Abstract
Background
Asthma has been associated with a higher risk for cardiometabolic disease. One possible explanation is the activation of type 2 inflammation. We aimed to investigate whether there is an association between type 2 inflammation and cardiometabolic disease and whether the association varies between different biomarkers.
Methods
This cross-sectional study included a total of 4277 non-smoking participants with data on cardiometabolic disease and type 2 inflammation (blood eosinophil count, exhaled nitric oxide fraction (FENO) and IgE sensitisation). The cut-off values of the biomarkers were ≥0.3×109 L−1 for blood eosinophil count, ≥25 ppb for FENO and ≥0.35 PAU·L−1 for IgE sensitisation.
Results
There was a higher prevalence of cardiometabolic disease among participants with any type 2 biomarker: diabetes (5.2% versus 3.3%; p=0.002), cardiovascular disease (CVD) (3.3% versus 1.8%; p=0.002) and hypertension (23.7% versus 20.2%; p=0.007). Diabetes had the strongest association with having all three biomarkers elevated (OR 4.03 (95% CI 1.84–8.87)), followed by elevation of both blood eosinophil count and FENO (OR 2.38 (95% CI 1.15–4.91)) and of only blood eosinophil count (2.02 (95% CI 1.21–3.36); p=0.007). CVD was associated with the combination of elevated blood eosinophil count and IgE sensitisation (OR 4.77 (95% CI 2.11–10.79)) and with elevated FENO (OR 2.57 (95% CI 1.31–5.06)). Hypertension was associated with elevated blood eosinophil count (OR 1.65 (95% CI 1.26–2.18)).
Conclusion
We found an association between type 2 inflammation and cardiometabolic disorder, but the association with the combination of markers varied between the diseases.
Shareable abstract
Having type 2 inflammation is not only associated with asthma but also with a higher prevalence of diabetes, cardiovascular disease and hypertension https://bit.ly/4fQV9LC
Introduction
Type 2 inflammation, a part of the immune system that protects from parasitic helminths and toxic environmental substances, is associated with chronic inflammatory diseases, including asthma [1]. The type 2 immune response is initiated when environmental factors stimulate group 2 innate lymphoid cells and T-helper 2 lymphocytes to produce the cytokines interleukin (IL)-4, IL-5 and IL-13 [2]. Biomarkers downstream of these cytokines have been identified and are now routinely used in clinical practice to assess asthma phenotype. These include sputum and blood eosinophils, total and antigen-specific IgE, and exhaled nitric oxide fraction (FENO) [3]. The Global Initiative for Asthma 2022 report advocates classifying asthma into type 2-high and type 2-low phenotypes to identify patients at risk of exacerbations and individualise treatment [4].
There is evidence suggesting a connection between asthma and cardiometabolic disease. A higher prevalence of coronary heart disease [5–7], cerebrovascular disease [6, 8, 9], hypertension [10] and diabetes [11] has been found among asthmatic patients than in non-asthmatic patients in several studies. A recent prospective study of a population-based cohort followed over 35 years found asthma to be a risk factor for cardiovascular disease (CVD) after adjusting for potential confounders [12]. Several common traits have been suggested to explain the connection, including common risk factors and systemic inflammation.
The most well-studied biomarker of type 2 inflammation in connection to cardiometabolic disease is blood eosinophil count, with differential findings. Higher blood eosinophil count is associated with an increased risk of developing coronary heart disease, heart failure and stroke/transient ischaemic attack [13], as well as with a higher prevalence of hypertension, diabetes and hyperlipidaemia [14]. In other studies, no relationship between higher blood eosinophil count and cardiometabolic diseases has been seen [15], and even opposite findings have been reported showing a protective effect [16]. Studies exploring the relationship between IgE sensitisation and cardiometabolic disease have been conflicting. Both total IgE and allergen-specific IgE levels have been found to increase the risk of CVD [17, 18], hypertension [19] and diabetes [20] in some studies. In contrast, others have not found any association [21]. In summary, evidence suggests a link between type 2 inflammation and cardiometabolic disease, but the evidence is far from conclusive.
