Analysis of circulating angiopoietin-like protein 3 and genetic variants in lipid metabolism and liver health: the DiOGenes study
Hess et al. Genes & Nutrition
Analysis of circulating angiopoietin-like protein 3 and genetic variants in lipid metabolism and liver health: the DiOGenes study
Anne Lundby Hess 1
Jérôme Carayol 3
Trine Blaedel 1
Jörg Hager 3
Alessandro Di Cara 2
Arne Astrup 1
Wim H. M. Saris 4
Lesli Hingstrup Larsen 0 1
Armand Valsesia 0 3
0 Equal contributors
1 The Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen , Rolighedsvej 26, 1958 Frederiksberg C , Denmark
2 Precision for Medicine , Geneva , Switzerland
3 Nestlé Institute of Health Sciences , Lausanne , Switzerland
4 The Department of Human Biology, NUTRIM School for Nutrition, Toxicology and Metabolism, Maastricht University Medical Centre , Maastricht , Netherlands
Background: Angiopoietin-like protein 3 (ANGPTL3), a liver-derived protein, plays an important role in the lipid and lipoprotein metabolism. Using data available from the DiOGenes study, we assessed the link with clinical improvements (weight, plasma lipid, and insulin levels) and changes in liver markers, alanine aminotransferase, aspartate aminotransferase (AST), adiponectin, fetuin A and B, and cytokeratin 18 (CK-18), upon low-calorie diet (LCD) intervention. We also examined the role of genetic variation in determining the level of circulating ANGPTL3 and the relation between the identified genetic markers and markers of hepatic steatosis. Methods: DiOGenes is a multicenter, controlled dietary intervention where obese participants followed an 8-week LCD (800 kcal/day, using a meal replacement product). Plasma ANGPTL3 and liver markers were measured using the SomaLogic (Boulder, CO) platform. Protein quantitative trait locus (pQTL) analyses assessed the link between more than four million common variants and the level of circulating ANGPTL3 at baseline and changes in levels during the LCD intervention. Results: Changes in ANGPTL3 during weight loss showed only marginal association with changes in triglycerides (nominal p = 0.02) and insulin (p = 0.04); these results did not remain significant after correcting for multiple testing. However, significant association (after multiple-testing correction) were observed between changes in ANGPTL3 and AST during weight loss (p = 0.004) and between ANGPTL3 and CK-18 (baseline p = 1.03 × 10−7, during weight loss p = 1.47 × 10−13). Our pQTL study identified two loci significantly associated with changes in ANGPTL3. One of these loci (the APOA4-APOA5-ZNF259-BUD13 gene cluster) also displayed significant association with changes in CK-18 levels during weight loss (p = 0.007). Conclusion: We clarify the link between circulating levels of ANGPTL3 and specific markers of liver function. We demonstrate that changes in ANGPLT3 and CK-18 during LCD are under genetic control from trans-acting variants. Our results suggest an extended function of ANGPTL3 in the inflammatory state of liver steatosis and toward liver metabolic processes.
Angiopoietin-like protein 3; Liver markers; Liver steatosis; Lipid metabolism; Lipoprotein lipase; Protein quantitative trait locus; Single nucleotide polymorphisms
The metabolic syndrome is a cluster of risk factors that
increases the risk of diseases such as type 2 diabetes,
hypertension, hyperlipidemia, and non-alcoholic fatty
liver disease. The prevalence of the metabolic syndrome
increases due to a parallel rise in the occurrence of
obesity and insulin resistance [
]. This highlights the need
for a more detailed understanding of the underlying
One of the key components in the etiology of the
metabolic syndrome is dyslipidemia. Angiopoietin-like
proteins (ANGPTLs) have been reported to be involved
in the regulation of lipid metabolism [
]. The human
gene of angiopoietin-like protein 3 (ANGPTL3) is
located on chromosome 1 and encodes one of several
structurally similar secreted glycoproteins in the
ANGPTL family. The ANGPTLs consists of a signal
sequence at the N-terminal followed by an α-helical
region forming coiled coil domains, and a
fibrinogenlike domain at the C-terminal. ANGPTL8 differs in
structure, as it lacks a C-terminal fibrinogen-like
domain. ANGPTL3 is found in plasma both as a native
protein and in cleaved form [
]. The coiled coil
domains at the N-terminal decrease the hydrolysis of
plasma triglyceride (TG) through inhibition of
lipoprotein lipase (LPL) activity and thereby affect the lipid and
lipoprotein metabolism . ANGPTL3 is predominantly
expressed in the liver and is secreted by the liver both in
mice and in humans [
]. ANGPTL3 deficiency results
in a dramatic reduction of the plasma concentration of
TG and cholesterol [
], and loss of function mutations
in ANGPTL3 are the cause of a recessive form of familial
combined hyperlipidemia [
In addition to stimulation of lipolysis, ANGPTL3 may
be a determining factor in increasing hepatic lipid
storage and affecting free fatty acid (FFA)-induced insulin
resistance. One study reported a positive association
between circulating ANGPTL3 and non-alcoholic
steatohepatitis (NASH) [
]. Altogether, ANGPTL3 may be
involved in the pathogenesis of the metabolic syndrome
and increase the risk of hepatic steatosis.
