Abstract
Objective
To explore the influence of serum metabolites on the risk of psoriasis.
Methods
In the initial stage, we applied Mendelian randomization to evaluate
the association between 1,400 serum metabolites and the risk of
psoriasis. Causal effects were primarily assessed through the
Inverse-Variance Weighted method and Wald Ratio’s odds ratios, and 95%
confidence intervals. False Discovery Rate was used for multiple
comparison corrections. Sensitivity analyses were conducted using
Cochran’s Q Test, MR-PRESSO. MR-Steiger Test was employed to check for
reverse causality. In the validation stage, we sought other sources of
psoriasis GWAS data to verify the initial results and used
meta-analysis to combine the effect sizes to obtain robust causal
relationships. In addition, we also conducted metabolic pathway
enrichment analysis on known metabolites that have a causal
relationship with the risk of psoriasis in both stages.
Results
In the initial stage, we identified 112 metabolites causally associated
with psoriasis, including 32 metabolite ratios and 80 metabolites (69
known and 11 unknown). In the validation stage, 24 metabolites (16
known, 1 unknown, and 7 metabolite ratios) were confirmed to have a
causal relationship with psoriasis onset. Meta-analysis results showed
that the overall effect of combined metabolites was consistent with the
main analysis in direction and robust in the causal relationship with
psoriasis onset. Of the 16 known metabolites, most were attributed to
lipid metabolism, with 5 as risk factors and 8 as protective factors
for psoriasis. Peptidic metabolite Gamma-glutamylvaline levels had a
negative causal relationship with psoriasis, while exogenous metabolite
Catechol sulfate levels and amino acid 3-methylglutaconate levels had a
positive causal relationship with the disease onset. The metabolites
associated with psoriasis risk in the two stages are mainly enriched in
the following metabolic pathways: Glutathione metabolism, Alpha
Linolenic Acid and Linoleic Acid Metabolism, Biosynthesis of
unsaturated fatty acids, Arachidonic acid metabolism,
Glycerophospholipid metabolism.
Conclusion
Circulating metabolites may have a potential causal relationship with
psoriasis risk, and targeting specific metabolites may benefit
psoriasis diagnosis, disease assessment, and treatment.
Keywords: psoriasis, Mendelian randomization, metabolites, causal
effect, implication
1. Introduction
Psoriasis is a chronic inflammatory disease that affects the skin and
joints and is now considered a systemic condition due to its frequent
association with multiple systemic disorders. Metabolic Syndrome (MetS)
is the most common comorbidity of psoriasis and a risk factor for
cardiovascular disease, representing a principal cause of mortality in
patients with psoriasis ([44]1). The prevalence of MetS in patients
with psoriasis ranges from 20% to 50%, and it increases with the
severity of the psoriasis condition ([45]2, [46]3).
Multiple studies employing metabolomics have identified a metabolic
profile in the plasma of psoriasis patients, uncovering extensive
metabolic disturbances in lipids and amino acids among these
individuals ([47]4–[48]6). Chen et al. observed significant alterations
in the metabolism of amino acids and carnitines in patients with
psoriasis, particularly involving the metabolism of amino acids,
branched-chain amino acids, and carbon monoxide ([49]5). Zeng et al.
found significant differences in the components of glycerophospholipid
metabolism, such as lysophosphatidic acid (LPA),
lysophosphatidylcholine (LysoPC), phosphatidic acid (PA),
phosphatidylinositol (PI), and phosphatidylcholine (PC), between the
plasma of psoriasis patients and healthy individuals ([50]4). The
concurrence of psoriasis and metabolic dysregulation is increasingly
becoming a significant public health issue, yet the pathomechanisms of
their comorbidity remain unclear.
Although a definitive causal relationship has not been established,
genetic susceptibility, common inflammatory pathways, and environmental
factors may contribute to the metabolic abnormalities observed in
patients with psoriasis ([51]1). In the field of genetics research,
numerous single nucleotide polymorphisms have been identified in
proximity to loci associated with innate and adaptive immune genes
relevant to psoriasis, such as antigen-presenting genes (HLA-C, ERAP1)
and Th17 cell activation genes (IL23R, IL23A, IL12B, TRAF3IP2) ([52]7).
Alterations in metabolic processes can lead to epigenetic
abnormalities. Therefore, studying differential metabolic biomarkers in
psoriasis is of significance for identifying potential therapeutic
targets for the disease.
Mendelian randomization (MR) is an epidemiological methodology that
utilizes genetic variants, primarily single nucleotide polymorphisms
(SNPs), as instrumental variables (IVs) to proxy for target exposure
variables, with the aim of assessing the causal relationship between
exposures and specific outcomes ([53]8). Published Genome-Wide
Association Studies have integrated the associations between
circulating metabolites and SNPs, facilitating the causal inference of
the relationship between metabolic products and the risk of psoriasis.
