asthma (P for nonlinear <0.001). Rather than a straightforward linear decline, the prevalence of asthma exhibited a dynamic and nonlinear pattern, progressively decreasing as LC9 scores increased (Figure 2).

Figure 2 Dose-response relationship between LC9 scores and adult asthma based on a restricted cubic spline (3 knots). The solid purple curve shows the multivariable-adjusted OR, with the shaded area indicating the 95% CI. Horizontal dashed black line at OR =1.0 serves as the reference for no association. The vertical dashed black represents the minimum threshold for beneficial associations. The association was adjusted for age (continuous), gender, race, educational level, poverty-income ratio, marital status and asthma family history. CI, confidence interval; LC9, Life’s Crucial 9; OR, odds ratio.

LC9 components and asthma

Table 3 presents the associations between individual LC9 components and asthma. Stronger inverse associations were observed for psychological health (OR =0.87, 95% CI: 0.85–0.89), diet quality (OR =0.96, 95% CI: 0.94–0.98), sleep health (OR =0.92, 95% CI: 0.90–0.94), BMI (OR =0.92, 95% CI: 0.91–0.94), and blood glucose (OR =0.94, 95% CI: 0.92–0.97), all with P<0.001. Blood pressure and nicotine exposure also demonstrated significant, though weaker, associations. In contrast, physical activity and blood lipid scores were not significantly related to asthma prevalence. The tabulation format of component-level analysis was adapted from our previous work (33).

Subgroup analysis and interaction test

In all subgroups, there was a negative correlation between LC9 scores and the prevalence of adult asthma (P<0.05). Additionally, interaction tests revealed that education level and PIR had a significant interactive effect on this correlation (P<0.05 for interaction tests). The negative association between LC9 scores and adult asthma prevalence was more pronounced in populations with low levels of education and poverty (Figure 3).

Figure 3 Forest plot illustrating the relationship between LC9 scores and adult asthma in different subgroups. The association was adjusted for age (continuous), gender, race, educational level, poverty-income ratio, marital status and asthma family history. CI, confidence interval; OR, odds ratio; PIR, poverty-income ratio.

Sensitivity analysis

A total of 2,179 individuals with a prior diagnosis of CVD (heart attack, angina, congestive heart failure, and coronary heart disease) were removed from the study to ensure a more precise assessment of the targeted associations. The sensitivity analysis confirmed that, even after excluding individuals with a history of CVD, the results of the fully adjusted weighted logistic regression model remained consistent with the primary findings (Table 4).

Discussion

Based on national data from NHANES 2005–2018, this study revealed an inverse relationship between LC9 scores and asthma prevalence in adults. Subgroup analyses revealed that the inverse relationship was notably more significant among individuals from disadvantaged socioeconomic backgrounds, particularly those with limited education and lower income levels.

There are already some studies investigating the relationship between CVH and asthma incidence (21,22,34). A prospective cohort study by Zhang et al. followed 249,713 participants for over a decade and found that individuals with higher LE8 scores had a lower risk of developing asthma (22). Xu et al. provided compelling evidence that individuals with higher LE8 scores exhibit a significantly reduced risk of asthma, highlighting the potential protective role of LE8 in asthma prevention (34). However, the aforementioned studies have all explored the relationship between CVH and asthma based on the LE8 framework, while neglecting the role of mental health. In recent years, numerous studies have demonstrated a strong association between mental health and both the risk and prognosis of CVD (35-37). Moreover, there is a bidirectional relationship between mental health and asthma (23). Therefore, investigations into the association between CVH and asthma should not overlook the influence of mental health. In our study, we included a sufficiently large sample and applied weighted analysis. Our findings indicated that when the LC9 score was treated as a continuous variable, it was negatively correlated with the prevalence of adult asthma. Furthermore, after stratifying LC9 scores into tertiles, we observed that individuals in the highest LC9 score group had the lowest prevalence of asthma. The RCS analysis further demonstrated a dose-response relationship, indicating that asthma prevalence declined progressively as LC9 scores increased, thereby reinforcing the inverse association between LC9 scores and adult asthma prevalence.