This study examines the role of ANGPTL3 in lipid
metabolism and liver health in the DiOGenes (Diet,
Obesity and Genes) study. The DiOGenes study was a
randomized, controlled dietary intervention that showed
that a reduction in the glycemic index (GI) and an
increase in dietary protein content led to an improvement
in weight maintenance after an 8-week low-calorie diet
(LCD) weight loss in adults [
]. In this study, we first
analyze ANGPTL3 concentration in relation to body
mass index (BMI), lipid profile, and markers of hepatic
steatosis before and during weight loss. Afterwards, we
identified genetic variants determining variations of
circulating ANGPTL3 level through protein quantitative
trait locus (pQTL) analysis and tested their association
to ANGPTL3-related covariates.
The DiOGenes study (registered at
http://www.clinicaltrials.gov, NCT00390637) was an intervention study
carried out in eight European centers (Bulgaria, the Czech
Republic, Denmark, Germany, Greece, the Netherlands,
Spain, and the UK). The primary purpose was to
examine the effects of dietary protein and GI on weight regain
and metabolic and cardiovascular risk factors in
overweight and obese families [
]. The study included
families with at least one overweight or obese parent less
than 65 years of age. The participants aimed to lose ≥
8% of their initial body weight during 8 weeks of a LCD
(800 kcal/day with additional use of 200 g of vegetables/
day). Subjects achieving ≥ 8% weight loss were included
in a 6-month weight maintenance period. Here, the
participants were randomized to one of four ad libitum
diets differing in GI and dietary protein content or a
control diet following the national dietary guidelines in
each of the countries [
The study was approved by the different local ethical
committees. Written informed consent was obtained
from all participants, and the study was performed in
accordance with the Declaration of Helsinki.
In the study, height was measured at the initial screening
visit. Body weight was measured on all of the clinical
investigation days together with fasting blood sampling.
Total cholesterol, high-density lipoprotein cholesterol
(HDL-C), TG, fasting glucose, and insulin were analyzed
at the Research Laboratory, Department of Clinical
Biochemistry, Gentofte University Hospital, Denmark.
Low-density lipoprotein cholesterol (LDL-C) was
calculated according to Friedewald’s equation [
Plasma concentrations of ANGPTL3, alanine
aminotransferase (ALT), aspartate aminotransferase (AST),
adiponectin, fetuin A, fetuin B, and cytokeratin 18
(CK-18) were quantified before and after the LCD
intervention using a multiplexed aptamer-based proteomic
technology developed by SomaLogic Inc. (Boulder, CO)
and measured as relative fluorescence units (RFU)
]. Data was normalized and calibrated by
SomaLogic™ according to standard operating
procedures . This was done to remove systematic biases
and correct plate-to-plate variation. Additional
postprocessing steps removed subjects with potential cell
lyses as indicated with high hemoglobin levels (> 9 × 105
RFU) and outliers as detected with principal component
analyses. Proteins were also checked for outliers and
proportion of missing values before log transformation for
]. Data were available for 1129 proteins in 512
DiOGenes participants. Protein change during the weight
loss intervention was computed as the log2 fold change
between the end and the beginning of the intervention.
DNA was extracted from EDTA blood buffy coats with a
salting out method. The DNA samples were quality
checked, quantified, and normalized to approximately
100 ng/ml and 2.0 mg before genotyping. Genotyping
was done using Illumina 660 W-quad according to
manufacturer’s protocols (Illumina, San Diego, CA). Detailed
information about this dataset can be found in Carayol et al.
]. Briefly, 498,233 single nucleotide polymorphisms
(SNPs) were genotyped; after quality check, additional
SNPs were imputed using the Michigan Imputation Server
] and the European 1000 Genomes set reference panel.
SNP information was mapped onto NCBI version 37.
Information was available for 4,020,654 SNPs in 494
participants with proteomics data.
A complete description of the QTL mapping is available
in Carayol et al. [
]. In summary, association between
SNPs and circulating ANGPTL3 was tested at baseline
and during weight loss using linear mixed effect models
as implemented in GCTA software adjusting for baseline
BMI or change in BMI, center, age, and gender as fixed,
and a genetic relationship matrices as random effect [
In order to handle the multiple comparisons, p values
were corrected using SLIDE (Sliding-window method for
Locally Inter-correlated markers with asymptotic
Distribution Errors corrected), a method based on a multivariate
normal distribution similar to classical permutation but
much faster [
]. Considering the large number of tests
performed, significance levels were defined at adjusted
alpha 10%. Genomic inflation factors (GIF) were estimated
for the two pQTL analyses using estlambda function
available in the GenABEL R package [
]. Pairwise linkage
disequilibrium (LD) was calculated with LDlink, a
webbased application using 1000 Genome phase 3 data [
Association between circulating ANGPTL3 and clinical
variables (BMI, fasting glucose and insulin levels, total
lipid levels, C-reactive protein (CRP) levels) was
performed using a linear model, adjusting for center, age,
gender, and baseline BMI. SNP effects were tested as
additive effects. In the analyses of data from the weight
loss period, models were adjusted for change in BMI.
Adjustment for multiple testing was performed applying
a Bonferroni correction considering tests performed on
data available at baseline and during the LCD
intervention separately. Statistical analyses were performed using
R version 3.2.3.