The present study investigated whether there is an association between type 2 inflammation and cardiometabolic disease, whether the association varies between the different biomarkers, and whether having more than one elevated biomarker impacts the association. We hypothesised that the association between cardiometabolic diseases and type 2 inflammation varies between different combinations of type 2 biomarkers.
Methods
Participants were randomly selected from the Swedish population registry and invited to participate in the Swedish CArdioPulmonary bioImage Study (SCAPIS). Between 2013 and 2018, a total of 30 154 randomly selected men and women aged 50–64 years were recruited nationally at six different study sites (Gothenburg, Linköping, Malmö/Lund, Stockholm, Umeå and Uppsala) using information from the Swedish population registry. The participation rate at the Uppsala University site was 46.8%. The recruitment of participants was carried out between October 2015 and June 2018. The study aimed to identify relevant risk predictors for cardiopulmonary and metabolic disease. To do so, the participants participated in a comprehensive questionnaire and underwent blood sample testing, physical examinations, lung function testing and imaging. The methods and selection criteria for SCAPIS have been described in detail elsewhere [22]. Type 2 inflammation biomarkers were analysed as an add-on to the core SCAPIS protocol at Uppsala University. They included blood samples for eosinophils, IgE antibodies and breath-out tests for FENO. A total of 5036 participants were evaluated at Uppsala University, and this subgroup of participants constitutes the population of this study.
The study was approved by the Regional Ethical Review Board in Umeå (2010-228-31 M), and the analyses of the SCAPIS study were approved by the Regional Ethical Review Board in Uppsala (2018-272). The study participants gave their written and informed consent.
Current smokers (n=424) and participants with missing data on smoking history (n=355) were excluded (figure 1) since smoking previously has been shown to reduce FENO levels markedly [23]. COPD, diabetes and stroke were significantly more prevalent among smokers than non-smokers. Participants with missing data on blood eosinophil count (n=20), FENO (n=884) and IgE sensitisation (n=69) were excluded from the corresponding analyses. The proportion of women was slightly higher (54.9% versus 50.9%; p=0.031) among participants with missing data. FENO measurement was an add-on measurement. During the data collection (December 2016 to April 2017), no add-ons (including FENO measurement) were performed due to staffing issues, explaining in part the high number of participants with missing data on FENO.
FIGURE 1.
Study flow and grouping of study participants. SCAPIS: Swedish CArdioPulmonary bioImage Study. FENO: exhaled nitric oxide fraction.
Participants were grouped based on the presence of the three biomarkers of type 2 inflammation. Further subgroups were formed based on whether participants had more than one biomarker elevated (figures 1 and 2).
FIGURE 2.
Distribution of type 2 inflammation biomarkers in the 3214 individuals where all three biomarkers were measured. FENO: exhaled nitric oxide fraction.
Medical history
The data on the cardiometabolic diseases examined in this study, diabetes, myocardial infarction, stroke and hypertension, as well as on asthma and COPD, were obtained from the questionnaire. The participants were asked, “Which of these diseases have you been diagnosed with by a doctor?” and given multiple choices including the earlier mentioned diseases. Myocardial infarction, angina and stroke were collectively considered CVD.
Smoking history
Information about smoking history was obtained from the questionnaire, and participants were divided into groups (current, former and never-smokers) based on their responses to the question “Do you smoke?”.
Blood eosinophils
Venous blood samples were collected from all study participants. Blood eosinophil count was measured using the Cell-Dyn 4000 automated haematology analyser (Abbott Laboratories, Abbott Park, IL, USA). Blood eosinophils were considered elevated if ≥0.3×109 L−1 [23].
Exhaled nitric oxide fraction
FENO was measured during a single breath-out test using NIOX Vero (previously Aerocrine, currently Circassia, Oxford, UK) as recommended by the American Thoracic Society/European Respiratory Society guidelines [24], apart from only one breath test being performed [25]. After emptying their lungs, the participant was asked to place their mouth on the mouthpiece of the analyser and fill their lungs with nitric oxide-free air. After that, they were asked to exhale with a constant flow of 50 mL·s−1 for 10 s. FENO ≥25 ppb was considered elevated [23].
