Abstract
Background.
The association of asthma and metabolic syndrome (MetS) among adolescents and young adults (AYAs) remains unclear, as well as the role of obesity in this relationship.
Methods.
AYAs aged 12-25 years who participated in the 2011-2020 National Health and Nutrition Examination Survey were included in this cross-sectional analysis. The moderating effect of obesity (age- and sex-adjusted body mass index ≥ 95th%ile for adolescents or ≥ 30kg/m2 for adults) on asthma and MetS were evaluated in four groups: 1) both asthma and obesity; 2) asthma and no obesity; 3) obesity and no asthma; and 4) healthy controls with no obesity/asthma.
Results.
A total of 7,709 AYAs (53.9% aged 12-18 years, 51.1% males, and 54.4% non-Hispanic White) were included in this analysis. 3.6% (95% CI 2.8-4.3%) had obesity and asthma,7.6% (95% CI 6.8-8.4%) had asthma and no obesity, 21.4% (95% CI 19.6-23.2%) had obesity and no asthma, and 67.4% (95% CI 65.4-69.4%) had neither obesity nor asthma. The estimated prevalence of MetS was greater among those with both obesity and asthma versus those with only asthma (4.5% [95% CI 1.7-7.3%] vs. 0.2% [95% CI 0-0.5%], P<0.001). Compared to healthy controls, those with both obesity and asthma had ~10 times higher odds of having MetS (aOR 10.5, 95% CI 3.9-28.1).
Conclusions.
Our results show the association between MetS and asthma is stronger in AYAs with BMI-defined obesity. Efforts to prevent and treat obesity may reduce MetS occurrence in AYAs with asthma.
Keywords: Asthma, obesity, metabolic syndrome, adolescents and young adults
Introduction
Asthma and metabolic syndrome (MetS) are both chronic conditions with increasing prevalence in adolescents and young adults (AYAs) ages 12 to 25 years old.1,2,3 Asthma is a chronic respiratory disease characterized by airway inflammation and hyperresponsiveness, while MetS is a cluster of metabolic abnormalities including central obesity (excess accumulation of visceral fat ), hypertension, hyperglycemia, and dyslipidemia.1,2 Emerging evidence suggests that asthma and MetS are linked,4,5 especially among individuals who also have concurrent obesity.6
Obesity is a major public health challenge that is a risk factor for numerous comorbidities, including asthma and MetS.7,8 The presence of obesity in individuals with asthma can lead to a cycle of worsening symptoms and increased morbidity.7 The underlying mechanisms linking obesity, asthma, and MetS are complex and multifactorial, but may involve chronic low-grade inflammation, oxidative stress, and adipokine dysregulation.9-11 Recent studies have shown that weight loss interventions, such as diet, exercise, and bariatric surgery, can improve asthma outcomes in patients with obesity, including a reduction in asthma medication use, severity and improved lung function. 12-14 The association between MetS and obesity is complex due to its overlap with central obesity, typically measured by waist circumference, (versus whole body obesity measured by body mass index or BMI). To address this complexity, we focus on the relationship of central obesity measured by abdominal circumference, and obesity represented by BMI and how BMI may moderate the association between MetS and asthma in particular.
Previous studies have shown that asthma and MetS are associated in older adults regardless of their weight status,15-17 and individuals with MetS may have worse asthma outcomes due to chronic inflammation.4,5 However, such an association between asthma and MetS has not been fully investigated among the AYA population as well as the role obesity has played. Given that AYA is a time of transitioning from dependent childhood to independent adulthood, AYAs are often at a higher risk of uncontrolled asthma due to substantial physical and psychological changes, which may lead to a higher level of inflammation.18 Based on previous literature, it was hypothesized that AYAs with concurrent asthma and obesity are more likely to have MetS compared to their counterparts. The primary aim of our study was to assess the moderating role of obesity on the association between asthma and MetS in the AYA population. We also (1) estimated the national (United States) prevalence of MetS among AYAs with asthma and (2) compared asthma outcomes, including asthma attacks and emergency department visits due to asthma, among AYAs with MetS versus no MetS.
Methods
This report follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. The Institutional Review Board at the University of Texas Health Science Center at Houston ruled this study to be exempt from review and informed consent because of the use of publicly available, deidentified data for analysis.
