Dietary Intervention for Low Free Sugars in Children With Non-alcoholic Fatty Liver Disease: a Randomized Controlled Trial
Phase: NA
Conditions: NAFLD, Low-free Sugar Dietary, Child Obesity
Sponsor: China Medical University, China

Dietary intervention for low free sugars in children with
non-alcoholic fatty liver disease: a randomized controlled
trial
12-January-2018

Study design and participants
This study is a randomised, two-arm, parallel dietary intervention trial. A
48-week dietary intervention programme was conducted in children aged 5-14 years
with a diagnosis of NAFLD. The study participants were randomized into a low free
sugar diet group and a regular diet group. As this was a dietary intervention study, the
participants could not be double-blinded for the grouping and intervention. The
primary objective of this study is to determine whether a low free sugar dietary
intervention improves liver function, fasting glucose, fasting insulin, lipid profile and
hepatic steatosis in children with NAFLD. The effects of the intervention will be
analysed and compared at weeks 12, 24 and 48.
Overweight and obese children and their guardians who attended Shengjing
Hospital of China Medical University between October 2018 and April 2020 were
selected as study subjects. All guardians signed an informed consent form before
participating in the intervention. Out of 150 subjects initially included, 146 were
eligible for analysis after excluding those missing key variables.
Intervention
After baseline data collection, the intervention group received a 30-60 minute
educational session with a dietitian and clinicians, including a health education
prescription. Interventions occurred at weeks 1, 12 and 24, with routine evaluations of
disease, diet, physical activity, and adherence. Follow-ups at 48 weeks assessed
NAFLD extent, dietary profile, and health knowledge for both subjects and guardians.
Blinding was not possible due to the researchers' roles in the intervention.

However,laboratory
and
imaging
tests
were
conducted
by
separate
hospital
departments, with physicians unaware of group assignments, minimizing potential
bias.
Statistical analysis
The questionnaire and laboratory examination data were entered into a database
using EpiData 3.1 software, and the disputed data were determined by looking at the
original data. The statistical descriptions of continuous variables were expressed as
mean±standard deviation (Mean±SD), and after the normality test, t-test or analysis of
variance (ANOVA) was used to compare the groups; the paired t-test was used to
determine the differences between baseline and post-intervention hepatic fat scores,
body composition, and serum analytical variables in the groups; the statistical
descriptions of the categorical variables were expressed in the form of frequency (rate)
and analyzed by the χ2 (chi-squared) test; the post-intervention versus baseline
indices were analyzed by the χ2 (chi-squared) test. ) test was used for analysis; the
difference between the post-intervention and baseline indicators was used as an
evaluation index of the intervention effect. The data were processed and analyzed
using SPSS 23.0 software, and the two-sided P value of less than 0.05 was statistically
different, with a test level of α=0.05.
In the present study, the physical and laboratory indicators of children with
NAFLD were measured longitudinally by repeated measures over a 48-week period,
and generalized estimating equations (GEEs) were used to deal with the internal
correlation, to test for the interactions between time and subgroups and the potential

subgroup-time interactions, and to obtain a generalized estimating equation (GEE).
potential subgroup-time interactions to obtain valid estimates of intervention effects
[105]. The dependent variable of this study is denoted as Yij, representing that there
are i individuals (i= 1,..., n), each with j observations ( tj = 1,..., p), and the covariate
is denoted as Xij. The constructed models are: (1) E(Yij) = μij, g( μij ) = X'ij β = β0 +
β1Xij1 + β 2Xij2 + ... + βjXijp, g( μij ) is the linkage function, the dependent
variables in this study include continuous variables, ordered categorical variables, and
the linkage function is selected as a linear function, Logit function, to establish a
functional relationship between Yij and Xij; (2) Var( Yij ) = v( μij ) - φ, to establish a
functional relationship between the variance of Yij and the mean, where v( μij ) =
v( μij ) - φ, to establish a functional relationship between the variance of Yij and the
mean. relationship, where v( μij ) is the known variance function and φ is the
estimated discrete parameter; (3) Ri (α) is a p × p dimensional operational correlation
matrix of Yi = (Yi1, ..., Yip ) 's indicating the magnitude of the correlation between
repeated measurements of the dependent variable. The generalized estimating
equation constructed is: S ( β; α,φ) = ∑i Vi-1 (ui; α) (yi - ui) = 0, where Vi is the
covariance matrix of Yi, Vi = φA i 1/2Ri (α) A i 1/2 . Based on the given estimates of
α and φ, the above equations are solved by iteratively repeating the weighting to
finally arrive at a covariance estimate of β. The covariance of α and φ is estimated by
the following equation.