The onset and progression of asthma vary among individuals and are influenced by a combination of lifestyle, environmental factors, psychological state, and overall health conditions. A well-documented asthma risk factor is smoking, as it not only increases the likelihood of disease onset but also exacerbates symptoms, impairs lung function, and reduces treatment effectiveness (26). Alwarith et al. suggested that increasing fruit and vegetable intake while reducing saturated fat consumption may lower the risk of developing asthma, highlighting dietary modifications as a key preventive strategy (38). A meta-analysis of eleven studies revealed that aerobic exercise plays a beneficial role in asthma management by enhancing lung function and improving overall disease control (39). As a newly incorporated factor in CVH assessment, sleep health is gaining increasing attention. Kavanagh et al. highlighted that poor sleep quality exacerbates asthma control and diminishes overall quality of life (25). Obesity is undeniably a major risk factor for asthma, and reducing BMI has been shown to improve lung function and asthma outcomes (40). Diabetes, hypertension, and dyslipidemia have also been identified as risk factors for asthma (41-43). LC9 not only encompasses the aforementioned eight factors but also incorporates mental health, making it a more suitable predictor of asthma risk. To explore the association between LC9 scores and asthma across different demographic groups, we conducted a subgroup analysis. We found a negative association between LC9 scores and asthma across all subgroups, with this relationship being more pronounced among individuals with lower education levels and lower income. Meanwhile, a meta-analysis found that low income and low education levels are risk factors for CVH (44). Thus, individuals exhibiting these clinical traits should place a strong emphasis on CVH and actively cultivate long-term healthy habits. Recognizing that a history of CVD could distort LC9 scores and influence our findings, we performed a reanalysis after excluding individuals with prior CVD. The consistency of the results confirms the stability and reliability of our conclusions. In addition to overall LC9 scores, we further examined the contribution of its individual components. Our component-level analysis revealed that psychological health, sleep, BMI, and blood glucose contributed most strongly to the inverse association between LC9 and asthma. These results highlight the roles of mental well-being, metabolic regulation, and sleep quality in respiratory health. Interestingly, physical activity and blood lipids were not significantly associated with asthma in this cross-sectional setting. This discrepancy may reflect measurement limitations in NHANES (e.g., self-reported physical activity, single lipid measurement) or complex pathophysiological pathways in asthma that extend beyond classical metabolic markers. Further longitudinal and mechanistic research is warranted to clarify these relationships.

While the underlying mechanisms accounting for the inverse relationship between LC9 and adult asthma risk remain largely uncertain, potential insights may be derived through a comprehensive analysis of the individual components constituting the LC9 framework. Nicotine exposure triggers a cascade of immunological responses, notably the upregulation of pro-inflammatory cytokines, which exacerbates mucosal inflammation and oxidative damage—pathophysiological processes closely linked to increased asthma susceptibility (45). Produced by gut microbiota during fermentation of dietary fiber, short-chain fatty acids contribute to immunomodulation and mucosal homeostasis in the lungs, resulting in a protective effect against asthma (46). Individuals with elevated BMI are more likely to exhibit excessive adipose accumulation, particularly in the thoracic and abdominal regions. This excess fat can mechanically compromise lung function by reducing pulmonary compliance, while also promoting systemic and airway inflammation—both of which have been implicated in the increased risk of asthma observed in obese populations (47). Reduced physical activity has been associated with elevated BMI and persistent low-grade systemic inflammation, both of which are recognized risk factors in the development and progression of obesity-related asthma (48,49). Poor sleep quality may disrupt melatonin production, diminishing its regulatory role in inflammation and oxidative stress, and thereby heightening susceptibility to asthma (50). Depression may exacerbate asthma through dysregulation of the hypothalamic-pituitary-adrenal axis and increased systemic inflammation, marked by elevated cytokines such as IL-6 and TNF-α (51,52). Chronic inflammation and elevated levels of pro-inflammatory mediators associated with diabetes may contribute to the development or exacerbation of asthma, suggesting a potential mechanistic link between metabolic dysregulation and respiratory diseases (53). Dyslipidemia may contribute to asthma by promoting Th17 cell differentiation and arachidonic acid metabolism, both of which are key drivers of airway inflammation (54,55). As a final point, in hypertensive patients, β2-receptor blockers may induce bronchoconstriction by disrupting airway smooth muscle relaxation, potentially worsening asthma symptoms (56).