Consequently, this study employs Mendelian Randomization to explore the
causal link between circulating metabolites and psoriasis risk from a
genetic variation perspective, aiming to identify potential metabolites
related to the onset of psoriasis in hopes of discovering targets for
disease activity assessment, diagnosis, and treatment.
2. Subjects and methods
2.1. Study design
To elucidate the causal relationship between serum metabolites and the
risk of developing psoriasis, we embarked on this MR study. Initially,
we employed a two-sample MR approach to screen for causal associations
between 1,400 metabolites and psoriasis. We also conducted multiple
sensitivity analyses to ensure the robustness of our findings.
Subsequently, we validated the results from the initial stage using
psoriasis GWAS data from other sources. Lastly, we applied a
meta-analysis to integrate effect sizes of metabolites with causal
effects on both psoriasis outcomes, thereby obtaining a robust causal
inference ([54]Figure 1).
Figure 1.
[55]Figure 1
[56]Open in a new tab
Mendelian randomization’s three hypotheses and research flowchart.
2.2. Data source
The summary statistics for serum metabolites utilized in this study
were sourced from The Canadian Longitudinal Study of Aging (CLSA)
([57]9). The study conducted genome-wide genotyping and circulating
plasma metabolite assessments on 8,299 unrelated European individuals.
Following stringent pre-GWAS genotype quality control, approximately
15.4 million SNPs were retained for GWAS assessment. The levels of
1,458 metabolites in plasma samples were quantified using the Ultrahigh
Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS)
platform. After standardization, which involved the removal of entities
representing systemic artifacts, misassignments, and background noise,
a rigorous quality control process ascertained 1,091 metabolites (850
known substances and 241 unknown entities) and 309 metabolite ratios
for inclusion in the genome-wide association analyses. The 850
metabolites with established identities were categorized into eight
super-pathways: carbohydrates, amino acids, nucleotides, vitamins,
lipids, peptides, energy metabolism products, and xenobiotics. Given
that many metabolites serve as substrates and products of enzymatic
reactions, investigating the genetic determinants of substrate-product
ratios can enhance the understanding of broader biological processes;
hence, the 309 metabolite ratios were also incorporated into this
study.
The initial stage outcome data were derived from the FinnGen biobank
analysis consortium, comprising 9,267 psoriasis patients and 364,071
healthy controls ([58]10). Validation data were sourced from a study
published by Stuart PE in 2021, including 15,976 psoriasis cases and
28,194 healthy controls ([59]11). The genetic data utilized in this
study conform to ethical standards and have been approved by local
ethics review committees, representing legally public research data.
2.3. Selection of instrumental variables
Instrumental variables were selected through the following steps. (1)
Genetic variants associated with metabolic traits were identified at a
genome-wide significance threshold set at P< 5×10^-7. (2) The
independence of the aggregated SNPs was assessed based on linkage
disequilibrium, selecting instrument variables without linkage effects
in the metabolic data (parameters set at r^2< 0.01 within a 5000 kb
distance). (3) These instrumental variables were then extracted from
the psoriasis dataset, excluding any IVs related to the outcomes and
discarding palindromic SNPs. (4) The strength of individual SNPs was
tested by calculating the F-statistic, retaining those with an
F-statistic > 10.The F-statistic is calculated using the formula
[MATH:
F=R2×(N−K−1)K(1−R2<
/mn>) :MATH]
, where R^2 is the proportion of variance in the exposure that is
explained by the instrumental variables, given by
[MATH:
R2=2×EAF×(1−EAF)×β22×EAF×<
/mo>(1−EAF)×β2+2×EAF×(1−EAF)×N×S<
msup>E2 :MATH]
. Within this formula, N denotes the sample size for the exposure, K
represents the number of instrumental variables, EAF is the effect
allele frequency, β is the effect size, and SE is the standard error of
the effect size ([60]12, [61]13).
2.3. Two-sample Mendelian randomization
The present study primarily employs the inverse-variance weighted (IVW)
method, weighted median (WM), MR-Egger, Weighted mode, simple mode, and
Wald Ratio for analysis. The IVW method, assuming all SNPs are valid
and independent, constrains the regression intercept to zero and uses
the reciprocal of the outcome variance as weights for fitting ([62]14).
When each genetic variant satisfies the assumptions of an instrumental
variable, IVW is presumed to provide the most accurate results; hence,
the IVW outcomes are often regarded as the major standard for the
assessment of causal effects ([63]14, [64]15). In this study, the
P-value of IVW is used as the primary indicator for assessing the
causal effect between exposure and outcome. If only a single
instrumental variable is obtained, the P-value from the Wald Ratio is
used for assessment. Other methods serve to supplement the evaluation
of MR results, and a consistent direction of effect size (β-value)
across different methods indicates robust findings. The False Discovery
Rate (FDR) correction is applied to adjust for multiple testing of all
P-value obtained from IVW and Wald Ratio methods.Pvalueof FDR less than
0.05 indicates a clear causal relationship between exposure and
outcome, while greater than 0.05 indicates a potential causal
relationship.
