Plasma Metabolomics Indicates Potential Biomarkers and Abnormal Metabolic Pathways in Female Melasma Patients
,1
Yi Chen,2
Hedan Yang,1
Hui Ding,1
Pingping Cai,1
Yiping Ge,1
Huiying Zheng,1
Xiaojie Sun,1
Yin Yang,1
Xinyu Li,2
and Tong Lin3
Abstract
Background
Melasma is a common and chronic pigmentary disorder with complex pathogenesis, and the relationship between melasma and metabolic syndrome remains elusive. Thus, metabolomics might contribute to the early detection of potential metabolic abnormalities in individuals with melasma.
Objective
The present study aims to analyze changes in plasma metabolites of female melasma patients and identify disease markers as well as explore potential therapeutic targets.
Methods
Plasma samples from 20 female patients with melasma and 21 healthy female controls that were comparable in terms of age and body mass index were collected for untargeted metabolomics investigations. Ultra-high performance liquid chromatography-mass spectrometry was used to analyze metabolites in the plasma. Metabolic pathway analyses were employed to identify significantly differentially expressed metabolites in melasma patients. Receiver operating characteristic curves were constructed, and correlation analyses were performed using the modified Melasma Area and Severity Index and oxidative stress levels.
Results
In contrast to healthy subjects, melasma patients showed significant alterations in 125 plasma metabolites, including amino acids, lipids, and carbohydrate-related metabolites. KEGG pathway analysis suggested that primary pathways associated with the development of melasma include tryptophan metabolism, as well as the biosynthesis of phenylalanine, tyrosine, and tryptophan. Importantly, based on receiver operating characteristic curves and correlation analyses, several metabolites were identified as robust biomarkers for melasma.
Conclusion
Collectively, this study identified significant changes in plasma metabolites in melasma patients, providing new insights into the pathogenesis of melasma and opening novel therapeutic avenues.
INTRODUCTION
Melasma is a chronic pigmentary disorder characterized by symmetrical light to dark-brown macules and patches on the face. It predominantly affects women, with only around 10% of affected patients being men1. Notably, this condition impacts up to 30% of childbearing women in certain cohorts, leading to a poor quality of life in those individuals2. However, the treatment of melasma remains extremely challenging.
Melasma is a complex disease influenced by genetic susceptibility, ultraviolet radiation, hormone imbalance, vascularization, oxidative stress, and impaired barrier function, among others3. Prior transcription analysis of melasma lesions versus adjacent non-lesional skin identified more than 300 differentially expressed genes, underscoring the complexity of melasma4, 5. A normal metabolic landscape is indispensable for maintaining physiological homeostasis6. The potential association between metabolic syndrome and melasma has received increasing attention recently7, 8, 9. The dysregulation of metabolites in vivo is also an important potential factor influencing the pathogenesis of melasma.
Metabolomics is a powerful tool that can help identify potential biomarkers and therapeutic targets for many diseases. Herein, we investigated the potential role of plasma metabolites in melasma pathogenesis by employing untargeted metabolomics performed using ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS). Plasma metabolite changes were comparatively analyzed in melasma patients and healthy controls to identify dysregulated metabolites that could contribute to melasma pathogenesis.
MATERIALS AND METHODS
Plasma sample collection
This study was approved by the Medical Ethics Committee of the Hospital for Skin Disease (Institute of Dermatology), Chinese Academy of Medical Science (approval number: [2022] immediate approval No. 009). A total of 20 female melasma patients and 21 age-matched healthy female volunteers, with Fitzpatrick skin types III–IV were recruited for this study. All the patients and healthy controls were aged between 30 and 60 years old. Subjects who were pregnant or lactating, had other concurrent systemic diseases, or took medications that could influence the outcome of this study were excluded. Each participant provided written informed consent prior to blood sample collection and study initiation. The modified Melasma Area and Severity Index (mMASI) score was used to assess the severity of melasma. Fasting peripheral venous blood was collected in an ethylenediaminetetraacetic acid anticoagulant tube and centrifuged at 3,000 rpm for 10 minutes at 4°C within 2 hours following sample collection to obtain the supernatant for analysis. All samples were stored at −80°C until analysis.
Sample preparation
Each sample was mixed with 700 µL of extractant containing internal standard d3-Leucine (methanol: acetonitrile: water = 4:2:1, v/v/v) for one minute, and then refrigerated at −20°C for two hours. After centrifuging for 15 minutes at 25,000 g, 4°C, 600 µL of the supernatant was transferred to a split Eppendorf tube and placed in a drying machine. The residue was then reconstituted with 180 µL pure water and methanol (1:1 v/v) and centrifuged again. The supernatant was then transferred to autosampler vials for UPLC-MS analysis. 50 µL of the supernatant from each sample was mixed to form the quality control (QC) sample.