To our knowledge, this is the first study to explore the association between LC9 and adult asthma in a US population. The findings may inform future approaches to asthma prevention and control. From a clinical perspective, comprehensive risk profiling such as LC9 may complement phenotype-directed care for eosinophilic exacerbations, where biologics have shown utility (57). In addition, device-based interventions like bronchial thermoplasty can reduce exacerbations and improve quality of life in asthma with type-2 inflammation (58). Several limitations should be acknowledged. First, the cross-sectional design restricts our ability to draw causal inferences between LC9 and adult asthma, highlighting the need for longitudinal research. Second, the potential pathways through which LC9 may influence asthma risk remain speculative, underscoring the importance of mechanistic studies. Third, several LC9 metrics (such as dietary intake and psychological health assessed via PHQ-9) and asthma diagnosis were obtained through self-reported questionnaires. Although these instruments are widely validated and used in large-scale epidemiological studies, potential misclassification and response bias cannot be excluded, which may attenuate the robustness of our findings. Future research using objective clinical assessments and biomarkers is needed to confirm these associations. Fourth, although we conducted sensitivity analyses excluding participants with prior CVD and adjusted for multiple covariates, residual confounding from unmeasured variables (e.g., environmental exposures not captured in NHANES) cannot be completely ruled out. Therefore, our findings should be interpreted with caution. Finally, psychological health is a broad construct that includes multiple domains beyond depression. Because NHANES only provides validated data on depressive symptoms (PHQ-9), our operationalization of psychological health may not capture the full spectrum of mental health. This limitation should be considered when interpreting the results.

