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,
FeNO, physical fitness, and SDB scores. The MR 2SLS
analysis is a well-known method in MR for binary exposure and binary
outcomes.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.30 Observational mediation analysis was
implemented using a mediation package for one mediator in R
software.31 MR mediation analysis and proportion were
calculated according to equations established by Burgess et
al.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),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.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.