Abstract
Objective
Although DNA methylation has been suggested to be a potential predictor
of the progression of obesity and obesity-related diseases, little is
known about its potential role as predictive marker of successful
weight loss after bariatric surgery.
Methods
20 patients who underwent sleeve gastrectomy were classified according
to the percentage of excess weight loss (%EWL) 1 year after bariatric
surgery, using 60% as the cut-off point. Blood DNA methylation was
analyzed prior to surgery using the Infinium Methylation EPIC Bead Chip
array-based platform.
Results
A total number of 76,559 differentially methylated positions (DMPs)
(p < 0.05) were found between <60% EWL and >60% EWL groups. Of them,
59,308 DMPs were annotated to genes. KEGG enrichment analysis showed
that pathways involved in the signalling of MAPK, Wnt, mTor, FoxO and
AMPK, among others, were involved in weight loss trajectory.
A stepwise logistic regression using the DMPs with an absolute Δβ >0.2
showed that higher methylation levels in the CpG sites cg02405213
(mapping to JAK2) (OR: 1.20098, [0.9586, 1.5044]) and cg01702330 (OR:
2.4426, [0.5761, 10.3567]), were shown to be associated with a higher
probability of achieving >60 %EWL after sleeve gastrectomy, whereas
higher methylation levels in the CpG site cg04863892 (mapping to HOXA5)
were associated with lower probability of achieving >60 %EWL after
sleeve gastrectomy (OR: 0.7966, [0.5637, 1.1259]).
Conclusions
Our results show a different pre-surgery methylation pattern according
to %EWL. We identified three CpG sites (cg04863892, cg02405213,
cg01702330) with potential value as predictor markers of weight loss
response to bariatric surgery.
Keywords: DNA methylation, Sleeve gastrectomy, Weight loss, Predictive
biomarker
Highlights
* •
Bariatric surgery has been shown to induced changes in DNA
methylation.
* •
Little is known about the role of DNA methylation in bariatric
surgery response.
* •
DNA methylation patterns differ according to %EWL 1 year after
surgery.
* •
We identified CpG sites that could predict the weight loss after
bariatric surgery.
* •
DNA methylation could contribute to the variability in bariatric
surgery response.
1. Introduction
Obesity is a chronic multifactorial disease associated with a higher
risk of developing many chronic diseases, whose prevalence has
increased in the last decades [[37]1]. If this trend continues, the
global prevalence of obesity might reach 17.5% by 2030 [[38]2].
First line of therapy for excess weight management includes diet,
physical activity and pharmacotherapy. For severe obesity when
nonsurgical treatments fail, bariatric surgery is the most effective
treatment to achieve sustainable and substantial weight loss along with
the amelioration or remission of obesity-associated comorbidities
[[39]3].
Weight loss outcomes after bariatric surgery show a high variability,
although the majority of patients achieve an optimal degree of weight
loss, there is a relevant percentage of patients, ranged from 7 to 25%,
who fail to achieve this goal [[40]4]. Multiple factors have been shown
to be involved in the weight loss trajectory after bariatric surgery.
Understanding those factors underlying this variability could help to
optimize bariatric surgery outcomes. In the last years, attention has
been paid to those preoperative factors that may influence on bariatric
surgery outcomes [[41]5,[42]6]. However, factors described so far, only
explain a small percentage of variability in weight loss trajectory.
Epigenetics has been shown to play a role in health and disease.
Epigenetics includes all those processes that can alter gene expression
without altering the DNA sequence, being DNA methylation the most
common epigenetic modification. DNA methylation has been suggested to
be a potential biomarker of the onset and progression of metabolic
diseases including obesity [[43]7,[44]8] and it has been associated
with the modulation of functions related to body weight regulation such
as appetite, glucose and lipid metabolism and adipogenesis [[45]9].
Moreover, evidence suggests that bariatric surgery induces changes in
DNA methylation that could be involved in the restoring of metabolic
health [[46][10], [47][11], [48][12], [49][13]].
The study of DNA methylation in peripheral blood represents a
non-invasive approach for the identification of promising biomarkers of
prognosis and diagnosis of diseases. DNA methylation has been shown to
be a potential predictor of the progression of obesity and associated
diseases [[50]14,[51]15] and evidence suggests that DNA methylation
could predict the response to weight loss interventions
[[52]16,[53]17], although whether DNA methylation could predict the
response to bariatric surgery has been little investigated [[54][18],
[55][19], [56][20]].
