Research progress of metabolomics in psoriasis

Abstract

Psoriasis is a chronic inflammatory skin disease with significant physical and psychological burdens. The interplay between the innate and adaptive immune systems is thought to contribute to the pathogenesis; however, the details of the pathogenesis remain unclear. In addition, reliable biomarkers for diagnosis, assessment of disease activity, and monitoring of therapeutic response are limited. Metabolomics is an emerging science that can be used to identify and analyze low molecular weight molecules in biological systems. During the past decade, metabolomics has been widely used in psoriasis research, and substantial progress has been made. This review summarizes and discusses studies that applied metabolomics to psoriatic disease. These studies have identified dysregulation of amino acids, carnitines, fatty acids, lipids, and carbohydrates in psoriasis. The results from these studies have advanced our understanding of: (1) the molecular mechanisms of psoriasis pathogenesis; (2) diagnosis of psoriasis and assessment of disease activity; (3) the mechanism of treatment and how to monitor treatment response; and (4) the link between psoriasis and comorbid diseases. We discuss common research strategies and progress in the application of metabolomics to psoriasis, as well as emerging trends and future directions.

Introduction

Psoriasis is an immune-mediated, chronic inflammatory skin disease with an overall prevalence that ranges from 0.1% in east Asia to 1.5% in western Europe, and imposes a significant physical and psychological burden on patients with the condition.^[1] Clinically, psoriasis can be divided into psoriasis vulgaris (PV), pustular psoriasis, erythrodermic psoriasis, and psoriatic arthritis (PsA).^[2] PV is the most common form characterized by well-demarcated, erythematous plaques with silvery scales, observed in about 90% of patients. Pustular psoriasis is characterized by multiple, coalescing sterile pustules.^[3] Erythrodermic psoriasis is the most severe and the rarest type, occurring in 3% of patients, and its pathogenesis is poorly understood. Psoriasis may also affect the joints, resulting in PsA.

The pathogenesis of psoriasis involves complex interplay between the innate and adaptive immune systems centered on T-cell immunity. The autoantigens produced by keratinocytes (KC) activate plasmacytoid dendritic cells, which produce interferon (IFN)-γ, tumor necrosis factor (TNF), and IL-23. The autoimmune CD4+ T helper (Th) 17 cells and CD8+ T cytotoxic (Tc) 17 cells are then stimulated and migrate to the epidermis where they recognize epidermal autoantigens, leading to the production of IL-17 and other inflammatory cytokines. The cytokines produced by Th17 cells drive the development of psoriatic phenotypes and sustain inflammation.^[4] However, the detailed molecular basis of psoriasis pathogenesis is not fully understood, resulting in unmet clinical needs.

Over the past decade, omics-based technologies, including genomics, transcriptomics, and proteomics, have been extensively applied to the field of psoriasis and have contributed to a deeper understanding of the pathogenesis. Large-scale genome-wide association studies have been conducted to identify new loci associated with psoriasis and explain the genetic heritability of PV.^[5] Transcriptome analysis showed that genes involved in the IFN-γ, IL-17, and TNF signaling pathways are up-regulated in patients with psoriasis.^[6] A recent proteomics study identified dysregulation of proteins involved in lipid metabolism and the immune response in plasma of psoriatic patients.^[7] In recent years, metabolomics has provided a new perspective on psoriasis pathogenesis. Specific features of psoriasis, including its association with metabolic comorbidities, indicate that the metabolomic signature is important to disease pathogenesis and treatment. In addition, given that definitive diagnostic criteria have yet to be established, diagnosis is usually made based on clinical findings and morphologic evaluation of the skin lesions, which sometimes involves invasive procedures,^[8] and thus, more accurate and non-invasive diagnostic tools are needed. Furthermore, precise methods of evaluating disease activity are lacking. Metabolomics has unique advantages as a methodological approach that could help clarify some of these aspects of psoriasis.

Patients with psoriasis are at increased risk of developing other serious health problems, including metabolic syndrome, cardiovascular (CV) diseases, gastrointestinal and kidney diseases, malignancies, depression, and infections, which may directly impact patients’ mortality.^[9] Metabolomics is as an ideal technique for establishing the link between psoriasis and comorbidities and identifying patients at high risk of developing specific comorbidities. In the future, early identification and intervention might be possible. There are a variety of treatments available for psoriasis, including conventional drugs, targeted therapies, and some emerging treatments. With the aid of metabolomics, the mechanism of treatments could be revealed, providing an evidence base for widespread clinical use. Furthermore, metabolomics technologies have the potential to enable monitoring of treatment response and the influence of specific diets on patients. This review summarizes metabolomics studies of psoriatic disease that have been published in recent years. We discuss common research strategies and progress regarding all types of psoriasis, including generalized pustular psoriasis (GPP) and PsA. Finally, we address emerging trends and future directions.

Brief overview of metabolomics

Metabolomics is an emerging science aimed at comprehensively characterizing the metabolome. The metabolome is usually defined as the complete collection of metabolites present in a biological system that could be cell, organ, tissue, or biofluid.^[10] Metabolites are low molecular weight molecules, typically 50–1500, including lipids, peptides and amino acids, carbohydrates, nucleic acids, organic acids, and vitamins.^[11] Because metabolites are the most downstream indicators of interactions between genetics and the environment, they can indicate what is actually happening in an individual rather than what might happen.

