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Published in final edited form as: Ann Allergy Asthma Immunol. 2025 Oct 24;136(2):173–179.e3. doi: 10.1016/j.anai.2025.10.023

Hyperglycemia, receptor for advanced glycation end products, and small airways dysfunction in asthma

Paula Sierra Salas *, Dennis T Villareal †,‡, Vickram Tejwani §,ǁ, Amrit Koirala , Cristian Coarfa ¶,#, Meredith C McCormack **, Nicola A Hanania *, Tianshi David Wu *,††
PMCID: PMC12973237  NIHMSID: NIHMS2139207  PMID: 41553201

Abstract

Background:

Diabetes and insulin resistance are associated with higher risk of asthma exacerbation and preserved ratio impaired spirometry (PRISm), but the pathophysiology is unclear. These conditions may cause small airways dysfunction (SAD), which is an under-recognized cause of PRISm.

Objective:

To determine whether insulin resistance and poor glucose control associate with SAD and to explore whether proteins prognostic of diabetes end-organ complications predict lung function abnormalities.

Methods:

We recruited a prospective cohort of adults with physician-diagnosed asthma. Fasting insulin, glucose, hemoglobin A1c, and concentrations of 13 proteins prognostic of diabetes end-organ complications were measured. Static insulin resistance was calculated by the homeostatic model assessment of insulin resistance. Lung function was measured by spirometry, and SAD was assessed by impulse oscillometry.

Results:

A total of 65 participants were recruited. Mean body mass index was 32.7 kg/m2 and 77% were female. There was effect modification by whether participants were taking medications for diabetes, dyslipidemia, or hypertension. Among 53 participants without treated metabolic disease, elevated hemoglobin A1c level was associated with oscillometry abnormalities consistent with SAD and higher odds of PRISm (adjusted odds ratio 5.12; 95% CI 1.15–22.55). Serum soluble receptor for advanced glycation end products, a protein protective of diabetes end-organ complications and which also suppresses airway inflammation in asthma, was associated with lower systemic inflammation, lower hemoglobin A1c level, and improved lung function.

Conclusion:

Hyperglycemia was associated with SAD and PRISm in asthma. Soluble receptor for advanced glycation end products may be a biomarker of hyperglycemia-associated lung dysfunction. Longitudinal study is necessary to validate biomarker relationships and understand structural and physiological consequences.

Introduction

Type 2 diabetes mellitus and insulin resistance are associated with worse outcomes in asthma, but the specific pathophysiology is unknown.1 Epidemiologically, these conditions are associated with a preserved ratio impaired spirometry (PRISm) pattern—a lower forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC) with a normal FEV1/FVC ratio.2 This relationship has been variously postulated to be due to restrictive lung disease, secondary to fibrosis or neuromuscular weakness, or to decreased chest wall compliance from obesity. However, results are inconsistent across studies, and the principal explanation for this pattern remains unknown.

Small airways dysfunction (SAD) is a complex physiological signature present in all severities of asthma characterized by nonparticipation of the small airways secondary to bronchiolar or alveolar narrowing, remodeling, and inflammation.3 Individuals with asthma driven predominantly by small airways involvement can present with PRISm rather than obstruction on spirometry.4,5

Insulin resistance and hyperglycemia are 2 core features of diabetes. In animal models, hyperinsulinemia, which arises from insulin resistance, increases peribronchial and alveolar septal membrane collagen deposition and airway smooth muscle mass.6 Hyperglycemia induced by streptozotocin also increases bronchiolar and alveolar septal thickness and causes non-allergic airway inflammation.7 If such effects occur in humans, these changes may manifest as SAD.

Advancement of high-throughput technologies has led to the discovery of numerous prognostic biomarkers for diabetes complications; interrogated through Mendelian randomization, many of these biomarkers are implicated as having a causal relationship, suggesting that they may represent mechanistic pathways involved in end-organ injury. Should the lung be a target organ for insulin resistance and hyperglycemia, then biomarkers prognostic of diabetes end-organ complications may also associate with SAD.

We hypothesized that insulin resistance and dysglycemia would be associated with SAD in asthma. We further hypothesized that bio-markers related to end-organ complications in diabetes may have value in identifying pulmonary dysfunction due to these conditions. To test these hypotheses, we performed a prospective cohort study of adult participants with asthma.