Ige sensitisation
Blood samples were analysed for IgE antibodies using ImmunoCAP Phadiatop (Phadia AB/Thermo Fisher Scientific, Uppsala, Sweden). The assay includes a mix of common aeroallergens, and the IgE antibody values are presented as Phadia arbitrary units per litre (PAU·L−1). Participants with IgE levels ≥0.35 PAU·L−1 were considered IgE sensitised [26].
Statistical analyses
All statistical analyses were performed using Stata (StataCorp, College Station, TX, USA). Continuous variables are presented as mean with standard deviation. Comparison between groups was done using independent t-tests. Categorical data are presented as counts with percentages. Comparison between groups was done using Chi-squared tests. Multivariate logistic regression analyses were performed to analyse the correlation between type 2 inflammation and cardiometabolic diseases adjusted for age, sex, body mass index (BMI) and smoking history. In sensitivity analyses, we used two other models: one adjusting for asthma with the aforementioned variables, and the other only adjusted for age, sex and smoking history. The results are presented as odds ratios with 95% confidence intervals.
Results
Characteristics
The baseline characteristics of the study population, divided into groups based on whether the participants had elevated levels of one or more of the biomarkers of type 2 inflammation or not, are presented in table 1. A total of 4277 participants were included in the study, and 1975 of them had at least one elevated biomarker for type 2 inflammation. No significant difference in age, smoking history or prevalence of COPD could be seen between the two groups. In the group without any biomarker of type 2 inflammation, the proportion of women was higher, and they had a slightly higher BMI. There was a significantly higher prevalence of several of the cardiometabolic diseases analysed in this study (diabetes, myocardial infarction, angina and hypertension) and of asthma in the group with at least one biomarker of type 2 inflammation elevated. Data on all three biomarkers were available from 3872 participants. When comparing these participants with all the others (n=1164) that were part of the study sample, the responders were less likely to be current smokers (9.1% versus 12.1%; p<0.0001), but there were no significant differences regarding sex, age, BMI, asthma, COPD or cardiometabolic diseases.
TABLE 1.
Baseline characteristics
| No type 2 biomarker elevated (n=2032) | ≥1 type 2 biomarkers elevated (n=1975) | p-value | |
|---|---|---|---|
| Age (years) | 57.8±4.4 | 57.5±4.5 | 0.093 |
| Female | 1353 (58.8) | 863 (43.7) | <0.001 |
| BMI (kg·m−2) | 26.8±4.4 | 27.2±4.4 | 0.002 |
| <30 kg·m−2 | 1860 (80.8) | 1542 (78.1) | 0.028 |
| ≥30 kg·m−2 | 442 (19.2) | 433 (21.9) | |
| Smoking history | 0.138 | ||
| Never-smoker | 1464 (63.6) | 1299 (65.8) | |
| Former smoker | 838 (36.4) | 676 (34.2) | |
| COPD | 8 (0.4) | 13 (0.7) | 0.148 |
| Asthma | 94 (4.1) | 197 (10.1) | <0.001 |
| Diabetes | 76 (3.3) | 102 (5.2) | 0.002 |
| CVD | 41 (1.8) | 65 (3.3) | 0.002 |
| Myocardial infarction | 17 (0.7) | 33 (1.7) | 0.005 |
| Angina | 8 (0.4) | 17 (0.9) | 0.028 |
| Stroke | 20 (0.9) | 21 (1.1) | 0.515 |
| Hypertension | 465 (20.2) | 467 (23.7) | 0.007 |
Data are presented as mean±sd or n (%), unless otherwise stated. BMI: body mass index; CVD: cardiovascular disease.