The aim of this study is to identify DNA methylation patterns and
related metabolic pathways in peripheral blood associated with weight
loss response to bariatric surgery. Second, to identify epigenetic
marks that could be potential predictive markers of bariatric surgery
outcomes.
2. Material and methods
2.1. Design and subjects
The study was carried out at the Virgen de la Victoria University
Hospital (Málaga, Spain). The inclusion criteria were patients with
morbid obesity who underwent laparoscopic sleeve gastrectomy procedure
between January 2020 and December 2020 and attended both visits (before
surgery and 12 months after surgery). Patients were excluded if they
had cardiovascular disease, acute inflammatory disease, infectious
disease, a history of or newly diagnosed cancer, and those had
complications after the surgery.
Informed consent was obtained from each participant, and the study was
reviewed and approved by the Biomedical Research Ethic Coordinator
Committee of Andalucía (CCEIBA).
2.2. Procedures
Weight and height measurements were obtained before surgery and 12
months after surgery. Body mass index (BMI) was calculated as: weight
(kg)/height^2 (m^2). Relevant clinical data, such as the presence of
type 2 diabetes, were collected. Blood samples were collected after
10-12-hour fast before surgery and 12 months after surgery. Serum was
separated and immediately frozen at −80 °C. Serum biochemical
parameters such as glucose, triglycerides, total cholesterol and
high-density lipoprotein cholesterol (HDL-c) were analysed in an ADVIA
Chemistry XPT autoanalyzer (Siemens Healthcare Diagnostics, North Ryde,
NSW, Australia). Insulin was determined by radioimmunoassay (ADVIA
Centaur Immunoassay System, Siemens Healthcare Diagnostics, North Ryde,
NSW, Australia). The homeostasis model assessment of insulin resistance
(HOMA-IR) was calculated as followed: fasting insulin (μIU/ml) x
fasting glucose (mmol/L)/22.5.
Percentage of excess weight loss (%EWL) at 1 year after surgery was
used as a measure of response to bariatric surgery and was calculated
as: (initial weight (kg) – post-surgery weight (kg))/(initial weight
(kg) – ideal weight (kg)), in which ideal weight is defined by the
weight with a BMI of 25 kg/m^2.
2.3. DNA methylation assay
DNA methylation assay was performed in blood samples before surgery.
DNA was extracted from peripheral blood using the QIAmp DNA Blood Mini
Kit (Qiagen, Hilden, Germany) following the manufacturer's
instructions. DNA concentration was quantified with a Qubit 3.0
Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) using Qubit
dsDNA HS Assay Kit Fluorometer (Thermo Fisher Scientific, Waltham, MA,
USA). Genomic DNA was bisulfite-treated using a Zymo EZ-96 DNA
Methylation™ Kit (Zymo Research Corp, Irvine, CA, USA) and purified
using a DNA-Clean-Up Kit (Zymo Research Corp, Irvine, CA, USA).
Over 850,000 methylation sites were interrogated with the Infinium
Methylation EPIC Bead Chip Kit (Illumina, San Diego, CA, USA) following
the Infinium HD Assay Methylation protocol, and raw data were obtained
from iS (Illumina) software.
2.4. Methylation data analysis
All the methylation data analysis were performed on R (version 4.1.3).
Patients were classified according to the %EWL. To determine the
cut-off point of %EWL that best cluster the population, a no supervised
K-means clustering analysis with two groups was performed in the cohort
using anthropometric and biochemical variables of the patients 1 year
after bariatric surgery. This clustering method showed that the best
cut-off point was ≥60% EWL in the 90% of the cases.
Raw data were processed using ChAMP package (ver. 2.30.0). First, a
filtering step was applied and all SNP-related CpG sites, as well as
CpG sites from X and Y chromosomes and CpG sites with multi-hit probes,
were removed. Afterwards, filtered data was normalized using the
function champ.norm, the methylation β-values and the BMIQ method, as
suggested by the ChAMP package. This step lets us correct the technical
effect derived from the differences between the hybridization
chemistries of CpG sites with type-I probes and type-II probes.
