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Effect of Very-Low-Calorie Ketogenic Diet on Psoriasis Patients: A
Nuclear Magnetic Resonance-Based Metabolomic Study
Giuseppe Castaldo, Imma Pagano, Manuela Grimaldi, Carmen Marino, Paola Molettieri, Angelo Santoro,
Ilaria Stillitano, Rocco Romano, Paola Montoro, Anna Maria D’Ursi,*and Luca Rastrelli*
Cite This: J. Proteome Res. 2021, 20, 1509−1521
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sıSupporting Information
ABSTRACT: Psoriasis is an inflammatory disease of the epidermis based on an
immunological mechanism involving Langerhans cells and T lymphocytes that
produce pro-inflammatory cytokines. Genetic factors, environmental factors,
and improper nutrition are considered triggers of the disease. Numerous studies
have reported that in a high number of patients, psoriasis is associated with
obesity. Excess adipose tissue, typical of obesity, causes a systemic inflammatory
status coming from the inflammatory active adipose tissue; therefore, weight
reduction is a strategy to fight this pro-inflammatory state. This study aimed to
evaluate how a nutritional regimen based on a ketogenic diet influenced the
clinical parameters, metabolic profile, and inflammatory state of psoriasis
patients. To this end, 30 psoriasis patients were subjected to a ketogenic
nutritional regimen and monitored for 4 weeks by evaluating the clinical data,
biochemical and clinical parameters, NMR metabolomic profile, and IL-2, IL-
1β, TNF-α, IFN-γ, and IL-4 concentrations before and after the nutritional
regimen. Our data show that a low-calorie ketogenic diet can be considered a successful strategy and therapeutic option to gain an
improvement in psoriasis-related dysmetabolism, with significant correction of the full metabolic and inflammatory status.
KEYWORDS: psoriasis, obesity, 1H NMR metabolomics, very-low-calorie ketogenic diet, biomarkers
■INTRODUCTION
Psoriasis is a chronic inflammatory and multifaceted disease.
This condition affects approximately 2−3% of the world’s
population
1
and is less common in children than in adults.
Psoriasis is associated with morbidity and mortality.
2
Generally, significant differences among individuals from
various ethnic groups and geographical location have been
recorded, with an increased incidence in individuals living at
high latitudes.
3
From a sex viewpoint, some studies reported
differences between males and females.
4,5
Patients with
psoriasis have a decreased quality of life,
6
with anxiety and
depression. Psoriasis is a disorder of multifactorial etiology
with both genetic and trigging factors. Numerous studies have
reported the identification of genetic loci, in particular, 10 loci
as susceptibility regions. The factors identified as triggers for
the development of psoriasis are trauma, obesity, infections,
medications, sunlight, stress, alcohol, smoking, and endocrine
factors.
7
Histologically, this disease is characterized by hyper-
proliferative keratinocytes and the infiltration of prominent T
cells, dendritic cells, and neutrophils in the dermis. The
diagnosis is usually clinical, including an examination of the
primary lesion and affected areas. General presentations
include plaque, inverse, guttate, erythrodermic, and pustular
forms, with cutaneous manifestations and nail, scalp, and joint
abnormalities. The psoriasis area and severity index (PASI),
body surface area (BSA), and dermatology life quality index
(DLQI) are tools that are commonly used for the classification
of plaque psoriasis.
8
A systematic review and meta-analysis summarize the
epidemiological association between psoriasis and obesity,
indicating a higher prevalence and incidence of obesity in
psoriasis patients, compared with the general population.
9−11
Moreover, numerous studies have reported evidence of a
causal relationship between obesity and psoriasis, investigating
the connection between body mass index (BMI) and
psoriasis.
10,12,13
A few studies of weight-loss interventions have been shown
to improve psoriasis and to increase the response to
treatment,
12
especially adherence to a low-calorie dietary
regimen.
14−17
Fatty tissue is an active endocrine tissue and
causes a pro-inflammatory state in obese patients.
18
The
clinical effect of weight loss is the reduction of adipose tissue as
Received: August 20, 2020
Published: November 9, 2020
Articlepubs.acs.org/jpr
© 2020 American Chemical Society 1509
https://dx.doi.org/10.1021/acs.jproteome.0c00646
J. Proteome Res. 2021, 20, 1509−1521
a source of pro-inflammatory cytokines. From this perspective,
a very-low-calorie diet with adequate protein content is
responsible for weight loss and the reduction of visceral fat
mass.
19−22
The ketogenic diet is a nutritional regimen characterized by
a reduction in carbohydrates and a relative increase in protein
and fat.
23
At a biochemical level, the ketogenic diet induces a
switch to ketone metabolism, causing a reduction in blood
glucose and an increase in blood ketones and mitochondrial
function.
24
Recent scientific studies have shown the
therapeutic potential of ketogenic diets in many diseases,
such as diabetes, polycystic ovary syndrome, acne, neurological
diseases, cancer, and the amelioration of respiratory and
cardiovascular disease risk factors.
25−30
Moreover, the
ketogenic diet has been identified as an effective remedy for
obesity and psoriasis, with a significant reduction in
inflammatory components that are possibly localized in visceral
adipose tissue.
17,22
In recent years, metabolomic studies have played a
significant role in revealing biomarkers, identifying the
biochemical pathways involved in many diseases, and in
providing information related to pathway perturbations.
Nuclear magnetic resonance (NMR) spectroscopy represents
a robust and suitable technique for metabolomic approaches:
low-molecular-mass compounds can be concurrently qualita-
tively and quantitatively detected in biological samples.
31
In
this novel study, we performed an NMR-based metabolomic
analysis of the sera of psoriasis patients subjected to a
ketogenic nutritional regimen for 4 weeks. Metabolomic data
analyzed with the aid of univariate and multivariate statistical
methods were correlated with the biochemical and clinical
parameters, including IL-2 and IL-1βcytokines. Metabolomic
profiles of psoriasis patients compared to those of healthy
controls before and after a 4 week ketogenic diet provide
preliminary indications to identify candidate biomarkers useful
in the theranostic control of psoriasis. Results of the metabolic
pathway analysis reveal the therapeutic potential of a dietary
regimen and provide new insights into the etiopathogenesis of
psoriasis.
■MATERIALS AND METHODS
Participants
The study was conducted at the NutriKeto_LAB, Azienda
Ospedaliera “San Giuseppe Moscati”, Avellino, Italy, between
October 1, 2018, and March 1, 2019. Consecutive participants
were recruited from hospital wards. Demographics and clinical
information are reported in Table 1.
