Abstract
Background
The bidirectional relationship between asthma and cardiovascular disease (CVD) among aging populations remains insufficiently explored, especially regarding potential mediating mechanisms. The objective of this study is to explore the bidirectional effects between asthma and CVD, with focus on frailty as a potential mediator.
Methods
We utilized longitudinal data (2011–2018) from the China Health and Retirement Longitudinal Study (CHARLS) to analyze two cohorts: 7,910 CVD-free participants and 8,897 asthma-free participants. Frailty was evaluated based on a 29-component frailty index (FI). Bidirectional associations were examined using Cox proportional hazards regression models, with progressive adjustments for sociodemographic, lifestyle, and clinical variables. Mediation analysis assessed the mediating role of FI, utilizing bootstrap methods.
Results
baseline asthma correlated with a 74.6% heightened risk of new-onset CVD (HR = 1.746, 95% CI: 1.351–2.256) after full adjustment, whereas baseline CVD correlated with a 62.1% heightened risk of developing incident asthma (HR = 1.621, 95% CI: 1.207–2.176). Bidirectional associations were examined using Cox regression models. FI accounted for 15.57% of the relationship between CVD and asthma, as indicated by the mediation analysis (indirect effect = 0.005, 95% CI: 0.002–0.007), but did not mediate the opposite pathway. Subgroup analyses revealed consistent effects across demographic and clinical subgroups (all interaction p > 0.05).
Conclusions
This research offers new insights into bidirectional associations between asthma and CVD in older Chinese adults, with partial mediation by frailty in the CVD→ asthma pathway. Findings highlight the need for integrated management and suggest frailty may serve as a target for intervention.
Supplementary Information
The online version contains supplementary material available at 10.1186/s41043-026-01278-x.
Keywords: Asthma, Cardiovascular disease, Bidirectional relationship, Frailty, CHARLS
Introduction
Asthma represents a persistent inflammatory condition affecting the airways, typically manifesting through repeated occurrences of wheezing, difficulty breathing, and a sensation of constriction in the chest [1]. Globally, asthma affects approximately 300 million individuals [2]. In China, recent epidemiological surveys indicate an adult prevalence rate of 4.2%, with a continuing upward trend [3]. As age advances, the clinical manifestations and pathophysiological features of asthma undergo significant changes. Middle-aged and older patients with asthma frequently present with atypical symptoms, multiple comorbidities, and suboptimal responses to treatment [4]. Epidemiological data demonstrate that asthma is not only a significant contributor to global mortality but also results in substantial economic burdens for healthcare systems [5, 6].
Importantly, the health impacts of asthma extend beyond the respiratory system, with growing evidence supporting its association with cardiovascular morbidity and mortality [7, 8]. Cardiovascular disease (CVD) constitutes a leading cause of global mortality and morbidity [9]. According to the Global Burden of Disease Study, CVD was responsible for over 19 million deaths globally in 2019, with the prevalence and absolute numbers of CVD cases continuing to rise, particularly in aging populations [10, 11]. The underlying mechanisms linking asthma and CVD are intricate and multifactorial. A substantial body of evidence has established that asthma patients may face a heightened risk of CVD. Asthma has been recognized as an independent predictor of CVD risk in prior research [7, 8]. A multiethnic cohort research indicated a 40% elevated risk of coronary heart disease among individuals with asthma, with notably higher risks observed in persistent asthma compared to intermittent asthma [12]. Chronic systemic inflammation, oxidative stress, and shared risk factors have been proposed as key pathways linking asthma to subsequent CVD development [13, 14].
However, while substantial evidence supports the impact of asthma on CVD risk, the reverse relationship that whether CVD increases the risk of incident asthma remains understudied and is primarily speculative [10]. A recent hypothesis paper by Adrish et al. (2023) proposed a potential bidirectional association, suggesting that shared risk factors, systemic inflammation, and even medications for CVD (e.g., β-blockers) might contribute to new-onset asthma, but longitudinal evidence is lacking [15]. Therefore, a critical gap remains in prospectively examining this bidirectional hypothesis. Furthermore, emerging evidence suggests that frailty, characterized by cumulative decline in multiple physiological systems and increased vulnerability to stressors, may serve as a key mechanism linking chronic diseases [16, 17]. We hypothesize that CVD, a major driver of physiological decline, may accelerate the development of frailty [18, 19]. This state of reduced physiological reserve could, in turn, increase susceptibility to other age-related diseases, including asthma, potentially by impairing respiratory function or amplifying systemic inflammation. Nevertheless, few studies have explored the mediating role of frailty in the bidirectional asthma-CVD relationship.