Subsequently, blood cell proportion was computed using EpiDISH package
(ver. 2.16.0). Additionally, Principal Component Analysis was performed
to study the effect of the groups of interest, blood cell proportion,
and pre-surgery clinical variables including sex, age, type 2 diabetes
medication, glucose levels, HOMA-IR, BMI, cholesterol, triglycerides,
HDL-c and LDL-c levels. Finally, data were corrected by blood cell
proportion using ChAMP workflow and a partially modified version of the
champ.refbase function. Then, β-values were converted back into
methylation M-values, and they were adjusted once again using the
removeBatchEffect function from limma (ver. 3.56.2) and gender data.
2.5. Methylation data analysis – differential methylation analysis
Differential methylation analysis was performed using the
non-parametric U Mann–Whitney test from base R function Wilcox.test was
applied to each CpG site. Afterwards, methylation β-values were
computed using M-values, and Δβ was calculated as (>60% EWL
[MATH: β¯ :MATH]
) – (<60% EWL
[MATH: β¯ :MATH]
). A threshold p-value of 0.05 and a threshold |Δβ| of 0.2 were taken
into consideration when determining significance. The identified CpG
sites were annotated with information about its location, gene and CpG
island association using the Illumina Infinium MethylationEPIC
manifest.
After the differential methylation analysis, a new PCA was carried out
using the identified CpG sites and the FactoMineR package (ver. 2.9).
Heatmaps were also generated using the selected CpG sites.
2.6. Functional analysis
Pathway enrichment analysis was performed to identify functional
pathways affected by weight loss. For the analysis only genes
associated with the identified CpG sites were included. Gene ontology
(GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
enrichment analysis was performed using ClusterProfiler package (ver.
4.8.3). The Enrichplot package (ver. 1.20.3) was used for the
visualization of the results.
Enrichment p-values were adjusted using Benjamini–Hochberg correction,
and FDR <0.05 was considered statistically significant.
2.7. Identification of predictive DNA methylation markers
Following, a stepwise logistic regression was applied to select the
best model for the discrimination of both groups. In this step all
differentially methylated CpG sites and clinical variables were used.
The CpG sites and clinical variables were scaled and correlated
variables were removed before fitting the first model in order to
reduce collinearity effect. Then, a complete generalized logistic model
was fitted using the filtered variables, all the samples and the glm
function from the stats package. Stepwise selection was carried out
using stepAIC function from the MASS package (ver. 7.3–60.0.1), and the
AIC statistic was the one used for determining which model was the best
at classifying the samples. The final logistic model was fitted using
the bias reduction method brflmfit2 of the brglm package (ver. 0.9.2)
to avoid the effect of perfect separation due to the small sample size.
Afterwards, the final model was compared against a null model using the
ANOVA method, and a p-value was obtained. Lastly, the Leave-One-Out
Cross-Validation (LOOCV) method was used to evaluate the ability of the
final model to generalize and classify new cases. ROC plot and the area
under the curve (AUC) value were generated using pROC package (ver.
1.18.5).
2.8. Differentially methylated regions (DMRs) detection
After studying differentially methylated probes, DMRs were identified
using ChAMP function champ.DMR applying the Bumphunter method [[57]21],
and selecting the default value for each parameter. All corrected CpG
probes that passed the filtering steps were used as input data for the
function. Finally, CpG sites of interest (p-value ≤0.05 and |Δβ| ≥ 0.2)
found in each DMR were annotated.
A flow-chart summarizing the methylation data analysis is shown in
[58]Supplementary Figure 1.
2.9. Statistical analysis of biochemical and anthropometric variables
Statistical analysis and comparison were performed using R software
(version 4.1.3) to study differences in anthropometric and biochemical
variables with Kruskall–Wallis test for continuous data and chi-square
test for categorial data. Data are expressed as the mean ± standard
deviation, or as percentage. Values were considered to be statistically
significant when p < 0.05.
3. Results
The main biochemical and anthropometric characteristics of the patients
are shown in [59]Table 1. Before surgery, there were no statistically
significant differences between groups. As it was expected, 1 year
after bariatric surgery, weight and BMI were significantly higher in
<60% EWL group compared to >60% EWL group (p = 0.012 and p < 0.001,
respectively). There were no statistically significant differences in
the rest of the variables studied.