In total, 769 participants from the DiOGenes study were
included in the analyses. The baseline characteristics are
described in Table 1 and have been extensively discussed
in previous DiOGenes publications [
10, 23, 24
participants were on average 41 years of age, with
baseline BMI of 34.5 ± 4.9 kg/m2 (mean ± sd) and were
nondiabetics (mean glucose levels = 5.12 ± 0.74 mmol/l and
insulin levels = 11.48 ± 8.57 μIU/ml). After the weight
loss period, the average BMI was decreased to 30.7 ± 4.
5 kg/m2, and glycemic profiles improved to 4.82 ± 0.
54 mmol/l for fasting glucose and 8.15 ± 6.12 μIU/ml for
Circulating ANGPTL3 and clinical measurements
During the weight loss period, ANGPTL3 plasma
concentration was marginally associated with weight loss (p = 0.
056, see Table 2). Furthermore, ANGPTL3 concentration
was positively associated with TG concentration (p = 0.02)
and with fasting insulin levels (p = 0.04). For both variables,
the associations were independent of weight loss. However,
these associations were not significant after adjustment for
multiple testing. For other variables (total cholesterol,
HDL-C, LDL-C, FFA, glucose, and CRP), there were no
significant associations between ANGPTL3 and their
concentration at baseline or changes during the weight loss period
Circulating ANGPTL3 and liver markers
The association between ANGPTL3 and plasma levels of
specific liver markers (AST, ALT, adiponectin, fetuin A
and B, and CK-18) were tested (Table 3). We observed a
strong positive association between circulating
ANGPTL3 and CK-18 both at baseline (p = 1.03 × 10−7)
and during the weight loss period (p = 1.47 × 10−13).
Significant association was also seen between changes in
AST and ANGPTL3 levels during weight loss
intervention (p = 0.004). All these associations remained
significant, even after adjustment for multiple testing.
During weight loss, adiponectin displayed marginal
association with ANGPTL3 (with nominal p value = 0.
03; Bonferroni-adjusted p value = 0.18 and FDR-adjusted
p value = 0.06).
ANGPTL3 pQTL analyses
Furthermore, we investigated the possible link between
circulating ANGPTL3 levels (at baseline and changes
during LCD) and genetic markers. We thus
performed genome-wide pQTL analyses testing more
than 4 million common variants (see the “Methods”
section). The results are shown as Manhattan plots
in Figs. 1 and 2, respectively for the baseline and
LCD pQTLs. Baseline pQTL analysis did not
highlight any genome-wide significant signals (at adjusted
alpha < 0.10).
The top SNPs (with nominal p < 1 × 10− 4) are
presented in Table 4. However, in the LCD pQTL, three
variants were considered genome-wide significant
(Table 5). The two first SNPs, rs4360730 (NC_000011.9:
g.116488748T>C) and rs74234276 (NC_000011.9:g.
116488753G>A) are in perfect LD (R2 = 1) and localized
within an intergenic region located 120 kb downstream
from BUD13 gene (Fig. 3). This gene belongs to a gene
Coefficient (β), corresponding 95% confidence intervals, and associated p value from a linear regression are provided. Data are presented as back-transformed β-coefficients
in percent with regard to results at baseline. Thus, an increase in ANGPTL3 of 1 RFU results in β (95%CI) percent change of the given variable. The regression models were
adjusted for center, age, gender, and BMI. Models with data from the weight loss period were adjusted for the change in BMI due to the weight loss
CI Confidence interval, CRP C-reactive protein, FFA free fatty acids, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol,
Mean ± sd
Coefficient (β), corresponding 95% confidence intervals, and associated p value from a linear regression are provided (in italics, p values passing Bonferroni correction).
Data are presented as back-transformed β-coefficients in percent with regard to results at baseline. Thus, an increase in ANGPTL3 of 1 RFU results in β (95%CI) percent
change of the given variable. In italics, p values passing Bonferroni correction (p < 0.05/6 = 0.0083). The regression models were adjusted for center, age, gender, and
BMI. Models with data from the weight loss period were adjusted for the change in BMI due to the weight loss
ALT alanine aminotransferase, AST aspartate aminotransferase, CI Confidence interval, CK-18 cytokeratin 18, RFU relative fluorescence units, TNF-α tumor necrosis
cluster together with APOA4, APOA5, and ZNF259.
The third SNP, rs9994520 (NC_000004.11:g.
154882844G>C), is located 170 kb upstream from
SFRP2 gene (Fig. 4). For both pQTL analyses, no
significant p value inflation was observed (GIF were
1.00 and 0.99, respectively for baseline and weight
loss pQTL, Additional file 1: Figure S1 and
Additional file 2: Figure S2). This indicated no bias
due to population substructure.
Association between genetic markers and liver markers
Based on the pQTL results, rs4360730 and rs9994520
were chosen for further analysis. Specifically, we assessed
whether the two liver markers (CK-18 and AST)
associated with ANGPTL3 levels were also under genetic
control. rs74234276 was not included due to complete
LD with rs4360730. Regarding the rs4360730 SNP, we
observed a significant association with CK-18 during
weight loss period (with nominal p = 0.007 and
Bonferroni adjusted p = 0.028, see Additional file 3: Table
S1) and marginal association at baseline (p = 0.086).
