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\begin{document}

\title{Mediators Linking Obesity to Childhood Asthma: the Role of Puberty,
Physical Fitness, and Pulmonary Function}



\author[1]{Yang-Ching Chen}%
\author[2]{Ming-Wei Su}%
\author[3]{Wen-Harn Pan}%
\author[4]{Yungling Lee}%
\affil[1]{Institute of Preventive Medicine, College of Public Health, National Taiwan University}%
\affil[2]{EInstitute of Epidemiology and Preventive Medicine}%
\affil[3]{Institute of Biomedical Sciences Academia Sinica}%
\affil[4]{Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University}%


\vspace{-1em}



  
  \date{May 28, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Background: Obesity and asthma are highly associated, but the mechanisms
underlying the association remain unknown. We examined five mediators
linking obesity with childhood asthma: (1) pulmonary function
impairment, (2) airway inflammation, (3) physical fitness, (4)
sleep-disordered breathing (SDB), and (5) early puberty. Methods: A
Mendelian randomization (MR) study with mediation analysis of data
obtained from 5,965 children as part of the Taiwan Children Health
Study. Cross-sectional regression analysis, MR two-stage least squares
method, and MR sensitivity analysis were carried out to investigate each
causal pathway. Prospective cohort analyses were used to confirm the
findings. Results: The increased asthma risk associated with obesity was
found to be mostly mediated through impaired pulmonary function, low
physical fitness, early puberty. In the MR analysis, body mass index was
negatively associated with FEV1/FVC and physical fitness index (\selectlanguage{greek}β\selectlanguage{english}= -2.17
and -0.71; 95\% CI, -3.92 to -0.42 and -1.30 to -0.13, respectively) and
positively associated with early puberty (OR, 1.09; 95\% CI,
1.02--1.17). High FEV1/FVC and physical fitness index reduced the risk
of asthma (OR, 0.98 and 0.93; 95\% CI, 0.97--0.99 and 0.88--0.98,
respectively), whereas SDB and early puberty increased the risk of
asthma (OR, 1.03 and 1.22; 95\% CI, 1.01--1.05 and 1.05--1.42,
respectively). The three main mediators were low physical fitness,
impaired pulmonary function, and early puberty, with mediation
proportions of 91.4\%, 61.6\%, and 28.3\%, respectively. Temporal
causality was further strengthened in prospective cohort analyses.
Conclusions: Interventions promoting physical fitness and pulmonary
function might effectively reduce obesity-induced asthma risk.%
\end{abstract}%



\sloppy


\textbf{Mediators Linking Obesity to Childhood Asthma: the Role of
Puberty, Physical Fitness, and Pulmonary Function}

Yang-Ching Chen\textsuperscript{1,2,3,4}, Yen-Tsung
Huang\textsuperscript{5}, Wen-Harn Pan\textsuperscript{6}, Yungling
L.Lee\textsuperscript{6,7}

\textsuperscript{1}Department of Family Medicine, Taipei Medical
University Hospital, Taipei Medical University, Taipei, Taiwan

\textsuperscript{2}Department of Family Medicine, School of Medicine,
College of Medicine, Taipei Medical University, Taipei, Taiwan

\textsuperscript{3}School of Nutrition and Health Sciences, College of
Nutrition, Taipei Medical University, Taipei, Taiwan

\textsuperscript{4}Graduate Institute of Metabolism and Obesity
Sciences, Taipei Medical University, Taipei, Taiwan

\textsuperscript{5}Institute of Statistical Science, Academia Sinica,
Taipei, Taiwan

\textsuperscript{6}Institute of Biomedical Sciences, Academia Sinica,
Taipei, Taiwan

\textsuperscript{7}College of Public Health, China Medical University,
Taichung, Taiwan

\textbf{Short running title: Link between adiposity and asthma}

\textbf{Correspondence and page proofs should be sent to:}

Yungling Leo Lee, MD, PhD

Institute of Biomedical Sciences, Academia Sinica, 128 Academia Road,
Sec. 2, Taipei 11529, Taiwan

Tel: +886-2-2652-3013

E-mail: leolee@ibms.sinica.edu.tw

\textbf{Financial disclosure:} This work was supported by the National
Health Research Institutes of Taiwan {[}NHRI-EX110-10718PC and
NHRI-EX109-10606PI{]}.

\par\null

\textbf{Conflicts of interest:} The authors declare no conflict of
interest.

\textbf{Author contributions:} Dr. Yang-Ching Chen collected the cohort
data, interpreted the data, and wrote the manuscript. Prof. Yen-Tsung
Huang and Prof. Wen-Harn Pan assisted in the critical part of the
statistical analysis, interpreted data, and revised it critically for
crucial intellectual content. Prof. Yungling Lee reviewed the study
design, collected data, interpreted data, supervised the study, and
revised the manuscript critically for valuable intellectual content. All
authors approved the final manuscript as submitted and published and
agree to be accountable for all aspects of the work.