Table 1.
Anthropometric and biochemical characteristics of patients included in
the study.
Study point <60% EWL (n = 11) >60%EWL (n = 9)
Sex (M/F) 1/10 3/6
Age (years) 50.55 ± 9.67 46.44 ± 5.34
Weight (kg) Baseline 127.06 ± 17.66 124.44 ± 25.95
1-year follow-up 98.15 ± 11.44 84.08 ± 14.14[60]^a
BMI (kg/m^2) Baseline 50.90 ± 9.12 46.06 ± 7.10
1-year follow-up 39.18 ± 4.75 31.15 ± 3.26[61]^a
Type 2 diabetes (y/n) Baseline 8/3 6/3
1-year follow-up 3/8 2/7
Glucose (mg/dl) Baseline 115.64 ± 20.04 114.78 ± 28.97
1-year follow-up 92.55 ± 11.83 90.44 ± 8.15
Insulin (μUI/ml) Baseline 24.39 ± 12.39 23.32 ± 18.19
1-year follow-up 11.19 ± 5.38 10.07 ± 6.80
HOMA-IR Baseline 7.18 ± 4.35 6.58 ± 4.98
1-year follow-up 2.69 ± 1.53 2.26 ± 1.60
Cholesterol (mg/dl) Baseline 186.45 ± 29.20 176.44 ± 35.94
1-year follow-up 210.55 ± 42.73 192.78 ± 39.78
HDL-cholesterol (mg/dl) Baseline 41.45 ± 4.91 43.11 ± 10.52
1-year follow-up 57.27 ± 7.90 61.00 ± 14.53
LDL-cholesterol (mg/dl) Baseline 113.80 ± 27.46 101.02 ± 28.54
1-year follow-up 130.80 ± 37.98 109.60 ± 42.84
Triglycerides (mg/dl) Baseline 172.10 ± 72.47 161.56 ± 59.43
1-year follow-up 105.82 ± 48.11 110.67 ± 34.63
[62]Open in a new tab
^a
p < 0.05. Mann–Whitney test for comparison between groups.
3.1. Differentially methylated positions
After filtering 815,568 probes, 742,912 remained for further analysis.
The distribution of the CpG sites in the study groups is represented in
[63]Figure 1A. A total number of 76,559 differentially methylated
positions (DMPs) were found between <60% EWL and >60% EWL groups
(unadjusted p < 0.05) ([64]Figure 1B).
Figure 1.
[65]Figure 1
[66]Open in a new tab
A. Principal Component Analysis of the CpG sites analyzed. B. Volcano
plot representing the distribution of CpG sites according to p-value
and Δβ value.
59,308 DMPs were annotated to genes. Considering those genes with more
than 5 DMPs, the gene with the highest percentage of DMPs was LINC0029
(75%), followed by GPR75 (58.82%) and DDX39 (57.14%) ([67]Table2).
Table 2.
Genes with highest percentage of DMP.
Gene Number of DMP Total number of CpG sites Percentage (%) Mean ± SD
(Δβ)
LINC00029 6 8 75 0.0405 ± 0.0306
GPR75 10 17 58.82 −0.0257 ± 0.0126
DDX39 8 14 57.14 0.0037 ± 0.0317
SSSCA1 9 16 56.25 0.0043 ± 0.0050
MIR639 9 16 56.25 0.0049 ± 0.0022
MIR140 5 9 55.56 −0.0644 ± 0.0232
LRRC67 5 9 55.56 0.0074 ± 0.0023
FAM173A 6 11 54.55 0.0159 ± 0.0312
SNORD68 7 13 53.85 0.0062 ± 0.0044
TOR1B 8 15 53.33 0.0057 ± 0.0205
MRPL41 8 16 50 0.0351 ± 0.0521
DNAAF1 6 12 50 0.0142 ± 0.0187
LOC80054 6 12 50 −0.0122 ± 0.0217
[68]Open in a new tab
DMP: Differentially methylated positions. Δβ (mean ± SD) > 60%EWL -
<60%EWL.