Methods

Cohort Description

We recruited 65 adult patients with physician-diagnosed asthma from Baylor College of Medicine between January 2023 to July 2024. Recruitment sources were patients treated at the pulmonary clinic, study advertisements, and contact of prior research participants. Exclusion criteria were active smoking or former smoking of greater than 10 pack-years, major cardiovascular disease, lung disease other than asthma, and active or recent asthma exacerbation. Patients taking diabetes medications were initially excluded, but a protocol modification allowed enrollment of these patients to permit accrual of individuals with more severe insulin resistance, as long as they were not treated with insulin (n = 2, both only treated with metformin). Participants were characterized by an in-person visit at baseline and a 3-month phone call for assessment of asthma outcomes. The study was performed with Institutional Review Board approval, and all participants gave written informed consent.

Metabolic Characterization

At the baseline visit, we collected fasting blood for metabolic characterization. Serum insulin was measured by an electrochemiluminescence immunoassay (Elecsys insulin), serum glucose by a hexokinase assay (GLUC3), and hemoglobin A1c by a turbidimetric inhibition immunoassay (Tina-quant A1c) on Roche cobas analyzers. The homeostatic model assessment of insulin resistance (HOMA-IR) as an epidemiologic surrogate for insulin resistance was calculated from fasting glucose and insulin based on the following formula: (insulin [in mIU/L]) × glucose [in mg/dL])/405.8 Elevated fasting glucose was identified if its serum concentration was more than or equal to 100 mg/dL, elevated hemoglobin A1c if more than or equal to 5.7%, elevated HOMA-IR if more than 2.5, and elevated fasting insulin if more than or equal to 22 mIU/L (based on the local laboratory’s reference range).9,10 We also evaluated these values as continuous variables, with HOMA-IR and insulin log-transformed as a sensitivity analysis.

Respiratory Physiology Characterization

Participants were instructed to hold all asthma therapies before their baseline visit. The hold time was dependent on the half-life of each medication to allow adequate washout.

Small airways function was characterized by respiratory oscillometry (Thorasys Tremoflo C-100; Montreal, Canada).11 In oscillometry, participants tidally breathe against a vibrating mesh which transmits pressure waves of varying frequencies into the respiratory system; the resultant pressure-flow relationships are analyzed to determine the resistance (in-phase changes to pressure and flow) and reactance (out-of-phase changes to pressure and flow) of the lungs.12 High-frequency (approximately 30 Hz) waves mainly reflect the function of the large airways, whereas low-frequency (approximately 5 Hz) waves reflect function of the entire respiratory tree; thus, their difference represents the function of the small airways. SAD is identified on the resistance curve by a higher difference in airway resistance between high and low frequencies (also termed frequency dependence of resistance; R5-R20) and on the reactance curve by a more negative reactance at low frequencies (at 5 Hz for this study, labeled as X5), an increased area under the reactance curve (AX), and a higher resonant frequency (Fres). Percentage of predicted values for R5, R5-R20, AX, and Fres was calculated based on the equations of Oostveen et al,13 which incorporated adjustment for biological sex, height, age, and weight.

Pre-bronchodilator spirometry was performed according to technical standards (ndd EasyOne Air; Andover, Massachusetts).14 FEV1, FVC, and FEV1/FVC were measured. Percentage of predicted values was calculated based on the race-neutral Global Lung Initiative equations, which incorporated adjustment for sex, height, and age. PRISm was defined by a FEV1/FVC more than or equal to 0.7 with a FEV1 percent-predicted less than 80%.15

Patient-Reported Outcomes

Participants answered questionnaires regarding their past medical history and active medication use. Asthma control was measured by the Asthma Control Test questionnaire, which ranges from 5 to 25 with a score of less than or equal to 19 indicating uncontrolled asthma, and an asthma exacerbation ascertained if the participant reported going to the emergency department, being hospitalized, or being prescribed a course of oral steroids for asthma.16,17

Plasma Proteomics

The proteome from ethylenediaminetetraacetic acid plasma was measured by the SomaScan 11k assay (SomaLogic; Boulder, Colorado).18 This assay uses modified single-stranded oligonucleotides, called aptamers, to measure relative protein concentrations with high sensitivity and dynamic range. After data quality control and standardization by SomaLogic, the relative fluorescence of proteins was log transformed for analysis. For proteins measured by more than 1 aptamer, the mean fluorescence was taken after confirmation that all target measurements were correlated with each other.