The basic metabolomics study workflow consists of several steps. First, the goal of the research is proposed, leading to the study design identifying the experimental objects and samples. The second step is to use high-throughput technological platforms to measure and analyze the samples. Next is the statistical analysis, aimed at explaining the results and identifying significant metabolites that could answer the questions proposed.^[12] Pre-treatment of the samples is a particularly critical step because it has a marked effect on the metabolite profile and could influence the accuracy of the results.

The most common methods used to identify and quantify metabolites include nuclear magnetic resonance (NMR), gas chromatography–mass spectrometry (GC–MS), and liquid chromatography–mass spectrometry (LC–MS). Other techniques including capillary electrophoresis–mass spectrometry (CE–MS) are also promising.^[13] Each technique has its own advantages and disadvantages. NMR spectroscopy is quantitative, reproducible, and non-destructive, requires little sample preparation, allows detection of almost all organic molecules, and can provide crucial structural information, but is very expensive.^[12] GC–MS is an analytical platform with excellent selectivity and sensitivity that is used to separate and identify small molecule metabolites (<650) and volatile compounds, requiring chemical derivatization to make compounds volatile for gas chromatography.^[13,14] This method can be used in both untargeted and targeted applications. Targeted GC–MS is used for discovery and semiquantification of novel metabolites, whereas untargeted GC–MS is used to quantify pre-selected metabolites using internal or external standards.^[14] In LC–MS-based metabolomics, chemical derivatization is not required. Owing to its high sensitivity and versatility, LC–MS is widely used.^[15] Most of the studies in this review used this method. NMR and mass spectrometry (MS) are compared in Table 1.

Table 1 - Comparing different platforms for metabolomics studies.

Properties	NMR	MS
Sample preparation	Relatively easy	Necessary and complex
Quantitation	Intrinsically quantitative	Requiring standard for quantification
Sensitivity	Low	High
Selectivity	Non-selective	More selective
Reproducibility	High	Low
Sample recovery	Sample preserved	Sample destroyed
Specialty	Application in large cohorts and longitudinal studies; providing structural information	The analysis of wide range metabolites with different chemical properties

MS: Mass spectrometry; NMR: Nuclear magnetic resonance.

The data obtained using metabolomics techniques need to be further analyzed by bioinformatics tools, and the identified metabolites need to be assigned to specific metabolic pathways. Metabolite identification requires metabolite databases (e.g., Human Metabolome Database, LipidMaps).^[16] The statistical tools used to perform these analyses mainly include SPSS, R, Simca P, and MetaboAnalyst. To establish the link between the metabolites and biochemical pathways, MetaboAnalyst is commonly used for pathway analysis.

The results lead to a deeper understanding of disease pathogenesis and help in finding potential biomarkers for diagnosis and assessment of disease activities. The findings could also explain the mechanism of treatments and help in monitoring treatment response. In addition, some studies have made progress in establishing links between psoriasis and comorbid diseases. Recent metabolomics-based studies of psoriasis are discussed below and summarized in Table 2.

Table 2 - The comparison of different metabolomics studies.