\textbf{Word count:} 250 for abstract; 3000 for text.

\textbf{Definition of abbreviations}

BMI: body mass index

CI: confidence interval

CSHQ: Children's Sleep Habits Questionnaire

Fe\textsubscript{NO}: fractional exhaled nitric oxide

FEV1/FVC: forced expiratory volume in 1 second/forced vital capacity

GRS: genetic risk score

IV: instrumental variable

IVW: inverse-variance weighted

MR: Mendelian randomization

OR: odds ratio

RCT: randomized controlled trial

SDB: sleep-disordered breathing

SNP: single-nucleotide polymorphism

TCHS: Taiwan Children Health Study

TDCS: Tanner-derived composite stage

2SLS: two-stage least square

\textbf{ABSTRACT}

\textbf{Background:} Obesity and asthma are highly associated, but the
mechanisms underlying the association remain unknown. We examined five
mediators linking obesity with childhood asthma: (1) pulmonary function
impairment, (2) airway inflammation, (3) physical fitness, (4)
sleep-disordered breathing (SDB), and (5) early puberty.

\textbf{Methods:} A Mendelian randomization (MR) study with mediation
analysis of data obtained from 5,965 children as part of the Taiwan
Children Health Study. Cross-sectional regression analysis, MR two-stage
least squares method, and MR sensitivity analysis were carried out to
investigate each causal pathway. Prospective cohort analyses were used
to confirm the findings.

\textbf{Results:} The increased asthma risk associated with obesity was
found to be mostly mediated through impaired pulmonary function, low
physical fitness, early puberty. In the MR analysis, body mass index was
negatively associated with FEV1/FVC and physical fitness index (\selectlanguage{greek}β\selectlanguage{english}= -2.17
and -0.71; 95\% CI, -3.92 to -0.42 and -1.30 to -0.13, respectively) and
positively associated with early puberty (OR, 1.09; 95\% CI,
1.02--1.17). High FEV1/FVC and physical fitness index reduced the risk
of asthma (OR, 0.98 and 0.93; 95\% CI, 0.97--0.99 and 0.88--0.98,
respectively), whereas SDB and early puberty increased the risk of
asthma (OR, 1.03 and 1.22; 95\% CI, 1.01--1.05 and 1.05--1.42,
respectively). The three main mediators were low physical fitness,
impaired pulmonary function, and early puberty, with mediation
proportions of 91.4\%, 61.6\%, and 28.3\%, respectively. Temporal
causality was further strengthened in prospective cohort analyses.

\textbf{Conclusions:} Interventions promoting physical fitness and
pulmonary function might effectively reduce obesity-induced asthma risk.

\textbf{Keywords:} Obesity; Asthma; Mediation analysis; Mendelian
randomization study; Pulmonary function



\textbf{INTRODUCTION}

Obesity and asthma are common in children, and obesity has been
identified as a significant risk factor for asthma in several
longitudinal\textsuperscript{1} and Mendelian randomization
(MR)\textsuperscript{2} studies, but the mechanism underlying their
association remains unknown. Genetic factors, environmental
exposures,\textsuperscript{3} mechanical effects (e.g.,
obesity-associated lung function decline), inflammation (as measured by
fractional exhaled nitric oxide (Fe\textsubscript{NO})), lifestyle
(e.g., physical activity and diet), common comorbidities, and endocrine
factors (e.g., adipokines and reproductive hormone) have all been
implicated.\textsuperscript{4} However, the relationship between obesity
and airway inflammation is inconsistent;\textsuperscript{5} and although
obesity-related physical activity levels and poor physical fitness have
been found to trigger the onset of asthma,\textsuperscript{6-8} few
studies have evaluated whether physical fitness mediates an increased
risk of obesity-induced asthma. Similarly, comorbidities of obesity such
as dyslipidemia, gastroesophageal reflux, and sleep-disordered breathing
(SDB), are known to provoke asthma;\textsuperscript{4} both SDB and
asthma involve airway obstruction and share ``obesity'' as their
significant risk factor,\textsuperscript{9} and treatment of SDB can
mitigate asthmatic symptoms.\textsuperscript{10} However, a possible
mediating role of SDB in the relationship between obesity and asthma has
yet to be unexplored. Finally, though endocrine factors (which are
expressed as early puberty in the current study) can be a crucial
mediating pathway from obesity to asthma,\textsuperscript{11} and a
causal relationship between early puberty and asthma has been
suggested,\textsuperscript{11,12} but no estimates of mediation
proportion from obesity and early puberty to asthma have been reported.