Correlation between cardiometabolic disease and type 2 inflammation
To further characterise the correlation between cardiometabolic diseases and type 2 inflammation, we analysed the prevalence of each diagnosis in groups of participants based on whether they had elevated or non-elevated blood eosinophil count, elevated or non-elevated FENO and present or non-present IgE sensitisation. The prevalence of diabetes, myocardial infarction, angina and hypertension was significantly higher in the group with elevated blood eosinophil count compared to the group with non-elevated blood eosinophil count. No difference in the prevalence of cardiometabolic disease could be seen between participants with elevated or non-elevated FENO nor with or without IgE sensitisation (table 2). These results remained also after adjustment for sex, age, smoking, asthma and BMI (table 2).
TABLE 2.
Prevalence of and adjusted associations between cardiometabolic disease and type 2 biomarkers
| Blood eosinophils | F ENO | IgE | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Low (n=3431) | Elevated (n=826) | p-value | Low (n=2703) | Elevated (n=690) | p-value | Non-sensitised (n=3123) | Sensitised (n=1086) | p-value | |
| Prevalence | |||||||||
| Diabetes | 111 (3.2) | 66 (8.0) | <0.001 | 102 (3.8) | 35 (5.1) | 0.12 | 127 (4.1) | 48 (4.4) | 0.61 |
| CVD | 72 (2.1) | 34 (4.1) | 0.001 | 57 (2.1) | 23 (3.3) | 0.06 | 73 (2.3) | 30 (2.8) | 0.43 |
| Hypertension | 684 (19.9) | 240 (29.1) | <0.001 | 591 (21.9) | 155 (22.5) | 0.73 | 696 (22.3) | 216 (20.1) | 0.13 |
| Adjusted# estimate (OR (95% CI)) | |||||||||
| Diabetes | 1 | 2.07 (1.54–2.78) | 1 | 1.20 (0.81–1.78) | 1 | 1.12 (0.81–1.55) | |||
| CVD | 1 | 1.70 (1.16–2.50) | 1 | 1.23 (0.76–2.00) | 1 | 1.17 (0.78–1.75) | |||
| Hypertension | 1 | 1.43 (1.21–1.69) | 1 | 0.91 (0.74–1.12) | 1 | 0.94 (0.79–1.11) | |||
Data are presented as n (%), unless otherwise stated. CVD: cardiovascular disease. #: adjusted for sex, age, body mass index and smoking history.
Combinations of biomarkers for type 2 inflammation
The prevalence of cardiometabolic disease varied significantly between participants with different patterns of type 2 inflammation. The highest prevalence of diabetes was observed among participants with all three type 2 biomarkers elevated. The prevalence was also markedly increased among participants with elevated blood eosinophil count and those with both elevated blood eosinophil count and FENO. For CVD, the prevalence was highest among participants with elevated blood eosinophil count and IgE sensitisation. The prevalence of hypertension, on the other hand, was highest among participants with only elevated blood eosinophil count (figure 3).
FIGURE 3.
Prevalence of cardiometabolic disease among participants with different combinations of elevated biomarkers of type 2 inflammation: a) diabetes, b) cardiovascular disease (CVD) and c) hypertension. FENO: exhaled nitric oxide fraction.
In the adjusted analyses, diabetes was independently associated with having elevated blood eosinophil count, having both elevated blood eosinophils and FENO, and having an elevation of all three biomarkers. CVD was associated with elevated FENO and a combination of elevated blood eosinophil count and IgE sensitisation. Hypertension was only associated with having elevated eosinophil count. BMI, age and male gender were also associated with higher odds for cardiometabolic diseases (table 3). Including asthma as a potential confounder in the models had only a small effect on the estimates (figure 4). The same was true when only adjusting for age, sex and smoking history (supplementary table S1). Excluding the 229 participants who reported that they had used medication against asthma in the previous 2 weeks did not change the estimate (data not shown).
TABLE 3.