Reference	Techniques	Population characteristics	Samples	Metabolites found	Main comments
Armstrong et al [17]	GC–TOF-MS	10 Ps only; 10 Ps and PsA; 10 HC	Serum	Ps had higher α-ketoglutaric acid, lower asparagine, glutamine, and beta-sitosterol compared with HC.	The results indicated cellular hyperproliferation, altered rates of protein synthesis, and dysregulated immune system took part in pathogenesis.
Lu et al [36]	HPLC	41 PV before and after Yinxieling treatment; 19 HC	Urine	12a-Hydroxy-3-oxocholadienic acid was increased after treatment. The altered metabolites were mainly in lipid metabolism. The metabolic profile recovered toward normal after Yinxieling treatment.	It revealed the mechanism and proof concerning the effectiveness of Yinxieling.
Kamleh et al [38]	LC–HRMS	32 mild Ps, 32 severe Ps, 32 HC; and 16 severe Ps after 12-week Etanercept treatment	Plasma	Threonine, citrulline, and ornithine were positively correlated with PASI scores. Etanercept treatment could normalize the majority of dysregulated metabolites, including ornithine, arginine, proline, citrulline, glycine, glutamine, threonine, and methionine.	The results indicated increasing demand for protein and collagen synthesis. Levels of amino acids were useful for monitoring the severity of disease and therapeutic response to anti-TNFα treatment.
Dutkiewicz et al [31]	nanoDESI-MS	100 Ps; 100 HC	Skin excretions	Ps skin had higher choline, glutamic acid, and phenylalanine, and lower lactic acid, urocanic acid, and citrulline. The combination of these six biomarkers could reach the AUC of 0.992.	Non-invasive diagnostic tool for Ps was built.
Kang et al [18]	GC–MS	14 Ps; 15 HC	Serum	Ps had higher lactic acid, urea, asparagine, aspartic acid, isoleucine, phenylalanine, ornithine, and proline, and lower crotonic acid, azelaic acid, ethanolamine, and cholesterol.	Glycolysis pathway and amino acid metabolism were increased due to higher demand for protein biosynthesis and keratinocyte hyperproliferation.
Ottas et al [20]	HPLC–MS	55 PV; 51 HC	Serum	PV had higher ornithine, glutamate, urea, phytol, and 1,11-undecanedicarboxylic acid, and lower acylcarnitines and PCs.	Classification models were built based on the metabolomics data.
Zeng et al [22]	UPLC–MS/MS	45 Ps; 45 HC	Plasma	Ps had higher LPA, LPC, and PA, and lower PC and PI.	Glycerophospholipid metabolism was altered in Ps.
Sarkar et al [25]	LC–MS	6 Ps; 6 HC	Skin	Ps skin had lower glucocorticoids, cortisone, and cortisol, and higher taurine and homoserine.	The results revealed locally glucocorticoid deficiency in Ps.
Wang et al [41]	N/A	12 Ps; 9 HC	Skin	13-HODE was significantly altered.	13-HODE might be a mechanistic link between psoriasis and CV comorbidities.
Sorokin et al [21]	LC–MS-MS	Skin and serum: 8 Ps, 7 HC; Plasma: 60 Ps, 30 HC	Skin; Serum; Plasma	Ps skin had higher 8-, 12-, 15-HETE, and 13-HODE. Ps plasma had higher omega-3/6 polyunsaturated fatty acids, glycerol, and acylcarnitines, and lower glutathione and primary and secondary bile acids.	Local and circulating lipid mediators played a role in modulating skin inflammation in Ps.
Sorokin et al [45]	LC–MS-MS	7 PV; 7 HC	Skin; Serum; Plasma	DHA-derived pro-resolving mediators, including Resolvin D5, Protectin D1, and Protectin Dx were enriched in Ps skin.	The results identified the role of pro-resolving lipid mediators in pathogenesis.
Li et al [23]	UPLC–Q-TOF/MS	12 PV; 12 HC	Plasma	PV had higher threonine, leucine, phenylalanine, tryptophan, palmitamide, linoleic amide, oleamide, stearamide, cis-11- eicosenamide, trans-13-docosenamide, uric acid, LPC (16:0), LPC (18:3), LPC (18:2), and LPC (18:1). PV had lower oleic acid, arachidonic acid, and N-linoleoyl taurine.	Disrupted lipid and amino acid metabolism took part in pathogenesis.
Bai et al [26]	UPLC–QTOF/MS	Ps and HC	Epidermis	Serine that serves as one-carbon donor was down-regulated, while taurine that serves as an output of trans-sulphuration pathway of one-carbon metabolism was up-regulated in Ps.	One-carbon metabolism was activated in Ps.
Pohla et al [24]	HPLC–MS	20 Ps; 19 HC	Skin	Ps skin had higher glutamate, methionine, arginine, acylcarnitines, biogenic amines, LPCs, PC, histamine, and ADMA.	The local inflammatory process that drove the increased cell proliferation took part in the pathogenesis.
Luczaj et al [27]	LC–MS	6 PV; 6 HC	KC and FB in the skin	KC in PV had higher CER[NS], CER[NP], CER[AS], CER[ADS], CER[AP], and CER[EOS], and lower CER[NDS]. FB in PV had higher CER[AS], CER[ADS], and CER[EOS].	The keratinocyte-fibroblast crosstalk took part in the etiopathogenesis of PV.
Kishikawa et al [29]	CE–TOFMS, LC–TOFMS	50 PV; 42 PsA; 38 HC	Plasma	Ps had higher ethanolamine phosphate, and lower XA0019, nicotinic acid, and 20α-hydroxyprogesterone. Vitamin digestion and absorption pathway was enhanced in PV compared with PsA.	The results reflected the pathophysiology of Ps.
Chen et al [19]	LC–MS-MS	45 PV; 45 HC	Plasma	PV had higher essential amino acids and branched-chain amino acids, and lower glutamine, cysteine, and asparagine. PV had lower palmitoylcarnitine, and higher hexanoylcarnitine and 3-OH-octadecenoylcarnitine.	Metabolism of amino acids and carnitines were significantly altered in pathogenesis of Ps.
Chen et al [28]	GC–MS	32 severe plaque Ps; 29 HC	Plasma	The dysregulation of several lipids, xanthine, d-ribose 5-phosphate, and uric acid were correlated with the alterations in skin microbiome.	The skin microbiota could have great influence on lipid and nucleotide metabolism in Ps.
Yu et al [30]	GC–MS; UPLC–MS/MS	24 GPP; 12 PV; 12 HC	Serum	GPP had lower glycine, histidine, asparagine, methionine, threonine, lysine, valine, isoleucine, tryptophan, tyrosine, alanine, proline, taurine, and cystathionine compared with HC. Tryptophan, histidine, tyrosine, alanine, and taurine were correlated with disease severity of GPP.	GPP serum exhibited an amino acid starvation signature. The decreased amino acids could be useful in monitoring the severity of GPP.
Tarentini et al [32]	NMR	7 new-onset Ps within the last 6 months; 7 HC	Skin; Serum	Ps skin had higher ascorbate, glutathione, lactate, taurine, creatine, phosphocholine, lysine, glutamine, glutamate, methionine, and valine, and lower scyllo-inositol. Ps serum had higher dimethylglycine and isoleucine.	Specific molecular signatures constituted by IL-6, IL1-ra, dimethylglycine, CCL4, isoleucine, glycine, and IL-8 were established to identify new-onset Ps.
Zhao et al [37]	UPLC/Q-TOF–MS	13 PV of treatment responders with the LiangXueJieDu formula	Serum	Disturbed glycerophospholipid metabolism and steroid hormone biosynthesis were reversed using the LiangXueJieDu formula treatment.	The study revealed the complex anti-psoriatic therapeutic mechanism of the Chinese herbal formula.
Cao et al [39]	UHPLC–MS	Cohort 1: 28 Ps pre- and post- 12-week treatment of ixekizumab, 28 HC; Cohort 2: 17 Ps, 17 HC, 17 Ps with CHD, 17 CHD patients without Ps	Serum	Ps had higher LPCs, LPIs, LPAs, and free fatty acids, but lower acylcarnitines and DAs. After ixekizumab treatment, LPCs and GPC were decreased in Ps, while LPLs, DAs, and acylcarnitines returned to normal levels in Ps with CHD. LPCs were higher in Ps with CHD than in Ps without CHD.	The results indicated that ixekizumab could normalize the dysregulated lipid metabolism in Ps. The study also established the link between Ps and CV diseases.
Castaldo et al [40]	NMR	30 Ps before and after 4-week ketogenic diet; 30 HC	Serum	Ps had lower l-tryptophan, l-tyrosine, l-lysine, l-histidine, l-methionine, l-arginine, l-ornithine, and l-glutamine. After ketogenic diet, l-leucine, l-alanine, pyruvic acid, and choline were decreased, while glutamine and glutamate were increased.	The results indicated a rebalancing of the metabolome after ketogenic diet. The low-calorie ketogenic diet could be considered a therapeutic option for Ps.
Colaco et al [42]	NMR	977 Ps, 70 patients with incident CV events included	Serum	Glycoprotein acetyls, apolipoprotein B, remnant cholesterol, triglycerides, LDLs, VLDL cholesterol, and very small VLDL particles were associated with higher CV risk, while unsaturation of fatty acids, alanine, tyrosine, total HDL cholesterol, and medium and large HDL particles were associated with lower CV risk.	Novel metabolites that had the potential to predict CV risk in Ps were identified.