Inclusion and Exclusion Criteria
Participants eligible for inclusion criteria in the psoriasis group
were overweight patients, aged 18−65 years old, with plaque
psoriasis: 35 patients were screened, and 30 patients were
recruited and completed the intervention study. The exclusion
criteria were pregnancy; breastfeeding; insulin treatment;
heart, kidney, or liver disease; obesity due to hypothyroidism;
neoplastic disease; intentional or unintentional weight loss of
more than 5 kg up to 3 months before the study; active
treatment or treatment in the past 2 weeks with topical drugs
or retinoic acid, ultraviolet light therapy or systemic therapy
(in the recent 4 weeks), or biological preparations in the past
12 weeks; and recent history of drug addiction or alcohol
abuse.
Study Design
Blood serum samples were collected from 30 healthy subjects
used as controls and 30 subjects diagnosed with psoriasis
according to the mentioned psoriasis diagnostic criteria. The
institutional ethical committee of Azienda Ospedaliera “San
Giuseppe Moscati”, Avellino, Italy, approved the study
protocol, which followed the 1964 Declaration of Helsinki
and its later amendments, and all subjects gave written
informed consent.
Clinical Assessments
The diagnosis of plaque psoriasis was made by dermatologists
based on clinical characteristics. PASI, BSA, the DLQI, and the
VAS for itch ratings were employed to measure psoriasis
severity. All the participants had height and body weight
measured by calibrated flat scales equipped with a telescopic
vertical steel stadiometer (SECA 711, Hamburg, Germany).
BMI was calculated as the weight (kg) divided by the height
squared (m2) kg/m2.Aflexible plastic tape was used to assess
waist and hip circumferences. Blood samples were analyzed in
the clinical laboratory using automated analyzers and available
commercial kits. Quantitative evaluation of the following
clinical parameters was performed: hemoglobin, total lympho-
cyte count, creatinine, uric acid, glucose, insulin, C-peptide,
glycated hemoglobin, growth hormone (GH), total cholesterol,
high-density lipoprotein (HDL) cholesterol, low-density lip-
oprotein (LDL) cholesterol, triglycerides, apolipoproteins A1
and B (Apo A1 and Apo B, respectively), albumin,
cholinesterase, serum aspartate aminotransferase (AST),
alanine aminotransferase, gamma glutamyl transferase (γGT),
lactate dehydrogenase, sodium, potassium, magnesium,
calcium, phosphorus, the homeostasis model assessment
insulin resistance (HOMA-IR), bilirubin, hematocrit, pro-
thrombin activity, cortisol, vitamin B12, folic acid, azotemia,
insulin, and homocysteine. For the assessment of visceral
adipose tissue (VAT), ultrasound measurement of the
aortomesenteric fat thickness (AMFT) was performed
according to a previous procedure.
32
Dietary Intervention and Assessment
The recommendations for daily nutrient intake were met
during the entire study time. The participants met a study
dietician every week to verify food intake and adherence to
administered dietary intervention. During group meetings, the
diet regimen was given to subjects with encouragement and
Table 1. This Table Describes the Demographics and
Clinical Information of the Participants
a
parameter psoriasis group
(N= 30) control group
(N= 30) P
sex (male/female) 11/19 10/20
age (mean ±SD, years) 42.8 ±14.04 50.0 ±9.90 0.003
BMI (kg/m2) 30.82 ±5.96 28.4 ±1.61 0.044
disease duration (mean ±
SD, years) 5.09 ±1.80 NA
BSA (mean ±SD) 16.02 ±3.39 NA 0.069
b
PASI (mean ±SD) 8.69 ±1.80 NA 0.007
b
PsO/PsO + PsA 25/5 NA
smokers 60% 50%
a
Pvalue obtained by the Mann−Whitney Utest between the psoriasis
group and control group. BMI, body mass index; NA, not applicable;
BSA, body surface area; PASI, psoriasis area and severity index; PsO,
psoriasis; PsA, psoriatic arthritis.
b
Pvalue obtained by the
Mann−Whitney Utest of psoriasis group between T0 and T1.
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instructions for the use of dietary supplements. Treatment
efficacy was assessed at baseline (T0) and after 4 weeks (T1).
The control group (healthy controls) adopted a conventional
diet and was instructed to eat ordinary food according to the
national guidelines for a healthy diet.
Diet recommendations for psoriasis patients included the
consumption of a very-low-calorie (<500 kcal/day) protein-
based diet providing 10−20 g of carbohydrates (from
vegetables, 400−500 g/day), 20−30 g of lipids, and 1.4 g
per kg of ideal body weight (calculated using Lorentz’s
equation) of protein per day. Half of this protein dosage is
sufficient to supply 12 g of 90% whey protein, with the
addition of L-arginine, α-ketoglutarate, L-ornithine, L-carnitine,
L-glutamine, taurine, L-citrulline, L-cysteine, and vitamin B6.
Other dietary supplements were alkalizing substances (calcium
carbonate, magnesium citrate, potassium bicarbonate, potas-
sium citrate, and sodium bicarbonate) and herbal remedies
(with diuretic, anti-inflammatory, hepatoprotective, and
antioxidant activities), such as garcinia (Garcinia cambogia),
hawthorn (Crataegus oxyacantha), java tea (Orthosiphon
stamineus), dandelion (Taraxacum officinale), thistle fruit
extract (Silybum marianum), a multivitamin (C, D, K, and
A)/multimineral supplement, and 10 g of hydrolyzed collagen
powder. Ashwagandha (Withania somnifera) and bacopa (
Bacopa monnieri) were also administered for psychophysical
balance, and Triphala (Phyllanthus emblica,Terminalia chebula,
and Terminalia bellirica) was administered to implement the
correct intestinal function. Patients were advised to drink at
least 2 L of bicarbonate-rich alkaline water per day (not tea or
coffee) and to not use table salt but to salt their food with
potassium chloride. All treatments with hypoglycemic agents
and diuretics were interrupted.
Outcomes (Primary and Secondary)
The primary outcome was PASI, an index of psoriasis severity:
the PASI was measured at baseline (T0) and at 4 weeks (T1),
along with BSA assessment. The secondary outcome was
DLQI to determine the quality of life, the reduction in BSA,
the improvement in itch severity, and weight loss.