Based on these considerations, this study employs longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), spanning 2011 to 2018, to: (1) examine the effect of baseline asthma on incident CVD during follow-up; (2) investigate the effect of baseline CVD on incident asthma during follow-up; (3) conduct subgroup analyses to explore potential effect modification by sex, age, and urban-rural differences; and (4) incorporate frailty as a potential mediator to elucidate possible mechanisms underlying this bidirectional association. The findings will furnish essential proof for clinical practice, highlighting the need for integrated management of asthma and CVD, while informing the development of customized preventative and management strategies for the middle-aged and the elderly.
Method
Study design and data source
The China Health and Retirement Longitudinal Study (CHARLS) collected baseline data from June 2011 to March 2012 using a multistage stratified probability sampling method, encompassing 17,705 residents aged 45 years or older from 450 urban and rural communities across 28 provinces [20]. This cohort systematically collected multidimensional data on population health, socioeconomic status, and mental health through face-to-face computer-assisted personal interviews (CAPI), standardized physical examinations, blood sample analyses. The sample is nationally representative of middle-aged and older adults in China. All subjects granted written informed consent, which was archived at the National School of Development, Peking University. The project updates cohort data through biennial follow-ups, and detailed study design and methodology are available at: https://charls.charlsdata.com/pages/Data/2018-charls-wave4/zh-cn.html. This study utilized longitudinal data from the CHARLS baseline (2011) to Wave 4 (2018). Wave 5 (2020) data were not included to ensure a consistent pre-pandemic follow-up period and to avoid potential confounding effects of the COVID-19 pandemic on healthcare access, diagnosis patterns, and health outcomes related to asthma and CVD. We excluded 4,754 participants for the following reasons: (1) age below 45 years, (2) missing baseline asthma or CVD information, or (3) incomplete demographic data. In Phase I, after excluding participants with baseline CVD and those lost to follow-up, the study contained 7,910 people. In Phase II, following the exclusion of participants with baseline asthma and those lost to follow-up, 8,897 participants were retained. A flowchart detailing participant inclusion and exclusion for both analytical phases is provided in Fig. 1.
Fig. 1.
Flowchart of sample screening
Assessment of asthma and CVD
Asthma was defined based on self-report in CHARLS. Specially trained researchers conducted face-to-face interviews using the question: “Has a doctor ever diagnosed you with asthma?” Responses were coded as “0” (no physician-diagnosed asthma) or “1” (physician-diagnosed asthma). In accordance with prior research, CVD occurrences encompassed cardiac disease and stroke [21, 22]. CVD was assessed using standardized questions: “Has a doctor ever diagnosed you with stroke or heart disease?” and “Are you currently receiving treatment for stroke or heart disease or its complications?“. A participant was defined as having baseline CVD if they answered “yes” to either the diagnosis question or the treatment question.
Assessment of frailty
Frailty was evaluated employing the Frailty Index (FI), constructed according to standardized procedures encompassing all major health domains. For the purpose of mediation analysis and to ensure the mediator was conceptually distinct from the exposure variables (asthma/CVD), we constructed a FI based on 29 health deficits that explicitly excluded items directly defining CVD (e.g., heart disease, stroke) and asthma. This allows for a more conservative and specific test of frailty as a mediating pathway independent of the core disease definitions. The FI comprised 29 selected items [23], including comorbidities (excluding asthma, heart disease, and stroke), physical function, disability, depression, and cognitive function. All items except item 29, were dichotomized as 0 (indicating no deficit) or 1 (indicating presence of deficit) based on established cutoff values. Item 29 was treated as a continuous variable spanning from 0 to 1, such that elevated scores are indicative of reduced cognitive function. The complete list of included items is shown in Supplementary Table S1, the FI score was computed for each participant as the ratio of the total number of deficits to 29. According to previously established criteria, participants were classified into three distinct cohorts for analysis: robust (FI ≤ 0.10), frail (FI ≥ 0.25), and pre-frail (0.10 < FI < 0.25) [24, 25].
