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In 2016, asthma affected 9.3% of people and caused ~93,000 hospitalizations in the United Kingdom (U.K.). Asthma hospitalizations result from severe or poorly controlled disease and account for a high proportion of asthma-related healthcare costs. Whereas insulin resistance and glucose dysregulation have been associated with asthma and asthma severity, the role of an elevated glycated hemoglobin A1c (HbA1c) on asthma control or severity is less clear. In a study, we hypothesized that chronic hyperglycemia would lead to worse lung function and asthma hospitalizations among adults with asthma but no physician-diagnosed diabetes mellitus. Using data from the UK Biobank (UKB), we examined the relation between HbA1c level and hospitalizations for asthma and lung function measures in a cohort of 47,606 British adults with self-reported physician-diagnosed asthma but no diagnosis of diabetes. It was found that an elevated HbA1c level was significantly associated with increased risk of at least one hospitalization for asthma, as well as small decrements in FEV1 and FVC (but not in FEV1/FVC). In summary, this study suggests that an elevated HbA1c level is linked to increased risk of asthma hospitalizations and lower FEV1 and FVC among British adults with asthma but no physician-diagnosed diabetes. Longitudinal studies are needed to determine whether improved glycemic control reduces the risk of asthma hospitalizations.
The prediction of asthma status using clinical and genetic variables
Asthma is a common and complex disease with substantial heritability. It has been shown that asthma is associated with clinical variables and genetics, but their association to asthma is still not sufficiently characterized. Currently, there is no gold standard for diagnosis of asthma because of its heterogeneity and complexity. Thus, an accurate asthma prediction model is on demand. With the growth of big data in the biomedical community, researchers are able to use a large sample size and large scale of variables to predict disease status with higher accuracy. Thus, in this study, we propose to use deep learning to train a prediction model for asthma diagnosis by using the UK Biobank samples. Our final prediction model will be freely accessible to the research community. This can help asthma to be diagnosed more accurately.