Effect size per genotype groups are indicated in
Additional file 3: Table S1. Association tests with ALT
levels did not reveal any significant effect of rs4360730.
rs4360730 was not previously identified in published
GWAs (EBI GWAs catalog, 01/01/2018 release) nor was
it previously identified as an eQTL SNP in GTEX
(release 7) [
]. For rs9994520, we did not observe any
significant association with CK-18 or ALT levels (at
baseline and changes during LCD, see Additional file 3:
rs74571086 8 37,049,958 G A 0.070 0.184 0.041
Results from the association between SNPs and ANGPTL3 level at baseline
A1 and A2 the minor and major alleles, bp basepair, Chr chromosome, Coef estimated association coefficient, MAF minor allele frequency, se standard error, SNP
single nucleotide polymorphism
rs8122922 20 23,632,776 C T 0.236 − 0.089 0.018
Results from the association between SNPs and ANGPTL3 protein level change during weight loss intervention
A1 and A2 the minor and major alleles, bp basepair, Chr chromosome, Coef estimated association coefficient, MAF minor allele frequency, se standard error, SNP
Single nucleotide polymorphism
*SNPs with adjusted p value < 0.10 upon the SLIDE (permutation) p value adjustments
In the current study, we addressed the link between
circulating ANGPTL3 levels and clinical improvements
(weight, plasma lipid, and insulin profile) during LCD in
a large clinical study. We assessed the link between
ANGPTL3 and liver markers (released in circulation),
and whether ANGPTL3 levels were under genetic
control. Finally, we investigated the contribution from
genetic markers modulating ANGPTL3 levels on liver
We observed a positive association between circulating
ANGPTL3 and TG concentration following weight loss.
However, this association was modest and did not
remain, when correcting for multiple testing. In general,
results on the relationship between circulating
ANGPTL3 concentration and plasma lipids in humans
are inconsistent [
]. In contrast to what could be
expected, Robciuc and colleagues reported a negative
correlation between ANGPTL3 and TG concentration
. This correlation did not remain significant after
adjusting for HDL-C and apolipoprotein concentrations.
A large study including 1770 participants of European
Caucasian ancestry did not observe a correlation
between plasma ANGPTL3 and concentration of TG
]. However, they did report positive correlations
between ANGPTL3 concentrations and LDL-C, HDL-C,
and total cholesterol. Despite conflicting results
concerning the relationship between ANGPTL3 and lipid
parameters in humans, there is a consensus about the
physiological role of ANGPTL3 regarding inhibition of
LPL. But the functional evidence is derived from animal
] and the exact inhibitory mechanisms of
ANGPTL3 on LPL in humans are not fully understood.
Earlier findings indicate that cleavage is crucial for the
function of ANGPTL3. The N-terminal fragment
containing the coiled coil domains of the protein is more
efficient in inhibiting LPL than the full-length ANGPTL3
. In this study, we used a detection method based on
protein binding of aptamers, which are reported to have
many advantages, compared to antibodies [
in this and several other studies, the methods used for
detecting ANGPTL3 cannot distinguish between the
different fragments of the protein, nor post-translational
modification. It is suggested that the functional fraction
of ANGPTL3 might not be found in circulation, but
exists bound to the endothelial surface of the adipose
tissue, cardiac muscle, and skeletal muscle for
LPLmediated lipolysis [
]. This further specifies the need of
an improved understanding regarding the LPL inhibitory
function of ANGPTL3 and further improvement of the
methods to detect and quantify the fragments of the
A study reported that the ANGPTL8 is the
ratelimiting protein for the activity of ANGPTL3 [
Co-expression of ANGPTL3 and ANGPTL8 in cultured
hepatocytes resulted in the appearance of a 33-kDa-sized
protein corresponding to the N-terminal domain of
ANGPTL3, whereas only full-length ANGPTL3 were
detected in cells that did not express ANGTPL8.
ANGPTL8 was not assayed on the Somalogic panel, and
it was not possible to study the relationship with
ANGPTL3 within the DiOGenes study. However, recent
in vivo studies have further indicated that ANGPTL3
and ANGPTL8 cooperate in the regulation of plasma
TG levels [
]. Davies and colleagues demonstrated
that ANGPTL3 and ANGPTL8 as a complex exhibited a
greatly enhanced ability to bind LPL compared to either
protein alone. This complex was formed more
efficiently, when the two proteins were co-expressed .
This has led to the suggestion of interplay between
ANGPTL3, ANGPTL4, and ANGPTL8 in the regulation
of lipid metabolism [
]. ANGPTL8 is induced by
feeding and possibly activates the inhibitory effects of
ANGPTL3 on LPL in cardiac and skeletal muscles,
directing circulating TG to the adipose tissue for storage.
In this study, the concentration of circulating ANGPTL3
and lipid parameters were measured in a fasted state,
which could explain the lack of significant associations.
It is likely that an ANGPTL3 response is only observed
post-prandial, and thus, a meal-test challenge would be
required to study the dynamics of ANGPTL3. ANGPTL4
is very similar to ANGPTL3 both in structure and in
function and is induced by fasting and might inhibit LPL in
adipose tissue during energy restriction, directing TG to
cardiac and skeletal muscle for oxidation [
Consistent with the conflicting results regarding
ANGPTL3 and lipid metabolism, the link between
ANGPTL3 and glucose metabolism remains unclear
]. Our results showed a marginal association
between circulating ANGPTL3 and fasting insulin
concentrations. The mechanisms by which ANGPTL3
influence the insulin remains unclear, but there might
be a potential role of the protein to indirectly
regulate glucose metabolism.