To validate that our tailored 29-item measure retained the core construct of frailty, we conducted a sensitivity analysis. Specifically, we calculated the Pearson correlation coefficient between this measure and a standard FI that included the CVD-related items excluded from our measure. The extremely high correlation (Stage I: r = 0.998, P < 0.001; Stage II: r = 0.995, P < 0.001) supports the construct validity of our measure; detailed results are presented in Supplementary Table S2.
Covariates
The covariates included three major categories: sociodemographic, lifestyle, and health status indicators. Sociodemographic variables included age, sex, educational attainment (primary school or below, middle school, or college or above), residential location (town or village), and marital status (married or non-married). Lifestyle factors included smoking status (current smoker, ex-smoker, or non-smoker) and drinking status (never, less than once a month or more than once a month). Health-related covariates included comorbidities (hypertension, diabetes, chronic kidney disease, and chronic lung disease), FI classified into robust, pre-frail, and frail categories, body mass index (BMI) was further divided as underweight (≤ 18.5 kg/m²), normal weight (18.5–24 kg/m²), or obese (> 24 kg/m²) [26], and biochemical markers including TG, HDL, CRP, and GFR.
Statistical analysis
Continuous variables were presented as mean (standard deviation), while categorical variables were presented as frequency (percentage). The chi-square test was employed to compare baseline characteristics between groups for categorical variables and the Student’s t-test for continuous variables. The bidirectional association between asthma and CVD was examined using Cox proportional hazards models. Two primary analyses were conducted: (1) evaluating baseline asthma as the exposure and incident CVD as the outcome, and (2) assessing baseline CVD as the exposure and incident asthma as the outcome. For each analysis, three progressively adjusted models were constructed: Model 1 adjusted for age and sex; Model 2 additionally adjusted for marital status, education, location, smoking status, and drinking status; and Model 3 further adjusted for comorbidities, FI, BMI, TG, HDL, CRP, and GFR. Analyses by subgroups were carried out with respect to age, sex, residential location, smoking status, and BMI categories, with interaction terms tested between exposure variables and each stratification factor. Sex, age, and urban-rural location were chosen a priori as stratification variables due to their established roles as major determinants of health outcomes and potential modifiers of disease risk and healthcare access in the Chinese population. To explore the mediating function of FI within the bidirectional association, the bootstrap method was employed to execute mediation analysis, with 1,000 replications., and the mediation proportion calculated as the ratio of the indirect effect to the total effect. R software (version 4.4.2) was employed to conduct all statistical analyses. Statistical significance was established at p < 0.05 for two-tailed tests.
Result
Stage Ⅰ: Longitudinal association between baseline asthma and incident CVD
The baseline CVD-free cohort included 7,910 participants, and their baseline characteristics are shown in Table 1. Participants with asthma were significantly older than those without asthma. (p < 0.001). Asthma was more prevalent among males and individuals with elevated CRP levels or chronic lung disease (p < 0.001). Other characteristics significantly associated with asthma included hypertension, higher HDL levels, and higher GFR (p < 0.05). Throughout the seven-year follow-up duration, 106 participants (36.1%) in the asthma group developed CVD. Figure 2A shows the Kaplan-Meier cumulative incidence curves for CVD risk according to asthma status.
Table 1.