The panel of proteins causally associated with diabetes end-organ complications was chosen from a recent Mendelian randomization study which identified 15 proteins prognostic of at least 1 end-organ complication of type 2 diabetes and which had a significant causal effect.19 Of these, 13 were measurable by the SomaScan assay. The specific UniProt and SomaScan identifiers for these proteins are listed in eTable 1.

Receptor for Advanced Glycation End Products Effector Proteins

Given initial proteomic results identifying the relevance of the receptor for advanced glycation end products (RAGE) pathway, we sought to evaluate this pathway in closer detail. Absolute concentration of the soluble receptor for advanced glycation end products (sRAGE) was measured in serum by automated enzyme-linked immunosorbent assay using the Ella platform (Bio-Techne; Minneapolis, Minnesota). We evaluated the Kyoto Encyclopedia of Genes and Genomes20 consensus pathway for RAGE activity and extracted SomaScan data for effector proteins related to RAGE activation: systemic non-type 2 inflammation (interleukin-1 alpha [IL-1], interleukin-6 [IL-6], interleukin-8 [IL-8], tumor necrosis factor [TNF-α], and C-reactive protein [CRP]), vascular adhesion and immune cell migration ([intercellular adhesion molecule 1 [ICAM-1], vascular cell adhesion protein 1 [VCAM-1], and monocyte chemotactic protein 1 [MCP-1]), angiogenesis (vascular endothelial growth factor A [VEGF-A]), and other growth factors (transforming growth factor beta-1 [TGF-β1]).

Statistical Analysis

Descriptive statistics and distributions were reviewed for plausibility and data error. Participant characteristics stratified by use of metabolic medications were generated, and statistical comparisons between the 2 groups were done using χ2 (for categorical), t test (for continuous normally-distributed), and Kruskal-Wallis test (for continuous skewed) as appropriate. Due to known log-normal population distributions, the oscillometry parameters AX, X5, Fres, and R5 were log-transformed for analysis. Because X5 is negative, the absolute value of X5 was log-transformed and then made negative.21

The relationship between each metabolic parameter with spirometry, oscillometry, and patient-reported asthma outcomes was estimated with linear or logistic regression. We considered age, sex, body mass index (BMI), and steroid exposure as critical confounders (eg, causally related to both insulin resistance and lung physiology); as age and sex were already represented in the spirometry and oscillometry predictive equations, covariate-adjusted models were also adjusted for BMI and steroid exposure, defined by inhaled steroid use or oral steroid use within the past 3 months. Further adjustment was limited by the risk of overfitting due to sample size.

Differences in protein concentrations by metabolic parameters were compared by t test after log transformation. Pairwise correlations between proteins were represented by matrix plots. A 2-sided P value of less than .05 defined statistical significance. For protein anal-yses, no adjustments were made to multiple comparisons due to the exploratory nature of the analysis. All analyses were performed in Stata 18 (StataCorp; College Station, Texas).

Results

Study Population

A total of 65 participants were recruited. Mean (SD) age was 42.3 ± 16.8 years, 50 (77%) were female, and 12 (19%) were of Hispanic ethnicity. Mean BMI was 32.7 kg/m2, which was within the obese range. Participants had spirometry values lower and oscillometry values higher than predicted from normative equations, both consistent with abnormal lung function from asthma.

Using a HOMA-IR threshold of 2.5 to define insulin resistance, 42 (65%) were classified as insulin resistant. Despite a high prevalence of self-reported exacerbations in the year prior, approximately half were currently using inhaled corticosteroids and half had not well-controlled asthma. No participants reported using oral steroids chronically.

Of the 65 participants, 22 (34%) reported being previously told that they had diabetes, dyslipidemia (high cholesterol or triglycerides), or hypertension; 12 of 65 (18%) were actively taking medications for these conditions. Specific to diabetes, 6 (9%) reported being diagnosed with this condition, of which 2 were drug treated with metformin. Table 1 reports data stratified by whether participants were using medications for dyslipidemia, hypertension, or diabetes. These individuals were older, more insulin resistant, and had higher fasting glucose and hemoglobin A1c levels. However, their asthma treatment and baseline lung physiology were not statistically different.

