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\begin{document}

\title{Childhood Asthma and Type 1 Diabetes Mellitus: A Meta-analysis and
Bidirectional Mendelian Randomization Study}



\author[1]{Xiaowen Zhang}%
\author[1]{Junyang Xie}%
\author[1]{Gui Chen}%
\author[1]{Tianhao Liang}%
\author[1]{Ang Li}%
\author[1]{Weixing Liu}%
\author[1]{Yiyan Wang}%
\author[1]{Xiaofen Wang}%
\author[1]{Xiaoxuan Kuang}%
\author[1]{Wenjing Liao}%
\author[1]{Lijuan Song}%
\affil[1]{First Affiliated Hospital of Guangzhou Medical University}%


\vspace{-1em}



  
  \date{May 18, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
\textbf{Objectives:} World-wide incidence and prevalence of both asthma
and type 1 diabetes mellitus (T1DM) has been increasing in past decades.
Association between the two diseases has been found in some but not
other studies. We conducted a meta-analysis to verify such an
association, and bidirectional Mendelian randomization analysis to
examine the potential cause-effect relationships. \textbf{Methods:}
Three databases(PubMed, Embase and Web of Science) were searched from
their inception to February 1, 2021. Pooled hazard ratios (HR) or odds
ratios (OR), and 95\% confidence intervals, were calculated.
Associations between single-nucleotide polymorphisms with childhood
asthma and T1DM were selected based on genome-wide association studies.
The outcome datasets were obtained from FinnGen study. We used the
inverse variance-weighted, weighted median and MR-Egger methods to
estimate causal effects. To assess robustness and horizontal pleiotropy,
MR-Egger regression and MR pleiotropy residual sum and outlier test was
conducted. \textbf{Results:} In meta-analysis, childhood asthma was
associated with an increased risk of T1DM (HR=1.30, 95\% CI 1.05--1.61,
\emph{P}=0.014), whereas T1DM was not associated with the risk of asthma
(HR=0.98, 95\% CI 0.64--1.51, \emph{P}=0.941; OR=0.84, 95\% CI
0.65--1.08, \emph{P}=0.168). MR analysis indicated increased genetic
risk of T1DM in children with asthma (OR=1.308; 95\% CI 1.030-1.661; P
=0.028). Analysis using the IVW method indicated decreased genetic risk
of asthma in children with T1DM (OR = 0.937, 95\%CI 0.881-0.996,
\emph{P} = 0.037). \textbf{Conclusions:} Childhood asthma is a risk
factor for T1DM; T1DM is a possible protective factor for childhood
asthma.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

Asthma is one of the most frequent chronic disease in
children.\textsuperscript{1,2} Type 1 diabetes mellitus (T1DM) occurs at
an incidence of 8-20 per 100,000 each year,\textsuperscript{3} and is
one of the most common immune-related diseases in children. The
incidence and prevalence of both childhood asthma and T1DM have been
increasing over the past decades,\textsuperscript{1,3} suggesting common
susceptibility factors.\textsuperscript{4}

A meta-analysis published in 2003 suggested a weak inverse association
between childhood asthma and T1DM.\textsuperscript{5} However, majority
of the studies included in the meta-analysis are case-control studies.
In addition, cohort studies included in the meta-analysis are highly
heterogeneous in disease definition and age spectrum. In addition, a
positive association between the two diseases , which now attracts
growing interest, has also been reported in several retrospective cohort
studies with stronger quality of evidence.\textsuperscript{6-8}

\par\null

Since exaggerated immune response and inflammation underlie both asthma
and T1DM, reciprocal cause-effect relationship between the two diseases
is likely. Mendelian randomization (MR) analysis uses genetic variants
as instrumental variables (IVs) for investigating potential cause-effect
relationships between different human traits.\textsuperscript{9} With
increasing number of genome-wide association studies (GWAS), MR has been
increasingly used. Bidirectional MR analysis is an extension to the
basic MR approach that could evaluate whether an ``exposure'' causes the
``outcome'' or vice versa.\textsuperscript{10} We conducted an updated
meta-analysis of original studies that examined the association between
childhood asthma and T1DM. Data were analyzed using bidirectional MR
analysis to examine the possible cause-effect relationship.