Study Design and Data Source
This is a cross-sectional analysis using nationally representative data from the most recent National Health and Nutrition Examination Survey (NHANES) data (2011–2012, 2013–2014, 2015–2016, and 2017–March 2020 cycles). NHANES is a survey conducted every two years by the National Center for Health Statistics and the Centers for Disease Control and Prevention (CDC) to monitor the health and nutrition status of the US population.19 Due to the interruption of data collection during the COVID-19 pandemic in 2020, the NHANES data cycle of 2019–March 2020 was merged with the 2017–2018 cycle to produce a nationally representative sample.20
Data Collection Procedures
Demographics and medical conditions were self-reported and collected through face-to-face household interviews using the Computer-Assisted Personal Interview system by trained interviewers. Physical examinations, including anthropometric measures and the collection of all biospecimens, are conducted in the Mobile Examination Center by trained health workers. All participants aged 18 years old or above had written consent, and parental permission and assent were obtained for adolescents younger than 18 years old.
Measurement and Assessment
Metabolic Syndrome (MetS)
In this study, MetS was defined based on the recommendation of the International Diabetes Federation (IDF), which is also endorsed by the American Academy of Pediatrics (Supplemental Table 1).21 Specific criteria include having central obesity (i.e., waist circumference ≥90th percentile for adolescents aged 12-16 years or >94 cm in males and >80 cm in females for those aged 16-25 years) and the presence of at least two of the following four criteria: (1) Hypertension: Elevated systolic or diastolic blood pressure, defined as ≥130 mm Hg or diastolic blood pressure ≥ 85 mm Hg or known hypertension diagnosis; (2) Hyperglycemia: A fasting glucose level ≥ 100 mg/dL or known type 2 diabetes mellitus; (3) Hypertriglyceridemia: Elevated triglyceride was defined as ≥ 150 mg/dL (1.7 mmol/L); and (4) Low High Density Lipoprotein (HDL) Cholesterol: Low HDL cholesterol was defined as <40 mg/dL (1.03 mmol/L) for 12-16 year-olds or <40 mg/dL (1.03 mmol/L) in males or <50 mg/dL (1.29 mmol/L) in females for those ≥16 years old.
Asthma
Asthma diagnosis was assessed during household interviews by the following two questions: (1) “Has a doctor or other health professional ever told you that you have asthma?” and (2) “Do you still have asthma?” Participants younger than 16 years old had proxy-assisted interviews, and those aged 16 years old and above answered independently. If children/guardians answered “yes” to both questions, those participants were categorized as having asthma, otherwise no asthma.
Obesity
As previously mentioned, the body measures data, such as weight and height, was obtained in the Mobile Examination Center by trained health technicians. For young adults aged >19 years old, if they had Body Mass Index (BMI; weight in kilograms divided by height in meters squared) ≥ 30kg/m2, those participants were classified as having obesity. Conversely, those with BMI ≤ 30kg/m2 were classified as not having obesity. For adolescents aged <19 years old, BMI percentile was used for age and sex, which was calculated for children and adolescents aged 2 to 19 years at time of examination and based on the CDC growth chart.22 Specifically, this study considered BMI ≥ 95th%ile as having obesity in adolescents, otherwise no obesity. 22
Asthma outcomes
In NHANES, the following two questions were used to assess asthma attacks and ED visits due to asthma: (1) In NHANES, the two following questions were asked to collect asthma outcomes: “During the past 12 months, {have you/has SP} had an episode of asthma or an asthma attack?” and (2) “[During the past 12 months], {have you/has SP} had to visit an emergency room or urgent care center because of asthma?” If a participant answered yes, he/she was considered to have had asthma attacks or ED visits.