[A]: α-hydroxy fatty acid; [DS]: 3 sphingoid bases: dihydrosphingosine; [EO]: Esterified ω-hydroxy fatty acid; [P]: Phytosphingosine; [S]: Sphingosine; 13-HODE: 13(S)-hydroxy-9Z,11E-octadecadienoic acid; ADMA: asymmetric dimethylarginine; AUC: Area under the ROC curve; CER[N]: Ceramide containing non-hydroxy fatty acid; CHD: Coronary heart disease; CV: Cardiovascular; DA: Dicarboxylic acid; FB: Fibroblasts; GC: Gas chromatography; GPC: Glycerophosphocholine; GPP: Generalized pustular psoriasis; HC: Healthy controls; HDL: High-density lipoprotein; HETE: Hydroxyeicosatetraenoic acid; HPLC: High Performance Liquid Chromatography; HRMS: High-resolution mass spectrometry; KC: Keratinocytes; LC: Liquid chromatography; LDL: Low-density lipoprotein; LPA: Lysophosphatidic acid; LPC: Lysophosphatidylcholine; LPI: Lysophosphatidylinositol; LPL: Lysophospholipid; MS: Mass spectrometry; MS/MS: Tandem quadrupole mass spectrometry; nanoDESI: Nanospray desorption electrospray ionization; NMR: Nuclear magnetic resonance; PA: Phosphatidic acid; PASI: Psoriasis area and severity index; PC: Phosphatidylcholine; PI: Phosphatidylinositol; Ps: Psoriasis; PsA: Psoriatic arthritis; PV: Psoriasis vulgaris; Q-TOF: Quadrupole time-of-flight; TOF: Time-of-flight; UPLC: Ultra-performance liquid chromatography; VLDL: Very low-density lipoprotein.

Metabolomics leads to a deeper understanding of the pathogenesis of psoriatic disease

Many metabolomics-based studies of psoriatic disease have tried to deepen and broaden our understanding of psoriasis pathogenesis and pathophysiology from a new perspective. The ability of metabolomics to reflect the status of a biological system using its most downstream output can reveal what is really happening and being perturbed within an individual patient. In 2014, Armstrong et al^[17] analyzed serum samples of psoriasis patients and healthy controls (HC) using gas chromatography–time-of-flight mass spectrometry (GC–TOF-MS). They found that the level of α-ketoglutaric acid was increased, whereas the levels of asparagine, glutamine, and beta-sitosterol were decreased in psoriasis patients compared with HC, which might indicate cellular hyperproliferation, altered rates of protein synthesis, and a dysregulated immune system.