Several pathways might explain the pathophysiology underlying the
association between obesity and asthma. However, past evidence is mostly
limited to observational studies, whose analyses are limited by
confounding factors and reverse causation. MR
studies\textsuperscript{13} use genetic variants that can divide the
observed population into different subgroups according to the exposure
level; therefore, it is analogous to a randomized controlled trial
(RCT). Network MR studies use genetic variants as instrumental variables
(IVs) to investigate mediation in causal
pathways.\textsuperscript{14}When genetic IVs are obtained for obesity
and mediators in the obesity--asthma link, the direct and indirect
effects of obesity on asthma can be estimated through network MR. If a
mediation pathway is crucial, then an intervention focusing on reducing
mediators may be as effective as weight reduction intervention in
reducing asthma risk.

To improve our understanding of mechanisms and pathways that underlie
obese asthma, we undertook a network MR analysis of data from the Taiwan
Children Health Study (TCHS) to study and quantify the influence of five
possible mediators of obesity-induced asthma: (1) pulmonary function
impairment, (2) airway inflammation, (3) low physical fitness, (4) SDB,
and (5) early puberty. Then, significant mediators identified from MR
analysis will be confirmed in prospective cohort analyses. Our results
enable the importance of each mediator to be compared, which will inform
the prioritization of future interventional studies targeting the
mediating pathway of obese children, to prevent asthma.



\textbf{MATERIALS AND METHODS}

\textbf{Study Population}

The TCHS was a population-based prospective study that comprised two
cohorts of children recruited from 14 communities across Taiwan. Cohort
1 was initiated in 2007 and cohort 2 in 2010; children were aged between
9 and 12 years old at the time of enrollment; and were followed until
they turned 18 years old. Data collection includes parent- and
children-filled questionnaires on atopy diseases, adiposity measures,
pulmonary function tests, airway inflammation measures, physical fitness
assessment, pubertal development record, and oral mucosa samples for DNA
extraction. The cross-sectional analysis, involved 3233 children in
cohort 1 and 2732 children in cohort 2, with complete data on adiposity,
asthma outcomes, mediators at the age of 12 years, and genotype data.
Detailed study design and recruitment strategy have been
published\textsuperscript{15} and described on the online supplementary
file. Informed consent was obtained from all participating parents and
children. This study was approved by the ethics committee of institute
and was conducted according to the Declaration of Helsinki.

After identification of the critical mediators, we further used the
annual follow-up data which assessed the mediators from age 10 to age 12
in TCHS to approve the temporal mediating causality. The analyses herein
involved data beginning from 2010 Survey which contained adiposity
measurement, asthma outcome, puberty stage assessment, physical fitness
measurement, and pulmonary function tests. Detailed study design were
described in the online supplementary material.

\textbf{Assessment of Asthma Outcomes}

Participants were defined to have active asthma based on an affirmative
response to both of the following two questions: ``Has a doctor ever
diagnosed your child as having asthma?'' and ``Did your child ever
experience difficulty breathing, or did you observe any wheezing or
whistling from his or her chest in the past 12 months?''

\textbf{Measurement of Mediators}

Spirometry was performed as per a previously standardized
protocol.\textsuperscript{16} In our previous study, the ratio of forced
expiratory volume in 1 second (FEV1) to forced vital capacity (FVC)
showed the strongest association with body mass index
(BMI).\textsuperscript{17} Hence, FEV1/FVC was used as a surrogate for
pulmonary function performance.

Fe\textsubscript{NO}, a marker of airway inflammation, was measured
using a portable NO analyzer NIOX MINO\selectlanguage{ngerman}® Airway Inflammation Monitor
(Aerocrine AB, Solna, Sweden) according to standardized
protocols.\textsuperscript{18}

Physical fitness was assessed based on a combination of all physical
fitness tests (800 m sprint, jump and reach, sit-ups, and sit-and-reach)
administered during school visits using a standardized
protocol.\textsuperscript{19} Physical fitness parameters included
cardiorespiratory endurance, muscular strength, endurance, and
flexibility. We added them up to formulate a physical fitness index and
transformed them into z-scores using age- sex- specific reference. A
higher physical fitness index indicates better physical fitness.

Sleep behaviors were assessed using a parent-administrated
questionnaire---the Children's Sleep Habit Questionnaire (CSHQ). The
CSHQ was designed and developed to assess the sleep behaviors of
school-aged children.\textsuperscript{20} It was proven to be a valid
tool for detecting the presence of SDB by comparing it with objective
polysomnography-defined obstructive sleep apnea.\textsuperscript{21}

A validated Chinese version of the Tanner-derived composite stage
(TDCS)\textsuperscript{22} was used to evaluate the Tanner stage.
Children with early puberty were defined as those reaching a certain
pubertal stage earlier than the median age for that
stage\textsuperscript{23} based on large-scale population-based Chinese
studies.\textsuperscript{24,25}

\textbf{Other Covariates}

BMI was calculated and then converted into age- and sex-specific
z-scores (z-BMI).\textsuperscript{26} Potential confounding factors such
as age, sex, parental educational level, and household second-hand
cigarette smoke exposure were assessed at recruitment.