3.2. Enrichment analysis
In order to examine the biological functions of the 59,308 DMPs
annotated to genes, GO and KEGG enrichment analyses were performed. In
biological process category, pathways involved in positive regulation
of DNA metabolic process, protein localization and transferase
activity, as well as, small GTPase mediated signal transduction and
proteasome-mediated ubiquitin-dependent protein catabolic process were
shown to be altered ([69]Figure 2A), among others, whilst in molecular
function category, pathways involved in DNA-binding transcription
activator activity, protein serine/threonine and tyrosine kinase
activity and GTPase regulator activity were shown to be altered
(FDR-adjusted p-value <0.05) ([70]Figure 2B). In KEGG, pathways
involved in the signalling of MAPK, AMPK, Rap 1, Wnt, mTOR and FoxO,
among others, were altered ([71]Figure 2D).
Figure 2.
[72]Figure 2
[73]Open in a new tab
Pathways influenced by weight loss trajectory. A. Biological Process.
B. Molecular Function. C. Cellular Component. D. KEGG Process.
3.3. Potential biomarkers of bariatric surgery success
A total number of 48 DMPs (unadjusted p < 0.05) showed an absolute Δβ
>0.2. A PCA representing the distribution of these CpGs in the study
groups is shown in [74]Figure 3A. Heatmap representing the methylation
levels of the 48 DMPs (unadjusted p < 0.05) with absolute Δβ >0.2 is
shown in [75]Figure 3B. In [76]Supplementary Table 1 is shown the
information related to the 48 DMPs (unadjusted p < 0.05) with absolute
Δβ >0.2. The top five of the most hypermethylated and hypomethylated
DMPs is shown in [77]Table 3.
Figure 3.
[78]Figure 3
[79]Open in a new tab
A. Principal Component Analysis of DMPs (p < 0.05) with absolute Δβ
>0.2. B. Heatmap representing methylation levels of DMPs (p < 0.05)
with absolute Δβ >0.2. B.
Table 3.
Top 5 of the most hypermethylated and hypomethylated DMP in >60%EWL
group compared to <60 %EWL.
Hypomethylated in >60%EWL
__________________________________________________________________
CpG site p-value Δβ Chr Gene Feature CGI
cg11173636 0.02734492 −0.39180916 10 IGR Opensea
cg26116556 0.01833735 −0.34851618 6 PLEKHG1 5′UTR Opensea
cg15914672 0.00062894 −0.34218597 22 IGR Opensea
cg05875700 0.03313281 −0.31763154 8 ERICH1 Body Island
cg14755254 0.03339815 −0.31595401 8 ERICH1 Body Island
__________________________________________________________________
Hypermethylated in >60%EWL
__________________________________________________________________
CpG site p-value Δβ Chr Gene Feature CGI
__________________________________________________________________
cg03126799 0.00783543 0.3182684 8 LOXL2 Body Opensea
cg02405213 0.01489907 0.32497882 9 JAK2 Body Shore
cg16402757 0.00783543 0.35033946 10 CUL2 Body Opensea
cg20485607 0.00183993 0.35063268 1 IGR Opensea
cg05079227 0.02974743 0.36320477 15 ADAMTS17 Body Opensea
[80]Open in a new tab
DMP: Differentially methylated positions. %EWL: Percentage of excess
weight loss. Chr: Chromosome.
Of the 48 DMPs (unadjusted p < 0.05) with absolute Δβ >0.2, 29 DMPs
were annotated to genes, and only three genes presented multiple DMPs
(cg14893161, cg05841700 and cg11965913 annotated to gene PM20D1;
cg04863892 and cg02005600 annotated to gene SMOC2; cg05875700,
cg14755254, cg01053087 annotated to gene ERICH1).
A stepwise logistic regression was performed using the DMPs (unadjusted
p < 0.05) with an absolute Δβ >0.2 and anthropometric and biochemical
variables to evaluate the prediction power of the weight loss. The
final model selected three CpG sites, cg04863892, cg02405213,
cg01702330, as the best potential markers to predict the %EWL after
bariatric surgery (AUC = 0.97). Higher methylation levels in the CpG
sites cg02405213 (annotated to gene JAK2) and cg01702330 were shown to
be associated with a higher probability of achieving >60 %EWL after
bariatric surgery, whilst higher methylation levels in the CpG site
cg04863892 (annotated to gene HOXA5) was shown to be associated with a
lower probability of achieving >60 %EWL after bariatric surgery
([81]Table 4).