Major changes in the levels of amino acids, carnitines, and lipids have been found in patients with psoriasis. In 2017, using GC–MS, Kang et al^[18] identified a group of up-regulated amino acids in serum of psoriasis patients, including asparagine, aspartic acid, isoleucine, phenylalanine, ornithine, and proline. These results indicated that amino acid metabolic activity was increased in psoriasis patients, which may be associated with an increased demand for protein biosynthesis and keratinocyte hyperproliferation. There are both consistencies and contradictions compared with other studies. Chen et al^[19] also discovered significantly up-regulated amino acids in plasma of psoriasis patients, including essential amino acids and branched-chain amino acids. However, different from previous studies, the level of asparagine was found to be decreased. Levels of glutamine and cysteine were also decreased, indicating that glutamine supplementation might be beneficial and protective.

Altered carnitine levels have also been identified in some studies. Ottas et al^[20] discovered down-regulation of acylcarnitines in serum of plaque psoriasis, which might be associated with increasing fatty acid oxidation due to enhanced energy consumption. Similarly, another study found that the levels of 14 carnitines including palmitoylcarnitine were decreased.^[19] Especially, palmitoylcarnitine level was negatively correlated with psoriasis area and severity index (PASI) score, suggesting that it might play a protective role in the pathogenesis of psoriasis. However, the study conducted by Sorokin et al^[21] showed opposite results. They found that acylcarnitine levels were increased in plasma of psoriasis patients, which might be associated with dysfunctional fatty acid transportation and impaired oxidation. Further studies should be conducted to resolve these controversies.

Lipids metabolomics is an important part in psoriasis metabolomics. Multiple lipids were differentially expressed in plasma of psoriasis patients compared with HC.^[22] Among them, lysophosphatidic acid (LPA), lysophosphatidylcholine (LPC), and phosphatidic acid (PA) were significantly increased, while phosphatidylcholine (PC) and phosphatidylinositol (PI) were decreased. Lipids participate in inflammatory-associated diseases through different signaling pathways, and the cellular mechanisms of these lipids require further investigation.^[22] Li et al^[23] also confirmed up-regulated LPC in plasma of psoriasis patients. On the contrary, another metabonomics study found that LPC was down-regulated in serum of PV patients, which needs to be confirmed by further studies. These findings suggest that LPC may play an important role in psoriasis progression.

The metabolomic profiles of skin biopsies were also unraveled by some studies. Pohla et al^[24] identified dysregulated metabolites in skin lesions of plaque psoriasis using high performance liquid chromatography–mass spectrometry. Among them, glutamate, methionine, arginine, acylcarnitine, biogenic amine, LPC, and PC levels were increased. The altered metabolomic profiles were mainly due to increased cell proliferation. Lower glucocorticoid and cortisone levels were found in psoriatic skin, demonstrating local glucocorticoid deficiency.^[25] Arachidonic acid metabolites, including 8-, 12-, and 15-hydroxyeicosatetraenoic acid, and linoleic acid metabolites, including 13-hydroxyoctadecadienoic acid, with anti-inflammatory properties, were up-regulated in the skin.^[21] Furthermore, Bai et al^[26] discovered activation of one-carbon metabolism in the epidermis of psoriasis. Serine, which serves as a one-carbon donor, was down-regulated, while taurine, which is the output of the trans-sulphuration of one-carbon metabolism pathway, was up-regulated.

New results can be obtained when changing the samples for metabolomics research. For example, Luczaj et al^[27] isolated KC and fibroblasts from skin samples to investigate the ceramide profiles of PV patients. They found different altered ceramides species in the KC and fibroblasts, which may be related to alterations in the epidermal barrier and inflammatory processes, as well as to the different signaling functions of ceramide species in the dermis and epidermis.

Most recently, Chen et al^[28] combined metabolomics with microbiome analysis. They explored the plasma metabolites of severe plaque psoriasis and combined the results with analysis of skin microbiota alterations. They proposed new perspectives that dysregulation of metabolites, including several lipids, xanthine, d-ribose 5-phosphate, and uric acid, correlated with alterations in the skin microbiome. The skin microbiota could strongly influence lipid and nucleotide metabolism in psoriasis patients.

In summary, there are both consistencies and conflicting results in these studies. Several common metabolites were identified in different studies that analyzed serum, plasma, and skin. These metabolites, along with their associated pathways, are presented in Figure 1. Elevated levels of pyruvic acid, lactic acid, α-ketoglutaric acid, fatty acid, alanine, phenylalanine, ornithine, citrulline, and aspartic acid were found in serum of psoriasis patients compared to HC. Upregulated fatty acid, PA, LPC, acylcarnitine, threonine, phenylalanine, leucine, tryptophan, ornithine, citrulline, and threonine levels were found in plasma of psoriasis patients. Elevated levels of acylcarnitine, LPC, PC, glutamine, glycine, phenylalanine, lysine, citrulline, and arginine were found in the skin of psoriasis patients. Downregulated acylcarnitine, PC, glutamine, tryptophan, lysine, and tyrosine levels were found in the serum of psoriasis patients. Decreased levels of cysteine, glutamine, asparagine, and PC were found in the plasma of psoriasis patients. Lactic acid and serine levels were decreased in the skin of psoriasis patients. These dysregulated metabolites might be associated with an increased demand for keratinocyte hyperproliferation, rapid protein synthesis, increased collagen production, oxidative stress, and chronic inflammation burden. There are also conflicting results. Asparagine was shown to be up-regulated in serum in one study,^[18] but down-regulated in serum and plasma in two other studies.^[17,19] Different analytical platforms and sample resources could both have influence on the results. For example, glutamine levels were increased in the skin, but decreased in plasma and serum, which might be due to circulating consumption and local demand for keratinocyte hyperproliferation.