\textbf{Genotypes}

A valid MR\textsuperscript{13} study is predicated on three assumptions:
(i) each genetic variant is associated with a risk factor; (ii) it
affect the outcome only through their effect on the risk factor; and
(iii) each genetic variant is independent of potential confounders. We
used the following criteria to identify the candidate single-nucleotide
polymorphism (SNP) for obesity and the mediators: (i) minor allele
frequency of \selectlanguage{english}[?]5\%, (ii) genotyping call rate of \textgreater{}98\% for
all children, (iii) no linkage disequilibrium with candidate SNPs, and
(iv) primers designed using the National Genotyping Center of the
Academia Sinica platform
(\emph{http://lims.ngc.sinica.edu.tw/service/}).\textsuperscript{27}Genotyping
was performed using Sequenom iPLEX matrix-assisted laser
desorption/ionization--time of flight mass spectrometry at the National
Center for Genome Medicine, Taiwan.

Using genomic information from large genome-wide association studies, we
chose well-known genetic variants associated with BMI or the mediators.
To address the concern of potential pleiotropic effects of genetic
variants, SNPs previously reported to be associated with asthma and
confounders were excluded. The online supplementary file demonstrates
the details of how candidate SNPs were selected. In summary, we selected
28, 16, 4, 13, 14, and 11 SNPs for BMI, FEV1/FVC, Fe\textsubscript{NO},
physical fitness, SDB, and early puberty, respectively. Weighted genetic
risk scores (GRSs) composed of SNPs of BMI and the mediators were used
as genetic IVs. The dosage of effect allele was multiplied by the
regression coefficients of each gene on associated BMI/mediators divided
by the mean value of all regression coefficients to generate
GRSs.\textsuperscript{28}

\textbf{Statistical Analysis}

To investigate the causal relationship between obesity, mediators, and
asthma outcomes, we conducted observational, MR two-stage least squares
(2SLS) and MR sensitivity analyses for all relationships (Figure 1).
Observational associations were analyzed using logistic regression
models for binary asthma outcome/early puberty, and linear regression
models were used for continuous outcomes, such as pulmonary function,
Fe\textsubscript{NO}, physical fitness, and SDB scores. The MR 2SLS
analysis is a well-known method in MR for binary exposure and binary
outcomes.\textsuperscript{29} The first stage involves the regression of
the BMI/mediators on genetic scores, generating predicted values of
BMI/mediators. The second stage involves a regression analysis of asthma
on the predicted BMI/mediators. To test for directional pleiotropy, we
used sensitivity analysis methods (MR inverse-variance weighted
{[}IVW{]}, Egger {[}slope{]}, and weighted median method) to support the
validity of causal inference from the MR analysis with multiple genetic
variants.\textsuperscript{30} Observational mediation analysis was
implemented using a mediation package for one mediator in R
software.\textsuperscript{31} MR mediation analysis and proportion were
calculated according to equations established by Burgess et
al.\textsuperscript{14} The proportion of mediation was calculated as
the estimate of the indirect effect divided by the estimate of the total
effect, considering one mediator at a time. All model regression and
sensitivity analyses were performed using R software (vers. 3.3.2,
RStudio, Inc., Boston, MA, USA).

To evaluate the longitudinal effect of obesity on mediators, and of
mediators on asthma throughout the three surveys in cohort 2, we used
generalized estimating equations (GEE),\textsuperscript{32} which
account for correlations between different measurements at different
times in the same individual. A specific type of GEE with a Time-lag
Model was used to approve temporal causality.\textsuperscript{33} In the
Time-lag Model, predictors (adiposity status in the 2010 and 2011
surveys) were modeled using data that preceded the outcome variables
(mediators assessed in 2011 and 2012); the model comprised the specific
time-varying nature of adiposity status. The Time-lag Model also
accounts for the temporal sequence in a possible cause-and-effect
relationship.



\textbf{RESULTS}

This study comprised 5,965 children aged 12 years in TCHS; data of 3233
children from cohort 1 were collected in 2007, and data of 2732 children
from cohort 2 were collected in 2012 (Table 1). The BMI and prevalence
of active asthma were similar among the two cohorts. In total, 43.2\% of
the children reported exposure to household cigarette smoking, and
nearly half of the children were breastfed.