Sample Pretreatment for NMR Analysis
NMR sample preparation and NMR spectra acquisition were
performed as previously reported.
31,33
To obtain the blood
serum, the whole blood was collected into tubes not containing
anticoagulant and allowed to clot at room temperature for 30
to 120 min. After centrifugation at 12,000 g, the blood serum
was aliquoted and stored at −80 °C in Greiner cryogenic vials
before NMR spectroscopy measurements. Before being
transferred to a 5 mm heavy-walled NMR tube, samples
were thawed at room temperature. NMR samples were
prepared by mixing 300 μL of blood sera with 200 μLof
phosphate buffer, including 0.075 M Na2HPO4·7H2O, 4%
NaN3,andH
2O. Trimethylsilyl propionic-2,2,3,3-d4acid,
sodium salt (0.1% TSP in D2O) was used as an internal
reference for the alignment and quantification of NMR signals;
the mixture, homogenized by vortexing for 30 s, was
transferred to a 5 mm NMR tube (Bruker NMR tubes) before
analysis started.
31
NMR Data Acquisition
NMR experiments were carried out on a Bruker DRX600 MHz
spectrometer (Bruker, Karlsruhe, Germany) equipped with a 5
mm triple-resonance z-gradient CryoProbe. TOPSPIN, version
3.0, was used for spectrometer control and data processing
(Bruker Biospin, Fällanden, Switzerland). For nonfiltered
biofluids, low-mass metabolites coexist with high-mass
biomolecules, such as lipids, proteins, and lipoproteins;
therefore, to selectively observe small-molecule components
in solutions, Carr−Purcell−Meiboom−Gill (CPMG) experi-
ments were performed. 1D 1H pulse-sequence CPMG
experiments comprised a spectral width of 7 kHz, 32,000
data points, water presaturation applied over 3.5 s of relaxation
delay, and a spin-echo delay of 80 ms.
34
The pulse sequence
used included an excitation sculpting routine for the
suppression of the water signal.
35
Due to the effect of
excitation sculpting on the signal height of resonances in the
region close to the water resonance,
36,37
the metabolites that
have resonances close to this region (ascorbate, glucose,
mannose, and pyroglutamate) were quantified using reso-
nances from those metabolites in other spectral regions. A
weighted Fourier transform was applied to the time domain
data with a 0.5 Hz line-broadening followed by a manual phase
and baseline correction in preparation for targeted profiling
analysis.
NMR Data Processing
NMR spectra were manually phased and baseline-corrected.
The quantification of serum metabolites was achieved using
Chenomx NMR-Suite v8.0 (Chenomx Inc., Edmonton,
Canada).
33,38
Briefly, the Chenomx profiler is linked to the
Human Metabolome Database (HMDB), containing more
than 250 metabolite NMR spectral signatures encoded at
different 1H spectrometer frequencies, including 600 MHz
(http://www.hmdb.ca). A comparison of the spectral data
obtained for each serum sample with the Chenomx metabolite
library results in a list of compounds together with their
respective concentrations based on the known concentration of
the added internal reference compound TSP-d4(5.8 mM).
Statistical Analysis
Multivariate statistical analysis, principal component analysis
(PCA), and partial least-squares discriminant analysis (PLS-
DA) were conducted with normalized metabolomics data
using MetaboAnalyst 4.0 (http://www.metaboanalyst.ca/).
The performance of the PCA and PLS-DA model was
evaluated using the coefficient Q2(using the 7-fold internal
cross-validation method) and coefficient R,
2
defining the
variance predicted and explained by the model, respectively.
The loading plot was used to identify significant metabolites
responsible for maximum separation in the PLS-DA score plot,
and these metabolites were ranked according to their variable
influence on projection (VIP) scores. VIP scores are weighted
sums of squares of the PLS-DA weights, which indicate the
importance of the variable.
Quantitative Analysis
The data relative to the metabolite concentrations were
analyzed using the PRincipal COmponent Normalization
Algorithm (PRICONA),
39
and the normalization strategy
was applied.
40−43
Accordingly, the proportional variations of
1H NMR signals were described by normalization factors, and
the normalization constant was calculated as the score relative
to the normalization setting.
Cytokine Analysis: ELISA
Serum concentrations of the cytokines IL-2, IL-1β, TNF-α,
IFN-γ, and IL-4 were determined by enzyme-linked
immunosorbent assay (ELISA) commercial kits (Diaclone
SAS (Besançon Cedex, France)) according to the manufac-
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turer’s instructions. All tests were performed in duplicate. The
ranges of the sensitivity standard curve of the ELISA kits were
31.2−1000 pg/mL for IL-2, 15.6−500 pg/mL for IL-1β,25−
800 pg/mL for TNF-α, 12.5−400 pg/mL for IFN-γ, and 1.1−
35 pg/mL for IL-4.
Standard diluents, the capture antibody, and the detection
antibody were obtained as a complete kit for each cytokine.
Standard diluent−serum samples were used to obtain a Cedex
standard curve. The samples in the multiwell plate were mixed
by repeated aspirations and ejections, taking care not to scratch
the inner surfaces. Freeze-dried control vials were also
reconstituted with the most appropriate standard diluent to
the sample to gain a solution at the concentration stated on the
vial. Biotinylated anticytokines, biotinylated secondary anti-
body, and streptavidin-HRP were also prepared according to
the manufacturer’s protocol. The absorbance value of each well
was read on a Thermo Scientific Multiskan GO spectropho-
tometer using 450 nm as the primary wavelength and 620 nm
as the reference wavelength (610 to 650 nm is acceptable).
■RESULTS
Clinical Analysis
Clinical data for 30 patients with psoriasis before and after the
ketogenic diet were statistically analyzed using the Mann−
Whitney Utest to assess their significance and Pvalue. The
VIP score was calculated using the R package to identify the
variables that discriminated between the two groups. The
variables with VIP scores >1 were considered significant for the
analysis. The VIP score values for selected variables concerning
psoriasis patients before and after the ketogenic diet are given
in Table 2.
44
Table 2 shows clinical and biochemical clinical parameters
for psoriasis patients. An analysis of the data indicated that all
parameters related to psoriasis improved after the ketogenic
diet. In particular, the DLQI, visual analogue scale (VAS)
pruritus, VAS pain, and PASI improved, indicating that the
ketogenic diet reduced psoriasis symptoms.