Table 1.

Participant Characteristics

Treated for metabolic disease
Characteristic No (n = 53) Yes (n = 12) P value
BMI (kg/m2) 32.1 ± 8.9 35.1 ± 12 .34
Asthma treatments
 Inhaled corticosteroid 22 (42%) 7 (59%) .29
 Long-acting beta agonist 18 (34%) 5 (42%) .61
 Long-acting muscarinic agent 4 (8%) 1 (8%) .93
 Allergen immunotherapy 6 (11%) 1 (8%) .76
Asthma morbidity
 Prior-year exacerbation 31 (59%) 7 (58%) .99
 Uncontrolled asthma 32 (60%) 10 (83%) .14
Metabolic labs
 Insulin (mIU/L) 12 (8–18) 18 (13–24) .06
 ≥22 mIU/L 9 (17%) 5 (42%) .06
 HOMA-IR 2.8 (1.7–4.1) 4.3 (3.0–5.7) .04
 >2.5 32 (60%) 10 (83%) .13
 Glucose (mg/dL) 92.4 ± 10.1 102.3 ± 21.0 .02
 ≥100 mg/dL 9 (17%) 4 (33%) .20
 Hemoglobin A1c, %a 5.5 ± 0.5 6.0 ± 1.0 .03
 ≥5.7% 14 (26%) 8 (67%) .01
Spirometry
 FEV1, % of predicted 86.5 ± 22.4 84.8 ± 23.7 .81
 FVC, % of predicted 92.0 ± 22.9 91.4 ± 22.8 .93
 FEV1/FVC 78.4 74.0 .07
 PRISm 12 (23%) 2 (17%) .65
Oscillometry
 AX (cmH2O/L) 8.2 (5.0–21.3) 13.4 (6.9–32.3) .26
 Fres (Hz) 18.2 (12.8–26.2) 23.2 (16.4–28.3) .14
 X5 (cmH2Os/L) −1.64 (−2.6 to-1.3) −1.6 (−3.2 to −1.2) .89
 R5 (cmH2Os/L) 4.1 (3.6–5.7) 4.5 (3.9–6.1) .46
 R5-R20 (cmH2Os/L) 0.5 (0.2–1.6) 1.1 (0.5–1.8) .07
 AX, % of predicted 224 (151–406) 219 (128–761) .72
 Fres, % of predicted 132 (107–161) 146 (118–207) .43
 R5, % of predicted 116 (92–127) 88 (81–113) .09
 R5-R20, % of predicted 93 (19–259) 217 (70–495) .17
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Values are presented in median (interquartile range) or mean ± standard deviation. Abbreviations: AX, area of reactance; BMI, body mass index; FEV1, forced expiratory volume in 1 second; Fres, resonant frequency; FVC, forced vital capacity; HOMA-IR, homeostatic model assessment of insulin resistance; PRISm, preserved ratio impaired spirometry; R5, respiratory system resistance at 5 Hz; R5-R20, difference in respiratory system resistance between 5 Hz and 20 Hz; X5, respiratory system reactance at 5 Hz.

a

Missing for 1 participant (in the treated for metabolic disease category) due to laboratory error.

Association of Metabolic Indices to Baseline Outcomes

We found statistically significant interactions of the association of metabolic indices to study outcomes by whether the participant was taking medications for hypertension, diabetes, or dyslipidemia. Relationships of glucose, hemoglobin A1c, and HOMA-IR with Fres, of A1c and HOMA-IR with R5-R20, and of A1c with R5 and FEV1 percent-predicted values were all weakened by use of these medications (P interaction < .05 for all comparisons). Stratified models were performed as a result.

Among patients taking medications for these conditions, no association was identified between metabolic indices with study outcomes (not shown). Table 2 reveals relationships with percent-predicted spirometry and oscillometry values for the remaining sub-group of 53 patients who were not taking medications (relationships with raw values are found in eTable 2). Elevated glucose and hemoglobin A1c levels were associated with higher percent-predicted total airway (R5) and frequency-dependent airway resistance (R5-R20) and exaggerated frequency dependence of airway reactance, reflected by a higher AX and Fres (Fig 1).

Table 2.