\textbf{Observational and MR association: obesity and asthma}

Our previous bidirectional MR analysis established that adiposity causes
asthma, but that asthma does not lead to adiposity
accumulation.\textsuperscript{2} In the current study, we confirmed the
causal direction from obesity to asthma using TCHS cohorts 1 and 2
(Figure E1). A one-unit increase in z-BMI was associated with an
elevated risk of active asthma according to both observational analysis
(odds ratio {[}OR{]}, 1.15; 95\% confidence interval {[}CI{]},
1.04--1.28) and MR 2SLS analysis (OR, 1.07; 95\% CI, 1.00--1.14). In
addition, the sensitivity analysis also yielded evidence of a positive
causal relationship between z-BMI and asthma.

The GRSs of the 28 BMI-SNPs were robustly correlated with z-BMI (Table
E1, F-statistics = 44.41, p \textless{} 0.001) and were associated with
active asthma (Table E1, p = 0.04). To prove that the individual SNP
used was independent of asthma and confounders, we showed that most of
the 28 BMI-SNPs were associated with z-BMI (Table E3), but that there
was little evidence to support an association between individual
BMI-SNPs and active asthma and confounders (Tables E3 and E4).

\textbf{Observational, MR, and longitudinal association: obesity and the
mediators}

Figure 2 depicts the associations between BMI z-scores and mediators in
both observational and MR 2SLS analyses. According to the MR analysis, a
one-unit increase in the BMI z-score was associated with a decrease in
FEV1/FVC ratio (\selectlanguage{greek}β, \selectlanguage{english}-\selectlanguage{english}2.17; 95\% CI, -3.92 to -0.42), physical fitness
index (\selectlanguage{greek}β, \selectlanguage{english}-\selectlanguage{english}0.71; 95\% CI, -1.30 to -0.13), and an increased risk of
early puberty (OR, 1.09; 95\% CI, 1.02 to 1.17). Both observational and
MR 2SLS analyses consistently supported the causal association of
obesity with FEV1/FVC, physical fitness, and early puberty, with a
similar causality direction. Although the observational association
between obesity and SDB was significant (\selectlanguage{greek}β, \selectlanguage{english}0.21; 95\% CI, 0.06--0.35),
the MR 2SLS analysis did not support causal relationships. Furthermore,
most of the results of the MR sensitivity analysis (IVW and weighted
median method) confirmed the direction of causal relationships (Figure
E2). No evidence supported the association of individual BMI-SNPs with
the mediators (Tables E5 and E6). The GRSs of the 28 BMI-SNPs showed
strong evidence of an association of obesity with low FEV1/FVC, low
physical fitness, high SDB scores, and high early puberty risk (Table
E1). Prospective cohort analyses supported the finding of Figure 2 that
obesity was associated with a decline in FEV1/FVC, physical fitness, and
increased risk of early puberty (Table 2).

\textbf{Observational, MR, and longitudinal association: mediators and
asthma}

Both observational and MR studies supported the causal relationship
between mediators and asthma (Figure 3). High FEV1/FVC and physical
fitness reduced the risk of active asthma (OR, 0.98 and 0.93; 95\% CI,
0.97--0.99 and 0.88--0.98, respectively), whereas SDB and early puberty
increased the risk of active asthma (OR, 1.03 and 1.22; 95\% CI,
1.01--1.05 and 1.05--1.42, respectively). The IVW results of the MR
sensitivity analysis supported the findings of observational and MR
analyses (Figure E3). However, the results of the causal effect of
Fe\textsubscript{NO} on asthma based on observational and MR analyses
were in the opposite direction, indicating that Fe\textsubscript{NO} is
unlikely to mediate the association between obesity and asthma. The
prospective cohort analyses also demonstrated that higher FEV1/FVC and
physical fitness were associated with lower risk of active asthma
(RR\textless{}1), whereas early puberty increased the risk of active
asthma (RR\textgreater{}1) (Table 3).

The GRSs of the mediators served as an adequate indicator for the
mediators, except for the GRS of Fe\textsubscript{NO}. The F statistics
for FEV1/FVC, Fe\textsubscript{NO}, physical fitness, SDB, and early
puberty were 16.25, 3.22, 10.27, 19.93, and 88.54, respectively (Figure
3). Furthermore, the GRSs of all the mediators, except for
Fe\textsubscript{NO}, were significantly associated with active asthma
(Table E2). We did not observe strong associations of the individual
SNPs of the mediators with active asthma (Tables E7--S11) and the
confounders (Tables E12--E16).