Baseline Characteristics of Participants in Stage I and Stage II
| Charactertic | Stage I (N = 7,910) | Stage II (N = 8,897) | ||||
|---|---|---|---|---|---|---|
| Asthma | CVD | |||||
| Yes (N=294) | No (N=7,616) | P value | Yes (N=949) | No (N=7,946) | P value | |
| Age | 61.41±8.33 | 57.85±8.71 | <0.001 | 60.06 (8.41) | 57.97 (8.75) | <0.001 |
| Sex N (%) | <0.001 | <0.001 | ||||
| Female | 121 (41.2) | 4043 (53.1) | 590 (62.2) | 4231 (53.2) | ||
| Male | 173 (58.8) | 3573 (46.9) | 359 (37.8) | 3715 (46.8) | ||
| Education N (%) | 0.005 | <0.001 | ||||
| Primary school or below | 229 (77.9) | 5259 (69.1) | 51 (5.4) | 210 (2.6) | ||
| Middle school | 58 (19.7) | 2157 (28.3) | 266 (28.0) | 2222 (28.0) | ||
| College or above | 7 (2.4) | 200 (2.6) | 632 (66.6) | 5514 (69.4) | ||
| Marital status N (%) | 0.277 | 0.249 | ||||
| Married | 257 (87.4) | 6822 (89.6) | 835 (88.0) | 7094 (89.3) | ||
| Non-married | 37 (12.6) | 794 (10.4) | 114 (12.0) | 852 (10.7) | ||
| Location N (%) | 0.350 | <0.001 | ||||
| City or town | 50 (17.0) | 1131 (14.9) | 254 (26.8) | 1188 (15.0) | ||
| Village | 244 (83.0) | 6485 (85.1) | 695 (73.2) | 6758 (85.0) | ||
| Smoking N (%) | <0.001 | <0.001 | ||||
| Current smoker | 102 (34.8) | 2397 (31.6) | 221 (23.3) | 2473 (31.2) | ||
| Ex-smoker | 50 (17.1) | 530 (7.0) | 108 (11.4) | 576 (7.3) | ||
| Non-smoker | 141 (48.1) | 4669 (61.5) | 619 (65.3) | 4874 (61.5) | ||
| Drinking N (%) | 0.774 | |||||
| More than once a month | 64 (23.3) | 1588 (22.0) | 131 (14.3) | 1634 (21.7) | <0.001 | |
| Less than once a month | 25 (9.1) | 603 (8.4) | 68 (7.4) | 627 (8.3) | ||
| Never | 186 (67.6) | 5023 (69.6) | 719 (78.3) | 5263 (69.9) | ||
| Diabetes N (%) | 0.968 | <0.001 | ||||
| No | 279 (95.2) | 7215 (95.4) | 823 (87.5) | 7515 (95.3) | ||
| Yes | 14 (4.8) | 344 (4.6) | 118 (12.5) | 370 (4.7) | ||
| Hypertension N (%) | 0.038 | <0.001 | ||||
| No | 219 (74.7) | 6057 (79.9) | 468 (49.5) | 6240 (78.9) | ||
| Yes | 74 (25.3) | 1525 (20.1) | 477 (50.5) | 1671 (21.1) | ||
| Chronic Kidney Disease N (%) | 0.484 | <0.001 | ||||
| No | 276 (94.5) | 7256 (95.6) | 843 (89.1) | 7557 (95.4) | ||
| Yes | 16 (5.5) | 337 (4.4) | 103 (10.9) | 361 (4.6) | ||
| Chronic Lung Disease N (%) | <0.001 | <0.001 | ||||
| No | 116 (39.5) | 7150 (94.0) | 843 (89.0) | 7455 (94.0) | ||
| Yes | 178 (60.5) | 456 (6.0) | 104 (11.0) | 477 (6.0) | ||
| FI N (%) | <0.001 | <0.001 | ||||
| Robust | 70 (23.8) | 811 (10.6) | 236 (24.9) | 4233 (53.3) | ||
| Pre-Frail | 154 (52.4) | 2684 (35.2) | 462 (48.7) | 2818 (35.5) | ||
| Frail | 70 (23.8) | 4121 (54.1) | 251 (26.4) | 895 (11.3) | ||
| BMI | <0.001 | <0.001 | ||||
| <18.5 | 37 (12.6) | 481 (6.3) | 43 (4.5) | 507 (6.4) | ||
| 18.5-24 | 144 (49.0) | 4178 (54.9) | 389 (41.0) | 4327 (54.5) | ||
| ≥24 | 113 (38.4) | 2957 (38.8) | 517 (54.5) | 3112 (39.2) | ||
| TG | 121.00±74.49 | 132.70±112.97 | 0.121 | 148.56±113.50 | 132.77±112.91 | <0.001 |
| HDL | 53.55±15.71 | 51.43±15.31 | 0.040 | 48.51±14.63 | 51.43±15.34 | <0.001 |
| CRP | 4.16±11.22 | 2.45±6.89 | <0.001 | 2.51±5.37 | 2.44±6.77 | 0.790 |
| GFR | 90.71±13.78 | 93.37±14.05 | 0.005 | 89.28±14.87 | 93.20±14.13 | <0.001 |
Continuous variables were presented as mean ± standard deviation, while categorical variables were summarized as frequencies (percentages)
Abbreviation: FI, frailty index; BMI, body mass index; TG, triglyceride; HDL, high-density lipoprotein; CRP, C-reactive protein; GFR, glomerular filtration rate
Fig. 2.