2.4. Sensitivity analysis
Diverse methodologies are employed to evaluate the heterogeneity and
pleiotropy of the results. Heterogeneity was tested by Cochran’s Q
statistic where a P-value greater than 0.05 suggests an absence of
heterogeneity ([65]16). Pleiotropy can be evaluated using the MR-PRESSO
test and the MR-Egger regression intercept; if the P-value for the
MR-PRESSO global Test and the MR-Egger intercept are greater than 0.05,
this suggests that pleiotropy is not present ([66]17, [67]18). It is
worth noting that when the number of SNPs is insufficient (3 for
MR-PRESSO, 2 for MR-Egger), pleiotropy test will not be performed.
The Steiger test is used to examine the directionality of the
instrumental variable’s effect on the outcome, avoiding reverse
causation, thereby confirming whether the results support the initial
hypothesis. P-value of Steiger greater than 0.05 for the instrumental
variable indicates the presence of reverse causation ([68]19, [69]20).
2.5. Replication stage and meta-analysis
We sought additional sources of outcome data for psoriasis and
conducted MR validation on the initial stage results under the same
conditions, retaining metabolites and metabolite ratios that still
exhibited a causal effect with psoriasis. Subsequently, we merged the
two MR results (odds ratios, OR; 95% confidence intervals, CI) through
a meta-analysis. If the merged results showed heterogeneity, indicated
by an I^2 value greater than 50%, a random-effects model was utilized;
otherwise, a fixed-effects model was applied. The statistical threshold
for the meta-analysis was set at 0.05. The aforementioned study was
primarily conducted using the Two Sample MR and meta packages within
the R statistical software, version 4.2.1, with a significance level
(alpha) of 0.05.
2.6. Metabolic pathway enrichment analysis
The HMDB IDs of known metabolites were retrieved from The Human
Metabolome Database ([70]https://hmdb.ca/), and enrichment analysis of
the metabolic pathways associated with these metabolites was conducted
using MetaboAnalyst 5.0 ([71]https://www.metaboanalyst.ca/). The
pathway libraries selected for this analysis were the Small Molecule
Pathway Database (SMPDB) and the Kyoto Encyclopedia of Genes and
Genomes (KEGG). The enrichment method employed was the Hypergeometric
Test, and the significance level for metabolic pathway analysis was set
at 0.01.
3. Results
3.1. Initial stage Mendelian randomization analysis results
Within a pool of 1,400 metabolites, 112 metabolites were discerned to
have a causal association with psoriasis, including 32 metabolite
ratios and 80 individual metabolites (of which 69 were identified
metabolites and 11 were unidentified) ([72]Figure 2). After adjustment
for FDR, 7 metabolites or metabolite ratios remained significantly
causally associated with psoriasis. Additionally, the 375 SNPs finally
associated with psoriasis causality were all linked to strong
instrumental variables (with an F-statistic > 10).Detailed information
of IVs can be found in the [73]Supplementary Table 1.
Figure 2.
[74]Figure 2
[75]Open in a new tab
Classification of 112 metabolites.
The 69 identified metabolites predominantly belonged to the categories
of lipids, xenobiotics and amino acids. Of the 35 lipid metabolites
analyzed, the strongest protective effect was identified as Carnitine
C14 levels (OR: 0.49, 95% CI: 0.38-0.64, P[Wald Ratio]=2.43×10^-7,
P[FDR]=3.13×10^-4), while the metabolite with the most potent risk
effect was Dihomo-linoleate (20:2n6) levels (OR: 2.07, 95% CI:
1.00-4.28, P[Wald Ratio] =0.049, P[FDR]=0.559).
Among the 11xenobiotic, the most significant positive and negative
causal association with the onset of psoriasis was observed with
elevated Umbelliferone sulfate levels (OR: 2.06, 95% CI:
1.53-2.79,P[Wald Ratio]=2.49×10^-6, P[FDR]=0.002) and elevated
4-vinylphenol sulfate levels (OR: 0.53, 95% CI: 0.28-0.97, P[Wald
Ratio]= 0.041, P[FDR]= 0.538).
Among the 10amino acids metabolites, the increase in Spermidine levels
exhibited the most significant positive causal effect on the incidence
of psoriasis (OR: 1.37, 95%CI: 1.10-1.71, P[IVW] = 0.005,
P[FDR]=0.239), whereas the increase in Histidine levels was associated
with the most significant negative causal effect (OR: 0.8, 95% CI:
0.69-0.92, P[IVW]=0.002, P[FDR]=0.192). Additionally, of the 32
metabolite ratios with a causal link to psoriasis, detailed information
can be found in [76]Supplementary Table 2.
