Patterns of Growth and Decline in Lung Function in Persistent Childhood Asthma.

BACKGROUND: Tracking longitudinal measurements of growth and decline in lung function in patients with persistent childhood asthma may reveal links between asthma and subsequent chronic airflow obstruction. METHODS: We classified children with asthma according to four characteristic patterns of lung-function growth and decline on the basis of graphs showing forced expiratory volume in 1 second (FEV1), representing spirometric measurements performed from childhood into adulthood. Risk factors associated with abnormal patterns were also examined. To define normal values, we used FEV1 values from participants in the National Health and Nutrition Examination Survey who did not have asthma. RESULTS: Of the 684 study participants, 170 (25%) had a normal pattern of lung-function growth without early decline, and 514 (75%) had abnormal patterns: 176 (26%) had reduced growth and an early decline, 160 (23%) had reduced growth only, and 178 (26%) had normal growth and an early decline. Lower baseline values for FEV1, smaller bronchodilator response, airway hyperresponsiveness at baseline, and male sex were associated with reduced growth (P<0.001 for all comparisons). At the last spirometric measurement (mean [±SD] age, 26.0±1.8 years), 73 participants (11%) met Global Initiative for Chronic Obstructive Lung Disease spirometric criteria for lung-function impairment that was consistent with chronic obstructive pulmonary disease (COPD); these participants were more likely to have a reduced pattern of growth than a normal pattern (18% vs. 3%, P<0.001). CONCLUSIONS: Childhood impairment of lung function and male sex were the most significant predictors of abnormal longitudinal patterns of lung-function growth and decline. Children with persistent asthma and reduced growth of lung function are at increased risk for fixed airflow obstruction and possibly COPD in early adulthood. (Funded by the Parker B. Francis Foundation and others; ClinicalTrials.gov number, NCT00000575.).

A polymorphism in HLA-G modifies statin benefit in asthma.

Several reports have shown that statin treatment benefits patients with asthma; however, inconsistent effects have been observed. The mir-152 family (148a, 148b and 152) has been implicated in asthma. These microRNAs suppress HLA-G expression, and rs1063320, a common SNP in the HLA-G 3'UTR that is associated with asthma risk, modulates miRNA binding. We report that statins upregulate mir-148b and 152, and affect HLA-G expression in an rs1063320-dependent fashion. In addition, we found that individuals who carried the G minor allele of rs1063320 had reduced asthma-related exacerbations (emergency department visits, hospitalizations or oral steroid use) compared with non-carriers (P=0.03) in statin users ascertained in the Personalized Medicine Research Project at the Marshfield Clinic (n=421). These findings support the hypothesis that rs1063320 modifies the effect of statin benefit in asthma, and thus may contribute to variation in statin efficacy for the management of this disease.

Predicting inhaled corticosteroid response in asthma with two associated SNPs.

Inhaled corticosteroids (ICS) are the most commonly used controller medications prescribed for asthma. Two single-nucleotide polymorphisms (SNPs), rs1876828 in corticotrophin releasing hormone receptor 1 and rs37973 in GLCCI1, have previously been associated with corticosteroid efficacy. We studied data from four existing clinical trials of asthmatics, who received ICS and had lung function measured by forced expiratory volume in 1 s (FEV1) before and after the period of such treatment. We combined the two SNPs rs37973 and rs1876828 into a predictive test of FEV1 change using a Bayesian model, which identified patients with good or poor steroid response (highest or lowest quartile, respectively) with predictive performance of 65.7% (P=0.039 vs random) area under the receiver-operator characteristic curve in the training population and 65.9% (P=0.025 vs random) in the test population. These findings show that two genetic variants can be combined into a predictive test that achieves similar accuracy and superior replicability compared with single SNP predictors.