A and B present the Kaplan-Meier cumulative curves for Stage I and Stage II
As shown in Table 2, The association between asthma and incident CVD was examined using three Cox proportional hazards models. In the initial model (Model 1), Asthmatic participants exhibited a markedly elevated risk of acquiring CVD in comparison to non-asthmatic counterparts (HR = 2.137, 95% CI: 1.752–2.607). After full adjustment in Model 3, the correlation persisted as statistically significant (HR = 1.746, 95% CI: 1.351–2.256).
Table 2.
Associations of CVD and Asthma with Hazard Ratios
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| HR (95%CI) | P value | HR (95%CI) | P value | HR (95%CI) | P value | |
| Stage I | ||||||
| No asthma | Ref | Ref | Ref | |||
| Asthma | 2.137(1.752-2.607) | < 0.001 | 2.178(1.776-2.669) | < 0.001 | 1.746(1.351-2.256) | < 0.001 |
| Stage II | ||||||
| No CVD | Ref | Ref | Ref | |||
| CVD | 1.985(1.571-2.501) | < 0.001 | 2.047(1.604-2.611) | < 0.001 | 1.621(1.207-2.176) | 0.001 |
Model 1 was adjusted for age and sex
Model 2 was adjusted for age, sex, education, marital status, location, smoking, drinking
Model 3 included the same adjustments as Model 2, with the further inclusion of diabetes, hypertension, chronic kidney disease, chronic lung disease, FI, BMI, TG, HDL CRP, GFR
Stage I: Comparison of CVD risk among individuals with and without possible asthma
Stage II: Comparison of asthma risk among individuals with and without possible CVD
Stage II: Longitudinal Association Between Baseline CVD and Incident Asthma
Stage Ⅱ included 8,897 participants. Individuals with CVD were older and more apt to live in rural locations than those without CVD. (p < 0.001). Regarding health status, participants with CVD had a greater incidence of numerous comorbidities, including hypertension, diabetes, chronic lung disease, and chronic kidney disease (p < 0.001). Table 1 lists the specific baseline characteristics of patients with and without CVD. Figure 2B presents the Kaplan-Meier cumulative incidence curves for asthma risk according to CVD status. Through 7 years of follow-up, 95 participants (10.0%) with baseline CVD developed asthma.
As shown in Table 2, three models were developed to evaluate the association between CVD and incident asthma. In Model 1, participants with CVD had a markedly elevated risk of getting asthma in comparison to those without CVD (HR = 1.985, 95% CI: 1.571–2.501). Following comprehensive adjustment for all factors in Model 3, the correlation persisted as statistically significant. (HR = 1.621, 95% CI: 1.207–2.176).
Subgroup analysis
Subgroup analyses were performed to explore possible variability in the relationship between asthma and CVD across different population strata. As illustrated in Fig. 3, we were stratified the cohorts by sex, age, location, BMI, chronic lung disease, hypertension, and diabetes status. Notably, none of the interaction terms reached statistical significance (all p-values for interaction > 0.05), suggesting that the effect estimates were relatively consistent across these demographic and clinical subgroups.
Fig. 3.
Subgroup Analyses of ORs for Stage I and Stage II
Forest plots show odds ratios (ORs) and 95%CIs for Stage I and Stage II adjusted for age, sex, education, marital status, location, smoking, drinking, diabetes, hypertension, chronic kidney disease, chronic lung disease, FI, BMI, TG, HDL CRP, GFR. Abbreviation: BMI, body mass index; CLD, chronic lung disease.
Mediating Effect of FI in the Asthma-CVD Association
As presented in Table 3., our findings indicate a statistically significant mediating effect of FI. A substantial direct effect was observed, indicating partial mediation by FI in the association between CVD and asthma. The direct effect of CVD on asthma was 0.030 (95% CI: 0.013–0.048), while the estimated indirect effect (CVD→ FI→ asthma) was 0.005 (95% CI: 0.002–0.007). FI accounted for 15.57% of the total effect (p < 0.001), confirming its significant role in the relationship between CVD and asthma. However, no mediating effect of FI in the association between asthma and CVD was observed in Table 3.