Odds ratios# (95% CIs) for cardiometabolic disease among participants with different combinations of elevated type 2 biomarkers
| Diabetes | CVD | Hypertension | |
|---|---|---|---|
| Elevated blood eosinophils | 1.63 (1.02–2.59) | 1.90 (1.02–3.55) | 1.58 (1.23–2.04) |
| Elevated FENO | 0.47 (0.20–1.10) | 2.48 (1.31–4.70) | 0.95 (0.70–1.29) |
| IgE sensitisation | 0.97 (0.56–1.69) | 1.69 (0.87–3.28) | 1.15 (0.89–1.48) |
| Blood eosinophils and FENO elevated | 2.79 (1.46–5.32) | 2.04 (0.77–5.41) | 1.36 (0.88–2.09) |
| Elevated blood eosinophils and IgE sensitisation | 1.69 (0.83–3.45) | 3.75 (1.76–7.97) | 1.35 (0.90−2.03) |
| Elevated FENO and IgE sensitisation | 1.24 (0.58–2.67) | 0.28 (0.04–2.07) | 0.86 (0.57–1.29) |
| All three biomarkers elevated | 3.39 (1.58–7.27) | 2.16 (0.63–7.38) | 1.23 (0.70–2.15) |
CVD: cardiovascular disease; FENO: exhaled nitric oxide fraction. #: adjusted for sex, age, body mass index and smoking history.
FIGURE 4.
Odds ratios (ORs (95% CIs)) for cardiometabolic disease among participants with different combinations of elevated biomarkers of type 2 inflammation: a) diabetes, b) cardiovascular disease (CVD) and c) hypertension. The colours, going from light to darkest red, represent non-significant negative OR, non-significant positive OR, significant OR <2, significant OR 2–4 and significant OR >4. The model made adjustments for sex, age, body mass index, smoking history and asthma. FENO: exhaled nitric oxide fraction.
Discussion
In this population-based study, we found that type 2 inflammation markers were associated with increased prevalence of the different manifestations of cardiometabolic disease investigated. However, the patterns of this association varied between the cardiometabolic disorders.
In our study, we could establish an association between diabetes and type 2 inflammation. The clearest association could be found with elevated blood eosinophil count. FENO and IgE sensitisation were not associated with diabetes on their own, but an additive effect of having both elevated blood eosinophil count and one or more other biomarkers of type 2 inflammation was found.
The association between elevated blood eosinophils and diabetes aligns with previous findings in a population-based retrospective study on a large cohort where the prevalence of diabetes increased as blood eosinophil counts increased [13]. Another population study by Amaral et al. [27] reported that diabetes was associated with higher blood eosinophil counts. Higher eosinophil levels were also associated with an increased prevalence of diabetes among patients undergoing coronary angiography [14]. Previous studies are, however, far from conclusive. In a recent review and meta-analysis, Benson et al. [28] showed that eosinophil distribution and range varied significantly between studies and were affected by a wide variety of clinical factors. For example, in a study by Hartl et al. [29], diabetes was not associated with higher blood eosinophil levels. Still, a significant association was found between elevated blood eosinophil count and metabolic syndrome. Amini et al. [15] also found no association between diabetes and blood eosinophil levels, but higher haemoglobin A1c was associated with increased blood eosinophils.
Our study found an association between CVD and type 2 inflammation, but the pattern is less clear. The prevalence of CVD was higher among participants with elevated blood eosinophil count and borderline significantly higher among participants with elevated FENO. The analysis of different combinations of type 2 inflammation biomarkers showed that participants with elevated blood eosinophil count and IgE sensitisation had the highest risk for CVD, followed by those with elevated FENO alone. Our results are partly in line with the study by Pongdee et al. [13], in which higher blood eosinophil levels were associated with a greater risk for future CVD events. Tanaka et al. [30] showed that eosinophil count is associated with coronary artery calcification, as evaluated with computer tomography in participants with suspected coronary heart disease. In a similar study, Guo et al. [17] found that IgE concentrations correlated to coronary heart disease verified with coronary angiography. We have found no other study that reported an association between FENO and CVD. For example, Amaral et al. [27] found no association between FENO and CVD when analysing this in the National Health and Nutritional Examination Surveys (NHANES) study. Elevated FENO has, however, been linked to an increased prevalence of metabolic syndrome [31].