45,46
Clinical data
identified by the VIP score also included several biochemical
and clinical parameters derived from blood analysis. In
particular, the concentrations of folic acid, vitamin B12, AST,
cortisol, calcium, and direct bilirubin were higher in patients at
T1 than at T0. LDL cholesterol, total cholesterol, and HOMA-
IR levels were lower in patients at T1 than at T0 (see Table 2).
Quantitative Analysis
To quantitatively evaluate the 1H NMR spectra of the serum of
participants at T0 in comparison to the NMR spectra of the
serum of the same participants at T1, the PRincipal
COmponent Normalization Algorithm (PRICONA) and
normalization strategy were applied. The PRICONA and
normalization strategy are based on the assumption that, since
concentration differences result in proportional variations of
spectral intensities, nonproportional changes most likely can be
attributed to the effects of the disease. The proportional
variations are described by the normalization factor R, which
must be calculated to compare the intensities of the T0 and T1
signals. When the first principal component (PC) explains the
major part of the variance of a spectral data set, it represents
the function shape of the sum of a group of proportional peaks
(normalization set), and its scores represent the proportion-
ality constants (normalization constants). Once the spectra are
normalized, peak intensities can be directly compared.
According to the normalization procedure described above,
signals that showed significant variations were quantified. At
the same time, an opportune strategy based on the PRICONA
is performed to achieve a reliable NMR metabolomic analysis.
The normalization algorithm is based on PCA with some
advantages: it allows simultaneous normalization of data sets of
spectra by identifying signals affected by the agent (in this case,
after the diet regimen) and quantitative measurement of
variations. In this way, the differences in peak intensities are
excluded, and the real differences do not depend on differences
in sample concentration. All extraneous sample-to-sample
variations and those within each metabolite are removed. After
spectral normalization, all differences related to metabolites
can be used to identify potential candidate biomarkers. Figure
1shows the most significant differences observed in the signals
of the spectra at T1 compared to those at T0: the variations in
concentration are indicated as log2(fold change); the
differences characterized by P< 0.05 are considered significant.
An analysis of the data indicated a significant increase in L-
serine, dimethyl sulfone, and hydroxybutyric acid and a
decrease in malonate, choline, and pyruvic acid.
Multivariate Data Analysis
Matrices, including metabolites and their concentrations as
derived from 1H NMR data collected in 1D-1H-CPMG,
34
were
analyzed according to multivariate statistical analysis using
MetaboAnalyst 4.0.
47
Multivariate analysis (MVA) was
performed to identify the metabolic profile of psoriasis
patients. The original matrix included the sera from 30
subjects with psoriasis and the sera from 30 healthy controls.
The data matrix, after normalization by sum, log trans-
formation, and Pareto scaling, was analyzed by PLS-DA
(Figure 2). To minimize false discoveries and to obtain robust
statistical models, t-tests and fold-change tests were applied
according to good standardized practice (see Tables S1 and S2,
Supporting Information).
47
In Figure 2, PLS-DA shows that
the data sets relative to psoriasis sera are well separated from
control sera. The first component explains 21.9% of the
variance, while the second explains 9.6%.
The creation of separate clusters indicates a different
metabolome characterizing patients with psoriasis and healthy
controls. This evidence is confirmed by applying VIP score
analysis (Figure 3). Accordingly, the metabolites characterized
Table 2. VIP Score and PValue Relative to Clinical Features
Calculated by the R Package
parameter VIP score T0 T1 P
a
DLQI 2.2108 + −1.3 ×10−05
folic acid 1.9214 −+ 4.3 ×10−05
VAS pain 1.7323 + −3.3 ×10−03
VAS pruritus 1.7504 + −2.5 ×10−03
vitamin B12 1.7297 −+ 6.3 ×10−04
AST 1.7140 −+ 3.4 ×10−02
LDL 1.6913 + −8.7 ×10−06
cortisol 1.6028 −+ 1.3 ×10−02
PASI 1.4916 + −7.0 ×10−03
calcium 1.3501 −+ 5.3 ×10−03
total cholesterol 1.2921 + −4.3 ×10−06
direct bilirubin 1.2533 −+ 1.9 ×10−02
HOMA-IR 1.2406 + −2.8 ×10−03
a
Pvalue calculated by the Mann−Whitney Utest.
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by a VIP score higher than 1 are considered good classifiers
between psoriasis patients and healthy controls. The
metabolites considered as discriminative of the metabolomes
of controls and those of subjects with psoriasis are represented
in the VIP score graph in Figure 3. In particular, we observed
an amino acid dysmetabolism correlated to a lower
concentration of L-tryptophan, L-tyrosine, L-lysine, L-histidine,
L-methionine, L-arginine, L-ornithine, and L-glutamine in
psoriasis patients.
The multivariate analysis was repeated, taking into account
patients before ketogenic diet (T0) and after the 4 week
ketogenic diet (T1). Original matrices were normalized
according to the concentration ranges of HMDB.
48
The data
matrix, after normalizationbysamplemedianandlog
transformation, was analyzed by PCA (see Figures S1 and
S2,Supporting Information) and PLS-DA (see Figure S3,
Supporting Information). To obtain robust statistical models
and to calculate the Pvalue, the Mann−Whitney Utest was
Figure 1. Histogram summarizing the fold change as log2(T1/T0) in the various metabolites quantified after the normalization procedure. Fold
changes are obtained by comparing the means of the metabolite signals of 30 psoriasis patients at T0 to those at T1. Positive differences are relative
to overexpressed metabolites at T1 with respect to T0. The differences were considered significant (P< 0.05). Metabolites with a significant
difference are marked with asterisks.
Figure 2. PLS-DA score scatter plot (A) and PLS-DA loading scatter plot (B) for the 1H NMR data collected in 1D-1H-CPMG spectra acquired at
600 MHz. Data represent the sera from 30 controls and 30 psoriasis patients before the ketogenic diet.
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applied.
49
For each sample, 38 metabolites were identified and
quantified.
An inspection of the PLS-DA score scatter plot (Figure 4A)
and loading scatter plot (Figure 4B) points to 3-hydrox-
ybutyrate, L-leucine, pyruvic acid, and choline as metabolites
that significantly discriminate patients at baseline from those
after 4 weeks of the diet. This evidence is confirmed through
the application of VIP score analysis (Figure 5) (for details, see
Table S3,Supporting Information). In particular, the graph
reported in Figure 5 shows that, before the diet, psoriasis
patient sera contain a higher concentration of L-leucine,
pyruvic acid, choline, L-alanine, and acetoacetate and a lower
concentration of 3-hydroxybutyrate and acetone than after the
diet.