The direction of effect estimates for most metabolites was consistent
across multiple analytical methods, rendering the results more
reliable. Specific MR analysis results for different methods are
presented in [77]Figure 3 and [78]Supplementary Table 2.
Figure 3.
[79]Figure 3
[80]Open in a new tab
Heat map of the results of six Mendelian randomization analysis methods
in the initial stage.
3.2. Sensitivity and reverse causality analysis
3 metabolites were identified with heterogeneous results usedCochran’s
Q test. Pleiotropy evaluation through the MR-PRESSO method detected
horizontal pleiotropy in the exogenous metabolite Threonine levels.
Detailed information is shown in [81]Supplementary Table 2.
The MR-Steiger directionality test confirmed the accuracy of the
exposure to outcome direction with all instrumental variables showing
P[Steiger]< 0.05. P-values for testing the reverse relationship between
each metabolite and psoriasis were all less than 0.05, suggesting no
evidence of potential reverse causation. Detailed information is shown
in [82]Supplementary Tables 2, [83]3.
3.3. Replication stage Mendelian randomization results and meta-analysis
Out of 112 metabolites with a putative causal relationship with
psoriasis, 24 (7 metabolite ratios, 16 known metabolites, and 1 unknown
metabolite) were successfully validated. [84]Figure 4A visualizes the
sensitivity analysis results of 24 metabolites. Detailed information of
main MR Results of 24 metabolites are shown in the [85]Supplementary
Table 4 and [86]Figure 5.
Figure 4.
[87]Figure 4
[88]Open in a new tab
(A) displays the heterogeneity and pleiotropy analysis results of 24
metabolites that have been successfully validated. (B) displays the
enrichment pathways of metabolites in KEGG and (C) displays the
enrichment pathways of metabolites in SMPDE database.
Figure 5.
[89]Figure 5
[90]Open in a new tab
Forest map displays the results of Meta Analysis, where * is the result
of Verification data.
For meta-analysis, heterogeneity test revealed heterogeneity in the
metabolite Arachidoylcarnitine (C20) levels (I^2 = 51%), while the rest
of the metabolites showed no evidence of heterogeneity. The
meta-analysis results indicated that the combined overall effect of the
24 metabolites was consistent with the direction of effect observed in
two stages MR analysis and confirmed astable causal relationship
between these metabolites and psoriasis (P<0.05).
Meta-analysis showed that among the 13 lipid metabolites, the increase
of 1-(1-enyl-palmitoyl)-2-linoleoyl-GPE (p-16:0/18:2) was a strong risk
factor for psoriasis (OR: 1.17, 95% CI: 1.07-1.28, P<0.001).
Considering the insufficient phenotypic interpretation of a single SNP,
the increase of Arachidoylcarnitine (C20) was a strong protective
factor for psoriasis (OR: 0.74, 95% CI: 0.64-0.86, P=0.004).
Additionally, the peptide metabolite Gamma-glutamylvaline levels were
inversely associated with the risk of psoriasis (OR: 0.71, 95% CI:
0.59-0.85, P<0.001), while the xenobiotics Catechol sulfate levels (OR:
1.34, 95% CI: 1.15-1.55, P<0.001) and the amino acid metabolite
3-methylglutaconate levels (OR: 1.11, 95% CI: 1.05-1.16, P<0.001) were
associated with an increased risk of psoriasis. Further details can be
found in [91]Figure 5.
3.4. Results of metabolic pathway enrichment analysis
We queried the HMDB IDs for the 69 known metabolites (16 for
replication stage) associated with psoriasis risk in the initial stage
and conducted metabolic pathway enrichment analysis on the 52 (11 for
replication stage) identifiable compounds. Two stages’ enrichment
results highlighted the primary metabolic pathways as Glutathione
metabolism, Biosynthesis of unsaturated fatty acids Alpha Linolenic
Acid and Linoleic Acid Metabolism (initial stage) and
Glycerophospholipid metabolism, Arachidonic acid metabolism
(replication stage). Due to the limited amount of metabolites, the
SMPDB database was unable to perform enrichment analysis in the
replication stage. Specific enrichment results are presented in
[92]Supplementary Table 3 and [93]Figures 4B, C.
4. Discussion
To our knowledge, there has been no study reported that investigates
the causal relationship between serum metabolites and psoriasis using
MR analysis. Our study pioneers the systematic exploration of the
association between serum metabolites and the risk of psoriasis through
MR analysis, complemented by pathway enrichment analysis. We
strengthened our preliminary findings with validation analyses and
meta-analyses, identifying 17 metabolites (16 known and 1 unknown)
causally linked to the risk of developing psoriasis. The known
metabolites are predominantly involved in lipid metabolism pathways.