\textbf{METHODS}

\textbf{Data Sources and Literature Search Strategy}

The meta-analysis was conducted according to the Preferred Reporting
Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and
registered in the Prospective Register of Systematic Reviews (PROSPERO
ID CRD42021236232).\textsuperscript{11} PubMed, Embase and Web of
Science were searched from inception to February 1, 2021 using the
following Medical Subject Heading (MeSH) terms, keywords and word
variants: ``insulin dependent'' or ``type 1 diabetes'' and ``asthma''.
Reference lists of the electronically identified articles were manually
searched.

\textbf{Eligibility Criteria}

For inclusion in analysis, studies must meet the following criteria: (1)
full-text published in English; (2) reported the odds ratio (OR),
relative risk (RR), or hazard ratio (HR), with 95\% confidence interval
(95\%CI), or adequate data to calculate these measures; (3) conducted
primarily in children (\textless{}18 years of age); (4) sample size of
at least 100. We excluded meta-analyses, review articles and case
reports. Two reviewers (J.X. and W.L.) screened the titles and
abstracts, and then assessed the full-text independently.

\textbf{Data Extraction and Quality Assessment}

Two investigators (J.X. and W.L.) extracted the following data from the
identified studies: full title, first author, publication year, study
design, follow-up period, sources of patient and participant, the number
of asthma patients, T1DM patients, and OR/RR/HR with 95\%CI. The quality
of eligible literature was assessed using the Newcastle-Ottawa Scale
(NOS), a tool recommended by the Cochrane Collaboration for the quality
assessment of observational studies.\textsuperscript{12} Eligible
studies with scores of 0-3, 4-6, and 7-9 were considered as low,
moderate, and high quality, respectively. Disagreements between the
investigators in data extraction and quality assessment were resolved by
consensus, and if consensus could not be reached, a third investigator
(X.Z.) was consulted for a final decision.

\textbf{Statistical Analysis}

Meta-analysis was conducted using Stata14.0 (StataCorp, College Station,
TX, USA). Heterogeneity was assessed by \selectlanguage{greek}\emph{χ²}\selectlanguage{ngerman} analysis. Data
synthesis was conducted using a random-effects model if \emph{I²}
\textgreater{} 50\%, and using a fixed-effects model otherwise. RR was
converted to OR for data synthesis. Subgroup analysis was performed with
stratification according to geographical location and quality grade.
Meta-regression analysis was conducted using year of publication,
region, incidence of asthma in the control group, sample size and
quality score as explanatory covariates. Sensitivity analysis was
conducted with the method of reducing one piece of literature at a time
to evaluate the impact of a single study on this meta-analysis.
Publication bias was examined using the funnel plot and Begg's bias test
if 10 or more studies were included. \emph{P}\textless{}0.05 (2-sided)
was considered to be statistically significant.

\textbf{Effect of Childhood Asthma on T1DM}

A total of 56 single nucleotide polymorphisms (SNPs) associated with
childhood asthma were selected for genome-wide significance (threshold:
P \textless{}5*10\textsuperscript{-8}) from the hitherto largest asthma
GWAS of UK Biobank (UKBB) (Table S1).\textsuperscript{13} These SNPs
explained 8.68\% of the variation in childhood asthma risk. The F
statistic is 2,881, indicating that the instruments could strongly
predict childhood asthma.\textsuperscript{14}We performed linkage
disequilibrium (LD)-clumping restricted to r2\selectlanguage{english} \textless{}\selectlanguage{english} 0.01, Clumping
distance \textgreater{} 5000kb and retained SNPs at a GWAS threshold of
statistical significance to eliminate LD. When a particular SNP was not
available in the outcome dataset, proxy SNPs were used instead through
LD tagging (Rsq \textgreater{} 0.8). Then 30 SNPs were selected as IVs
for MR analysis by removing the SNPs for lack of required information,
being palindromic or incompatible alleles. Effect estimates of these
childhood asthma-associated SNPs on the risk of T1DM were assessed using
the summary statistics from the FinnGen datasets (2,636 T1DM cases and
8,2655 controls of European ancestry). Detailed description is provided
on the FinnGen research project website (https://www.finngen.fi/en).