The results shown in Figure 5 are in agreement with those of
the quantitative analysis regarding the significant metabolite
identification, except for L-leucine. Furthermore, regarding the
Figure 3. Metabolites discriminating healthy controls from psoriasis
patients before the ketogenic diet according to VIP score values.
Figure 4. PLS-DA score scatter plot (A) and PLS-DA loading scatter plot (B) for the 1H NMR data collected in the 1D-1H-CPMG spectra
acquired at 600 MHz. Data are relative to sera of 30 psoriasis patients at T0 (before ketogenic diet) and those of 30 psoriasis patients at T1 (after 4
weeks of the diet).
Figure 5. Metabolites discriminating psoriasis patients at baseline
(T0) from psoriasis patients after 4 weeks of the diet (T1) according
to VIP score values. Only metabolites with VIP score > 1 are
discriminant between patients before diet (T0) and psoriasis patients
after 4 weeks of the diet (T1).
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trends of the concentrations of significant metabolites before
and after the diet, there are concordances with the PRICONA
analysis, such as the increase in hydroxybutyric acid and the
decrease in choline and pyruvic acid at T1 (see Figure 1).
To gain meaningful insight from these data, we applied
metabolic pathway analysis using MetaboAnalyst 4.0.
47
Similar
to the MVA, we carried out pathway analysis on two clusters:
(i) controls against psoriasis patients before the ketogenic diet
and (ii) psoriasis patients before the ketogenic diet (T0)
against psoriasis patients after 4 weeks from the ketogenic diet
(T1). Table 3 shows that the results of the pathway analysis
come from the comparison between patients at T0 (before the
ketogenic diet) and controls and the comparison between
psoriasis patients at T0 and psoriasis patients at T1 (after 4
weeks of diet). The Pvalue and false discovery rate (FDR)
50
are reported to confirm the significance of the pathways. We
also reported the most discriminating metabolite (P< 0.05)
belonging to the pathway and detected through the KEGG
database.
51
The comparison between controls and patient at T0
identified an amino acid dysmetabolism related to different
pathways, in particular, arginine and proline metabolism;
histidine metabolism; arginine biosynthesis; cysteine and
methionine metabolism; alanine, aspartate, and glutamate
metabolism; glycine, serine, and threonine metabolism; and
phenylalanine, tyrosine, and tryptophan biosynthesis. In
addition, the comparison between psoriasis patients at T0
and psoriasis patients at T1 highlighted the involvement of
pathways related to ketogenic diet, in particular, synthesis and
degradation of ketone bodies and butanoate metabolism,
which are not disregulated in comparison with previous
clusters. Also, in the comparison between psoriasis patients at
T0psoriaticpatientsatT1,thereisanaminoacid
dysmetabolism related to glycine, serine, and threonine
metabolism; arginine and proline metabolism; alanine,
Table 3. Metabolic Pathway Analysis Related to the Comparison between Control and Psoriasis Patients at T0 and the
Comparison between Psoriasis Patients at T0 and Psoriasis Patients at T1 (after 4 Weeks of Diet)
a
pathway (control vs psoriasis patients before ketogenic diet) raw PFDR discriminant metabolites C P
arginine and proline metabolism 2.59 ×10−25 9.58 ×10−25 arginine (P=3.34 ×10−08)+−
ornithine (P= 6.85 ×10−06)+−
proline (P= 0.0020) + −
aminoacyl-tRNA biosynthesis 3.86 ×10−19 7.14 ×10−18 histidine (P= 2.14 ×10−10)+−
lysine (P= 6.08 ×10−11)+−
tryptophan (P= 1.18 ×10−11)+−
tyrosine (P= 1.56 ×10−11)+−
glyoxylate and dicarboxylate metabolism 3.69 ×10−11 4.55 ×10−10 formate (P= 4.45 ×10−18)+−
L-glutamine (P= 8.81 ×10−05)+ −
L-serine (P= 3.42 ×10−07)+−
histidine metabolism 3.79 ×10−08 3.51 ×10−07 histidine (P= 2.14 ×10−10)+−
arginine biosynthesis 9.39 ×10−08 6.38 ×10−07 arginine (P= 3.34 ×10−08)+−
cysteine and methionine metabolism 1.21 ×10−07 6.38 ×10−07 methionine (P= 3.36 ×10−10)+−
β-alanine metabolism 5.18 ×10−07 2.05 ×10−06 histidine (P= 2.14 ×10−10)+−
alanine, aspartate, and glutamate metabolism 5.94 ×10−04 1.83 ×10−03 L-glutamine (P= 8.81 ×10−05)+ −
succinate (P= 6.71 ×10−05)−+
glycine, serine, and threonine metabolism 1.14 ×10−03 3.24 ×10−03 choline (P= 2.36 ×10−06)+−
phenylalanine, tyrosine, and tryptophan biosynthesis 1.73 ×10−03 4.27 ×10−03 tyrosine (P= 1.56 ×10−11)+−
pathway (psoriasis patients before ketogenic diet vs psoriasis patients after
ketogenic diet) raw PFDR discriminant metabolites PT0 PT1
synthesis and degradation of ketone bodies 2.27 ×10−13 4.33 ×10−12 3-hydroxybutanoate (P= 1.96 ×
10−13)
−+
butanoate metabolism 2.28 ×10−13 4.33 ×10−12 3-hydroxybutanoate (P= 1.96 ×
10−13)
−+
L-glutamate (P= 0.008) −+
glycine, serine, and threonine metabolism 7.95 ×10−03 7.55 ×10−02 choline (P= 2.11 ×10−07)+−
pyruvate (P= 4.21 ×10−05)+−
arginine and proline metabolism 4.19 ×10−06 3.11 ×10−05 proline (P= 0.007) +−
pyruvate (P= 4.21 ×10−05)+−
alanine, aspartate, and glutamate metabolism 3.94 ×10−06 3.11 ×10−05 L-alanine (P= 0.0019) +−
pyruvate (P= 4.21 ×10−05)+−
L-glutamate (P= 0.008) −+
aminoacyl-tRNA biosynthesis 8.84 ×10−06 4.20 ×10−05 L-alanine (P= 0.0019) +−
leucine (P= 1.96 ×10−11)+−
L-glutamate (P= 0.008) −+
proline (P= 0.007) +−
valine, leucine, and isoleucine degradation 7.95 ×10−06 4.20 ×10−05 leucine (P= 1.96 ×10−11)+−
valine, leucine, and isoleucine biosynthesis 1.16 ×10−05 4.87 ×10−05 leucine (P= 1.96 ×10−11)+−
pyruvate metabolism 1.59 ×10−05 4.87 ×10−05 pyruvate (P= 4.21 ×10−05)+−
a
The Pvalue, FDR value, and the most significant metabolites with the concentration variation related to the clusters taken in examination are
reported for each pathway.