Covariates
Covariates of this analysis include age (12–18 years old and 19-25 years old), sex (male and female), race/ethnicity (non-Hispanic White [NHW], non-Hispanic Black [NHB], Hispanic, and other/multi-race), family income-to poverty ratio (<1, 1–1.99. 2–2.99, 3–3.99, and ≥4), and healthcare utilization over the past year (0-5 times and > 6 times). This study also included household food insecurity (full food security, marginal food security, low food security, and very low food security) as a covariate because previous literature has shown that food insecurity is a risk factor for MetS.23
Statistical Analyses
For each survey cycle, weights are created in NHANES to account for the complex survey design, including survey non-response, oversampling, and matching total population counts from the Census Bureau.19 After applying appropriate sample weights, the NHANES data is representative of the U.S. civilian noninstitutionalized resident population. Due to the analysis of the combined dataset of 9.2 years, the sampling weights were also adjusted accordingly.19
We created four mutually exclusive comparison groups based on asthma and obesity status: 1) obesity and asthma; 2) no obesity and asthma;3) obesity and no asthma; and 4) no obesity and no asthma (healthy controls). Demographic variables were presented as raw frequencies and weighted percentages. Chi-square analysis was used to compare demographic characteristics and household food security among the four groups. The overall estimated prevalence of MetS and its individual components was reported using aggregated four cycles (2011–2020) and was compared between different groups. Logistic regression models were built to find the odds of MetS and each individual component among AYAs with obesity and asthma, no obesity and asthma, or obesity and no asthma, as compared to the healthy controls and controlling for age, gender, race, and household food security. Finally, we also assessed the odds of asthma attacks and ED visits of AYAs with MetS versus no MetS using logistic regression.
Sensitivity Analysis
We performed two sensitivity analyses. First, to determine if asthma is an independent risk factor for MetS, the sample was restricted to individuals with obesity only and compared the odds of MetS between AYAs with asthma and without asthma using logistic regression. Second, because age may also be a potential modifier, the results were stratified based on two age groups: adolescents versus young adults and then explored further if the odds of MetS differed by age groups.
Results
Our analytical sample comprised 7,709 AYAs (53.9% aged 12-18 years, 51.1% males, and 54.4% NHW, 13.8% NHB, 22.1% Hispanics, and 9.8% other/multi-race). In total, about 11.2% (95% CI 10.2-12.2%) AYAs had asthma. Furthermore, 3.6% (95% CI 2.8-4.3%) had both obesity and asthma, 7.6% (95% CI 6.8-8.4%) had asthma but no obesity, 21.4% (95% CI 19.6-23.2%) had obesity but no asthma, and the remaining 67.4% (95% CI 65.4-69.4%) of the sample had neither obesity nor asthma. Compared to those with asthma and no obesity, more AYAs with both obesity and asthma aged 19 years and above (52.1% vs 40.5%, p=0.043) were more likely to have a lower family income-to-poverty ratio (FIPR<1: 30.4% vs 23.0%; 1-1.99: 25.0% vs. 24.2%; 2-2.99: 23.7% vs. 15.2%; 3-3.99: 13.6% vs. 10.4%; and ≥ 4: 7.2% vs. 27.2%, p=0.001). About one-third of AYAs who had both obesity and asthma reported low or very low food security compared to about 21% of AYAs with asthma and no obesity (p=0.005). Similarly, among AYAs without asthma, those with obesity were more likely to report a lower FIPR and low/very low food security. (Table 1)
Table 1.
Participants’ characteristics by asthma and obesity status, NHANES 2011-2020.