Hypertension was only associated with elevated blood eosinophils in our study and not with any other biomarker or combination of biomarkers. Both the studies by Pongdee et al. [13] and Verdoia et al. [14] found that higher blood eosinophil counts were associated with increased prevalence of hypertension. A recent review by McCarthy et al. [32] supports the connection between hypertension and eosinophils.
The pathophysiological connection between cardiometabolic disease and type 2 inflammation is far from established. Our understanding of the role of inflammation and the different inflammatory cells in the development of diabetes has widened over the last decade [33]. For example, eosinophils in the adipose tissue have been identified as participants in maintaining glucose homeostasis in mouse models [34]. The immune system also plays an important role in the formation of atherosclerotic plaques, a hallmark of CVD [35], and some findings support eosinophilic involvement in atherosclerotic plaque formation [35, 36].
The strengths of this study are the large number of participants and the population-based inclusion, which does not limit the results to a specific subgroup of patients. The study also has some limitations. The main limitations are that all disease data are self-reported and that only people aged between 50 and 64 years were included. We also lack specific data on medication that could influence type 2 inflammation, such as inhaled and oral corticosteroids. Sensitivity analysis, where we excluded participants on current asthma medication, indicated that the lack of this kind of data probably did not influence our results. Another important limitation is that this is a cross-sectional study, which means that we cannot draw any conclusions on causality, and with inherent limitations such as recall bias and residual confounding.
In conclusion, we found an association between type 2 inflammation and cardiometabolic disorder. Further studies are needed to understand this better, but our results strengthen the theory that the pathophysiology of type 2 inflammation and cardiometabolic disease are interlinked and could explain the previously reported associations between asthma and cardiometabolic disease. Our results also highlight the need for better risk scores for CVD that take more risk factors, such as type 2 inflammation, into account.
Footnotes
Provenance: Submitted article, peer reviewed.
Ethics statement: The study was approved by the Regional Ethical Review Board in Umeå (2010-228-31 M), and the analyses of the SCAPIS study were approved by the Regional Ethical Review Board in Uppsala (2018-272). The study participants gave their written and informed consent.
Conflict of interest: None declared.
Support statement: The study is supported by the Swedish Heart and Lung Foundation, the main funding body of SCAPIS (Swedish CArdioPulmonary bioImage Study); the Knut and Alice Wallenberg Foundation; the Swedish Research Council; and VINNOVA (Sweden's Innovation Agency). Funding for the add-on studies with exhaled nitric oxide measurements and blood cell counts was provided by the Swedish Heart and Lung Foundation (20150609, 20170673 and 20200174) and the Swedish Asthma and Allergy Foundation (F2014-0042 and F2020-0025). Funding information for this article has been deposited with the Open Funder Registry.
Supplementary material
Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.