We found a strong positive association between
changes in ANGPTL3 levels and CK-18, together with a
negative association between changes in ANGPTL3 and
AST, both independently of weight loss. CK-18 is the
major intermediate filament protein in the liver.
Circulating CK-18 is associated with apoptotic cell death of
hepatocytes, and several studies have demonstrated the
elevation of CK-18 in the context of NASH and hepatic
]. AST is a transaminase enzyme
dependent on pyridoxal phosphate and important in the
amino acid metabolism. It is present as both cytoplasmic
and mitochondrial isoforms. In this study, we measured
the cytoplasmic isoform, which independently is a
marker of tissue injury. High levels of circulating AST is
not exclusively related to the liver steatosis, but could
also indicate diseases affecting other organs, as AST is
found in high concentrations in the liver, heart, skeletal
muscle, and kidney [
]. To our knowledge, only one
human study has analyzed circulating ANGPTL3
concentration in relation to liver steatosis. This study found
that ANGPTL3 concentration was significantly and
independently associated with NASH, but not in patients
with simple steatosis . Szalowska et al. induced
inflammation in human liver tissues in vitro and identified
ANGPTL3 as a biomarker associated with liver diseases
]. Together with our results regarding CK-18, it could
indicate that an increase in plasma ANGPTL3
concentration is the result of liver inflammation or that
ANGPTL3 plays a role in the development of the
diseased condition. Due to the controversy of non-invasive
biomarkers as measurement of liver diseases, additional
studies should include actual liver biopsies to further
evaluate the role of ANGPTL3 in liver steatosis.
Our pQTL study highlighted SNPs that were
modulating changes in circulating ANGPTL3 during the weight
loss period, of which one locus also seemed to modulate
CK-18 levels. Specifically, these pQTL studies revealed
three common genetic variants (rs4360730, rs74234276,
and rs9994520) associated with circulating ANGPTL3.
SNPs rs4360730 and rs74234276 are located near the
APOA4-APOA5-ZNF259-BUD13 gene cluster locus at
the chromosome region 11q23.3; and are in perfect LD.
Several genetic variants in this region have already been
associated to hyperlipidemia [
], serum lipid levels [
risk of developing metabolic syndrome [
], and plasma
TG level [
]. APOA4 and APOA5 encode apolipoproteins
involved in lipid metabolism [
]. ZNF259 encodes zinc
finger protein, a regulatory protein that is involved in cell
proliferation and signal transduction. BUD13 encodes for
BUD13 homolog protein, which is a subunit in the
retention and splicing (RES) complex that affects nuclear
premRNA retention. However, the exact function of ZNF259
and BUD13 in lipid mechanisms is unclear [
region is an interesting target knowing that ANGPTL3
regulates plasma lipid levels and is a potential therapeutic
target to treat combined hyperlipidemia [
]. The SNPs in
this region, rs4360730 and rs74234276, are trans-acting
genetic variants, probably working as distant regulators of
ANGPTL3 through mechanisms of the
APOA4-APOA5ZNF259-BUD13 gene cluster. We further demonstrated
that CK-18 levels at baseline and during the weight loss
period were under genetic control by the rs4360730 SNP.
The rs9994520 SNP is located near the SFRP2 gene.
This gene encodes the secreted Frizzled-related protein
2, which operates as soluble modulators of Wnt
signaling. The functional relationship between ANGPTL3 and
SFRP2 is not known. However, SFRP2 has been
associated to adipose tissue mass and may play a role in
adipose angiogenesis of which angiopoietin-like proteins
are regulation key factors [
Interestingly, the identified pQTLs affecting circulating
ANGPTL3 during the weight loss intervention were not
detectable at baseline. This is consistent with our recent
large-scale pQTL study on 1129 proteins [
], where the
identified pQTL during LCD could not be identified at
baseline. This can be explained by effect size
consideration (statistical power): very large sample size would be
required to identify potential baseline pQTL. By
contrast, a clinical intervention (such as LCD) would induce
drastic metabolic and physiological changes, thus would
lead to very large effect sizes and thereby significantly
improve our ability to detect pQTLs associated with
such drastic shift in homeostasis [
In conclusion, we uncover genetic regulators of
circulating ANGPTL3 during LCD and the link with markers of
liver function. We report several trans-acting pQTL on
changes in circulating ANGPTL3 during LCD. These
pQTLs were not detectable at baseline, suggesting a
change in the regulation of ANGPTL3 due to calorie
restriction. It was not possible to clarify the controversy
regarding the function of ANGPTL3 in lipid metabolism
as we found a very marginal association with total lipid
levels. However, our data suggest strong associations
with specific liver markers (CK-18 and AST). These
observations are supported by the identification of pQTL
signals that affect ANGPTL3 levels during the weight
loss period. Our analysis also suggests an extended
function of ANGPTL3 in the development of liver steatosis
and shows a common genetic regulation for both
ANGPTL3 and markers of liver function.
Additional file 1: Figure S1. QQ plot of the relationship between expected
and observed distribution at baseline. Quantile-quantile plot of baseline data.
The relationship between observed (y-axis) and expected (x-axis) distribution.
The statistical significance is measured by the negative log of the corresponding
p-value for each SNP. (JPEG 92 kb)
Additional file 2: Figure S2. QQ plot of the relationship between
expected and observed distribution during weight loss period.