Pathway analysis highlighted the importance of glutathione metabolism,
alpha-linolenic and linoleic acid metabolism, biosynthesis of
unsaturated fatty acids etc. We reviewed the literature on 16 known
metabolites and summarized the reported roles of 9 searchable
metabolites in different diseases in [94]Table 1.
Table 1.
The known interaction effects of metabolites in different diseases from
previous studies.
Metabolite Biological function Ref.
Eicosapentaenoate (EPA, 20:5n3) 1.Anti-inflammatory benefits for
various diseases.
2.Maintains gut health and immune function.
3.Boosts muscle function and athletic recovery.
4.Protects the heart and prevents blood clots.
5.Reduces lipid oxidation and enhances the effects of statins.
6.Alleviates psoriasis symptoms and lowers lipid toxicity.
7.Reduces inflammation by decreasing IL-17A T cells.
8.Enhances skin lipid balance and promotes skin health. ([95]21–[96]28)
Docosapentaenoate (n3 DPA, 22:5n-3) 1. Promotes endothelial migration.
2. Regulates gut flora for colitis relief.
3. n-3 DPA reduces macrophage inflammation.
4. Low n-3 DPA correlates with increased CRP and triglycerides.
5. Higher DPA reduces RSV risk in infants. ([97]29–[98]33)
linoleoyl-arachidonoyl-glycerol (18:2/20:4)
Linoeloyl-arachidonoyl-glycerol (18:2/20:4) correlates positively with
serum α-tocopherol concentrations. ([99]34)
Arachidonate (20:4n6) 1.Regulates blood pressure, diabetes.
2. Crucial for infant growth, neural, immune maturation.
3.Marker for COPD, nephrotic syndrome, prostate cancer.
4.Affects hBMSC immunomodulation. ([100]35–[101]39)
1-palmitoyl-2-palmitoleoyl-gpc (16:0/16:1) 1. Correlates with increased
depression risk.
2. Potential biomarker for diagnosis of colorectal cancer.
3.Aids in surgical boundary definition of oral cancer ([102]40–[103]42)
3-methylglutaconate 1.Linked to aciduria and congenital metabolic
defects in mitochondrial energy metabolism
2.Induces hepatic lipid peroxidation, disrupts redox balance by
altering enzymatic/non-enzymatic antioxidants.
3.Reduces non-enzymatic antioxidant defense in brain cortical cells,
triggers lipid oxidative damage. ([104]43–[105]46)
Catechol sulfate 1.Biomarker for ultra-processed food intake.
2.Assesses renal function and dialysis efficacy in CKD. ([106]47,
[107]48)
Gamma-glutamylvaline 1.Reducing colitis inflammation
2.Rrevents TNF-α-induced inflammatory cytokines in adipocytes,
protecting against inflammation-induced insulin resistance
3. Inhibiting the gamma-glutamyl cycle, exhibiting anticancer activity.
4.Suppresses LPS-induced pro-inflammatory cytokines in sepsis.
5.Lowers TNF-α-induced vascular inflammation by activating endothelial
CaSR and reducing adhesion molecules and cytokines. ([108]22,
[109]49–[110]53)
Carnitine C14 1.Induce inflammation via cytokine expression and JNK/ERK
phosphorylation.
2.Limits autophagosome-lysosome fusion, reduces autophagy.
3.Correlates with increased risk of diabetic cardiovascular disease.
4.Potential biomarker for conditions like neonatal hypoxic-ischemic
encephalopathy, liver injury, knee cartilage volume loss.
([111]54–[112]60)
[113]Open in a new tab
Numerous observational studies have identified a pervasive
dysregulation of lipid metabolism in patients with psoriasis, and our
research has also discerned a causal link between 13 lipid metabolites
and the onset of psoriasis. Lipid metabolites such as Docosapentaenoate
n3 (DPA 22:5n3), commonly referred to as n-3 DPA, and Eicosapentaenoate
(EPA; 20:5n3), known as EPA, are omega-3 polyunsaturated fatty acids.