Association of Metabolic Analytes With Spirometry, Oscillometry, and Asthma Control Among Patients Not Taking Metabolic Medications (n = 53)a

Mean difference (95% CI) in outcome associated with each analyte
Outcome Elevated fasting glucose Elevated A1c Elevated fasting insulin Elevated HOMA-IR
Spirometry, % predicted
 FEV1 −10.08 (−27.26 to 7.09) −28.31 (−40.93 to −15.68) 11.75 (−7.26 to 30.76) −7.78 (−21.10 to 5.54)
 FVC −12.08 (−29.24 to 5.08) −28.16 (−40.9 to −15.42) 8.04 (−11.23 to 27.31) −11.93 (−25.06 to 1.21)
 Odds of PRISmb 0.77 (0.12–5.11) 5.12 (1.16–22.55) 0.61 (0.08–4.43) 2.89 (0.51–16.34)
Oscillometry, % predicted
 AX 349.60 (97.22–601.99) 246.17 (22.96–469.37) −132.77 (−431.43 to 165.88) 177.19 (−27.17 to 381.54)
 Fres 49.49 (13.8–85.18) 34.34 (2.74–65.95) −21.01 (−63.16 to 21.14) 28.62 (−0.01 to 57.24)
 R5 38.95 (14.03–63.87) 33.10 (11.56–54.64) −10.25 (−40.38 to 19.88) 13.12 (−7.71 to 33.96)
 Frequency dependence of resistance (R5-R20) 445.95 (102.07–789.82) 218.15 (−91.88 to 528.19) −224.61 (−626.21 to 177) 31.35 (−253 to 315.71)
Odds of uncontrolled asthmab 5.63 (0.96–32.94) 3.25 (0.81–13.03) 0.43 (0.08–2.46) 4.12 (1.17–14.52)
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Abbreviations: AX, area of reactance; BMI, body mass index; FEV1, forced expiratory volume in 1 second; Fres, resonant frequency; FVC, forced vital capacity; HOMA-IR, homeostatic model assessment of insulin resistance; PRISm, preserved ratio impaired spirometry; R5, respiratory system resistance at 5 Hz; R5-R20, difference in respiratory system resistance between 5 Hz and 20 Hz; X5, respiratory system reactance at 5 Hz.

NOTE. Values in bold are statistically significant at P less than .05.

a

Adjusted for BMI and inhaled and oral steroid exposure.

b

Values in this row are odds ratios and associated 95% CI.

Figure 1.

Figure 1.

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Association of metabolic indices with the marginal difference in percent-predicted resonant frequency on impulse oscillometry, adjusted for body mass index and inhaled and oral steroid exposure. Asterisks denote a statistically significant difference at P less than .05. HOMA-IR, homeostatic model assessment of insulin resistance.

With respect to spirometry, elevated hemoglobin A1c level was associated with lower percent-predicted FEV1 and FVC and higher odds of PRISm. Associations with elevated fasting glucose level were not statistically significant. With respect to asthma control, only HOMA-IR was associated with a higher odds of uncontrolled asthma, although point estimates for elevated A1c and glucose were in a concordant direction.

Modeling of fasting glucose, hemoglobin A1c, log(HOMA-IR), and log(fasting insulin) as continuous variables revealed similar relationships (eTable 3). Of note, HOMA-IR was not statistically associated with asthma control in this model.

Association of Metabolic Indices to Follow-Up Outcomes

Of the 65 participants enrolled, 36 (55%) responded to the 3-month phone call. Of these, 3 participants (8%) reported an asthma exacerbation and 6 (21%) had uncontrolled asthma. There was no association between the metabolic parameters with odds of uncontrolled asthma at baseline. Because of the rarity of exacerbations, no statistical adjustment was possible; however, all 3 participants who had exacerbations had an elevated A1c level at baseline (Fisher’s exact P value = .02).

Soluble Receptor for Advanced Glycation End-Products as a Candidate Biomarker of Hyperglycemia-Associated Lung Dysfunction

Next, we tested whether proteins prognostic of diabetes end-organ complications were associated with spirometry (represented by percent-predicted FEV1), oscillometry (represented by percent-predicted Fres), and uncontrolled asthma (represented by ACT score ≤ 19) in multivariable linear and logistic regression models, adjusted for BMI and steroid exposure.