\textbf{Effect of T1DM on Childhood Asthma}

A total of 28 T1DM-associated SNPs were selected based on GWAS analysis
in the National Human Genome Research Institute (NHGRI) and the European
Bioinformatics Institute (EBI) GWAS Catalog
(https://www.ebi.ac.uk/gwas/) (Table S2).\textsuperscript{15-21}The
F-statistic ranges from 107 to 559 and could strongly predict T1DM
(F\textgreater{}10). LD-clumping were applied to remove dependent SNPs
and 20 of them were available in the outcome dataset. Effect estimates
of T1DM-associated SNPs on the risk of childhood were assessed using the
FinnGen study (992 childhood asthma cases and 65,481 controls of
European ancestry).

\textbf{Mendelian Randomization Analysis}

The random-effects inverse-variance weighted (IVW) method was used to
estimate the bidirectional causality between childhood asthma and T1DM
by meta-analyzing the SNP specific Wald ratio
estimates.\textsuperscript{22} The causal effect estimates (beta
coefficients) were transformed to odds ratios (OR). The weighted median,
MR-Egger methods were also implemented to assess the causality.

Since many IVs are associated with multiple traits (pleiotropy), we used
MR-Egger regression\textsuperscript{23} and MR pleiotropy residual sum
and outlier (MR-PRESSO)\textsuperscript{24} method to examine the
robustness of the main findings. When the MR-Egger intercept differs
from zero (at \emph{P} \selectlanguage{english} \textless{}\selectlanguage{english} 0.05), horizontal pleiotropy or
violation of the MR assumption is
indicated.\textsuperscript{25}MR-PRESSO method was used to correct the
IVW-estimate via outlier removal, when MR-PRESSO outlier test suggested
the presence of outlying variants. All MR analysis were performed using
R version (4.0.3) with the ``TwoSampleMR'' and ``MRPRESSO''
package.\textsuperscript{26}

\textbf{RESULTS}

\textbf{Study Selection}

The initial search identified 30,775 studies. 30,749 studies were
excluded after screening the titles or abstracts, and 11 studies were
excluded after reading the full text (Figure 1). We finally included 15
studies in the meta-analysis.\textsuperscript{6-8,27-38} Among them, 5
centers were included in the EURODIAB ACE(ED) study.

\textbf{Study Characteristics and Quality Assessment}

Table S3, S4 summarizes the detailed characteristics and quality
assessment of all studies, respectively. Most articles were case-control
studies. Geographical location included: Europe (12 studies), America (2
studies), and Asia (1 study). The total number in the 15 studies was
18,813 for cases (having T1DM) and 3,018,809 for controls (not having
T1DM). The prevalence of asthma was 8.51\% in the cases and 6.84\%, in
the controls. A moderate risk of bias was observed in the NOS
assessment. The selection domain was mostly related to a case-control
design; consecutive or representative sampling was not used. The
exposure domain was assessed using questionnaire, and thus subjected to
recall bias.

\textbf{Meta-analysis}

Two cohort studies reported HR as the measure for the risk of T1DM in
asthmatic patients. There was significant heterogeneity (\emph{I} 2 =
90.2\%, \emph{P} = 0.001). In random-effects model analysis, pooled HR
for the risk of T1DM was 1.30 (95\% CI 1.05--1.61, \emph{P} =0.014).
Figure 2 shows the forest plot of the included studies.