| Total | Asthma (N=873) |
No asthma (N=6836) |
|||
|---|---|---|---|---|---|
| Obesity | No Obesity | Obesity | No Obesity | ||
| Total Unweighted N | 7,709 | 285 | 588 | 1617 | 5219 |
| Age, n (weighted %) | |||||
| 12-18 | 5,059 (53.9%) | 178 (47.9%) | 418 (59.5%) | 960 (44.9%) | 3,503 (56.5%) |
| 19-25 | 2,650 (46.1%) | 107 (52.1%) | 170 (40.5%) | 657 (55.1%) | 1,716 (43.5%) |
| Gender, n (weighted %) | |||||
| Female | 3,791 (48.9%) | 164 (60.7%) | 296 (56.1%) | 819 (48.2%) | 2,512 (47.7%) |
| Male | 3,918 (51.1%) | 121 (39.3%) | 292 (43.9%) | 798 (51.8%) | 2,707 (52.3%) |
| Race/ethnicity, n (weighted %) | |||||
| Non-Hispanic white | 2,196 (54.4%) | 79 (53.7%) | 194 (61.1%) | 426 (48.2%) | 1,497 (55.7%) |
| Non-Hispanic black | 1,960 (13.7%) | 118 (22.8%) | 208 (17.8%) | 429 (14.8%) | 1,205 (12.5%) |
| Hispanic/Latino | 2,187 (22.1%) | 58 (17.1%) | 111 (13.0%) | 565 (29.0%) | 1,453 (21.2%) |
| Other | 1,366 (9.8%) | 30 (6.4%) | 75 (8.08%) | 197 (7.9%) | 1,064 (10.7%) |
| Family income-to-poverty ratio, n (weighted %) a | |||||
| <1 | 2,185 (22.9%) | 104 (30.4%) | 186 (23.0%) | 479 (25.3%) | 1,416 (21.7%) |
| 1-1.99 | 1,938 (24.2%) | 72 (25.0%) | 153 (24.2%) | 469 (28.3%) | 1,244 (22.9%) |
| 2-2.99 | 1,021 (16.4%) | 42 (23.7%) | 70 (15.2%) | 208 (15.9%) | 701 (16.3%) |
| 3-3.99 | 659 (12.2%) | 25 (13.6%) | 43 (10.4%) | 120 (10.3%) | 471 (12.9%) |
| ≥ 4 | 1,174 (24.3%) | 16 (7.2%) | 91 (27.2%) | 190 (20.1%) | 877 (26.2%) |
| Household food security, n (weighted %) | |||||
| Full food security | 4,251 (64.2%) | 116 (44.4%) | 323 (63.9%) | 784 (55.8%) | 3,028 (68.0%) |
| Marginal food security | 1,164 (13.2%) | 53 (21.1%) | 97 (14.0%) | 285 (16.7%) | 729 (11.6%) |
| Low food security | 1,309 (14.0%) | 71 (22.6%) | 87 (13.9%) | 306 (15.9%) | 845 (12.9%) |
| Very low food security | 772 (8.6%) | 37 (11.9%) | 65 (8.2%) | 199 (11.7%) | 471 (7.5%) |
The total family income divided by the poverty threshold.
4.4% (95% CI 1.6-7.1%) AYAs with both obesity and asthma had MetS compared to 0.2% (95% CI 0-0.5%) of those with asthma and no obesity (p<0.001). In addition, the prevalence estimates of central obesity (79.9% [95% CI 73.0-86.6] vs. 18.9% [95% CI 13.6-24.2], p<0.001), hypertriglyceridemia (16.6% [95% CI 7.7-25.4%] vs. 3.5% [95% CI 0.8-6.2%]), and low HDL cholesterol (41.9% [95% CI 34.2-49.6] vs. 14.2% [95% CI 8.9-19.5%], p<0.001) was significantly higher among those with obesity and asthma than AYAs with no obesity and asthma, respectively. Likewise, among AYAs without asthma, those who also had obesity had a higher prevalence of MetS and individual components compared to the healthy controls (All p≤0.001). (Table 2)
Table 2.
Prevalence of metabolic syndrome and individual components by asthma and obesity status among adolescents and young adults, NHANES 2011-2020.
| Asthma | No Asthma | |||||
|---|---|---|---|---|---|---|
| Obesity (%) | No obesity (%) | P-valuea | Obesity (%) | No obesity (%) | P-valuea | |
| Metabolic syndrome b | 4.4 (1.6-7.1) | 0.2 (0-0.5) | <0.001 | 5.3 (3.3-7.3) | 0.4 (0.2-0.7) | <0.001 |
| Central obesity c | 79.9 (73.0-86.6) | 18.9 (13.6-24.2) | <0.001 | 80.8 (78.7-83.0) | 17.7 (15.8-19.5) | <0.001 |
| Hypertension d | 3.5 (0-8.3) | 1.1 (0-2.8) | 0.239 | 2.1 (0.9-3.3) | 0.2 (0.1-0.4) | <0.001 |
| Hyperglycemia e | 28.2 (18.2-38.2) | 18.8 (12.1-25.5) | 0.089 | 38.4 (32.4-44.5) | 25.1 (21.5-28.6) | 0.001 |
| Hypertriglyceridemia f | 16.6 (7.7-25.4) | 3.5 (0.8-6.2) | 0.002 | 19.7 (15.3-24.2) | 5.3 (3.8-6.8) | <0.001 |
| Low HDL cholesterol g | 41.9 (34.2-49.6) | 14.2 (8.9-19.5) | <0.001 | 41.9 (37.7-45.1) | 16.5 (14.7-18.3) | <0.001 |
Chi-square analysis
Have central obesity plus two of the following conditions: hypertension, hyperglycemia, hypertriglyceridemia, and low HDL cholesterol.