\textbf{Observational and MR mediation analyses}

According to both observational and MR analyses, the two mediators with
the highest ranking of mediation proportions were FEV1/FVC and physical
fitness (Figure 4). For all the mediators, the mediation proportions of
the MR analysis (Figure 4, right) were higher than that of the
observational analysis (Figure 4, left). Fe\textsubscript{NO} did not
appear to mediate the influence of obesity on asthma, because the
mediation coefficient was in the opposite direction from obesity to
Fe\textsubscript{NO} (negative in Figure 2) and from
Fe\textsubscript{NO} to active asthma (positive in Figure 3), making the
indirect pathway coefficient negative; hence, the mediation proportion
was unable to be calculated. Furthermore, SDB was not considered a
strong mediator because the MR analysis showed that obesity-induced SDB
was insignificant (Figure 2). The mediators presented as consistent
causal relationships from obesity to the mediators and then from the
mediators to asthma were FEV1/FVC, physical fitness, and early puberty
in mediation proportions of 61.57\%, 91.42\%, and 28.28\%, respectively,
based on the MR methods; and 24.88\%, 21.05\%, and 10.32\% based on the
observational analysis. The mediation proportion implies the proportion
of the mediating pathway compared with the total effect from obesity to
asthma, considering one mediator at a time. Therefore, the mediation
proportion from all five mediators does not necessarily have to sum to
100\%. The more substantial mediation proportion indicates that the
mediator might play a more critical role.

The mediation analysis assumes that the exposure and mediators had no
interaction effects on the outcome.\textsuperscript{14} We did not
observe an obvious interaction effect of BMI or BMI-GRS and the
mediators on predicting active asthma after correction for multiple
comparisons (Table E17).



\textbf{DISCUSSION}

Our study has yielded new evidence for several potential mechanistic
factors underlying the causative relationship between obesity and
asthma. We used the network MR to examine causal mediation pathways and
to confirm the temporal causality using prospective cohort analyses.
Among the five mediators examined in the current study, impaired
pulmonary function, low physical fitness, and early puberty were found
to be robust mediators, whereas SDB was identified as a possible
mediator. However, airway inflammation did not appear to be a causal
mediator. Low FEV1/FVC and physical fitness were the top two leading
mediation pathways. A recent interventional study wherein obese
asthmatic patients were provided high-intensity pulmonary rehabilitation
programs did lead to experience significant improvements in asthma
control.\textsuperscript{34} Hence, the promotion of physical fitness to
improve pulmonary functions should be recommended for obese children, to
prevent asthma.

According to this network MR study, obesity-induced low physical fitness
and impaired pulmonary function are the main mediation pathways
presenting 91.42\% and 61.57\% of the mediation proportion, respectively
(Figure 4). Determining the causal links between physical fitness and
asthma incidence is crucial in guiding clinical practice. However, there
is limited proof for the causal relationship between physical fitness
and the incidence of asthma. In a Danish study of school-aged children,
Rasmussen et al. reported an association between poor physical fitness
in childhood and incidence of asthma in young
adulthood.\textsuperscript{6} The authors then combined this result with
a New Zealand-based study, and concluded that improving fitness during
childhood was associated with a higher lung volume in
adulthood.\textsuperscript{8} Additionally, Ortega et al. discovered
that among obese individuals, high cardiorespiratory fitness could
reduce the risk of asthma by half.\textsuperscript{35} Examining the
temporal sequence of low physical fitness and asthma development in a
longitudinal study is time-consuming and laborious, and testing this
hypothesis in a controlled trial is not feasible. To our knowledge, this
is the first MR study to investigate the causal relationship between
physical fitness and asthma. Additionally, the indirect effects mediated
through low physical fitness and impaired pulmonary function are much
higher than the direct effects, indicating that enhancing physical
fitness and pulmonary function could effectively prevent obesity-induced
asthma.

Childhood obesity is a major contributor of both early puberty and
asthma. In the current study, we identified a moderate degree of
mediation (mediation proportion, 28.28\%) for early puberty in the
pathogenesis from obesity to asthma. We previously confirmed the
causality between early puberty and asthma for both sexes through MR and
longitudinal studies.\textsuperscript{11} Boys and girls showed a 1.38-
and 1.11-fold increased risk of asthma, respectively, with early
puberty.\textsuperscript{11} Underlying causes linking early puberty and
asthma could be hormonal changes at
puberty,\textsuperscript{36}decreased pulmonary function in those with
early puberty,\textsuperscript{37} and hyperinsulinemia during the
pubertal growth stage.\textsuperscript{38} One case report stated that
pharmacological therapy (such as a gonadotropin-releasing hormone
analog) could be administered to prevent asthma in children experiencing
early puberty.\textsuperscript{39} To prevent obesity-induced asthma
risk, clinicians must assess for the presence of early puberty.

Our study, which used a MR network analysis, adds to the existing
evidence suggesting bi-directional relationships between SDB and asthma.
We confirmed the causality from SDB to asthma. Moreover, we observed
that SDB is a potential mediator with a moderate mediation proportion in
the pathogenesis of obesity-induced asthma. Although causal
relationships between SDB and asthma were never determined using a
cohort study,\textsuperscript{40} treatment of SDB, such as
adenotonsillectomy, showed improved asthma
outcomes.\textsuperscript{41}Possible mechanisms linking SDB and asthma
include airway edema or obstruction and systemic
inflammation.\textsuperscript{34} SDB-related hypoxia can induce reflex
bronchoconstriction, which further causes asthma.\textsuperscript{42}
Additional RCTs are needed to confirm that the treatment of SDB improves
pediatric asthma outcomes.