Quantilequantile plot for the analysis of the weight loss period. The relationship
between observed (y-axis) and expected (x-axis) distribution. The statistical
significance is measured by the negative log of the corresponding p-value
for each SNP. (JPEG 94 kb)
Additional file 3: Table S1. Effect of rs4360730 on BMI, Lipid Profile
and Liver Markers. Table S2 Effect of rs9994520 on BMI, Lipid Profile and
Liver Markers. (DOCX 21 kb)
ALT: Alanine aminotransferase; ANGPTL3/4/8: Angiopoietin-like protein 3/4/8;
ANGPTLs: Angiopoietin-like proteins; AST: Aspartate aminotransferase;
BMI: Body mass index; CK-18: Cytokeratin 18; CRP: C-reactive protein;
FDR: False discovery rate; FFA: Free fatty acids; GI: Glycemic index;
GIF: Genomic inflation factor; HDL-C: High-density lipoprotein cholesterol;
LCD: Low-calorie diet; LD: Linkage disequilibrium; LDL-C: Low-density
lipoprotein cholesterol; LPL: Lipoprotein lipase; NASH: Non-alcoholic
steatohepatitis; pQTL: Protein quantitative trait locus; RES complex: Retention
and splicing complex; RFU: Relative fluorescence units; sd: Standard
deviation; se: Standard error; SLIDE: Sliding-window method for Locally
Intercorrelated markers with asymptotic Distribution Errors corrected; SNPs: Single
nucleotide polymorphisms; TG: Triglycerides
We gratefully acknowledge all of the study participants for their
contributions to the DiOGenes study. We also wish to thank Mads Vendelbo
Lind, Christian Ritz, and Finn Sandø-Pedersen for the advice regarding the
The DiOGenes project was funded by a grant from the European Union
Food Quality and Safety Priority of the Sixth Framework Programme,
contract no. FP6-2005-513946.
Availability of data and materials
The datasets analyzed during the current study are available from the
corresponding author on reasonable request.
WS and AA conceived and designed the DiOGenes study; AV, JC, JH, and
ADC performed the experiments and data production; AV, JC, LHL, TB, and
ALH were responsible for the data analysis, interpretation of the results, and
the final version of the manuscript. All authors have read and approved the
findings of the study and the final version of the manuscript.
Ethics approval and consent to participate
The study was approved by the local ethical committees in the respective
countries, confirming that the study protocol was in accordance with the
Declaration of Helsinki.
Consent for publication
AA is an advisor to or a member of advisory boards for a number of food
and pharmaceutical producers: Basic Research, USA; Beachbody, USA;
BioCare Copenhagen, Denmark; Crossfit, USA; Dutch Beer Institute,
Netherlands; Feast Kitchen A/S, Denmark; Gelesis, USA; Groupe Éthique et
Santé, France; McCain Foods Limited, USA; Nestlé Research Center,
Switzerland; Novo Nordisk, Denmark; Pfizer, Germany; Saniona, Denmark;
Sanofi-Aventis, Germany; S-Biotek, Denmark; Scandinavian Airlines System,
Denmark; TetraPak, Sweden; Weight Watchers, USA; and from Zaluvida,
Switzerland. AA does not own stock in, or have other ownership interests in,
any of the companies to which he provides scientific advice, or in any
nutrition company other than those companies whose stock is held by various
mutual fund retirement accounts. Recent research at the University of
Copenhagen, Denmark, has been funded by unrestricted grants from or
contracts with DC-Ingredients, Denmark; Danish Dairy Foundation; Global Dairy
Platform; and Gelesis AS, USA. AA receives payment as associate editor of
The American Journal of Clinical Nutrition and as a member of the editorial
committee of Annual Review of Nutrition. AA is a recipient of honoraria as
speaker for a wide range of Danish and international concerns and of
royalties from textbooks and from popular diet and cookery books. AA is a
coinventor of a number of patents, including Methods of inducing weight loss,
treating obesity and preventing weight gain (licensee Gelesis, USA) and
Biomarkers for predicting degree of weight loss (licensee Nestec SA, CH),
owned by the University of Copenhagen, in accordance with Danish law. AA
is a co-founder and co-owner of the University of Copenhagen spin-out
companies Mobile Fitness A/S, Personalized Weight Management Research
Consortium ApS (Gluco-diet.dk), and Flaxslim ApS, where he is also a
member of the board. AA is not an advocate or activist for specific diets and is
not strongly committed to any specific diet, e.g., veganism, Atkins diet,
gluten-free diet, high animal protein diet, or dietary supplements. AV, JC, and
JH are full-time employees at Nestlé Institute of Health Sciences. The
remaining authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
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1. Grundy SM . Metabolic syndrome pandemic . Arterioscler Thromb Vasc Biol . 2008 ; 28 ( 4 ): 629 - 36 .
2. Ono M , et al. Protein region important for regulation of lipid metabolism in angiopoietin-like 3 (ANGPTL3): ANGPTL3 is cleaved and activated in vivo . J Biol Chem . 2003 ; 278 ( 43 ): 41804 - 9 .
3. Conklin D , et al. Identification of a mammalian angiopoietin-related protein expressed specifically in liver . Genomics . 1999 ; 62 ( 3 ): 477 - 82 .
4. Shan L , et al. The angiopoietin-like proteins ANGPTL3 and ANGPTL4 inhibit lipoprotein lipase activity through distinct mechanisms . J Biol Chem . 2009 ; 284 ( 3 ): 1419 - 24 .