Waist circumference (WC) ≥ 90th percentile of the same gender and age for children aged 12-16 years, WC ≥ 94cm for males aged >16 years, and WC ≥80 cm for females aged >16 years
Blood pressure ≥130/80 mmHg
Fast glucose ≥ 100mg/dL
Triglycerides ≥ 130 mg/dL (1.47 mmol/L)
HDL <40 mg/dL (1.03 mmol/L) for males aged 12-18 years and females aged 12-16 years, and HDL <50 mg/dL (1.29 mmol/L) for females aged >16 years.
Crude odds of having MetS was ~10 times (OR 10.1, 95% CI 4.1-24.8) greater among AYAs with both asthma and obesity compared to the health control (i.e., no asthma or obesity). While we did not find an association with MetS among those with no obesity and asthma (OR 0.4, 95% CI 0.1-2.0),Central obesity (OR 18.4, 95% CI 11.9-28.4) and hypertension (OR 18.4, 95% CI 3.5-97.5) were the two individual components of MetS that had the highest odds followed by low HDL cholesterol (OR 3.7, 95% CI 2.7-5.0) and hypertriglyceridemia (OR 3.6, 95% CI 1.7-.7.4). The patterns were consistent for those with obesity and no asthma. In addition, the adjusted OR remained significant after controlling for age, gender, race, and household food security. (Table 3)
Table 3.
Logistic regression to find the odds of metabolic syndrome and individual components among adolescents and young adults’ asthma and obesity status.
| Outcomes | Exposures | Crude Odds Ratio (95% CI) | P value | Adjusted Odds Ratio (95% CI)g | P value |
|---|---|---|---|---|---|
| Metabolic syndrome a | Asthma and obesity | 10.1 (4.1-24.8) | <0.001 | 10.5 (3.9-28.1) | <0.001 |
| Asthma, no obesity | 0.4 (0.1-2.0) | 0.266 | 0.5 (5.6-22.2) | 0.319 | |
| No asthma, obesity | 12.3 (6.7-22.6) | <0.001 | 11.1 (5.6-22.2) | <0.001 | |
| No asthma, no obesity | 1 (ref) | - | 1 (ref) | - | |
| Central obesity b | Asthma and obesity | 18.4 (11.9-28.4) | <0.001 | 51.7 (33.8-78.9) | <0.001 |
| Asthma, no obesity | 1.1 (0.8-1.5) | 0.640 | 1.0 (0.7-62.9) | 0.927 | |
| No asthma, obesity | 19.7 (16.3-23.8) | <0.001 | 48.2 (36.9-62.9) | <0.001 | |
| No asthma, no obesity | 1 (ref) | - | 1 (ref) | - | |
| Hypertension c | Asthma and obesity | 18.4 (3.5-97.5) | 0.001 | 28.8 (4.7-175.7) | <0.001 |
| Asthma, no obesity | 5.7 (0.9-34.6) | 0.056 | 6.7 (0.9-48.7) | 0.60 | |
| No asthma,obesity | 10.7 (3.5-32.9) | <0.001 | 11.5 (3.5-38.1) | <0.001 | |
| No asthma, no obesity | 1 (ref) | - | 1 (ref) | - | |
| Hyperglycemia d | Asthma and obesity | 1.2 (0.7-1.9) | 0.528 | 1.2 (0.7-2.1) | 0.494 |
| Asthma, no obesity | 0.7 (0.4-1.1) | 0.125 | 0.8 (0.5-1.2) | 0.249 | |
| No asthma, obesity | 1.9 (1.3-2.7) | 0.001 | 2.0 (1.4-2.8) | <0.001 | |
| No asthma, no obesity | 1 (ref) | - | 1 (ref) | - | |
| Hypertriglyceridemia e | Asthma and obesity | 3.5 (1.7-7.4) | 0.001 | 4.3 (1.9-9.6) | <0.001 |
| Asthma, no obesity | 0.6 (0.3-1.4) | 0.281 | 0.8 (0.4-1.8) | 0.608 | |
| No asthma, obesity | 4.4 (2.9-6.6) | <0.001 | 4.6 (2.9-7.1) | <0.001 | |
| No asthma, no obesity | 1 (ref) | - | 1 (ref) | - | |
| Low HDL cholesterol f | Asthma and obesity | 3.7 (2.7-5.0) | <0.001 | 3.4 (2.5-4.7) | <0.001 |
| Asthma, no obesity | 0.8 (0.5-1.3) | 0.446 | 0.9 (0.6-1.4) | 0.573 | |
| No asthma, obesity | 3.7 (3.0-4.4) | <0.001 | 3.4 (2.8-4.2) | <0.001 | |
| No asthma, no obesity | 1 (ref) | - | 1 (ref) | - |
Have central obesity plus two of the following conditions: hypertension, hyperglycemia, hypertriglyceridemia, and low HDL cholesterol.