Three studies reported HR as the measure for asthma risk in T1DM
patients. There was significant heterogeneity (\emph{I} 2 =
92.7\%,\emph{P} \textless{} 0.001). In random-effects model analysis,
pooled HR was 0.98 (95\% CI 0.64--1.51,\emph{P} =0.941). In the
meta-analysis of the 12 studies that reported OR as the measure for
asthma risk in T1DM patients, pooled OR was 0.84, 95\% CI 0.65--1.08,
\emph{P} =0.168). Figure 3 shows the forest plot of the included
studies. Subgroup analysis indicated that geographical location and
quality grade could not explain the heterogeneity (Table S4). In
meta-regression analysis, the observed heterogeneity could not be
explained by the year of publication, region, incidence of asthma in the
control group, sample size and quality score (Table S5). Sensitivity
analysis failed to show significant bias caused by any single study
(Figure S1). Funnel plots indicated some degree of publication bias
(Figure S2), but Begg's test did not confirm the bias (\emph{P} of
=0.82).

\textbf{Mendelian Randomization Analysis}

In the IVW analysis, children with asthma were at higher risk for T1DM
(OR=1.308, 95\% CI 1.030-1.661, P\selectlanguage{english} =0.028) (Table 1). Higher risk for
T1DM in children with asthma was also apparent in MR-Egger analysis
(OR=2.082, 95\%CI 1.115-3.888, P=0.029 ). Analysis using the weighted
median method failed to establish an association, but the direction of
the general finding was consistent (OR=1.033, 95\%CI 0.849-1.256,
P=0.745) . Figure S3 illustrates method comparison plot(a) and forest
plot(b). MR-Egger regression failed to identify evidence to support the
existence of unbalanced pleiotropy in the genetic instruments (intercept
\selectlanguage{greek}β \selectlanguage{english}= -0.071, P = 0.126). The MR-PRESSO outlier test detected 2 specific
horizontal pleiotropic outlier variants (rs4795399 and rs28407950).
However, these 2 SNPs were not genome-wide significant associated with
T1DM. The association of childhood asthma with T1DM was not
statistically significant in MR-PRESSO outlier-corrected IVW analysis
(OR = 1.175, 95\%CI 0.960-1.438, P = 0.118).

IVW analysis suggested a protective effect of T1DM on the risk of
childhood asthma (OR = 0.937, 95\%CI 0.881-0.996, \emph{P}  = 0.037).
Subsequent analysis using the MR-Egger (OR=0.918, 95\%CI 0.850-0.991,
P=0.041) and weighted median methods (OR=0.928, 95\%CI 0.872-0.987,
P=0.017) supported this finding. Figure S4 illustrates method comparison
plot(a) and forest plot(b). No evidence of pleiotropy or outlier
variants was found by either MR-Egger regression (intercept \selectlanguage{greek}β \selectlanguage{english}= 0.016, p
= 0.383) or MR-PRESSO outlier test.

\textbf{DISCUSSION}

In the current study, meta-analysis indicated an increased risk of T1DM
in children with asthma, but not increased risk for asthma in children
with T1DM. However, strong heterogeneity among the included studies was
noted. Subgroup analysis, meta-regression, and sensitivity analysis
failed to identify the sources of heterogeneity.

The results of MR analysis were not completely consistent with that of
the meta-analysis. Specifically, children with asthma had higher risk of
T1DM, whereas children with T1DM had lower risk of asthma. The
importance in sequential appearance of the two diseases was apparently
illustrates. However, in the majority of the included studies, it was
not clear whether an asthma diagnosis or symptoms preceded the T1DM
diagnosis, which could lead to reverse
causality.\textsuperscript{27-38}Only 3 cohort studies documented the
sequence of disease occurrence, but the results were still
conflicting.\textsuperscript{6-8} The results of MR analysis are
consistent with a previous cohort study by Metsala et
al\textsuperscript{7}. Similarly, Smew et al\textsuperscript{6}indicated
that childhood asthma was related to an increased risk of subsequent
T1DM, whereas T1DM was not associated with a decreased risk of
subsequent asthma. Hisiao et al\textsuperscript{8}, however, reported a
markedly higher incidence of asthma in patients with T1DM in the Asian
population. It is important to note that the MR analysis data came from
European population. Whether the results of MR analysis in the current
study could be extrapolated to other populations remained unknown.