5. Koishi R , et al. Angptl3 regulates lipid metabolism in mice . Nat Genet . 2002 ; 30 ( 2 ): 151 - 7 .
6. Romeo S , et al. Rare loss-of-function mutations in ANGPTL family members contribute to plasma triglyceride levels in humans . J Clin Invest . 2009 ; 119 ( 1 ): 70 - 9 .
7. Musunuru K , et al. Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia . N Engl J Med . 2010 ; 363 ( 23 ): 2220 - 7 .
8. Minicocci I , et al. Mutations in the ANGPTL3 gene and familial combined hypolipidemia: a clinical and biochemical characterization . J Clin Endocrinol Metab . 2012 ; 97 ( 7 ): E1266 - 75 .
9. Yilmaz Y , et al. Serum concentrations of human angiopoietin-like protein 3 in patients with nonalcoholic fatty liver disease: association with insulin resistance . Eur J Gastroenterol Hepatol . 2009 ; 21 ( 11 ): 1247 - 51 .
10. Larsen TM , et al. Diets with high or low protein content and glycemic index for weight-loss maintenance . N Engl J Med . 2010 ; 363 ( 22 ): 2102 - 13 .
11. Larsen TM , et al. The Diet, Obesity and Genes (Diogenes) Dietary Study in eight European countries-a comprehensive design for long-term intervention . Obes Rev . 2010 ; 11 ( 1 ): 76 - 91 .
12. Moore CS , et al. Dietary strategy to manipulate ad libitum macronutrient intake, and glycaemic index, across eight European countries in the Diogenes Study . Obes Rev . 2010 ; 11 ( 1 ): 67 - 75 .
13. Friedewald WT , Levy RI , Fredrickson DS . Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge . Clin Chem . 1972 ; 18 ( 6 ): 499 - 502 .
14. Gold L , et al. Aptamer-based multiplexed proteomic technology for biomarker discovery . PLoS One . 2010 ; 5 ( 12 ): e15004 .
15. Rohloff JC , et al. Nucleic acid ligands with protein-like side chains: modified aptamers and their use as diagnostic and therapeutic agents . Mol Ther Nucleic Acids . 2014 ; 3 : e201 .
16. SOMAscan Technical White Paper . 2017 [cited 2018 02- 02 -2018]; Available from: http://www.somalogic.com/somalogic/media/Assets/PDFs/SSM-002 - Rev- 2 -SOMAscan-Technical- White-Paper- 3 -7-15.pdf.
17. Carayol J , et al. Protein quantitative trait locus study in obesity during weight-loss identifies a leptin regulator . Nat Commun . 2017 ; 8 ( 1 ): 2084 .
18. Das S , et al. Next-generation genotype imputation service and methods . Nat Genet . 2016 ; 48 ( 10 ): 1284 - 7 .
19. Yang J , et al. GCTA: a tool for genome-wide complex trait analysis . Am J Hum Genet . 2011 ; 88 ( 1 ): 76 - 82 .
20. Han B , Kang HM , Eskin E . Rapid and accurate multiple testing correction and power estimation for millions of correlated markers . PLoS Genet . 2009 ; 5 ( 4 ): e1000456 .
21. Aulchenko YS , et al. GenABEL: an R library for genome-wide association analysis . Bioinformatics . 2007 ; 23 ( 10 ): 1294 - 6 .
22. Machiela MJ , Chanock SJ . LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants . Bioinformatics . 2015 ; 31 ( 21 ): 3555 - 7 .
23. Valsesia A , et al. Distinct lipid profiles predict improved glycemic control in obese, nondiabetic patients after a low-caloric diet intervention: the Diet, Obesity and Genes randomized trial . Am J Clin Nutr . 2016 ; 104 ( 3 ): 566 - 75 .
24. Armenise C , et al. Transcriptome profiling from adipose tissue during a lowcalorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects . Am J Clin Nutr . 2017 ; 106 ( 3 ): 736 - 46 .
25. MacArthur J , et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog) . Nucleic Acids Res . 2017 ; 45 ( D1 ): D896 - 901 .
26. Consortium GT , et al. Genetic effects on gene expression across human tissues . Nature . 2017 ; 550 ( 7675 ): 204 - 13 .
27. Hatsuda S , et al. Association between plasma angiopoietin-like protein 3 and arterial wall thickness in healthy subjects . J Vasc Res . 2007 ; 44 ( 1 ): 61 - 6 .
28. Shimamura M , et al. Angiopoietin-like protein3 regulates plasma HDL cholesterol through suppression of endothelial lipase . Arterioscler Thromb Vasc Biol . 2007 ; 27 ( 2 ): 366 - 72 .
29. Stejskal D , et al. Angiopoietin-like protein 3: development, analytical characterization, and clinical testing of a new ELISA . Gen Physiol Biophys . 2007 ; 26 ( 3 ): 230 - 3 .
30. Shoji T , et al. Plasma angiopoietin-like protein 3 (ANGPTL3) concentration is associated with uremic dyslipidemia . Atherosclerosis . 2009 ; 207 ( 2 ): 579 - 84 .
31. Robciuc MR , et al. Quantitation of serum angiopoietin-like proteins 3 and 4 in a Finnish population sample . J Lipid Res . 2010 ; 51 ( 4 ): 824 - 31 .