Table 3.
The mediation effect of FI in the relationship between asthma and CVD
| Effect Type | Path | Effect (95% CI) | Mediating Effect (%) | P |
|---|---|---|---|---|
| Stage I | ||||
| Total Effect | Asthma→ CVD | 0.135 (0.066,0.207) | < 0.001 | |
| Indirect Effect | Asthma→ CVD | -0.005 ( -0.012,0.003) | 0.220 | |
| Direct Effect | Asthma→ FI→ CVD | 0.140 (0.068,0.209) | < 0.001 | |
| Stage II | ||||
| Total Effect | CVD→ Asthma | 0.030 (0.013,0.048) | < 0.001 | |
| Indirect Effect | CVD→ Asthma | 0.005 (0.002,0.007) | 15.57 | < 0.001 |
| Direct Effect | CVD→ FI→ Asthma | 0.025 (0.008,0.043) | 84.43 | < 0.001 |
Abbreviations: CVD, cardiovascular disease; FI, frailty index
Discussion
This study is the first systematic investigation of the bidirectional association between asthma and CVD. Upon controlling for all confounding variables, baseline asthma correlated with a 74.6% heightened risk of developing incident CVD, whereas baseline CVD correlated with a 62.1% heightened risk of developing incident asthma. Notably, we found that the FI mediated 15.57% of the effect in the CVD–asthma pathway, but showed no significant mediating effect in the opposite pathway.
Previous studies have predominantly concentrated on the unidirectional impact of asthma on CVD. Our findings regarding asthma-associated CVD risk are consistent with the existing literature. A meta-analysis of 18 studies reported that people with chronic asthma had a 1.33-fold increased risk of CVD events relative to individuals without asthma [13]. Similarly, a longitudinal study conducted among Taiwanese adults demonstrated 23% and 32% increased risks of stroke and coronary heart disease, respectively, among individuals with asthma [27]. Another analysis of data from the Framingham Offspring Cohort revealed that, the association between asthma and the subsequent development of CVD persisted even after adjusting for cardiovascular risk factors (aHR: 1.28, 95% CI: 1.07–1.54) [28]. The higher risk observed in our study (HR = 1.638) may reflect unique disease patterns and risk profiles specific to the Chinese middle-aged and older population.
Various potential mechanisms have been suggested to elucidate the correlation between asthma and CVD. Chronic systemic inflammation may serve as a key link between these conditions [14]. Studies have identified increased concentrations of inflammatory markers, including interleukin-6 (IL-6), CRP, and tumor necrosis factor-α (TNF-α) in individuals with asthma [13], which are closely associated with atherosclerotic plaque formation and endothelial dysfunction [29]. Additionally, Oxidative stress may elevate the generation of reactive oxygen species. (ROS), further triggering systemic inflammatory responses that promote the onset and advancement of CVD [30].
While substantial evidence supports the impact of asthma on CVD risk, the reverse relationship remains understudied and is primarily speculative. Our study provides the first longitudinal evidence that CVD increases asthma risk, supporting Adrish et al.‘s hypothesis of a potential bidirectional association between asthma and CVD [15]. Shared risk factors, including obesity, physical inactivity, metabolic syndrome, smoking, and environmental exposures, may explain this relationship [31]. Furthermore, medications for CVD (e.g., β-blockers, statins, and antiplatelet agents) may adversely affect asthma outcomes [32].
Contrary to our findings, asthma and CVD have been associated with sex differences in previous research. A meta-analysis of 30 cohort investigations revealed the relationship between asthma and CVD to be 20% more pronounced in women than to males [33]. Overweight women with asthma (BMI 25–30) showed stronger associations with CVD than their non-overweight counterparts with asthma [34]. However, our study did not identify significant differences by sex or BMI.
A unique finding of our study is the mediating function of FI in the CVD–asthma pathway, which aligns with Rockwood’s theoretical framework of frailty as a cumulative effect of multisystem dysregulation [35]. CVD may accelerate systemic aging processes, leading to reduced functional reserve across multiple organ systems [18, 19], thereby increasing susceptibility to asthma. This suggests that CVD may indirectly influence asthma through complex systemic pathways. he absence of FI mediation in the asthma–CVD pathway implies that distinct pathological mechanisms may drive these bidirectional associations, emphasizing the necessity for more study to elucidate their molecular foundations and clinical implications.