Both nurture (i.e. the environment) and nature (i.e. genetics)
contribute to the etiology of both asthma and T1DM. Asthma and T1DM tend
to be comorbid and congregate within
families.\textsuperscript{6}Siblings of children with one disease have
higher risk of the other disease.\textsuperscript{6} For example, TLR2
rs3804100 T allele has been shown to be a susceptibility allele for both
childhood asthma and T1DM.\textsuperscript{39} In contrast, the T allele
of rs9273349 and rs1063355 in HLA-DQB1 seems to be a protective factor
to both diseases.\textsuperscript{16} Adding to the complexity,
susceptibility genes to one disease could be protective to the other
disease. For example, GIMAP5 SNP (rs6965571) has been associated with
increased risk for asthma but decreased risk for
T1DM.\textsuperscript{40} Future GWAS studies are required to better
characterize the potential link between them.\textsuperscript{41}

At a mechanistic level, lower risk of asthma in children with T1DM could
be attributed to the anti-inflammatory properties of insulin. High
levels of insulin promote Airway smooth muscle (ASM) contraction,
enhance contractile responses to methacholine, which are likely to
result in increased airway hyperresponsiveness, bronchoconstriction, and
airway remodeling.\textsuperscript{42} Insulin deficiency in T1DM could
prevent airway remodeling.\textsuperscript{43} A recent study in a mouse
model for asthma showed that insulin deficiency could prevent the
development of allergic inflammation, eosinophilic pulmonary
infiltration, and airway hyperresponsiveness in an asthma mouse
model.\textsuperscript{44} These findings mainly explain the reduced
risk of asthma in patients with T1DM, but lacking mechanistic research
on the increased risk of T1DM in children with asthma. The use of some
anti-asthmatic drugs (inhaled corticosteroids and inhaled \selectlanguage{greek}β-\selectlanguage{english}agonists)
was potentially associated with the risk of T1DM in a case-cohort study.
Therefore, future research is required to explore the underlying
biological mechanisms of this relationships.

MR analysis makes sense only when it uses IVs that are: (1) associated
with the exposure; (2) independent of factors confounding the
association of the exposure and outcome; and (3) associated with the
outcome only through exposure. With these premises, MR analysis can work
as an analogue to RCT, which is more plausible to identify causality
than observational analysis.\textsuperscript{45} To satisfy the first
assumption, we selected only SNPs with a genome-wide significant
association (P \textless{} 5*10\textsuperscript{-8}) and the F statistic
at \textgreater{} 10.\textsuperscript{14} The second assumption is not
verifiable. To satisfy the third assumption, MR-Egger regression was
used to estimate horizontal pleiotropy.\textsuperscript{23} Since these
premises were fulfilled in our MR analysis, we believe that our findings
were reached with least likelihood of confounding effects and maximal
avoidance of reverse causality.

The meta-analysis in the current study has several limitations. First,
majority of the studies included in the analysis are of case-control
design; only three cohort studies were included. Second, due to the low
prevalence of T1DM, most included studies were based on T1DM and
considered asthma as potential risk factors. Third, there was
significant heterogeneity among the included studies, but we were unable
to identify the source of heterogeneity. The MR analysis also has
several important limitations. First, the MR analysis used only one
dataset, and lacks validation. Second, some of the SNPs (such as
rs4795399 and rs28407950) utilized in this study are potentially
correlated with confounding factors which may also influence the risk of
T1DM or childhood asthma. We were unable to completely rule out the
possible impact of pleiotropic effects on the findings. Third, the MR
analysis data came from European population only, and whether the
findings could be extrapolated to other populations remains unknown.

\textbf{Conclusion}

Childhood asthma is a risk factor for T1DM. In contrast, T1DM is a
possible protective factor for childhood asthma. These findings
encourage T1DM screening in children with asthma.

\textbf{Contributors}

Conception and design: Junyang Xie, Weixing Liu and Xiaowen Zhang; (II)
Administrative support: Xiaowen Zhang; (III) Data analysis and
interpretation: Chen Gui, Tianhao Liang, Ang Li, Yiyan Wang, Xiaofen
Wang, Xiaoxuan Kuang, Wenjing Liao and Lijuan Song; (IV) Manuscript
writing: All authors; (V) Final approval of manuscript: All authors.