Table S1
01085-2024.SUPPLEMENT
References
- 1.Palm NW, Rosenstein RK, Medzhitov R. Allergic host defences. Nature 2012; 484: 465–472. doi: 10.1038/nature11047 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Hammad H, Lambrecht BN. The basic immunology of asthma. Cell 2021; 184: 1469–1485. doi: 10.1016/j.cell.2021.02.016 [DOI] [PubMed] [Google Scholar]
- 3.Szefler SJ, Wenzel S, Brown R, et al. Asthma outcomes: biomarkers. J Allergy Clin Immunol 2012; 129: Suppl. 3, S9–S23. doi: 10.1016/j.jaci.2011.12.979 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Reddel HK, Bacharier LB, Bateman ED, et al. Global Initiative for Asthma Strategy 2021: executive summary and rationale for key changes. Eur Respir J 2022; 59: 2102730. doi: 10.1183/13993003.02730-2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Wang L, Gao S, Yu M, et al. Association of asthma with coronary heart disease: a meta analysis of 11 trials. PLoS One 2017; 12: e0179335. doi: 10.1371/journal.pone.0179335 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Iribarren C, Tolstykh IV, Miller MK, et al. Adult asthma and risk of coronary heart disease, cerebrovascular disease, and heart failure: a prospective study of 2 matched cohorts. Am J Epidemiol 2012; 176: 1014–1024. doi: 10.1093/aje/kws181 [DOI] [PubMed] [Google Scholar]
- 7.Wee JH, Park MW, Min C, et al. Association between asthma and cardiovascular disease. Eur J Clin Invest 2021; 51: e13396. doi: 10.1111/eci.13396 [DOI] [PubMed] [Google Scholar]
- 8.Fang ZH, Li ZF, An ZY, et al. Meta-analysis of the association between asthma and the risk of stroke. Front Neurol 2022; 13: 900438. doi: 10.3389/fneur.2022.900438 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Schanen JG, Iribarren C, Shahar E, et al. Asthma and incident cardiovascular disease: the Atherosclerosis Risk in Communities Study. Thorax 2005; 60: 633–638. doi: 10.1136/thx.2004.026484 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Christiansen SC, Zuraw BL. Treatment of hypertension in patients with asthma. N Engl J Med 2019; 381: 1046–1057. doi: 10.1056/NEJMra1800345 [DOI] [PubMed] [Google Scholar]
- 11.Torres RM, Souza MDS, Coelho ACC, et al. Association between asthma and type 2 diabetes mellitus: mechanisms and impact on asthma control-a literature review. Can Respir J 2021; 2021: 8830439. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.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: 1338–1345. doi: 10.1016/j.chest.2020.11.053 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Pongdee T, Manemann SM, Decker PA, et al. Rethinking blood eosinophil counts: epidemiology, associated chronic diseases, and increased risks of cardiovascular disease. J Allergy Clin Immunol Glob 2022; 1: 233–240. doi: 10.1016/j.jacig.2022.09.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Verdoia M, Schaffer A, Cassetti E, et al. Absolute eosinophils count and the extent of coronary artery disease: a single centre cohort study. J Thromb Thrombolysis 2015; 39: 459–466. doi: 10.1007/s11239-014-1120-3 [DOI] [PubMed] [Google Scholar]
- 15.Amini M, Bashirova D, Prins BP, et al. Eosinophil count is a common factor for complex metabolic and pulmonary traits and diseases: the LifeLines Cohort Study. PLoS One 2016; 11: e0168480. doi: 10.1371/journal.pone.0168480 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Gao S, Deng Y, Wu J, et al. Eosinophils count in peripheral circulation is associated with coronary artery disease. Atherosclerosis 2019; 286: 128–134. doi: 10.1016/j.atherosclerosis.2019.05.027 [DOI] [PubMed] [Google Scholar]
- 17.Guo X, Yuan S, Liu Y, et al. Serum IgE levels are associated with coronary artery disease severity. Atherosclerosis 2016; 251: 355–360. doi: 10.1016/j.atherosclerosis.2016.05.020 [DOI] [PubMed] [Google Scholar]
- 18.Xu Z, Wang T, Guo X, et al. The relationship of serum antigen-specific and total immunoglobulin E with adult cardiovascular diseases. Int J Med Sci 2018; 15: 1098–1104. doi: 10.7150/ijms.25857 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ge W, Guo X, Song X, et al. The role of immunoglobulin E and mast cells in hypertension. Cardiovasc Res 2022; 118: 2985–2999. doi: 10.1093/cvr/cvac010 [DOI] [PubMed] [Google Scholar]