Figure 1: Metabolites found in metabolomics studies of psoriasis. The up arrow refers to the metabolites upregulated, while the down arrow refers to the metabolites downregulated. Sk, in skin. Se, in serum. P, in plasma. TCA: Tricarboxylic acid.

Based on known mechanisms of psoriasis and the novel metabolites found, we make a summary of the speculation of the pathogenesis. The activation of Th17 cells leads to the stimulation of keratinocyte proliferation. Then, the glycolysis, urea cycle, and amino acids metabolism are activated, accompanied by the upregulation of multiple amino acids. The metabolites provide energy and materials for protein synthesis and cell proliferation and sustain chronic inflammation.

As for PsA, Armstrong et al^[17] found that psoriasis patients with PsA had higher phosphoric acid, glucuronic acid, arabitol, and arabinose levels compared with HC. Meanwhile, psoriasis patients with PsA had lower α-ketoglutaric acid and higher lignoceric acid levels than patients with psoriasis alone. The change in metabolite levels might be associated with the joint destruction and enhanced inflammatory response in PsA.^[17] Kishikawa et al^[29] found that patients with PsA had higher tyramine and saturated fatty acid levels, and lower mucin acid levels than patients with PV, which might be related to psoriatic inflammation severity. Metabolomics-based studies of PsA are summarized in Table 3.

Table 3 - Metabolomics in PsA.

Reference	Techniques	Population characteristics	Samples	Metabolites found	Main comments
Armstrong et al [17]	GC–TOF-MS	10 Ps only 10 PsA and Ps 10 HC	Serum	Ps with PsA had higher phosphoric acid, glucuronic acid, arabitol, and arabinose compared with HC. Ps with PsA had lower α-ketoglutaric acid and higher lignoceric acid than Ps only.	The results reflected joint destruction and enhanced inflammatory response in pathogenesis.
Coras et al [35]	Reverse-phase LC/MS	41 PsA	Serum	Pro-inflammatory eicosanoids including PGE2 and HXB3 correlated with joint disease activity, as well as several anti-inflammatory eicosanoids including 11-HEPE, 12-HEPE, and 15-HEPE.	Disbalance between pro- and anti-inflammatory eicosanoids might take part in the pathogenesis of joint inflammation in PsA.
Souto-Carneiro et al [33]	NMR	49 NegRA; 73 PsA	Serum	PsA had higher alanine, threonine, leucine, valine, acetate, creatine, lactate, and choline, and lower phenylalanine. A diagnostic model that included age, gender, the levels of alanine, succinate, and creatine phosphate, and the lipid ratios L2/L1, L5/L1 and L6/L1 was proposed.	PsA and NegRA had different serum metabolomic signatures. A diagnostic model for PsA vs. NegRA was established.
Kishikawa et al [29]	CE–TOFMS, LC–TOFMS	50 PV; 42 PsA; 38 HC	Plasma	PsA had higher tyramine and lower mucin acid than PV. PsA had higher saturated fatty acids than PV.	The metabolites might be related to psoriatic inflammation severity. The findings helped to provide biomarkers for clinical subtypes of Ps.
Looby et al [34]	SPME–LC–HRMS	10 Mild PsA; 10 Moderate PsA; 10 Severe PsA; 10 HC	Serum	Levels of selected long-chain fatty acids including 3-hydroxytetradecanedioic acid and 3-hydroxydodecanedioic acid were increased in relation to disease activity.	Metabolites had the potential to provide biomarkers for PsA activity.

CE: Capillary electrophoresis; GC: Gas chromatography; HC: Healthy controls; HEPE: Hydroxyeicosapentaenoic acid; HRMS: High-resolution mass spectrometry; LC: Liquid chromatography; NegRA: Seronegative rheumatoid arthritis; NMR: Nuclear magnetic resonance; PGE: Prostaglandin; Ps: Psoriasis; PsA: Psoriatic arthritis; PV: Psoriasis vulgaris; SPME: Solid-phase microextraction; TOF: Time-of-flight; MS: Mass spectrometry.

Some researchers have also analyzed the metabolomics of pustular psoriasis. In 2021, Yu et al^[30] explored the serum metabolomic profiles of GPP patients. Amino acid starvation was found to be a major characteristic, which was different from PV. A group of amino acids including glycine, histidine, asparagine, methionine, threonine, lysine, valine, isoleucine, tryptophan, tyrosine, alanine, proline, taurine, and cystathionine were significantly decreased compared with HC. The findings help to shed light on the pathogenesis of GPP.