n-3 DPA is an intermediate between EPA and DHA. On the one hand, lower
n-3 DPA concentrations have been found to correlate with higher levels
of the inflammatory marker CRP ([114]32), and n-3 DPA can reduce the
expression of pro-inflammatory factors IL-6, IL-1β in RAW264.7
macrophages stimulated by LPS ([115]33). Therefore, n-3 DPA has a
certain anti-inflammatory effect, which may play a role by reducing the
inflammatory response of psoriasis lesions. On the other hand, it has
been reported that the disturbance of intestinal flora can destroy the
intestinal mucosal barrier, increase intestinal mucosal permeability,
and reduce the metabolism of probiotics and short chain fatty acids,
thus aggravating the intestinal inflammatory response in patients with
psoriasis ([116]61). n-3 DPA and EPA play an important role in
maintaining the integrity of intestinal barrier, increasing the
diversity of intestinal microbiota, and regulating intestinal immunity
([117]23, [118]29), both may play an important role in the
gut-cutaneous axis by maintaining the balance between the gut
microbiota, repairing the intestinal barrier, and reducing
inflammation. Additionally, EPA has been shown to reduce the proportion
of IL-17A producing T-cells and diminish the production of inflammatory
mediators by psoriatic keratinocytes and T-cells, thus exerting its
anti-inflammatory effects ([119]25, [120]26). Moreover, supplementation
with EPA can increase the content of EPA and n-3 DPA in the dermal and
epidermal phospholipids, balance the production of epidermal lipid
mediators, promote normal differentiation of psoriatic epidermis, and
restore homeostasis at psoriatic skin lesions ([121]25). The elevated
levels of DPA and EPA found in our study correlate with a reduced risk
of psoriasis, aligning with previous research. Supplementation of DPA
and EPA may mitigate the risk of psoriasis by regulating intestinal
immunity, modulating the balance of the gut microbiome, inhibiting
inflammatory responses, and promoting epidermal differentiation.
Gamma-glutamylvaline (γ-EV) is a dietary peptide ubiquitously found in
various foods. As demonstrated in [122]Table 1, γ-EV has been
identified to play a role in multiple inflammatory animal and cell
models, such as colitis in murine intestinal epithelial cells,
adipocytes, and human aortic endothelial cells ([123]50, [124]52,
[125]62). Additionally, research by Chee et al. has shown that oral
administration of γ-EV can reduce the expression levels of
pro-inflammatory cytokines TNF-α, IL-6, and IL-1β in the serum and
small intestine of sepsis-induced mice by LPS, exhibiting anti-septic
systemic inflammatory activity ([126]22) Zhang and colleagues have also
discovered that supplementation with γ-EV can alleviate intestinal
inflammation in a porcine model of colitis ([127]51). Although there is
no explicit research evidence directly linking γ-EV with psoriasis or
its impact, psoriasis is globally recognized as a chronic inflammatory
skin disease, and the interplay of inflammation and response is a
crucial pathogenic mechanism. Our study results have found
Gamma-glutamylvaline (γ-EV) to be a protective factor for psoriasis
(OR:0.71, 95%CI:0.59-0.85, P<0.001). γ-EV may exert a therapeutic
effect by reducing the level of inflammation in epidermal cells.
Therefore, the supplementation of γ-glutamylvaline could potentially be
beneficial for psoriasis management.
Our study also identified exogenous metabolite catechol sulfate levels
and the amino acid metabolite 3-methylglutaconate levels as risk
factors for the onset of psoriasis. Catechol sulfate is recognized as
one of the biomarkers for the intake of ultra-processed foods, with its
metabolite levels significantly correlating with the consumption of
such foods ([128]48) The increased consumption of ultra-processed foods
has been reported to elevate the risk of various diseases, including
diabetes, depression, and cancer ([129]63–[130]65). Therefore,
individuals with psoriasis should limit their intake of ultra-processed
foods to prevent the potential risk increase associated with high
levels of catechol sulfate metabolites. Dysregulation of
3-methylglutaconic acid metabolism has been noted in conditions such as
3-methylglutaconic aciduria and various congenital metabolic defects
where mitochondrial energy metabolism is impaired ([131]44, [132]45).
Elevated levels of 3-methylglutaconic acid detected in various
inherited metabolic diseases could lead to dysfunctions in the nervous
system, heart, liver, and other organs ([133]66, [134]67). Leipnitz and
colleagues have further found through in vitro experiments that the
accumulation of 3-methylglutaconic aciduria metabolites can increase
oxidative stress in cerebral cortical epithelial cells, potentially
contributing to mechanisms inducing brain damage ([135]46). At present,
there are no studies on the correlation between these two metabolites
and psoriasis, and our results confirm that increased levels of
catechol sulfate and 3-methylglutaconate increase the risk of
psoriasis, but further mechanism studies are still to be conducted.
Additionally, we found two metabolites that are risk factors in other
diseases, yet are protective factors in our results. As demonstrated in
[136]Table 1, research by van et al. has indicated a positive
correlation between the levels of 1-palmitoyl-2-palmitoleoyl-gpc
(16:0/16:1) and the risk of depression ([137]42). Although psoriasis
patients often have mental illnesses such as depression, our results
suggest a negative correlation between 1-palmitoyl-2-palmitoleoyl-gpc
(16:0/16:1) levels and the incidence of psoriasis. Carnitine C14 may
activate pro-inflammatory pathways to induce inflammatory responses and
is positively associated with the risk of diabetic cardiovascular
disease ([138]56, [139]58), suggesting that Carnitine C14 may be a
potential risk factor, which is inconsistent with our results. This
could potentially be due to a bias from the single SNP obtained under
the current screening criteria, therefore, it is necessary to relax the
screening criteria to include more SNPS to increase the interpretation
of phenotypes. Of course, in order to better understand the role of
metabolites in psoriasis, further clinical research or experimental
verification is more necessary.