Table 4.
Potential predictors of %EWL.
Chr Gene Feature CGI Beta Odds Ratio 95% CI p-value
Interception −0.6285851 0.5333459 0.0566, 5.0232 0.583
cg04863892 7 HOXA5 TSS200 Island −0.2272833 0.7966950 0.5637, 1.1259
0.198
cg02405213 9 JAK2 Body Shore 0.1831059 1.2009415 0.9586, 1.5044 0.111
cg01702330 6 IGR Shelf 0.8930834 2.4426496 0.5761, 10.3567 0.226
[82]Open in a new tab
Reference category: <60%EWL. %EWL: Percentage of excess weight loss.
This regression model was compared to a null model, which predicts
randomly, showing that outperforms the null model (p < 0.001).
Moreover, LOOCV technique was performed as a validation approach of the
model with 90% of overall accuracy.
3.4. Differentially methylated regions
We performed differently methylated region (DMR) analysis to
investigate whether DMPs with an absolute Δβ >0.2 are located in
regions that could predict the response to sleeve gastrectomy. We
identified 18 DMRs. Upon examining whether DMPs with an absolute Δβ
>0.2 are situated within these DMRs, we identified a region located in
chromosome 6 (29648161–29649084) (p-value Area<0.001) that comprises,
cg11747594. DMR located in chromosome 1 (205818668–205819609) (p-value
Area = 0.002) comprises cg05841700, cg11965913 and cg14893161, whilst
DMR located in chromosome 7 (27183133–27184375) (p-value Area<0.001)
comprises cg02005600 and cg04863892 ([83]Table 5).
Table 5.
Differentially methylated regions between <60%EWL and >60%EWL group.
Cluster Chr Start End Value Area L Cluster(L) p-value FWER p-value Area
FWER Area
1 chr6 29648161 29649084 −1.30 28.72 22 22 0.000135 0.044 0.000171
0.048
2 chr7 27183133 27184375 −0.57 15.51 27 40 0.000220 0.068 0.001862
0.408
3 chr1 205818668 205819609 1.74 13.90 8 9 0.000331 0.1 0.002866 0.532
4 chr19 55972504 55973338 1.20 11.97 10 10 0.000723 0.196 0.004752
0.676
5 chr3 182817190 182817626 1.08 11.86 11 11 0.001274 0.308 0.004973
0.684
6 chr6 33084479 33085063 0.66 9.99 15 15 0.001580 0.412 0.008256 0.864
7 chr6 29894619 29895175 1.24 9.94 8 9 0.002143 0.436 0.00832 0.864
8 chr17 48585216 48585470 −1.45 4.35 3 10 0.003123 0.512 0.04792 1
9 chr5 135415693 135416613 0.67 8.73 13 13 0.002523 0.584 0.011550
0.924
10 chr17 5402883 5403516 1.14 6.87 6 13 0.004128 0.64 0.019916 0.976
11 chr15 90792609 90793056 0.95 6.64 7 10 0.007496 0.824 0.021300 0.98
12 chr6 33560953 33561188 −0.87 4.34 5 8 0.012077 0.92 0.048162 1
13 chr13 36871646 36872346 −0.47 4.67 10 10 0.015139 0.96 0.042221 1
14 chr2 54086854 54087343 −0.47 4.66 10 12 0.015237 0.96 0.042417 1
15 chr10 123355268 123356336 −0.61 5.47 9 9 0.014943 0.964 0.031896 1
16 chr19 57741988 57742444 −0.57 5.17 9 9 0.016364 0.976 0.03525 1
17 chr22 24384105 24384400 0.69 4.81 7 8 0.019148 0.988 0.040164 1
18 chr6 146350131 146351044 0.47 4.25 9 10 0.023738 0.992 0.04992 1
[84]Open in a new tab
Chr: Chromosome. Start: Genomic start position. End: Genomic end
position. Value: Average difference in methylation in the bump between
groups. Area: Area of the bump with respect to the 0 line. L: number of
differentially methylated CpG sites in the region. Cluster(L): total
number of probes in the cluster. P-value: p-value differentially
methylation. FWER: Family-wise error rate (FWER) of the regions
estimated by permutation. P-value Area: p-value for area of bump. FWER
Area: FWER of area of bump.