Waist circumference (WC) ≥ 90th percentile of the same gender and age for children aged 12-16 years, WC ≥ 94cm for males aged >16 years, and WC ≥80 cm for females aged >16 years
Blood pressure ≥130/80 mmHg
Fast glucose ≥ 100mg/dL
Triglycerides ≥ 130 mg/dL (1.47 mmol/L)
HDL <40 mg/dL (1.03 mmol/L) for males aged 12-18 years and females aged 12-16 years, and HDL <50 mg/dL (1.29 mmol/L) for females aged >16 years.
Controlling for age, gender, race, and household food security.
The crude estimated prevalence of MetS did not differ between AYA with asthma (1.5% [95% CI 0.6-2.4%]) and those without asthma (1.6% [95% CI 1.0-2.2%]) (p=0.857). Similar findings were shown for each component, and no significant differences were shown by asthma status. (Supplemental Table 2) Sensitivity analysis results have also suggested that asthma is not independently associated with MetS because, after restricting the sample to AYAs with obesity only, the odds of MetS and its individual component between AYAs with asthma and no asthma did not differ among AYAs (all p-value>0.05). (Supplemental Table 3) In addition, there were no significant associations found between asthma and MetS after stratifying by the age group (i.e., adolescents versus young adults). (Data not shown)
Furthermore, compared to AYAs with no MetS, those with MetS did not have a greater odds of asthma attacks (aOR 0.8, 95% CI 0.2-2.8) or ED visits due to asthma (aOR 1.2, 95% CI 0.2-4.9). (Figure 1)
Figure 1.
Estimated prevalence and odds ratio of asthma attacks and emergency department visits among adolescents and young adults with metabolic syndrome versus those without metabolic syndrome.
Discussion
Our analysis is one of the first studies using population-based, nationally representative data showing that the link between MetS and asthma appears to be much stronger in AYAs with obesity, with MetS being significantly more prevalent in AYAs with both obesity and asthma compared to those with asthma and no obesity. This finding is consistent with a previous non-population-based study that only included Mexican adolescents, where authors found that those with obesity and asthma were more likely to have MetS compared to those with asthma but no obesity. The authors also found a significantly higher prevalence of MetS among male individuals with both asthma and obesity than those with obesity but no asthma.6
It is indeed recognized that central obesity is a key component of MetS and shares a common association with general obesity.2 However, they are different in terms of (1) measurement: general obesity is assessed using BMI, while central obesity is measured via waist circumference; (2) fat distribution: general obesity involves overall body fat accumulation, whereas central obesity specifically targets fat accumulation in the abdominal area only; (3) health Impact: both types of obesity are associated with health risks, but central obesity is notably linked to cardiovascular disease, diabetes, and metabolic syndrome due to its association with visceral fat.24-26 In our study, we explored the relationship between MetS and asthma in AYAs while taking into account this potential overlap. Our analysis focused on understanding whether the presence of MetS could have additional implications for asthma in AYAs already identified as having obesity based on their BMI.
The exact mechanism behind the association between MetS and asthma in AYAs with obesity remains unclear. However, it is suggested that underlying inflammation from having obesity may play an important role in the development of both conditions.10,11 The chronic low-level inflammation in people with obesity may contribute to airway inflammation in asthma, leading to the worsening of asthma symptoms.7-9 In addition, obesity may be associated with reduced lung function, increased airway resistance, and decreased responsiveness to asthma medications.12-14 These factors may partially explain the development and worsening of asthma symptoms in individuals with obesity. Further studies are needed to better understand the underlying mechanisms and implications for clinical management in this population. However, these findings highlight the importance of weight management to prevent and manage both MetS and asthma.
Another key finding of our study is that asthma is not independently associated with MetS among AYAs. A previous study suggested there was a significant association between asthma and MetS in older adults, independent from BMI.17 In addition, this study also found asthma and MetS are not associated in those aged younger than 18 years.17 Here, we have further extended this null finding to young adults aged <25 years old.