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aspartate, and glutamate metabolism; and valine, leucine, and
isoleucine degradation and biosynthesis.
In enrichment analysis, by increasing the number of
reference metabolites and evaluating concentrations of
metabolites present in our matrix, it is possible to identify a
change in the metabolome of psoriasis patients before and after
the diet regimen. All dysmetabolism identified in the pathway
analysis was confirmed by the enrichment analysis (see Table
S4,Supporting Information). The graph in Figure 6 shows the
pathways that best discriminate between the two classes (T0
and T1). In particular, in the pathway of fatty acids, there is a
physiological variation of the ketone bodies related to the
ketogenic diet, with an increase of 3-hydroxybutyrate and
acetic acid in the serum of patients after 4 weeks. Choline
pathways (phospholipid biosynthesis and phosphatidylcholine
biosynthesis) indicated a reduction in serum choline levels of
patients with post-diet psoriasis.
52
Amino acid dysmetabolism
was confirmed by a change in L-leucine, L-isoleucine, and L-
valine levels, which decreased after the diet,
53
and by the
dysfunction of the urea cycle.
53
Enrichment analysis was repeated using the parameter
“location-based metabolite set”to understand which tissue was
the most involved in the previously described dysmetabolism.
The graph in Figure 7 represents the output of the enrichment
analysis that showed a dysmetabolism related to the epidermis,
muscular tissue, and skeletal muscle tissue, which is already
known to be involved in the physiopathogenesis of psoriasis.
1
A more accurate analysis of the metabolic pathways involved
in psoriasis was carried out by Reactome analysis
54
(see Table
S5,Supporting Information). Pathway analysis by Reactome
confirmed all dysmetabolism individuated to pathway analysis
by MetaboAnalyst 4.0 and has shown a possible link to
SLC6A14 gene variations that may be associated with obesity.
Several studies have confirmed the correlation between obesity
and psoriasis.
10,12,13
To assess the benefits of ketogenic diet for patients with
psoriasis, the average of the concentrations of metabolites with
VIP score > 1 was calculated in the following groups: (i)
healthy controls, (ii) patients with psoriasis at T0 (before the
ketogenic diet), and (iii) patients with psoriasis at T1 (after 4
weeks of diet). Table 4 shows the average metabolite values for
each cluster and the difference between the metabolite’s mean
concentration of healthy controls and patients before and after
the diet. Analysis of the data shown in Table 4 indicates that
the mean concentration difference of the metabolites in the
patients vs controls at T1 is lower than the mean concentration
difference in the patients vs controls at T0. The metabolites
formate, L-histidine, methionine, L-arginine, choline, L-
ornithine, pyruvic acid, and L-alanine are the most discrim-
inant.
ELISA Results
ELISAs performed on serum samples to evaluate the levels of
IL-2, IL-1β, TNF-α, IFN-γ, and IL-4 showed that there were
significant differences in the levels of the cytokines IL-2 (P=
0.04) and IL-1βin patients between T0 and T1 (P= 0.006)
(Figure 8). The significance of the data was assessed by the
Wilcoxon statistical analysis test.
55
The graph in Figure 8
represents the significant difference in mean cytokine
Figure 6. Pathway enrichment analysis: the pathways related to a Pvalue that excludes randomness and is correlated with psoriasis.
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concentrations for the patient group before the ketogenic diet
(T0) and after 4 weeks of the diet (T1). No significant changes
in concentrations were detected for TNF-α(P= 0.47), IFN-γ
(P= 0.17), and IL-4 (P= 0.65).
■DISCUSSION
In the present study, we evaluated the effect of a ketogenic diet
on psoriasis disease progression. Thirty psoriasis patients were
subjected to a ketogenic nutritional regimen and monitored by
evaluating (i) the clinical symptoms, (ii) the blood biochemical
parameters, including IL-2, IL-1β, TNF-α, IFN-γ, and IL-4,
and (iii) the metabolomic profile, as derived from 1H NMR
analysis. As a preliminary step, we identified the psoriatic
patients’metabolomic profile and the healthy controls’
metabolomic profile.
1
Based on preliminary data previously
obtained in our laboratory and for reasons related to the
restrictions imposed from the nutritional regimen, the patients
were subjected to 4 week treatment. As the parallel evaluation
of ketogenic diet effects in psoriatic and healthy subjects was
not possible, we considered the healthy controls’metabolomic
profile as our experimental control. By following the
indications of the Italian Society of Endocrinology (ISE),
56
the enrollment of healthy subjects for the low-calorie ketogenic
nutritional regimen is not recommended as the low-calorie
ketogenic diet causes alteration of the metabolic profile in
healthy subjects, with potentially harmful implications in blood
pH equilibrium, calcium homeostasis, and lean mass balance.
Therefore, we considered the therapeutic effect of ketogenic
diets by comparing the metabolomic profile of psoriatic
patients at T1 with healthy subjects’metabolomic profile at
T0.
Data resulting from the clinical evaluation showed caloric
restriction-induced psoriasis disease regression after 4 weeks
with a significant reduction in the DLQI, PASI, VAS pain, and
VAS pruritus clinical scores (P< 0.05). In particular, the PASI
showed a reduction of approximately 50%.
Biochemical and clinical parameters indicated a general
improvement in the metabolites’concentration known to be
related to the condition of psoriasis:
57
folic acid, vitamin B12,
calcium, bilirubin, cortisol, LDL, and total cholesterol. High
levels of folic acid and vitamin B12 are known to improve the
pathological condition related to psoriasis.
57
As reported in
Figure 7. Metabolite set enrichment overview: the tissue related to a
Pvalue that excludes randomness and correlates with psoriasis.