- 20.Kim MK, Jeong JS, Han K, et al. House dust mite and cockroach specific immunoglobulin E sensitisation is associated with diabetes mellitus in the adult Korean population. Sci Rep 2018; 8: 2614. doi: 10.1038/s41598-018-20573-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Skaaby T, Husemoen LL, Thuesen BH, et al. The association of atopy with incidence of ischemic heart disease, stroke, and diabetes. Endocrine 2015; 48: 541–550. doi: 10.1007/s12020-014-0321-z [DOI] [PubMed] [Google Scholar]
- 22.Bergstrom G, Berglund G, Blomberg A, et al. The Swedish CArdioPulmonary BioImage Study: objectives and design. J Intern Med 2015; 278: 645–659. doi: 10.1111/joim.12384 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Mogensen I, Alving K, Jacinto T, et al. Simultaneously elevated FeNO and blood eosinophils relate to asthma morbidity in asthmatics from NHANES 2007-12. Clin Exp Allergy 2018; 48: 935–943. doi: 10.1111/cea.13137 [DOI] [PubMed] [Google Scholar]
- 24.American Thoracic Society/European Respiratory Society . ATS/ERS recommendations for standardised procedures for the online and offline measurement of exhaled lower respiratory nitric oxide and nasal nitric oxide, 2005. Am J Respir Crit Care Med 2005; 171: 912–930. doi: 10.1164/rccm.200406-710ST [DOI] [PubMed] [Google Scholar]
- 25.Khalili B, Boggs PB, Bahna SL. Reliability of a new hand-held device for the measurement of exhaled nitric oxide. Allergy 2007; 62: 1171–1174. doi: 10.1111/j.1398-9995.2007.01475.x [DOI] [PubMed] [Google Scholar]
- 26.Zaigham S, Zhou X, Molin M, et al. Importance of type and degree of IgE sensitisation for defining fractional exhaled nitric oxide reference values. Respir Med 2021; 188: 106621. doi: 10.1016/j.rmed.2021.106621 [DOI] [PubMed] [Google Scholar]
- 27.Amaral R, Jacinto T, Malinovschi A, et al. The influence of individual characteristics and non-respiratory diseases on blood eosinophil count. Clin Transl Allergy 2021; 11: e12036. doi: 10.1002/clt2.12036 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Benson VS, Hartl S, Barnes N, et al. Blood eosinophil counts in the general population and airways disease: a comprehensive review and meta-analysis. Eur Respir J 2022; 59: 2004590. doi: 10.1183/13993003.04590-2020 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Hartl S, Breyer MK, Burghuber OC, et al. Blood eosinophil count in the general population: typical values and potential confounders. Eur Respir J 2020; 55: 1901874. doi: 10.1183/13993003.01874-2019 [DOI] [PubMed] [Google Scholar]
- 30.Tanaka M, Fukui M, Tomiyasu K, et al. Eosinophil count is positively correlated with coronary artery calcification. Hypertens Res 2012; 35: 325–328. doi: 10.1038/hr.2011.191 [DOI] [PubMed] [Google Scholar]
- 31.Guo Y, Ma J, Lu W, et al. Associations of exhaled carbon monoxide and fractional exhaled nitric oxide with metabolic syndrome: a cohort study. Sci Rep 2016; 6: 24532. doi: 10.1038/srep24532 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.McCarthy CG, Saha P, Golonka RM, et al. Innate immune cells and hypertension: neutrophils and neutrophil extracellular traps (NETs). Compr Physiol 2021; 11: 1575–1589. doi: 10.1002/cphy.c200020 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Rohm TV, Meier DT, Olefsky JM, et al. Inflammation in obesity, diabetes, and related disorders. Immunity 2022; 55: 31–55. doi: 10.1016/j.immuni.2021.12.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Wu D, Molofsky AB, Liang HE, et al. Eosinophils sustain adipose alternatively activated macrophages associated with glucose homeostasis. Science 2011; 332: 243–247. doi: 10.1126/science.1201475 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Fernandez-Gallego N, Castillo-Gonzalez R, Mendez-Barbero N, et al. The impact of type 2 immunity and allergic diseases in atherosclerosis. Allergy 2022; 77: 3249–3266. doi: 10.1111/all.15426 [DOI] [PubMed] [Google Scholar]
- 36.Xiao L, Harrison DG. Inflammation in hypertension. Can J Cardiol 2020; 36: 635–647. doi: 10.1016/j.cjca.2020.01.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
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Table S1
01085-2024.SUPPLEMENT