Conclusions

In this cross-sectional analysis of NHANES 2005–2018, higher LC9 scores were associated with lower odds of prevalent asthma among U.S. adults. This inverse, non-linear association was robust across subgroups and in sensitivity analyses. Given the cross-sectional design and self-reported asthma, these results should be interpreted as associations rather than causal or predictive. Prospective studies are warranted to establish temporality, assess clinical utility, and elucidate potential pathways.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-839/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-839/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-839/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Ledford DK, Kim TB, Ortega VE, et al. Asthma and respiratory comorbidities. J Allergy Clin Immunol 2025;155:316-26. [Crossref] [PubMed]
2. Kim JH, Park BY, Choi SH, et al. Comorbidities associated with adult asthma according to severity: analysis of data from the National Health Insurance Sharing Service. J Thorac Dis 2025;17:1142-58. [Crossref] [PubMed]
3. Shin YH, Hwang J, Kwon R, et al. Global, regional, and national burden of allergic disorders and their risk factors in 204 countries and territories, from 1990 to 2019: A systematic analysis for the Global Burden of Disease Study 2019. Allergy 2023;78:2232-54. [Crossref] [PubMed]
4. Jayasooriya SM, Devereux G, Soriano JB, et al. Asthma: epidemiology, risk factors, and opportunities for prevention and treatment. Lancet Respir Med 2025;13:725-38. [Crossref] [PubMed]
5. Adrish M, Hanania NA. Asthma and cardiovascular disease: A bidirectional association? Respirology 2023;28:217-9. [Crossref] [PubMed]
6. Pollevick ME, Xu KY, Mhango G, et al. The Relationship Between Asthma and Cardiovascular Disease: An Examination of the Framingham Offspring Study. Chest 2021;159:1338-45. [Crossref] [PubMed]
7. He Y, Deng C, Liu P. Association of platelet-lymphocyte ratio, neutrophil-lymphocyte ratio, and monocyte-lymphocyte ratio with coronary heart disease prevalence in asthma patients: a cross-sectional analysis of NHANES 1999-2018. J Thorac Dis 2025;17:3831-9. [Crossref] [PubMed]
8. Garcia-Aymerich J. The Role of Smoking in the Association between Asthma and Cardiovascular Disease. An Example of Poorly Controlled Confounding. Am J Respir Crit Care Med 2015;192:123. [Crossref] [PubMed]
9. Wang X, Ran S, Xia H, et al. Ambient air pollution associated with incident asthma, subsequent cardiovascular disease and death: A trajectory analysis of a national cohort. J Hazard Mater 2023;460:132372. [Crossref] [PubMed]
10. Gottdiener JS. Intersection of 2 Epidemics: Asthma and Cardiovascular Disease. JACC Heart Fail 2017;5:505-6. [Crossref] [PubMed]
11. Lloyd-Jones DM, Hong Y, Labarthe D, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation 2010;121:586-613. [Crossref] [PubMed]
12. Lloyd-Jones DM, Allen NB, Anderson CAM, et al. Life's Essential 8: Updating and Enhancing the American Heart Association's Construct of Cardiovascular Health: A Presidential Advisory From the American Heart Association. Circulation 2022;146:e18-43. [Crossref] [PubMed]
13. Zhang Y, Jia X, Yang Y, et al. Change in the global burden of depression from 1990-2019 and its prediction for 2030. J Psychiatr Res 2024;178:16-22. [Crossref] [PubMed]
14. Krittanawong C, Maitra NS, Qadeer YK, et al. Association of Depression and Cardiovascular Disease. Am J Med 2023;136:881-95. [Crossref] [PubMed]
15. Harshfield EL, Pennells L, Schwartz JE, et al. Association Between Depressive Symptoms and Incident Cardiovascular Diseases. JAMA 2020;324:2396-405. [Crossref] [PubMed]
16. Qiu W, Cai A, Li L, et al. Association of depression trajectories and subsequent hypertension and cardiovascular disease: findings from the CHARLS cohort. J Hypertens 2024;42:432-40. [Crossref] [PubMed]
