DISCUSSION
Among the five obesity-related factors examined for causality with
childhood asthma, we obtained consistent evidence that obesity, SDB, and
sleep quality are observationally and genetically associated with
asthma. Prospective survival analysis also confirmed the temporal
relationship. When stratified by asthma subphenotypes, SDB was
associated with atopic incident asthma. Detection and treatment of
sleep-related symptoms may reduce the risk of asthma.
In our data, poor sleep quality and SDB were the strongest risk factors
contributing to asthma in the observational and MR analyses. Their
predicted prevalence of asthma was also the highest in the Q5 GRS. Two
previous prospective studies support our findings that insomnia
increases the risk of asthma in adults7, 8. Brumpton
et al. reported an increased risk of asthma in those having problems
initiating sleep, waking up too early, and experiencing nonrestorative
sleep8. Moreover, Lin et al. conducted a
population-based study in Taiwan and found that patients with insomnia
had a 2-fold risk of having new-onset asthma during the 4-year
follow-up7. On the other hand, our study is the first
to investigate this association among school-age children. Although the
prevalence of insomnia among school-age children is not high, the global
pandemic of sleep loss among children has gained the attention of public
health and school officials24. According to a
large-scale meta-analysis25, European adolescents
sleep for an average of 500 minutes per day, whereas Asian adolescents
sleep for only 400 minutes per day on weekdays. In our study, we used
the PSQI questionnaire, representing a combination of various components
of sleep parameters, to assess sleep quality in children. Poor sleep
quality was a robust risk factor for asthma in the three models. The
mechanism behind poor sleep quality leading to asthma may be explained
by the shifting of T helper (Th) cell activity toward a Th2 cytokine
profile under chronic sleep deprivation26. Moreover,
protracted upregulation of inflammatory markers, such as increased
interleukin (IL)-6, was also observed among patients with poor
sleep27. IL-6 production induced by poor sleep quality
might exacerbate airway hypersensitivity. Other potential mechanisms,
such as poor sleep–induced chronic inflammatory status, manifesting as
increased NF-kB level28 and decreased
IFN-γ29, may also explain why poor sleep quality
triggers new-onset asthma.
SDB was another strong risk factor for asthma in the present study.
Although researchers have postulated that SDB and asthma exhibit similar
airway inflammation, the temporal relationship between the two remains
unclear30. The current study is the first to confirm
the causal association using both MR analysis and a prospective cohort.
SDB is associated with excessive daytime sleepiness, the release of
proinflammatory cytokines, and increased leptin level, contributing to
the development of asthma31. Our finding that SDB
predicts atopic incident asthma is supported by one
study32 but contradicted by another related study,
which found that SDB is associated with neutrophilic airway
inflammation33. Further studies are warranted to
explain relevant mechanisms linking SDB and asthma.
The strength of the current analysis centers on the use of three methods
(observational, MR, prospective) to investigate the causal associations
between risk factors and asthma. Our review of the literature suggests
that no related study has employed three methods (observational, MR, and
survival analysis) to investigate modifiable obesity-related factors for
childhood asthma. The unique, population-based children cohort with
prospective follow-up surveys enabled this analysis. Our study used
self-reported physical activity
and sleep quality, which represents a potential limitation. However, the
TCHS employed globally used validated questionnaires to assess physical
activity and sleep quality, limiting the reporting bias. Moreover,
F-statistic suggests that the GRS of sleep quality is a weak instrument,
and we might be underpowered to detect small-moderate effect sizes, as
compared to large sample GWAS studies.