At a nominal P value of less than .05, lower AGER/RAGE (receptor for advanced glycation end products), lower HSPA1B (heat shock protein family A member 1B), and higher PTPN9 (tyrosine-protein phosphatase non-receptor type 9) were associated with lower (worse) percent-predicted FEV1. Higher GCKR (glucokinase regulator) was associated with a higher (worse) percent-predicted Fres (eFig 1). There were no proteins associated with asthma control. RAGE, HSPA1B, and GCKR were directionally consistent with their prognostic associations with diabetes progression and end-organ complications. Of these, only RAGE has prior literature supporting a role in lung disease and asthma.

Therefore, we evaluated RAGE in detail and measured its absolute concentration by enzyme-linked immunosorbent assay. Mean sRAGE was 961 ± 429 pg/mL. Participants with elevated hemoglobin A1c level had lower circulating levels of sRAGE (mean difference −272.9 pg/mL; 95% CI −532.8 to −13.0 pg/mL), consistent with its protective relationship with diabetes and diabetes end-organ complications (Fig 2A). Lower sRAGE was not associated with differences in oscillometry (Fig 2B for Fres and eTable 4 for other measures) but was associated with lower percent-predicted FEV1 and FVC (Fig 2C for FEV1 and eTable 4 for FVC). With additional adjustment for BMI and steroid exposure, a 1 SD increase in sRAGE was associated with a 7.72% (1.52%−13.91%) improvement in FEV1 and a 6.78% (0.47%−13.1%) higher FVC.

Figure 2.

Figure 2.

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Association of serum sRAGE with (A) hemoglobin A1c, (B) percent-predicted FEV1 on spirometry, and (C) percent-predicted resonant frequency on oscillometry. FEV1, forced expiratory volume in 1 second; sRAGE, soluble receptor for advanced glycation end products.

Next, we evaluated correlations of sRAGE with effector proteins of RAGE activation. We found that serum sRAGE was inversely correlated with IL-6 (P < .01) and CRP (P = .03), markers of metabolic inflammation, but positively correlated with VCAM-1 (P = .04) and MCP-1 (P < .01), markers of immune cell adhesion and migration (eFigs 2 and 3).

Discussion

In a prospective study of adults with physician-diagnosed asthma, glycemic control was associated with SAD and PRISm, but only among individuals not treated for metabolic disease. Our results, taken together with preclinical data, suggest that the physiological consequences of metabolic dysregulation may be localized to the small airways. In exploratory analysis, we further nominated sRAGE as a potential biomarker which connects poor glycemic control with abnormal lung function (eFig 4 for graphical summary of key results).

The small airways represent the distal portion of the respiratory tree less than 2 mm in diameter.22 SAD is common in asthma and is histologically characterized by airway smooth muscle hypertrophy and hyperplasia, basement membrane thickening, changes in composition of the extracellular matrix, and an increase in luminal inflammatory cells. In preclinical models, hyperinsulinemia associated with insulin resistance and hyperglycemia can precipitate similar structural alterations in the small airways as described for asthma, but whether such findings are relevant to humans has remained unclear.6,7,23

Using oscillometry to directly interrogate the small airways, we found that hyperglycemia was associated with a pattern of changes in lung resistance and reactance consistent with SAD. Importantly, these relationships were independent of elevated BMI as obesity has also been associated with peripheral airway dysfunction on oscillometry.24,25 Our results raise the possibility that prior findings could be contributed to by differences in metabolic health secondary to obesity. Further work, especially incorporating more nuanced parameters of adiposity beyond BMI, is necessary to confirm this possibility.

In exploratory analysis, we identified sRAGE as a candidate biomarker for hyperglycemia-associated lung dysfunction. RAGE is a transmembrane pattern receptor enriched in alveolar epithelial cells and whose activation induces cellular inflammation and oxidative stresss.26 Elevated glucose promotes the production of advanced glycation end products which directly serve as RAGE ligands. Parallel studies have implicated RAGE activation as important to asthma pathogenesis and both allergic and non-allergic inflammation, raising the hypothesis that the RAGE pathway is mechanistically involved in the pulmonary harms related to hyperglycemia.27,28 sRAGE is produced by alternative splicing and proteolytic cleavage of RAGE and experimentally inhibits its activity by acting as a decoy receptor.26,29 In our study, sRAGE was inversely correlated with IL-6 and CRP, supporting its role as an anti-inflammatory ligand sink, but studies in other chronic diseases have revealed opposing directionality.30 sRAGE may also have a proinflammatory role through effects on monocyte function, consistent with its positive correlation with MCP-1 in our study.31 Whether sRAGE is a valid biomarker in adult asthma will require prospective study, particularly as its relationship with oscillometry end points was weaker than spirometry, which may reflect preferential associations with large airway function or limited power due to sample size.

