Asthma persists as a widespread chronic respiratory condition globally, impacting the quality of life of millions of people (1). The global burden of asthma is considerable, with estimates suggesting that it affects approximately 262 million people and causes around 461,000 deaths annually (2). This burden is not only reflected in healthcare costs but also includes lost productivity and decreased activity levels. Moreover, the impact of asthma on adults is particularly profound, as it can complicate the management of other comorbid conditions such as cardiovascular disease and diabetes, which are more prevalent with advancing age (3). Unlike childhood asthma, which is frequently characterized by atopy and a clear allergic component, asthma in adults can present with a broader range of triggers and is often less responsive to conventional treatments (4). Thus, recognizing risk factors for asthma in adults and implementing suitable interventions are crucial for decreasing both the morbidity and mortality associated with asthma.

Obesity represents a severe worldwide health issue that has experienced a significant rise in prevalence during recent decades (5). Obesity in adults is often evaluated using the body mass index (BMI), where a BMI of 30 kg/m² or more is considered obese. This condition is not only a risk factor for traditional metabolic syndromes but also complicates respiratory conditions (6). Numerous studies have demonstrated that obesity raises the risk of developing asthma (7–9). After controlling for other variables, observational research conducted in the US discovered that obesity was an independent risk factor for asthma (8). A meta-analysis that included seven cohort studies with more than 333,102 participants showed that an increase in BMI had a dose–response effect on the incidence of asthma (9). While most research on obesity and asthma classifies participants solely based on BMI, this basic and frequently used anthropometric measure has significant limitations. Because it fails to differentiate between adipose mass and muscle mass, nor does it consider the distribution of body fat (10). Some studies (11, 12) have even found a U-shaped association between BMI and asthma, meaning that having a BMI that is too high or too low may both raise the chance of developing asthma. In certain instances, BMI might either overestimate or underestimate obesity. Therefore, new measures are required in order to correctly examine the link between obesity and asthma risk. Body fat distribution measurements have been used to predict conditions such as diabetes, cardiovascular disease, and other conditions with superior predictive value to BMI (13, 14). However, there is a dearth of evidence on the connection between fat distribution and adult asthma.

The National Health and Nutrition Examination Survey (NHANES) is a collection of surveys conducted at different times to evaluate the well-being of the U.S. population, track disease patterns and risk factors, and provide data to support the development and evaluation of health policies and programs (15). Mendelian Randomization (MR) is a method to evaluate the causal link between exposure and outcome. It uses genes strongly associated with traits as instrumental variables (IVs) to infer the causal relationship indirectly. Since genes are randomly allocated during gamete production and are minimally influenced by external environments after birth, MR can avoid confounding factors, reverse causation, and selection bias (16). To determine the impact of body fat distribution measurements (arm, leg, trunk, and total fat mass) on adult asthma risk, we first analyzed cross-sectional data from the NHANES database in this research. Afterwards, the causal association between them was evaluated using a bidirectional MR study.

We used a bidirectional MR analysis in conjunction with cross-sectional data from the NHANES 2011–2018 to investigate the relationship between body fat distribution measures and adult asthma. The results indicated that body fat distribution measurements were associated with an increased risk of adult asthma, and exhibited a unidirectional causal relationship in MR analysis.

Numerous studies have examined the link between obesity and asthma. In order to evaluate the association between adult asthma and obesity, Brumpton et al. (7) recruited 23,245 people from the Norwegian population for their prospective research. They defined general obesity as a BMI ≥30 kg/m². After 11 years of follow-up, they found that general obesity increased the risk of asthma in both adult men (OR = 1.84, p < 0.05) and women (OR = 1.96, p < 0.05). A multicenter clinical study by Liu et al. (12) included 11,868 US adults and found that both underweight (BMI < 18.5 kg/m²) and obesity (BMI ≥ 30 kg/m²) were risk factors for asthma. Although BMI is widely used and simple to measure, it has limitations. A study by Tomiyama et al. (26) included 40,420 US adults, who were stratified by BMI to investigate cardiac metabolic conditions across different groups. They found that 50% of the overweight individuals and 29% of those classified as obese were metabolically healthy, whereas over 30% of those with normal weight had unhealthy cardiac metabolism. Lahav et al. (27), after analyzing medical examination data from approximately 3,000 Israeli adult men and women, found that body fat percentage was a more reliable predictor of overall individual health and cardiac metabolic risk than BMI. Prillaman (28) pointed out that obesity should be redefined. He believed that BMI only considered height and weight, yet neglected other factors such as body fat, which might potentially have a more significant influence on one’s health. In light of this, in our study, we utilized body fat distribution measurements to evaluate the correlation between obesity and adult asthma.