\textbf{Declaration of interests}

All authors declare no competing interests.

\textbf{Funding}

This study was supported by High-level Construction Project of Guangzhou
Medical University, Upper Respiratory Disease Innovation and
Transformation Platform Construction Project of Guangdong Province,
China.

\textbf{Data sharing}

Our study is based on published data. The data supporting the findings
of this study are available within this article and the appendix, and
all data retrieved from original papers, together with tables and
figures arising from these data, are available to share.

\textbf{Acknowledgements}

We thank the participants and research teams from UK Biobank and FinnGen
that made the GWAS results publicly accessible.

\textbf{Ethical Considerations}

No ethical clearance was required for this research, since no human
subjects were directly involved in the development of the research
question or its outcome measures. Only secondary analysis was performed
using published GWAS summary statistics available in the public domain.

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\textbf{Table 1.} MR estimates of the causality between childhood asthma
and T1DM.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Exposure} & \textbf{Outcome} & \textbf{nSNP} & \textbf{method} &
\textbf{OR} & \textbf{95\% CI} & \textbf{P}\tabularnewline
\midrule
\endhead
Childhood asthma & T1DM & 32 & IVW & 1.308 & 1.030, 1.661 &
0.028\tabularnewline
& & & MR-Egger & 2.082 & 1.115, 3.888 & 0.029\tabularnewline
& & & Weighted median & 1.033 & 0.849, 1.256 & 0.745\tabularnewline
T1DM & Childhood asthma & 20 & IVW & 0.937 & 0.881, 0.996 &
0.037\tabularnewline
& & & MR-Egger & 0.918 & 0.850, 0.991 & 0.041\tabularnewline
& & & Weighted median & 0.928 & 0.872, 0.987 & 0.017\tabularnewline
\bottomrule
\end{longtable}

Note: MR: Mendelian randomization; T1DM: type 1 diabetes mellitus; nSNP:
number of single-nucleotide polymorphism; OR: odds ratios; CI:
confidence interval; IVW: inverse variance weighted.

\textbf{Figure legends}

\textbf{Figure 1} Flow diagram detailing the search strategy and
identification of studies used in meta-analysis. *Reasons for exclusion
of the 11 studies were: 2 studies used the replicated data from the same
study population, 1 study not published in English, 7 studies with small
sample sizes, and 1 study not related to the childhood period.

\textbf{Figure 2} Meta-analysis of studies of asthma and the risk of
type 1 diabetes using the random-effects model, ordered by date of
publication.

Note: HR: hazard ratio; CI: confidence interval.

\textbf{Figure 3} Meta-analysis of studies of type 1 diabetes and the
risk of asthma using the random-effects model, ordered by date of
publication.

Note: HR: hazard ratio; OR: odds ratio; CI: confidence interval.

\textbf{Supplementary materials}

\textbf{Figure S1} Sensitivity analyses. The results indicated that none
of the studies had a significant impact on the effects with regard to
the outcomes mentioned above.

\textbf{Figure S2} Funnel plots of observational studies of asthma
exposures and diabetes.

\textbf{Figure S3} Scatter plot and Forest plot. A: Scatter plot of SNP
potential effects of childhood asthma and the risk of T1DM, with the
slope of each line corresponding to estimated MR effect per method; B:
Forest plot of the MR-based effect sizes of childhood asthma exposure
instruments on FinnGen T1DM outcome.

Note: SNP: single-nucleotide polymorphism; IVW: inverse variance
weighted method.

\textbf{Figure S4} Scatter plot and Forest plot. A: Scatter plot of SNP
potential effects of T1DM and the risk of childhood asthma, with the
slope of each line corresponding to estimated MR effect per method; B:
Forest plot of the MR-based effect sizes of T1DM exposure instruments on
FinnGen childhood asthma outcome.

Note: SNP: single-nucleotide polymorphism; IVW: inverse variance
weighted method.\selectlanguage{english}
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