Notably, the pathogenesis of erythrodermic psoriasis – the most severe and rarest clinical type of psoriasis – is still very poorly understood. At the present time, few studies have explored the metabolomics of erythrodermic psoriasis; thus, future studies should focus on addressing this gap in our knowledge.

Metabolomics for psoriatic disease diagnosis and assessment of disease activity

The diagnosis of psoriatic diseases is usually based on clinical findings and dermatologists’ professional experience, as definitive diagnostic criteria are lacking. With the emergence of metabolomics, substantial research efforts have been focused on identifying specific biomarkers for disease diagnosis and assessment of disease activity.

In 2016, Dutkiewicz et al^[31] used non-invasive hydrogel micropatch probes combined with MS to investigate metabolites of skin excretions of psoriasis patients compared with HC. Six biomarkers, namely choline, glutamic acid, phenylalanine, lactic acid, urocanic acid, and citrulline, were identified and proposed as a way to diagnosis psoriasis, with an area under the ROC curve of 0.992. In 2017, Ottas et al^[20] established classification models of psoriasis based on serum metabolomic data.

Most recently, Tarentini et al^[32] analyzed and quantified metabolites in skin and serum samples of patients with new-onset (within the previous 6 months) psoriasis by high-resolution magic-angle spinning NMR. By combining metabolomic profiles with cytokine profiles, they discovered specific signatures that identified new-onset psoriasis patients. For serum samples, the signature constituted IL-6, IL1-ra, dimethylglycine, CCL4, isoleucine, glycine, and IL-8. For skin samples, the signature constituted IL-6, IL1-ra, CCL4, glycine, and IL-8. These signatures accurately distinguished psoriasis patients from healthy subjects and represent a candidate tool for the diagnosis of new-onset psoriasis.

These studies show that differentiation between psoriasis and healthy individuals is achievable based on analysis of dysregulated metabolites. The proposed diagnostic criteria need to be validated in a larger sample size.

Apart from psoriasis diagnosis, researchers have also attempted to identify appropriate biomarkers that distinguish PsA from other similar diseases, which are difficult to differentiate clinically. In 2020, Souto-Carneiro et al^[33] compared serum metabolites of PsA patients with seronegative RA patients using NMR. Nine metabolites were successfully identified as being significantly differentially expressed between the two groups, namely alanine, leucine, phenylalanine, threonine, valine, acetate, choline, creatine, and lactate. The study proposed a diagnostic model that included age, gender, the levels of alanine, succinate, and creatine phosphate, and the lipid ratios L2/L1, L5/L1, and L6/L1 that improved the sensitivity and specificity of the diagnosis of PsA.

In 2021, Looby et al^[34] analyzed the serum metabolome of patients with PsA of varying activity, with the aim of discovering potential biomarkers to assess PsA activity. Multivariate analysis showed that the levels of selected long-chain fatty acids, including 3-hydroxytetradecanedioic acid and 3-hydroxydodecanedioic acid, were increased in parallel with disease activity, with severe PsA patients presenting the highest levels. These metabolites could have the potential to serve as biomarkers for PsA activity.

Around the same time, Coras et al^[35] analyzed and quantified eicosanoids in serum of 41 PsA patients by reverse-phase LC/MS and then established correlations between metabolites and disease activity. The levels of pro-inflammatory eicosanoids, including PGE2 and HXB3, correlated with joint disease activity, as well as those of several anti-inflammatory eicosanoids including 11-HEPE, 12-HEPE, and 15-HEPE. The imbalance between pro- and anti-inflammatory eicosanoids might participate in the pathogenesis of joint inflammation in PsA and have great potential to evaluate disease activity.

In summary, studies have shown that metabolomics could serve as a powerful tool for assessing PsA disease activity. The results from these studies require further verification before clinical use.

As for pustular psoriasis, Yu et al^[30] discovered that a group of amino acids including tryptophan, histidine, tyrosine, alanine, and taurine, was significantly correlated with the Japanese Dermatological Association severity score for GPP, which might be useful for monitoring disease severity.

Metabolomics for explaining the mechanism of treatments and monitoring treatment response in psoriasis

Metabolomics could serve as a powerful technique to unravel the mechanism of various therapies and evaluate pharmacological response to treatment. In 2014, Lu et al^[36] analyzed the metabolomic profiles of urine samples from psoriasis patients before and after treatment with optimized Yinxieling formula, a Chinese traditional medicine, using high performance liquid chromatography (HPLC). As a result, the altered metabolites were mainly associated with lipid metabolism. The metabolic profiles normalized after treatment with optimized Yinxieling, demonstrating the effectiveness of this Chinese traditional medicine.

Similarly, Zhao et al^[37] illustrated the anti-psoriatic effectiveness of the LiangXueJieDu formula, a Chinese herbal medicine, using metabolomics. They analyzed metabolic changes in serum of psoriasis patients after an 8-week treatment and found that disrupted glycerophospholipid metabolism and steroid hormone biosynthesis could be reversed by treatment with the LiangXueJieDu formula. Chinese traditional medicine has been used empirically for psoriasis for many years. With the development of metabolomics, the effectiveness and safety of this treatment approach can now be monitored, providing an evidence base for future clinical use and popularization.