4. Discussion
Our results suggest that preoperative blood DNA methylation pattern
differs according to the weight loss achieved 1-year after sleeve
gastrectomy. Moreover, we identified three CpG sites that could be
potential predictive markers of weight loss trajectory after sleeve
gastrectomy. Higher methylation levels in the sites cg02405213 (gene
JAK2) and cg01702330 and lower methylation levels in the CpG site
cg04863892 (gene HOXA5) were associated with a higher probability of
achieving >60 %EWL after sleeve gastrectomy.
Although, changes in DNA methylation patterns following weight loss
intervention have been suggested to play a role in the beneficial
effect of weight loss on metabolic status [[85]22,[86]23], whether DNA
methylation could predict the response to weight loss intervention is
far from be elucidated.
Previous studies have shown that pre-intervention blood DNA methylation
patterns are associated with changes in glycaemic parameters induced by
weight low interventions including bariatric surgery [[87]24].
Regarding to weight loss outcomes, it has been suggested that blood DNA
methylation patterns could predict weight loss associated to lifestyle
intervention [[88]17,[89]25], whilst DNA methylation in adipose tissue
has been shown to predict weight increase in response to overfeeding
[[90]26]. However, the number of studies evaluating the potential role
of DNA methylation patterns as predictors of weight loss response to
bariatric surgery is limited. Coppedè F et al. failed in their attempt
to find blood DNA methylation that could predict %EWL 1-year after
Roux-en-Y gastric bypass (RYGB) [[91]18], but they only analyzed DNA
methylation patterns in genes involved in hormonal control of appetite
and body weight regulation (leptin, ghrelin, ghrelin receptor and
IGF2), whilst Chen G et al. showed that DNA methylation patterns of
visceral adipose tissue in the CpG sites cg03610073, cg03208951, and
cg18746357 (mapping to TLR2, EMP3, and RELA, respectively) to predict
the %EWL 1-year after sleeve gastrectomy [[92]19].
Evidence suggests a potential role of non-coding RNA in the prediction
of weight loss trajectory after bariatric surgery [[93]27,[94]28].
Non-coding RNA molecules have shown to play a relevant role in the
regulation of gene expression including body weight regulation. Our
results found non-coding RNA such as LINC00029, MIR639, MIR140, SNORD68
and LOC80054 with a high percentage of DMPs associated with %EWL 1-year
after sleeve gastrectomy. Little is known about the role of these
non-coding RNA in body homeostasis. MIR140 has been suggested to
regulate adipogenesis, inducing lipogenesis and adipogenic
differentiation [[95]29]. The role of DNA methylation-mediated
epigenetic regulation of non-coding RNA in body weight regulation
deserves more investigation.
We identified three CpG sites (cg04863892, cg02405213, cg01702330) that
could contribute to predict the weight loss trajectory after sleeve
gastrectomy. In the literature, there is little information about these
CpG sites. The cg04863892 site is mapped to the promoter region
(TSS200) of the gene HOXA5. HOXA5 encodes a homebox transcription
factor involved in adipocyte differentiation and lipid storage
[[96]30], showing a pivotal role in the regulation of the adipose
tissue function. Triglyceride levels have been associated with both DNA
methylation and gene expression of HOXA5 in subcutaneous adipose tissue
[[97]31]. Strong evidence suggests that HOXA5 gene regulation through
DNA methylation could contribute to the pathogenesis of metabolic
diseases. In animal model, long term exposure to high-fat diet have
been associated with hypermethylation of HOXA5 and a downregulation of
its mRNA and protein expression. Conversely, returning to a standard
chow diet restored HOXA5 levels to those comparable with animals fed a
chow diet throughout the entire study period [[98]32]. Previous studies
have shown that weight loss induced by bariatric surgery increases the
expression levels of HOXA5 in subcutaneous adipose tissue [[99]33]. An
increase in methylation in HOXA5 promoter has been shown to repress its
transcriptional activity [[100]31,[101]34], in line with our results
that showed an association between higher methylation in the CpG site
cg04863892 (annotated to gene HOXA5) and a lower probability of
achieving successful weight loss after sleeve gastrectomy. Moreover,
DMR analysis reinforces the relevance of this CpG site which is located
in a region that deserves further investigation due to its potential
role in the weight loss response to sleeve gastrectomy.