Following the adjustment for 11 covariates, including age and gender, the weighted logistic regression analyses in the cross-sectional study from NHANES 2011–2018 revealed that fat mass in the left arm, left leg, right arm, right leg, trunk, and overall body was linked to an increased risk of adult asthma. Furthermore, to assess the trend of asthma risk increasing with fat mass, we employed RCS for analysis. A U-shaped connection between BMI and asthma risk was found in a study by Sun et al. (11). However, our RCS analysis revealed a J-shaped relationship between six fat mass indicators and asthma risk, indicating that below a certain threshold, changes in fat mass did not affect asthma risk, but beyond this threshold, asthma risk increased with fat mass. Indeed, this J-shaped relationship simplifies the association between fat mass and asthma compared to BMI, which requires concern both for being too high and too low. However, cross-sectional studies can only establish the correlation between body fat distribution measurements and adult asthma, and cannot determine the causal relationship or direction between the two (29). Therefore, we further used a bidirectional MR study to investigate their causal link and direction. In the forward MR analysis evaluating the causal effect of fat mass on adult asthma, with the IVW method as the primary approach, we found that all six fat mass indicators increased the risk of asthma. Additionally, sensitivity analysis supported the reliability of the MR analysis. In the reverse MR analysis, the IVW method’s findings demonstrated that none of the six fat mass indicators were causally impacted by adult asthma.

Several reasons may account for the correlation between high fat mass and a higher incidence of asthma in adults. One possible reason is that the buildup of adipose tissue in the chest wall and abdominal wall may reduce the compliance of the respiratory system, thus affecting the circulation of gasses and resulting in reduced functional residual capacity (FRC) (30). Reduced FRC weakens the mutual support between the airways and the parenchyma, which in turn causes the collapse and narrowing of the airways (31). Additionally, a longitudinal study from Korea found that fat increased airway hyperresponsiveness in adults, which is an important feature of asthma (32). Another possible reason is that fat increases the risk of asthma by promoting inflammation. Large numbers of macrophages were found in the adipose tissue of obese individuals by Weisberg et al. (33). According to Periyalil et al. (34), the majority of the macrophages in the obese asthma patients’ adipose tissue were pro-inflammatory. Leptin is a hormone released by fat cells that has pro-inflammatory and immune-regulating effects (35). Sideleva et al. (36) found that the expression of leptin was increased in obese asthma patients, suggesting it might be a significant mediator in the occurrence of airway diseases in obese individuals. Further research is needed to validate our hypotheses regarding how fat increases the risk of asthma.

Our study’s strength is that we used cross-sectional studies in conjunction with MR analysis to assess the association between body fat distribution measurements and adult asthma risk. We considered both epidemiology and genetics, and using these two methods together greatly reduced the effects of reverse causation and confounding variables. The consistency of conclusions from both approaches also makes the research findings more reliable. Furthermore, the bidirectional MR analysis we employed not only established causality but also determined the direction of the causal relationships. However, our study is subject to certain limitations. Firstly, in cross-sectional studies, asthma diagnoses are often based on survey questionnaires, which can introduce bias. Secondly, in contrast to the NHANES, which mostly surveys Americans, MR covers people all around Europe. Disparities in race might influence the results. Thirdly, the impact of body fat mass on the risk of adult asthma across different genders was not examined due to data constraints. Finally, we did not go into the specific mechanisms via which excess fat increases the risk of adult asthma.

In conclusion, the results from both cross-sectional studies and MR analysis support a correlation between body fat distribution and the risk of adult asthma, with higher fat mass potentially increasing the risk of adult asthma. However, reverse MR did not find evidence that asthma could increase fat mass. Our study provides a new perspective on the prevention and treatment of asthma in adults, and more research is still needed to validate our findings in the future.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.

The studies involving humans were approved by National Center for Health Statistics. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because Data were obtained from public databases.

KW: Conceptualization, Methodology, Writing – original draft. ZC: Data curation, Methodology, Writing – original draft. ZW: Supervision, Writing – review & editing, Software. LH: Data curation, Methodology, Writing – original draft. LG: Conceptualization, Funding acquisition, Supervision, Writing – review & editing.