The reason behind this age-dependent association between asthma and MetS needs to be further investigated. However, several factors may contribute to this phenomenon. First, the prevalence of MetS increases with age.2 It has been shown that the incidence of MetS increases after the age of 40.2 Therefore, the higher prevalence of MetS in older adults may contribute to the stronger association observed. Second, the pathophysiologic mechanisms of asthma are different depending on age.27 AYAs are more likely to have an allergic phenotype of asthma, whereas older adults who develop asthma later in life commonly have non-allergic asthma, and usually obesity-related, therefore older adults with asthma may be at a higher risk of developing MetS than AYAs.28 Third, lifestyle factors such as physical inactivity, poor diet, and obesity, which are common risk factors for both asthma and MetS, may have a cumulative effect over time.29-31 These factors may contribute to the development of MetS and worsening of asthma symptoms in older adults. Lastly, the immune system and inflammatory response change with age.32 Aging is associated with a decline in immune function, which may affect the development and progression of asthma and MetS.32 Additionally, chronic low-grade inflammation is associated with both conditions, and this inflammation may increase with age, contributing to the stronger association observed in older adults.33,34
In addition, we found AYAs with concurrent asthma and obesity were more likely to have hypertension compared to healthy controls. Previous studies have also suggested that asthma and hypertension are linked through possibly different mechanisms, such as shared genes and drug side effects.35,36 Our finding suggests obesity may be another important risk factor contributing to this association. However, current clinical guidelines have not included screening for hypertension in individuals with asthma,37 given the high prevalence of hypertension in AYAs with asthma and obesity, there is a need to monitor blood pressure closely in this population to prevent cardiometabolic health risks in later life.
Without factoring in obesity as a potential modifier, the borderline significantly higher prevalence of central obesity among asthma patients underscores a potential link between central obesity and asthma. This is consistent with a previous study, which suggests that central obesity’s distinct fat distribution pattern may exert a more pronounced influence on respiratory health.26 Conversely, other MetS criteria—blood pressure, glucose, triglycerides, and HDL levels—seem to have minimal impact on asthma within this study population. These findings suggest that central obesity may hold a more prominent role in influencing MetS related to asthma in this study.
In the present study, we have also found having MetS did not increase the odds of asthma attacks or ED visits. In NHANES, the two following questions were asked to collect asthma outcomes: “During the past 12 months, {have you/has SP} had an episode of asthma or an asthma attack?” and “[During the past 12 months], {have you/has SP} had to visit an emergency room or urgent care center because of asthma?” Given those asthma outcomes were collected as a binary variable (Yes or No), future studies can focus on the actual number of episodes to further examine this association. Because there might be a potential bidirectional relationship between asthma and MetS, it is important for healthcare professionals to consider both conditions when evaluating and managing patients. Further research is needed to elucidate the underlying mechanisms and develop targeted interventions to improve health outcomes in these patients.
Limitations
This study has limitations. First, a temporal or causal relationship cannot be established due to the cross-sectional design. Second, we could not examine the impact of dosages or durations of asthma medications, such as inhaled corticosteroids, because NHANES does not collect this information. Third, as mentioned above, we could not examine the number of asthma exacerbations, rather they were treated as binary outcomes, which may lead to residual confounding effects. Fourth, asthma diagnosis is self-reported, which may be prone to recall/reporting bias. Finally, although there might be selection bias because of the modest response rates (52%–73%);34 however, the sample weights provided by NHANES helped decrease possible sampling bias.
Conclusion
In conclusion, the link between asthma and MetS remains a topic of ongoing research. Our study suggests that the association between the two conditions is much stronger in AYAs with concurrent obesity. The high prevalence of MetS among those with both asthma and obesity underscores the need for a multidisciplinary approach to management that addresses both the respiratory and metabolic aspects of these conditions. It is important to note that central obesity, a key element in the definition of MetS, plays an important role in this association. The overlap between obesity and MetS, particularly in terms of abdominal obesity, underscores the complex interplay between these factors in influencing asthma outcomes. Further research is needed to elucidate the precise mechanisms underlying this relationship and to inform more targeted interventions to improve outcomes and reduce the burden of these chronic diseases.
Supplementary Material
Footnotes
Conflict of Interest: None
References
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