Clinical and public health implications
The confirmation of a bidirectional relationship between asthma and CVD, partially mediated by frailty, carries substantial implications for clinical practice and public health. Firstly, our findings advocate for a shift toward integrated care models. In clinical settings, physicians managing older adults with asthma should be vigilant in screening for and managing cardiovascular risk factors, and vice versa. The assessment of frailty, using tools like the FI, could serve as a practical and efficient risk stratification tool to identify a subgroup of patients with either condition who are at the highest risk of developing the other. For instance, an older patient with CVD who is identified as frail may warrant more proactive monitoring and management of respiratory health. From a public health perspective, our results underscore the importance of developing combined prevention strategies that target both respiratory and cardiovascular health in aging populations. Interventions aimed at preventing or delaying the onset of frailty, such as physical resistance exercise, nutritional supplementation, and reduction of polypharmacy, may hold dual benefits in breaking the cycle between asthma and CVD. Future research should focus on designing and evaluating such multifaceted interventions to determine if mitigating frailty can effectively reduce the co-occurrence of asthma and CVD.
Advantages and drawbacks
There are several noteworthy strengths to this investigation: (1) it is the first systematic investigation of the bidirectional association between asthma and CVD, providing potential causal inferences; (2) the simultaneous examination of bidirectional associations across multiple subgroups enhances the comprehensiveness of our findings; (3) the innovative exploration of the frailty index (FI) as a potential mediator offers novel insights into disease mechanisms. However, several limitations should be acknowledged: (1) reliance on self-reported diagnoses for both asthma and CVD may introduce classification bias; (2) although multiple confounding factors were adjusted for, residual confounding from unmeasured variables may persist; (3) the lack of detailed treatment information limits our ability to assess the effects of medication; (4) potential bias due to mortality in the sample may have led to underestimation of the strength of the associations (5). The frailty measure used in our mediation analysis was adapted by excluding cardiovascular-related items to ensure conceptual distinctness from the exposure; although this adaptation was necessary for our research question, it may limit direct comparability with studies that use the standard frailty index for prognostic purposes.
Conclusion
Our study confirms a significant bidirectional association between asthma and CVD and reveals the partial mediating role of frailty in this relationship. These results emphasize the necessity of integrated management of multisystem diseases and provide scientific evidence to support improved health outcomes within this demographic. Subsequent research should further elucidate the molecular mechanisms underlying this bidirectional association and assess the efficacy of targeted strategies for mitigating chronic disease burden.
Supplementary Information
Acknowledgements
This study is based on data from the CHARLS dataset. We thank the CHARLS research team and field staff for their dedication to data collection and management, and all participants for providing valuable information.
Abbreviations
- CVD
Cardiovascular Disease
- FI
Frailty Index
- CHARLS
China Health and Retirement Longitudinal Study
- HR
Hazard Ratio
- CI
Confidence Interval
- BMI
Body Mass Index
- TG
Triglyceride
- HDL
High-Density Lipoprotein
- CRP
C-Reactive Protein
- GFR
Glomerular Filtration Rate
- OR
Odds Ratio
- IL-6
Interleukin-6
- TNF-α
Tumor Necrosis Factor-alpha
- CLD
Chronic Lung Disease
- aHR
Adjusted Hazard Ratio
- CAPI
Computer-Assisted Personal Interview
Author contributions
ZHW, SYZ, and TW conceived and designed the study. ZYW, KJW, MZC, and HYL contributed to data acquisition and database management. DCW and QHL performed the statistical analysis. SYZ, ZYW, and MZC interpreted the data. ZHW and TW drafted the initial manuscript. HYL and DCW critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.
Funding
This study was supported by the Jilin Provincial Department of Finance Project (No.010401052).
Data availability
The data supporting the findings of this study are available from the CHARLS repository: https://charls.charlsdata.com/.
Declarations
Ethics approval
The research received approval from the Biomedical Ethics Committee of Peking University (IRB00001052-11015).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data supporting the findings of this study are available from the CHARLS repository: https://charls.charlsdata.com/.