17. Gaffey AE, Rollman BL, Burg MM. Strengthening the Pillars of Cardiovascular Health: Psychological Health is a Crucial Component. Circulation 2024;149:641-3. [Crossref] [PubMed]
18. Ge J, Peng W, Lu J. Predictive Value of Life's Crucial 9 for Cardiovascular and All-Cause Mortality: A Prospective Cohort Study From the NHANES 2007 to 2018. J Am Heart Assoc 2024;13:e036669. [Crossref] [PubMed]
19. Huang S, Duan S, Choi S, et al. Higher Life's Crucial 9 protects against infertility among U.S. women aged 18-45 years. Front Endocrinol (Lausanne) 2025;16:1465022. [Crossref] [PubMed]
20. Gong H, Duan S, Huang S. Association between Life's Crucial 9 and overactive bladder: the mediating role of weight-adjusted-waist index. Front Nutr 2024;11:1508062. [Crossref] [PubMed]
21. Shen G, Yang Y, Wang N, et al. Association of life's essential 8 and asthma: mediating effect of inflammation and oxidative stress. J Asthma 2025;62:328-35. [Crossref] [PubMed]
22. Zhang H, Chang Q, Yang H, et al. Life's Essential 8, genetic predisposition, and risk of incident adult-onset asthma: a prospective cohort study. Am J Clin Nutr 2024;119:100-7. [Crossref] [PubMed]
23. Choi HG, Kim JH, Park JY, et al. Association Between Asthma and Depression: A National Cohort Study. J Allergy Clin Immunol Pract 2019;7:1239-1245.e1. [Crossref] [PubMed]
24. Wood LG. Diet, Obesity, and Asthma. Ann Am Thorac Soc 2017;14:S332-8. [Crossref] [PubMed]
25. Kavanagh J, Jackson DJ, Kent BD. Sleep and asthma. Curr Opin Pulm Med 2018;24:569-73. [Crossref] [PubMed]
26. Thomson NC, Polosa R, Sin DD. Cigarette Smoking and Asthma. J Allergy Clin Immunol Pract 2022;10:2783-97. [Crossref] [PubMed]
27. Staggers KA, Minard C, Byers M, et al. Metabolic Dysfunction, Triglyceride-Glucose Index, and Risk of Severe Asthma Exacerbation. J Allergy Clin Immunol Pract 2023;11:3700-3705.e2. [Crossref] [PubMed]
28. Ahluwalia N, Dwyer J, Terry A, et al. Update on NHANES Dietary Data: Focus on Collection, Release, Analytical Considerations, and Uses to Inform Public Policy. Adv Nutr 2016;7:121-34. [Crossref] [PubMed]
29. Levis B, Benedetti A, Thombs BD, et al. Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis. BMJ 2019;365:l1476. [Crossref] [PubMed]
30. Krebs-Smith SM, Pannucci TE, Subar AF, et al. Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet 2018;118:1591-602. [Crossref] [PubMed]
31. Kong W, Zhang X, Gu H, et al. Association between BMI and asthma in adults over 45 years of age: analysis of Global Burden of Disease 2021, China Health and Retirement Longitudinal Study, and National Health and Nutrition Examination Survey data. EClinicalMedicine 2025;82:103163. [Crossref] [PubMed]
32. Pan Y, Wu L, Yao S, et al. Association between triglyceride-glucose index and fractional exhaled nitric oxide in adults with asthma from NHANES 2007-2012. Lipids Health Dis 2024;23:335. [Crossref] [PubMed]
33. Wang K, Li C, He L, et al. Relationship between cardiovascular health and COPD andits impact on all-cause mortality in patients with COPD: analyses of NHANES 2005-2018. BMC Pulm Med 2025;25:397. [Crossref] [PubMed]
34. Xu J, Tang J. Associations between asthma and Life's Essential 8: a cross-sectional study. Front Med (Lausanne) 2025;12:1446900. [Crossref] [PubMed]
35. Correll CU, Solmi M, Veronese N, et al. Prevalence, incidence and mortality from cardiovascular disease in patients with pooled and specific severe mental illness: a large-scale meta-analysis of 3,211,768 patients and 113,383,368 controls. World Psychiatry 2017;16:163-80. [Crossref] [PubMed]
36. Levine GN, Cohen BE, Commodore-Mensah Y, et al. Psychological Health, Well-Being, and the Mind-Heart-Body Connection: A Scientific Statement From the American Heart Association. Circulation 2021;143:e763-83. [Crossref] [PubMed]