32. Mehta N , et al. Differential association of plasma angiopoietin-like proteins 3 and 4 with lipid and metabolic traits . Arterioscler Thromb Vasc Biol . 2014 ; 34 ( 5 ): 1057 - 63 .
33. Shimizugawa T , et al. ANGPTL3 decreases very low density lipoprotein triglyceride clearance by inhibition of lipoprotein lipase . J Biol Chem . 2002 ; 277 ( 37 ): 33742 - 8 .
34. Koster A , et al. Transgenic angiopoietin-like (angptl)4 overexpression and targeted disruption of angptl4 and angptl3: regulation of triglyceride metabolism . Endocrinology . 2005 ; 146 ( 11 ): 4943 - 50 .
35. Han K , Liang ZQ , Zhou ND . Design strategies for aptamer-based biosensors . Sensors . 2010 ; 10 ( 5 ): 4541 - 57 .
36. Sonnenburg WK , et al. GPIHBP1 stabilizes lipoprotein lipase and prevents its inhibition by angiopoietin-like 3 and angiopoietin-like 4 . J Lipid Res . 2009 ; 50 ( 12 ): 2421 - 9 .
37. Quagliarini F , et al. Atypical angiopoietin-like protein that regulates ANGPTL3 . Proc Natl Acad Sci U S A . 2012 ; 109 ( 48 ): 19751 - 6 .
38. Haller JF , et al. ANGPTL8 requires ANGPTL3 to inhibit lipoprotein lipase and plasma triglyceride clearance . J Lipid Res . 2017 ; 58 ( 6 ): 1166 - 73 .
39. Davies BSJ , et al. ANGPTL8 promotes the ability of ANGPTL3 to inhibit lipoprotein lipase . FASEB J . 2017 ; 31 : 1137 - 1149 .
40. Zhang R. The ANGPTL3- 4-8 model, a molecular mechanism for triglyceride trafficking . Open Biol . 2016 ; 6 ( 4 ): 150272 .
41. Dijk W , Kersten S. Regulation of lipid metabolism by angiopoietin-like proteins . Curr Opin Lipidol . 2016 ; 27 ( 3 ): 249 - 56 .
42. Robciuc MR , et al. Angptl3 deficiency is associated with increased insulin sensitivity, lipoprotein lipase activity, and decreased serum free fatty acids . Arterioscler Thromb Vasc Biol . 2013 ; 33 ( 7 ): 1706 - 13 .
43. Haridas PAN , et al. Regulation of angiopoietin-like proteins (ANGPTLs) 3 and 8 by insulin . J Clin Endocrinol Metab . 2015 ; 100 ( 10 ): E1299 - 307 .
44. Diab DL , et al. Cytokeratin 18 fragment levels as a noninvasive biomarker for nonalcoholic steatohepatitis in bariatric surgery patients . Clin Gastroenterol Hepatol . 2008 ; 6 ( 11 ): 1249 - 54 .
45. Giannini EG , Testa R , Savarino V . Liver enzyme alteration: a guide for clinicians . CMAJ . 2005 ; 172 ( 3 ): 367 - 79 .
46. Gowda S , et al. A review on laboratory liver function tests . Pan Afr Med J. 2009 ; 3 : 17 .
47. Szalowska E , et al. Comparative analysis of the human hepatic and adipose tissue transcriptomes during LPS-induced inflammation leads to the identification of differential biological pathways and candidate biomarkers . BMC Med Genet . 2011 ; 4 : 71 .
48. Aung LHH , et al. Association of the variants in the BUD13-ZNF259 genes and the risk of hyperlipidaemia . J Cell Mol Med . 2014 ; 18 ( 7 ): 1417 - 28 .
49. LHH A , et al. Association between the MLX interacting protein-like, BUD13 homolog and zinc finger protein 259 gene polymorphisms and serum lipid levels . Sci Rep . 2014 ; 4 : 5565 .
50. Lin E , et al. Association and interaction of APOA5, BUD13, CETP, LIPA and health-related behavior with metabolic syndrome in a Taiwanese population . Sci Rep . 2016 ; 6 : 36830 .
51. Fu Q , et al. Effects of polymorphisms in APOA4-APOA5-ZNF259-BUD13 gene cluster on plasma levels of triglycerides and risk of coronary heart disease in a Chinese Han population . PLoS One . 2015 ; 10 ( 9 ): e0138652 .
52. Delgado-Lista J , et al. Effects of variations in the APOA1/C3/A4/A5 gene cluster on different parameters of postprandial lipid metabolism in healthy young men . J Lipid Res . 2010 ; 51 ( 1 ): 63 - 73 .
53. Tikka A , Jauhiainen M. The role of ANGPTL3 in controlling lipoprotein metabolism . Endocrine . 2016 ; 52 ( 2 ): 187 - 93 .
54. Crowley RK , et al. SFRP2 is associated with increased adiposity and VEGF expression . PLoS One . 2016 ; 11 ( 9 ): e0163777 .
55. Courtwright A , et al. Secreted frizzle-related protein 2 stimulates angiogenesis via a Calcineurin/NFAT signaling pathway . Cancer Res . 2009 ; 69 ( 11 ): 4621 - 8 .
56. Hato T , Tabata M , Oike Y. The role of angiopoietin-like proteins in angiogenesis and metabolism . Trends Cardiovasc Med . 2008 ; 18 ( 1 ): 6 - 14 .