It is noteworthy that the metabolites identified in our study results
are primarily enriched in glutathione metabolism, as well as
alpha-linolenic and linoleic acid metabolism pathways. Glutathione is a
vital metabolic regulator with intracellular antioxidative and
anti-inflammatory functions, playing a role in maintaining normal
immunity. Oxidative stress markers are established to be elevated in
psoriasis and correlate with the disease’s course and severity
([140]68), which may be associated with the abnormal levels of the
antioxidant glutathione in the skin lesions and peripheral blood of
patients with psoriasis, as well as dysregulation of related
transferase activities ([141]69). Furthermore, basic experimental
evidence confirms that levels of antioxidant markers such as
glutathione (GSH) and superoxide dismutase (SOD) are decreased in
psoriasis mouse and cell models, which are reversed upon treatment
([142]70, [143]71). Impairment of glutathione metabolism in psoriasis
may relate to an imbalance in the oxidative-antioxidative system,
inflammatory responses, and immune system dysregulation. Research
reveals a strong positive correlation between glutathione and
inflammatory cytokines, angiogenic initiators in psoriasis ([144]72),
and it plays a significant role in various psoriasis-associated cells.
Liu et al. found increased numbers of MDSCs and M-MDSCs in the
peripheral blood and skin lesions of psoriasis patients, and acitretin
promotes the differentiation of MDSCs by increasing the expression of
glutathione synthetase (GSS) and accumulation of glutathione (GSH),
thus reducing ROS levels ([145]73). Campione et al. reported increased
activity of glutathione-S-transferase (GST) in the lesional areas of
psoriasis, exerting anti-inflammatory effects in the hyperproliferative
keratinocytes characteristic of psoriasis ([146]69). Additionally, GSH
can regulate the expression levels of IκBζ in macrophages ([147]74).
The oxidative stress levels in dendritic cells and neutrophils are
increased in the IMQ-induced psoriasis model ([148]72). In conclusion,
glutathione metabolism plays an important role in the pathological
processes of psoriasis such as oxidative stress and inflammation by
affecting a variety of cells, such as keratinocytes, macrophages, MDSC,
and dendritic cells.
Alpha-linolenic acid (ALA) and linoleic acid (LA), as essential
polyunsaturated fatty acids, play a pivotal role in human metabolism.
Vahlquist et al. observed significant reductions in the levels of
linoleic acid (18:2 omega-6) and α-linolenic acid (18:3 omega-3) in the
plasma lipid esters of patients with psoriasis compared to a healthy
control group. Notably, these reductions were even more pronounced in
patients with severe psoriasis, suggesting a possible correlation
between fatty acid levels and disease severity ([149]75). Methotrexate,
a cornerstone medication in psoriasis treatment, has been proven to
modulate linoleic acid metabolism within CD4^+ central memory T cells
and CD8^+ effector memory T cells ([150]76). Experimental studies have
elucidated that ALA and LA, under the action of lipoxygenase and
cyclooxygenase, give rise to eicosanoids such as prostaglandins (PGs),
thromboxanes, and leukotrienes (LTs), which play crucial roles in
inflammation and hemodynamic regulation, mediating inflammatory and
allergic diseases like psoriasis and atopic dermatitis
([151]76–[152]78). In metabolic pathways associated with psoriasis,
linoleic acid exhibits a negative correlation with AMPK and the PI3-Akt
signaling pathway. Specifically, ALA has been shown to mediate the Ras
signaling pathway, participating in cell adhesion and the
transendothelial migration of leukocytes ([153]79). Moreover, ALA
interacts directly with the NF-κB pathway and, as a ligand for PPARs,
expresses bioactivities with anti-inflammatory capacities ([154]80,
[155]81). The perspective of Simopoulos on dietary intake ratios
reveals the inflammatory impact of a high n-6 to n-3 fatty acid ratio
(20:1) prevalent in Western diets, which may enhance the production of
pro-inflammatory mediators. In contrast, a more balanced intake ratio
(approximately 1:1) is considered to have protective effects against
inflammation ([156]82). Synthesizing these insights regarding the
metabolic pathways of ALA and LA unveils their anti-inflammatory
attributes and accentuates the nuanced effects of varying dietary
intake ratios. This body of evidence provides compelling justification
for further investigation into the therapeutic potential of modulating
these fatty acid levels in the management of inflammatory diseases.