37. Mensah GA, Collins PY. Understanding mental health for the prevention and control of cardiovascular diseases. Glob Heart 2015;10:221-4. [Crossref] [PubMed]
38. Alwarith J, Kahleova H, Crosby L, et al. The role of nutrition in asthma prevention and treatment. Nutr Rev 2020;78:928-38. [Crossref] [PubMed]
39. Hansen ESH, Pitzner-Fabricius A, Toennesen LL, et al. Effect of aerobic exercise training on asthma in adults: a systematic review and meta-analysis. Eur Respir J 2020;56:2000146. [Crossref] [PubMed]
40. Miethe S, Karsonova A, Karaulov A, et al. Obesity and asthma. J Allergy Clin Immunol 2020;146:685-93. [Crossref] [PubMed]
41. Wu TD. Diabetes, insulin resistance, and asthma: a review of potential links. Curr Opin Pulm Med 2021;27:29-36. [Crossref] [PubMed]
42. Christiansen SC, Schatz M, Yang SJ, et al. Hypertension and Asthma: A Comorbid Relationship. J Allergy Clin Immunol Pract 2016;4:76-81. [Crossref] [PubMed]
43. Jiang T, Dai L, Li P, et al. Lipid metabolism and identification of biomarkers in asthma by lipidomic analysis. Biochim Biophys Acta Mol Cell Biol Lipids 2021;1866:158853. [Crossref] [PubMed]
44. Khaing W, Vallibhakara SA, Attia J, et al. Effects of education and income on cardiovascular outcomes: A systematic review and meta-analysis. Eur J Prev Cardiol 2017;24:1032-42. [Crossref] [PubMed]
45. Strzelak A, Ratajczak A, Adamiec A, et al. Tobacco Smoke Induces and Alters Immune Responses in the Lung Triggering Inflammation, Allergy, Asthma and Other Lung Diseases: A Mechanistic Review. Int J Environ Res Public Health 2018;15:1033. [Crossref] [PubMed]
46. Trompette A, Gollwitzer ES, Yadava K, et al. Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis. Nat Med 2014;20:159-66. [Crossref] [PubMed]
47. Littleton SW. Impact of obesity on respiratory function. Respirology 2012;17:43-9. [Crossref] [PubMed]
48. Iwamoto H, Hirano T, Amano Y, et al. Prospective Real-World Analysis of Asthma Patients With Preserved and Reduced Physical Activity. J Allergy Clin Immunol Pract 2023;11:2792-2800.e2. [Crossref] [PubMed]
49. Dimitri P, Joshi K, Jones N, et al. Moving more: physical activity and its positive effects on long term conditions in children and young people. Arch Dis Child 2020;105:1035-40. [Crossref] [PubMed]
50. Habtemariam S, Daglia M, Sureda A, et al. Melatonin and Respiratory Diseases: A Review. Curr Top Med Chem 2017;17:467-88. [Crossref] [PubMed]
51. Lee S, Rhee DK. Effects of ginseng on stress-related depression, anxiety, and the hypothalamic-pituitary-adrenal axis. J Ginseng Res 2017;41:589-94. [Crossref] [PubMed]
52. Du YJ, Li B, Zhang HY, et al. Airway inflammation and hypothalamic-pituitary-adrenal axis activity in asthmatic adults with depression. J Asthma 2013;50:274-81. [Crossref] [PubMed]
53. Khateeb J, Fuchs E, Khamaisi M. Diabetes and Lung Disease: A Neglected Relationship. Rev Diabet Stud 2019;15:1-15. [Crossref] [PubMed]
54. Kanno T, Nakajima T, Miyako K, et al. Lipid metabolism in Th17 cell function. Pharmacol Ther 2023;245:108411. [Crossref] [PubMed]
55. Monga N, Sethi GS, Kondepudi KK, et al. Lipid mediators and asthma: Scope of therapeutics. Biochem Pharmacol 2020;179:113925. [Crossref] [PubMed]
56. Huang KY, Tseng PT, Wu YC, et al. Do beta-adrenergic blocking agents increase asthma exacerbation? A network meta-analysis of randomized controlled trials. Sci Rep 2021;11:452. [Crossref] [PubMed]
57. Kim SH, Kim Y, Moon JY, et al. Insights and considerations in the use of biologics for managing eosinophilic exacerbations in asthma and chronic obstructive pulmonary disease. J Thorac Dis 2025;17:1795-7. [Crossref] [PubMed]
58. Angsuwatcharakorn T, Savigamin C, Thanthitaweewat V, et al. Bronchial thermoplasty reduces asthma exacerbation and improves quality of life in asthma with type 2 inflammation. J Thorac Dis 2025;17:1159-68. [Crossref] [PubMed]