The cg02405213 site is mapped to the transcriptional region of the gene
JAK2. JAK2 encodes a non-receptor tyrosine kinase that is part of a
signalling pathway of Janus kinase 2 (JAK2)/signal transduction and
transcription 3 activator (STAT3) (JAK2/STAT3) involved in many
processes such as the control of cellular growth, proliferation,
differentiation and apoptosis. Previous studies have evaluated the
association between JAK2 methylation levels and obesity. Higher
methylation levels in the promoter and lower methylation levels in the
transcriptional region of the gene JAK2 was related with obesity
[[102]35,[103]36]. Our results showed that higher methylation levels in
the CpG site cg02405213 located in the transcriptional region of the
gene JAK2 was associated with a higher probability of achieving >60
%EWL after bariatric surgery.
In vitro studies have shown that Hoxa5 inhibits adipocyte proliferation
by blocking JAK2/STAT3 signalling pathway [[104]37].
Moreover, JAK2/STAT3 is related to other signalling pathways, such as
MAPK, Wnt, AMPK, FoxO and mTOR signalling pathways, pathways related to
obesity and its comorbidities [[105]38], which were shown to be altered
in our results.
The regulation of gene expression of HOXA5 and JAK2 as markers of
weight loss response to bariatric surgery is an issue that deserves
further investigation and paves the way to develop new strategies in
weight management.
Our study presents some limitations that should be taken into
consideration. Due to small sample size, correction for multiple
testing could not be performed in the differential methylation
analysis. Uncorrected p-values below 0.05 are not as stringent, further
validation studies in larger populations would reinforce our findings
We used blood samples to assess differential DNA methylation, therefore
further research on tissue-specific methylation patterns would be
necessary to elucidate those pathways involved in adipose tissue
function that could contribute to develop new strategies in the
management of weight loss interventions. Additionally, although the
Infinium EPIC array is a very useful tool to interrogate CpGs sites, it
only covers 3–4% of the human methylome.
In conclusion, our results showed that pre-surgery blood DNA
methylation patterns differ according to weight loss trajectory 1 year
after sleeve gastrectomy. Moreover, we identified three CpG sites
(cg04863892, cg02405213, cg01702330) with potential value as predictor
markers of weight loss response to bariatric surgery. The CpG sites
cg04863892 and cg02405213 are mapped to the genes HOXA5 and JAK2
respectively, which are related to adipose tissue function.
The study of DNA methylation profiles represents a minimally invasive
clinical prognostic method that could, along with traditional
predictors such as age and preoperative BMI, be used in the future to
predict the individual response to bariatric surgery.
Funding
CGR is supported by Miguel Servet program from Instituto de Salud
Carlos III (CP20/00066), TMLP was supported by a grant from Instituto
de Salud Carlos III (FI19/00178). SM is supported by Nicolas Monardes
program from the Consejería de Salud de la Junta de Andalucía
(RC-0008-2021). This work was supported in part by a grant from the
Instituto de Salud Carlos III (PI15-01350). This study has been
co-funded by FEDER funds (“A way to make Europe”).
CRediT authorship contribution statement
Guillermo Paz-López: Writing – review & editing, Writing – original
draft, Visualization, Formal analysis, Data curation. Teresa M.
Linares-Pineda: Investigation. Andrés González-Jiménez: Writing –
review & editing, Visualization, Formal analysis, Data curation. Raquel
Sancho-Marín: Investigation. Luis Ocaña-Wilhelmi: Resources. Francisco
J. Tinahones: Writing – review & editing, Methodology,
Conceptualization. Sonsoles Morcillo: Writing – review & editing,
Writing – original draft, Visualization, Formal analysis. Carolina
Gutiérrez-Repiso: Writing – review & editing, Writing – original draft,
Visualization, Formal analysis.
Availability of data and materials
Data are available from the authors upon reasonable request.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.
Acknowledgments