In addition, unsaturated fatty acid related metabolites EPA and n-3 DPA
have been discussed above. Many elements in the glycerol phospholipid
metabolism pathway, such as PA, PC, and PI, have also been confirmed to
undergo significant changes in the plasma of psoriasis patients
([157]4). Drugs such (R)-salbutamol and Chinese Herbal formula can
alleviate psoriasis by improving glycophoric metabolism ([158]83,
[159]84). Arachidonic acid (AA) is a polyunsaturated fatty acid that
serves as a precursor to various bioactive lipid mediators, including
prostaglandins, leukotrienes, and thromboxanes. These eicosanoids are
potent regulators of inflammation and immune responses. In psoriasis,
the metabolism of arachidonic acid is often dysregulated. Clinical
studies have found that compared with female psoriasis patients,
psoriatic skin lesions are more severe in male patients and plasma AA
concentration is significantly lower than that in female patients. AA
is positively correlated with DLQI score, which may be related to
estrogen metabolism ([160]85). Ye et al. reported that CD4^+ T cells in
patients with psoriatic arthritis (PsA) increased the expression of the
poro-forming calcium channel component ORAI3, thereby increasing the
activity of calcium selective channels regulated by arachidonic acid,
making T cells sensitive to arachidonic acid and contributing to the
chronic inflammatory response of PsA ([161]86). In addition,
inflammatory mediators such as PGE2 and LTB4 produced by Arachidonic
acid and its derivatives catalyzed by cycoperoxidase can recruit and
activate T cells to participate in inflammation ([162]26,
[163]87).Therefore, arachidonic acid and its derivatives may play a key
role in the pathogenesis of psoriasis by influencing the function and
sensitivity of T cells and activating the inflammatory response.
Our study has several limitations: Firstly, we used strict screening
conditions (P< 5×10^-8, r^2< 0.001 within a 10000 kb distance) and the
SNPS of many metabolites could not be extracted. After we broadened the
screening criteria (P< 5×10^-7, r^2< 0.01 within a 5000kb distance),
some metabolites still obtained fewer SNPs under the currently set
screening conditions, which may lead to a bias in the results.
Secondly, many metabolites filtered during the initial stage failed to
achieve significance post-rigorous multiple testing adjustments.
Thirdly, the population data comes from European ancestry, and there
may be differences when the findings generalize to other ethnicities.
Therefore, more people with different genetic backgrounds should be
analyzed to improve the generality of the results. Lastly, the
employment of genetic variants linked to metabolites as instrumental
variables reflects prolonged exposure scenarios; conversely, transient
dietary supplementation may elicit disparate effects on the delineated
results.
In conclusion, our study found that 24 metabolites (7 metabolite
ratios, 16 known metabolites, and 1 unknown metabolite) are associated
with psoriasis risk, and most of these metabolites belong to lipid
metabolism. These metabolites may be biomarkers to predict the onset
and development of psoriasis. This study also provides recommendations
for dietary adjustment and nutritional intervention in clinical
psoriasis patients, such as increasing the intake of γ-EV or Omega-3
fatty acids such as DPA, EPA, and reducing the intake of
ultra-processed foods to reduce the production of catechol sulfate have
a positive effect on the control of psoriasis.
Funding Statement
The author(s) declare financial support was received for the research,
authorship, and/or publication of this article. This study was
supported by grants from Research Fund for Bajian Talents of Guangdong
Provincial Hospital of Chinese Medicine (No.BJ2022KY02), Science and
Technology Planning Project of Guangzhou (Nos.202201020353 &
202206080006 & 202201020332), Innovation Team and Talents Cultivation
Program of National Administration of Traditional Chinese Medicine
(No.ZYYCXTD-C-202204), National Natural Science Foundation of China
(No. U20A20397 & U23A6012 & 82374313), Science and Technology Planning
Project of Guangdong Province (No. 2022A1515110720 & 2023B1212060063).
Data availability statement
The original contributions presented in the study are included in the
article/[164]Supplementary Materials, further inquiries can be directed
to the corresponding authors.
Author contributions
YY: Data curation, Formal analysis, Writing – original draft. XZ: Data
curation, Visualization, Writing – original draft. HL: Methodology,
Formal analysis, Writing – original draft. BT: Data curation,
Visualization, Writing – original draft. YZ: Writing – review &
editing, Validation. QL: Software, Writing – review & editing. YB:
Methodology, Writing – review & editing. KY: Resources, Writing –
review & editing. HZ: Investigation, Writing – review & editing. HC:
Funding acquisition, Supervision, Writing – review & editing. CL:
Supervision, Writing – review & editing.
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary material
The Supplementary Material for this article can be found online at:
[165]https://www.frontiersin.org/articles/10.3389/fimmu.2024.1343301/fu
ll#supplementary-material
[166]DataSheet1.xlsx^ (129KB, xlsx)
References