Table 4. Mean Discriminant Metabolites (VIP Score > 1) Concentration and Difference Mean Concentration Relative to
Controls, Psoriasis Patients before Diet (T0), and Psoriasis Patients after 4 Weeks of Diet (T1)
a
metabolites (VIP > 1) M[]ctrl (μM) M[]p(T0) μM M[]p(T1) (μM) M[]ctrl −M[]p(T0) M[]ctrl −M[]p(T1)
formate 143.34 206.62 162.48 −65.76 −19.14
tryptophan 107.41 203.79 214.17 −96.38 −106.76
tyrosine 43.79 103.03 83.41 −59.24 −39.62
L-lysine 129.48 116.48 186.24 13 −56.76
L-histidine 303.27 348.10 300.10 −44.83 3.17
methionine 74.72 118.45 50.18 −43.71 24.56
L-arginine 17.83 53.31 14.00 −35.48 3.83
L-lactic acid 1235.86 13,490.45 10,661.03 −12,254.59 −9425.17
choline 39.83 116.86 39.41 −77.03 0.42
isoleucine 171.72 2507.27 1912.76 −2335.55 −1741.04
L-ornithine 70.17 136.96 91.41 −66.79 −21.24
succinate 12.69 401.03 509.34 −388.34 −496.65
L-glutamine 743.69 2087.62 2079.38 −1343.93 −1335.69
3-hydroxybutiric acid 69.34 385.90 4526.07 −316.56 −4456.73
L-leucine 21.48 101.48 19.62 −80 1.86
pyruvic acid 52.48 410.93 123.62 −358.45 −71.14
L-alanine 182.48 662.55 410.14 −480.07 −227.66
acetone 147.96 1336.14 2800.55 −1188.17 −2652.59
a
M[]ctrl, mean concentration in control group; M[]p(T0), mean concentration in psoriasis patients at T0; M[]p(T0), mean concentration in
psoriasis patients at T1.
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Table 2, our data indicated low levels of folic acid and vitamin
B12 at T0, whereas an increase in the concentration was
registered at T1. Hypocalcemia was observed as a risk factor in
psoriasis.
58
Our data show an increase in calcium levels in
subjects after the ketogenic diet. Bilirubin, an essential
antioxidant metabolite, was present in low concentrations in
subjects with psoriasis;
59
after our diet regimen, bilirubin
concentration increased. Significant variation was observed in
the concentrations of cortisol, LDL, and total cholesterol.
Recent scientific studies assert that low cortisol levels are
related to high stress levels in subjects with psoriasis.
60
According to the DLQI, investigating the quality of life in
subjects with psoriasis, cortisol levels following a low-calorie
ketogenic diet increased.
The ketogenic diet resulted in weight loss at T1,
corresponding to ∼10% of the initial body weight. Other
significant modifications of anthropometric measurements
were waist circumference and hip circumference (see Table
S6,Supporting Information). The weight loss was associated
with a significant increase in ketone bodies at T1, as shown in
the NMR-based metabolomic analysis. This effect was the
main physiological effect of the ketogenic diet. The basic
principle of the low-calorie ketogenic diet is to limit the
availability of carbohydrates, forcing the consumption of fats as
the primary energy source, with a resulting increase in fatty
acids, ketone bodies, and pyruvic acid. However, weight loss
and the increase in ketone bodies were not associated with any
alteration of those biochemical parameters that initially were in
the average concentration range, thus proving the safety of the
proposed dietary intervention.
In contrast, biochemical and clinical parameters previously
found in abnormal concentrations and related to carbohydrate
and lipid metabolism, such as glucose, total cholesterol, LDL,
Apo A1 and B, AST, γGT, insulin, and HOMA-I, returned to
healthy average ranges (see Table S6,Supporting Informa-
tion). A decrease in HDL was also observed due to the drop in
total cholesterol (from 52.07 ±18.23 to 44.20 ±14.22 mg/dL;
P= 0.079). Last, the reduction in the aortomesenteric fat
thickness (AMFT) proved a substantial decrease in VAT.
The ketogenic nutritional regimen aims to minimize insulin
levels and to increase GH secretion. The final effect is the
almost complete reduction of the visceral adipose tissue
responsible for insulin resistance and the insurgence of
inflammatory status. Insulin and estrogen act at the PPAR
(peroxisome proliferator-activated receptor) level by activating
the transcriptase for adipogenesis. Conversely, GH phosphor-
ylates PPAR and inhibits adipogenesis. As a result, reduced
insulin concentration favors a lipolytic route with the
mobilization of visceral localized fat deposits.
61−63
Earlier scientific studies have identified lactic acid as a
possible biomarker of psoriasis.
53,64
Confirming this evidence,
the VIP analysis (Figure 3) indicated increased lactic acid
concentration in psoriatic patients. On the contrary, the
concentrations of lactic acid and L-isoleucine, although
decreasing, do not fall into the physiological range of healthy
subjects, perhaps due to the short-term treatment.
The confirmationthattheketogenicdietinducesa
correction of the dysmetabolic condition related to psoriasis
disease results from the NMR metabolomics study performed
on psoriasis patients’blood sera. NMR metabolomics data on
the patient sera collected before the diet indicated abnormal
concentrations of metabolites that are related to the condition
of psoriasis,
65
and these concentrations were found in the
ranges of the healthy controls at T1, suggesting a rebalancing
of the metabolome after the ketogenic regimen.
65
Abnormally high concentrations of L-alanine and L-leucine
and a lower concentration of L-glutamine were previously
identified as biomarkers of psoriasis.
53
VIP score analysis based
on the multivariate statistical analysis of the NMR metab-
olomics data shows a decrease in L-leucine and L-alanine and
an increase in glutamine and glutamate at T1.
Moreover, significant variations in L-arginine, L-phenyl-
alanine, L-aspartic acid, and L-proline concentrations at T1
were also observable.
53
An interpretation of these metabolic
changes according to the pathway and enrichment analysis
(Figures 6 and 7) indicated that the regression of psoriasis was
related to the correction of amino acid metabolic pathways, in
particular, those of alanine, aspartate, and glutamate metabo-
lism; arginine and proline metabolism; valine, leucine, and
isoleucine degradation; valine, leucine, and isoleucine biosyn-
thesis; and tyrosine metabolism. Therefore, these data,
considered from a pathognomonic perspective, suggest that
important modifications in amino acid and glycolysis pathways
for psoriasis patients are ascribed to an increase in amino acid
and energy demand for protein biosynthesis and keratinocyte
hyperproliferation.