Our study has three key strengths. First, it is the large
population-based study on children that involved various mediator
measurements linking obesity and asthma. Second, we applied three
distinct statistical analyses (observational, MR, and prospective cohort
analyses) to prove the robustness of interrelations among obesity, the
mediators (impaired pulmonary function, low physical fitness, and early
puberty), and asthma. Moreover, several sensitivity analyses for MR
results were performed to support our findings. The present study also
has several limitations that should be addressed. First, SDB was defined
using a questionnaire-based assessment instead of measuring it through
polysomnography (PSG). This approach is common in the SDB/asthma
literature,\textsuperscript{43} where the cost involved in technology
and personnel make such a large population-based study cost-prohibitive.
However, the CSHQ is a well-established instrument for SDB assessment,
with good performance in population-based studies as compared with the
gold standard PSG.\textsuperscript{21} Second, a lack of the association
of elevated Fe\textsubscript{NO} with both obesity and asthma in the MR
analysis could be due to weak genetic instruments. In contrast to the
genome-wide association study approach, the candidate SNP approach was
limited by only a few available literatures on genetic variants relevant
to Fe\textsubscript{NO.}\textsuperscript{44}

In summary, impaired pulmonary function, low physical fitness, and early
puberty are prominent mediating mechanisms for obesity-induced asthma.
Therefore, lifestyle interventions aimed at promoting physical fitness
and pulmonary function might effectively reduce the risk of obese
asthma. Assessing these promising mediators may deepen our understanding
of its pathogenic features and may offer new treatment strategies for
specific phenotypes of obese asthma.

\textbf{Acknowledgments:}

The authors thank the field workers, teachers, and other school staff
who supported the data collection, as well as all the parents and
children who participated in this study. We also thank the NCGM for the
genotyping technical support.



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\textbf{Figure Legends}

\textbf{Figure 1. Flow diagram depicting the associations between
obesity and asthma and their respective mediators that were examined in
this study}

\textbf{Footnotes} of Figure 1: Arrows and accompanying Figures/Tables
indicate the associations/mediations studied in corresponding to order
of analyses in the text. \emph{Definition of abbreviations:} BMI=body
mass index; Fe\textsubscript{NO}=fractional exhaled nitric oxide;
FEV1/FVC = forced expiratory volume in 1 s/forced vital capacity

\textbf{Figure 2. Observational and Mendelian Randomization analysis of
the associations between BMI z-scores and mediators}

\textbf{Footnotes} of Figure 2: Observational and MR two-stage least
square associations were estimated in a total of 5965 children.
F-statistics of BMI genetic risk scores (28 SNP) =44.41. Observational
models were adjusted by age, sex, parental education, and household
environmental tobacco smoking. \emph{Definition of
abbreviations:}BMI=body mass index; Fe\textsubscript{NO}=fractional
exhaled nitric oxide; FEV1/FVC = forced expiratory volume in 1 s/forced
vital capacity

\textbf{Figure 3. Observational and Mendelian Randomization analysis of
associations between mediators and active asthma}

\textbf{Footnotes} of Figure 3: Observational and MR two-stage least
square associations were estimated in a total of 5965 children.
F-statistics of the mediators were listed. Observational models were
adjusted by age, sex, parental education, and household environmental
tobacco smoking. \emph{Definition of abbreviations:} BMI=body mass
index; Fe\textsubscript{NO}=fractional exhaled nitric oxide; FEV1/FVC =
forced expiratory volume in 1 s/forced vital capacity

\textbf{Figure 4. Mediation proportions (\%) as calculated in the
observational and Mendelian Randomization analyses}

\textbf{Footnotes} of Figure 4: The proportion of mediation indicates
the proportion of indirect mediation effect to total effect from obesity
to asthma. Mediation proportion of the observational models were
calculated by single-mediator analysis. Mediation proportion of the MR
models were calculated according to equations by \emph{Burgess et
al.}Observational models were adjusted by age, sex, parental education,
and household environmental tobacco smoking. \emph{Definition of
abbreviations:} BMI=body mass index; Fe\textsubscript{NO}=fractional
exhaled nitric oxide; FEV1/FVC = forced expiratory volume in 1 s/forced
vital capacity