Metabolic changes in psoriasis patients treated with targeted therapies have also been explored. Kamleh et al^[38] investigated the plasma profiles of patients with severe psoriasis before and after 12 weeks of treatment with the anti-TNFα drug Etanercept. The results showed that Etanercept treatment normalized the psoriasis-induced dysregulation of the majority of metabolites, including ornithine, arginine, proline, citrulline, glycine, glutamine, threonine, and methionine, but not cystathionine and sphingosine-1-phosphate. Thus, amino acid metabolism could be a potential marker for monitoring response to anti-TNFα treatment.

More recently, Cao et al^[39] explored the serum profiles of psoriasis patients before and after 12 weeks of ixekizumab treatment. After ixekizumab treatment, levels of LPC and glycerophosphocholine (GPC) were decreased, suggesting that this treatment approach can normalize dysregulated lipid metabolism. Furthermore, the levels of lysophospholipids (LPLs), dicarboxylic acids (DAs), and acylcarnitines returned to normal in psoriasis patients with coronary heart disease, indicating that anti-IL-17A monoclonal antibodies might reduce CV risk in patients with psoriasis.

Apart from drug therapy, the effects of dietotherapy have also been the focus of recent research. Castaldo et al^[40] investigated the serum metabolomic profiles of overweight psoriasis patients before and after a 4-week ketogenic diet using NMR analysis. After the dietary intervention, levels of l-leucine, l-alanine, and choline were decreased, whereas levels of glutamine and glutamate were increased, suggesting a rebalancing of the metabolome. These results give clinical doctors and patients more guidance and evidence that a low-calorie ketogenic diet could be a therapeutic option for psoriasis.

Metabolomics for establishing the link between psoriasis and comorbid diseases

Psoriasis is associated with various comorbid diseases, including metabolic syndrome, CV diseases, and other serious and chronic health conditions.^[41,42] A deeper understanding of the pathogenesis of comorbid diseases and early identification appears to be extremely important. Several metabolomics studies have made great progress.

In 2017, Wang et al^[43] discovered that 13(S)-hydroxy-9Z,11E-octadecadienoic acid might be a mechanistic link between psoriasis and CV comorbidities. In 2021, Cao et al^[39] found that LPC levels were higher in psoriasis patients with coronary heart disease than in those without, indicating that high blood levels of LPC could be a risk factor for CV diseases.

Most recently, Colaco et al^[44] applied targeted high-throughput NMR metabolomics platform to quantify serum metabolites of 977 patients with either psoriasis or PsA, 70 of whom developed CV events. The results showed that glycoprotein acetyls, apolipoprotein B, remnant cholesterol, triglycerides, low-density lipoproteins, very low-density lipoprotein (VLDL) cholesterol, and very small VLDL particles were associated with higher CV risk, whereas the degree of unsaturation of fatty acids, alanine, tyrosine, total high-density lipoprotein (HDL) cholesterol, and medium and large HDL particles were associated with lower CV risk. Furthermore, they established a model that significantly improved CV risk prediction accuracy (with an AUC of 79.9) that could help predict CV risk in patients with psoriatic diseases in the future.

In summary, some novel metabolites that are not routinely measured in clinical practice appear to be associated with an increased risk of comorbidities in psoriatic disease. Metabolomic profiles have the potential to aid in risk stratification and early identification of high-risk individuals in the future.

Conclusions

During the past decade, with the rapid development of metabolomics methodologies, metabolomics has been widely applied to psoriatic disease, offering new perspectives on this disease. Although different types of samples have been analyzed using different platforms, some common metabolites have been identified, including glutamine, ornithine, citrulline, phenylalanine, fatty acids, and acylcarnitines. These novel metabolites reflect increased protein synthesis, keratinocyte hyperproliferation, increased oxidative stress, and chronic inflammation, thereby providing more information about psoriasis. In conclusion, substantial progress has been made in understanding: (1) the molecular mechanisms of psoriasis pathogenesis; (2) psoriasis diagnosis and assessment of disease activity; (3) the mechanism of treatment and how to monitor treatment response; and (4) the link between psoriasis and comorbid diseases. However, the application of metabolomics to psoriasis is still at an early stage, and many gaps are yet to be filled. Thus, larger cohorts and more accurate experimental verification are needed. There are also some limitations due to the inherent properties of metabolomics, such as the lack of absolute quantification and standard metabolite concentrations, making it difficult to transform research findings to clinical applications, and so technical improvements are needed. Additionally, further validation and mechanistic researches are needed to obtain an in-depth understanding of the pathogenesis of psoriasis. The combination of genomics, transcriptomics, and proteomics techniques is expected to elucidate the signaling pathways and molecular mechanisms involved in this disease in the future. Ultimately, these research efforts will lead to earlier diagnosis, more definitive assessment of treatment response, more precise prediction of the development of comorbidities, and more effective targeted therapies.

Funding

This study was supported by grants from the National High Level Hospital Clinical Research Funding (No. 2022-PUMCH-B-092); the National Key Research and Development Program of China (No. 2022YFC3601800); and the National Natural Science Foundation of China (No. 82073450).

Conflicts of interest

None.