Prior epidemiologic work by our group and others have revealed an elevated hemoglobin A1c level as a risk factor for asthma exacerbations; these results were thought to originate from insulin resistance.32,33 However, although glucose and hemoglobin A1c were broadly associated with physiological measurements, associations with insulin and HOMA-IR were neutral. One explanation is that glucose rather than insulin is principally responsible for prior studies which have implicated IR as a risk factor for worsened asthma outcomes.34,35 Alternatively, because fasting insulin and HOMA-IR mainly identify central insulin resistance, they may not be an adequate representation of the exaggerated excursions in insulin in non-fasting and post-prandial conditions which are characteristic of poor peripheral insulin sensitivity.

The magnitude of reduction in FEV1 and FVC associated with an elevated hemoglobin A1c was significantly higher than that reported in other studies.33,36 In our study, participants withheld their asthma medications before pulmonary function testing, implying that these large differences in spirometry may be reversible. Because bronchodilator responsiveness testing was not performed, a follow-up study is necessary to confirm this speculation.

We found significant interactions by whether participants were taking medications for conditions associated with metabolic syndrome. Metformin, statins, angiotensin pathway medications, and calcium channel blockers have been described to have effects on lung function, which may explain why metabolic indices are less predictive of physiological outcomes in these individuals.37–39 Because of the low sample size of the stratum, the implications of this finding are not clear.

Several limitations are acknowledged. Participants were metabolically and physiologically characterized in a single visit, and key results should be interpreted as cross-sectional rather than longitudinal. Participants were also enrolled based on a physician diagnosis of asthma, without physiological criteria such as prior evidence of airway hyper-responsiveness, which represents a risk of misclassification. In addition, results related to the proteomic analyses should be considered exploratory, and many adjusted comparisons were underpowered, both due to the limited sample size. Despite accounting for what we considered the most influential confounders, sample size also prevented adjustment for a more comprehensive suite of covariates, such as air pollution exposure or physical activity. Finally, we did not measure lung volumes which are necessary to confidently exclude the presence of comorbid restrictive lung disease; however, participants in this study were relatively young without a significant smoking background, decreasing this likelihood.

In conclusion, among a cohort of adult participants with physician-diagnosed asthma, elevated fasting glucose and hemoglobin A1c levels were associated with abnormalities in respiratory oscillometry and spirometry consistent with SAD, only among participants not treated for dyslipidemia, diabetes, or hypertension. These results suggest that glycemic control is important to small airways physiology in asthma. Further research with more detailed longitudinal follow-up is necessary to define structural and clinical consequences of metabolic dysregulation in asthma.

Supplementary Material

Supplement

Funding

Dr Wu reports receiving support by the American Lung Association (grant number: 960953), the National Heart, Lung, and Blood Institute (K23HL151669), and the Department of Veterans Affairs, Office of Research and Development, Center for Innovations in Quality, Effectiveness, and Safety (VA HSRD CIN 13-413). Dr Coarfa reports receiving support from the Department of Veterans Affairs, Office of Research and Development, Military Exposure Research Program and the Center for Innovations in Quality, Effectiveness, and Safety (VA HSRD CIN 13-413). Dr Tejwani reports receiving support from the National Heart, Lung, and Blood Institute (K23HL173570). Dr McCormack reports receiving support from the National Institutes of Environmental Health Science (P2CES033415). Enzyme-linked immunosorbent assay measurements were done by the Antibody-Based Proteomics Core at Baylor College of Medicine which is supported by the Cancer Prevention and Research Institute of Texas Proteomics and Metabolomics Core Facility Support Award (RP210227) and the National Cancer Institute (P30CA125123). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the Department of Veterans Affairs, or the US government. The remaining authors have no funding sources to report.

Footnotes

Disclosures

The authors have no conflicts of interest to report.

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