53
NMR metabolomics data showed that choline levels were
decreased in psoriasis patients at T1 compared to T0. The
decrease in choline and nicotine concentrations is additional
evidence that the regression of psoriasis corresponds to the
regression of the inflammatory process: previous data showed
high choline and nicotine levels in psoriasis patients due to
mast cell degranulation.
52
Additionally, we measured the concentration of the
cytokines IL-4, TNF-α, INF-γ, IL-2, and IL-1βthat are
considered markers of inflammatory status. In agreement with
previous scientific studies pointing to altered interleukin serum
concentrations in psoriasis patients,
66,67
we found decreased
IL-2 and IL-1βconcentrations at T1 compared to T0
52,53
Figure 8. Mean resultant significant cytokine concentrations (P< 0.05) at T0 (before diet) and T1 (after 4 weeks of the diet).
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J. Proteome Res. 2021, 20, 1509−1521
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(Figure 3). The concentrations of IL-4, TNF-α, and INF-γ
were not significantly decreased, probably due to the
insufficient 4 week period to induce a significant variation of
pro-inflammatory cytokine concentrations.
■CONCLUSIONS
Taken together, our data suggest that a low-calorie ketogenic
diet can be considered a successful strategy and therapeutic
option for the management of psoriasis. IL-2 and IL-1β,
together with the concentrations of leucine, alanine, glutamine,
glutamate, and choline, can be considered promising
biomarkers for the early diagnosis and correct prognosis of
psoriasis patients.
Our data suggest that a low-calorie ketogenic diet can be
considered a successful strategy and therapeutic option for
psoriasis management. IL-2 and IL-1β, together with the
concentrations of L-leucine, L-alanine, L-glutamine, L-glutamate,
and choline, can be considered promising biomarkers for the
early diagnosis and correct prognosis of psoriasis patients. The
dietary program is feasible, with high compliance, and safe.
The main effects depend on reducing VAT, disrupting the
inflammatory environment, and the source of inflammatory
cytokines.
■ASSOCIATED CONTENT
*
sıSupporting Information
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00646.
Figure S1: green line on top showing the accumulated
variance explained and blue line underneath showing the
variance explained by individual PC; Figure S2: pairwise
score plots among the selected PCs; Figure S3: PLS-DA
classification using different numbers of components;
Table S1: important features identified by fold change
and logarithmic fold change (log2(FC)) parameters
calculated; Table S2: important features identified by t-
test values, Pvalues (threshold, <0.05), logarithmic P
values and false discovery rate (FDR) parameters
calculated for the most statistically significant com-
pounds; Table S3: important features identified by PLS-
DA and relative component VIP score; Table S4: results
from quantitative enrichment analysis; Table S5: path-
way analysis by Reactome and relative Pvalue and
number of hits; Table S6: clinical features of the patients
during the follow-up (PDF)
■AUTHOR INFORMATION
Corresponding Authors
Anna Maria D’Ursi −Department of Pharmacy, University of
Salerno, 84084 Fisciano, Salerno, Italy; orcid.org/0000-
0001-6814-8472; Phone: +39089969748; Email: dursi@
unisa.it
Luca Rastrelli −NutriKeto_LAB Unisa−“San Giuseppe
Moscati”National Hospital (AORN), 83100 Avellino,
Avellino, Italy; Department of Pharmacy, University of
Salerno, 84084 Fisciano, Salerno, Italy; orcid.org/0000-
0003-0718-5450; Phone: +39089969766;
Email: rastrelli@unisa.it
Authors
Giuseppe Castaldo −NutriKeto_LAB Unisa−“San Giuseppe
Moscati”National Hospital (AORN), 83100 Avellino,
Avellino, Italy; orcid.org/0000-0003-2887-5012
Imma Pagano −NutriKeto_LAB Unisa−“San Giuseppe
Moscati”National Hospital (AORN), 83100 Avellino,
Avellino, Italy; Department of Pharmacy, University of
Salerno, 84084 Fisciano, Salerno, Italy; orcid.org/0000-
0002-8678-7371
Manuela Grimaldi −Department of Pharmacy, University of
Salerno, 84084 Fisciano, Salerno, Italy; orcid.org/0000-
0001-7354-8008
Carmen Marino −Department of Pharmacy, University of
Salerno, 84084 Fisciano, Salerno, Italy
Paola Molettieri −NutriKeto_LAB Unisa−“San Giuseppe
Moscati”National Hospital (AORN), 83100 Avellino,
Avellino, Italy; orcid.org/0000-0001-6380-5250
Angelo Santoro −Department of Pharmacy, University of
Salerno, 84084 Fisciano, Salerno, Italy; orcid.org/0000-
0002-9690-907X
Ilaria Stillitano −Department of Pharmacy, University of
Salerno, 84084 Fisciano, Salerno, Italy
Rocco Romano −Department of Pharmacy, University of
Salerno, 84084 Fisciano, Salerno, Italy
Paola Montoro −Department of Pharmacy, University of
Salerno, 84084 Fisciano, Salerno, Italy
Complete contact information is available at:
https://pubs.acs.org/10.1021/acs.jproteome.0c00646
Author Contributions
G.C. designed research. I.P., P.M., A.S., I.S., and M.G.
conducted research and analyzed the data. C.M., R.R., and
P.M. performed the statistical analysis. A.M.D. and L.R. wrote
the paper.
Notes
The authors declare no competing financial interest.
■ABBREVIATIONS
PASI, psoriasis area and severity index; BSA, body surface area;
DLQI, dermatology life quality index; BMI, body mass index;
NMR, nuclear magnetic resonance; GH, growth hormone;
HDL, high-density lipoprotein; LDL, low-density lipoprotein;
AST, aspartate aminotransferase; γGT, gamma glutamyl
transferase; HOMA-IR, homeostasis model assessment insulin
resistance; VAT, visceral adipose tissue; AMFT, aortomesen-
teric fat thickness; CPMG, Carr−Purcell−Meiboom−Gill;
HMDB, Human Metabolome Database; MVA, multivariate
analysis; PCA, principal component analysis; PLS-DA, partial
least-squares discriminant analysis; TSP, trimethylsilyl pro-
pionic-2,2,3,3-d4acid, sodium salt; PRICONA, PRincipal
COmponent Normalization Algorithm; ELISA, enzyme-linked
immunosorbent assay; VIP, variable influence on projection;
VAS, visual analogue scale; FDR, false discovery rate; PPAR,
peroxisome proliferator-activated receptor
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