\textbf{Table 1. Characteristics of participants aged 12 years in the
Taiwan Children Health Study}\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{Characteristics} & \textbf{Total} & \textbf{Total} &
\textbf{Cohort 1} & \textbf{Cohort 1} & \textbf{Cohort 2} &
\textbf{Cohort 2} & \selectlanguage{ngerman}\textbf{P value§}\tabularnewline
\midrule
\endhead
\textbf{N} & 5965 & 5965 & 3233 & 3233 & 2732 & 2732 &\tabularnewline
\textbf{Age, yrs} & 12.45 & 0.42 & 12.70 & 0.34 & 12.15 & 0.30 &
\textless{}0.001\tabularnewline
\textbf{Male sex} & 3043 & 51.01 & 1669 & 51.62 & 1374 & 50.29 &
0.31\tabularnewline
\textbf{Breast Feeding} & 2480 & 49.50 & 1561 & 48.28 & 919 & 51.72 &
0.02\tabularnewline
\textbf{Parental Education} & & & & & & &\tabularnewline
\textbf{High school or below} & 3440 & 57.67 & 2085 & 64.49 & 1355 &
49.60 & \textless{}0.001\tabularnewline
\textbf{College or university} & 2163 & 36.26 & 1026 & 31.74 & 1137 &
41.62 &\tabularnewline
\textbf{Post-graduate school} & 362 & 6.07 & 122 & 3.77 & 240 & 8.78
&\tabularnewline
\textbf{Family income (USD)} & & & & & & &\tabularnewline
\textbf{\textless{}20,000} & 3274 & 54.89 & 2021 & 62.51 & 1253 & 45.86
& \textless{}0.001\tabularnewline
\textbf{20,001-330,000} & 1827 & 30.63 & 848 & 26.23 & 979 & 35.83
&\tabularnewline
\textbf{\textgreater{}330,001} & 864 & 14.48 & 364 & 11.26 & 500 & 18.30
&\tabularnewline
\textbf{Household cigarette smoke} & 2577 & 43.20 & 1525 & 47.17 & 1052
& 38.51 & \textless{}0.001\tabularnewline
\textbf{BMI (kg/m\textsuperscript{2}) at age 12} & 20.49 & 4.16 & 20.57
& 4.23 & 20.40 & 4.06 & 0.11\tabularnewline
\textbf{Active Asthma} & 233 & 3.91 & 127 & 3.93 & 106 & 3.28 &
0.54\tabularnewline
\bottomrule
\end{longtable}

\emph{Definition of abbreviations:} BMI=body mass index; FEV1/FVC =
forced expiratory volume in 1 s/forced vital capacity; USD = United
States dollars.

All data are presented as means \selectlanguage{ngerman}± standard deviation or numbers (\%).

The number of participants does not add up to the total number because
of missing data.

§P value indicates the difference between the two cohorts.

\textbf{Table 2. Prospective cohort analyses linking obesity to three
mediators (early puberty, poor physical fitness, poor pulmonary
function)}\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\textbf{Obesity measures}\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\textbf{FEV1/FVC} (\%)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\textbf{FEV1/FVC} (\%)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\textbf{FEV1/FVC} (\%)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\textbf{Physical fitness} (z-scores)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\textbf{Physical fitness} (z-scores)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\textbf{Physical fitness} (z-scores)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\textbf{Early puberty}\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\textbf{Early puberty}\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\textbf{Early puberty}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\selectlanguage{greek}\textbf{β}\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{*95\%CI}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{*95\%CI}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\selectlanguage{greek}\textbf{β}\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{*95\%CI}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{*95\%CI}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{RR}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{*95\%CI}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{*95\%CI}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{Overweight}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-1.24\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-1.66\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.82\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-1.04\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-1.22\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.86\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.54\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.27\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.87\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{Obesity}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-1.26\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-1.79\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.74\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-1.33\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-1.55\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-1.11\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.57\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.25\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.96\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{z-BMI}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.41\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.55\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.26\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.39\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.45\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
-0.33\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.26\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.18\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.35\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

*Models are adjusted by age, sex, parental educational level, and
household cigarette smoking exposure

\textbf{Table 3. Relative risk of active asthma for three mediators
during follow-ups survey at age 11, 12 years}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Active asthma} & \textbf{RR} & \textbf{* 95\%CI} & \textbf{*
95\%CI}\tabularnewline
\midrule
\endhead
\textbf{FEV1/FVC} (\%) & 0.96 & 0.94 & 0.98\tabularnewline
\textbf{Physical fitness} (z-scores) & 0.95 & 0.90 & 1.00\tabularnewline
\textbf{Early puberty} (yes to no) & 1.95 & 1.13 & 3.21\tabularnewline
\bottomrule
\end{longtable}

*Models are adjusted by age, sex, parental educational level, and
household cigarette smoking exposure\selectlanguage{english}
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\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/figure-1-0405/figure-1-0405}

\end{center}
\end{figure}\selectlanguage{english}
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\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/figure-2/figure-2}

\end{center}
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\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/figure-3/figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
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