UPLC-MS analysis
In this experiment, the ACQUITY UPLC system (Waters Ltd., Elstree, U.K.) was employed in conjunction with the XevoG2-XS Q Tof instrument (Waters, Manchester, UK) to facilitate the separation and detection of metabolites. The chromatographic conditions were as follows: chromatographic separation was performed using a Waters ACQUITY UPLC BEH C18 column (1.8 μm, 2.1 mm×100 mm, Waters Ltd.), with the column temperature maintained at 45°C. The mobile phase consisted of 0.1% formic acid (A) and acetonitrile (B) in the positive mode, while the negative mode contained 10 mM ammonium formate (A) and acetonitrile (B). The gradient conditions were as follows: 0–1 minutes, 2% B; 1–9 minutes, 2%–98% B; 9–12 minutes, 98% B; 12–12.1 minutes, 98% B to 2% B; and 12.1–15 minutes, 2% B. The flow rate was 0.35 ml/min, and the injection volume was 5 μl.
The mass spectrometry conditions were as follows: Waters XevoG2-XS Q Tof was used to perform primary and secondary mass spectrometry data acquisition. The full scan range was 70–1,050 m/z with a resolution of 120,000, and the automatic gain control (AGC) target for MS acquisitions was set to 3e6 with a maximum ion injection time of 100 ms. The top 3 precursors were selected for subsequent MS fragmentation with a maximum ion injection time of 50 ms and resolution of 30,000, while the AGC was maintained at 1e5. The stepped normalized collision energy was set to 20, 40, and 60 eV. Electrospray Ionization parameters were as follows: sheath gas, 40 psi; auxiliary gas, 10 psi; positive-ion mode spray voltage, 3.80 kV; negative-ion mode spray voltage, 3.20 kV; capillary temperature, 320°C; auxiliary gas heater temperature, 350°C.
Data processing and analysis
A data matrix containing information such as metabolite peak area, retention time (RT), and identification outcomes was obtained after importing offline mass spectrometry data into Progenesis QI software (Waters). Further analysis of the mass spectrometry data in combination with the HMDB database (https://hmdb.ca/) and ChemSpider online database (http://www.chemspider.com/) was conducted to identify metabolites. All metabolites were normalized by log2 conversion before analysis. To analyze metabolic profiles and identify metabolic differences, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminate analysis (OPLS-DA) were performed. MetaboAnalyst (https://www.metaboanalyst.ca) was used for the identification of metabolic pathways, differential metabolite hierarchy clustering, and differential metabolite correlation heat map. IBM SPSS statistics version 26 was used to perform receiver operating characteristic curves (ROC) analysis. Meanwhile, R Studio and related R packages were used to draw the volcano map.
Determination of oxidative stress parameters
Next, alterations in oxidative stress markers among female melasma patients and the control group were investigated. Superoxide dismutase (SOD) activity and malondialdehyde (MDA) content were respectively detected in plasma samples, according to the provided instructions (Shanghai Enzyme-linked Biotechnology Co., Ltd., Shanghai, China).
Statistical analysis
The data were represented as mean ± standard deviation. The significance of differences between groups was assessed by a one-way analysis of variance with Dunnett’s multiple comparisons or a two-tailed unpaired Student’s t-test using GraphPad Prism 9.0 (La Jolla, CA, USA). Statistical significance was defined as a p-value <0.05.
RESULTS
Characteristics of clinical data
The demographic characteristics of the individuals are presented in Table 1. The melasma group had an average age of 42.55±6.09 years, a body mass index (BMI) of 22.28±1.80 kg/m2, and a mMASI score of 8.25±3.53. Meanwhile, the control group had an average age of 39.05±7.98 years and a BMI of 21.58±1.94 kg/m2 (Table 1).
Table 1
Characteristics of the melasma patients and healthy control subjects
Stability analysis of metabolomics methodology and metabolites overall analysis
The base peak chromatograms of all QC samples exhibited overlapping results, with minimal fluctuations in RT and peak response intensity. This outcome indicates the instrument's optimal condition and stable signal throughout the entire process of sample detection and analysis, as illustrated in Fig. 1. By constructing a PCA model between the melasma group and control group, it was observed that samples from the same group were clustered within the 95% confidence interval and samples from different groups were distinguishable, indicating significant differences between female melasma patients and healthy subjects. To amplify the differences in metabolic profiles, PLS-DA and OPLS-DA were performed (PLS-DA analysis is shown in Supplementary Fig. 1). The evaluation parameters R2Y and Q2 of the model were obtained after 200 response permutation tests in positive mode (R2Y=0.963, Q2=0.949) and negative mode (R2Y=0.351, Q2=0.102), indicating good explanatory ability (Fig. 2).
Fig. 1
Stability analysis of metabolomics methodology. Base peak chromatograms overlapping spectrum of QC samples in the positive mode (A) and negative (B) mode. In each individual panel, the graph above shows the base peak chromatograms of a single QC sample and the overlapping of the base peak chromatograms of total QC samples.
QC: quality control.
Fig. 2
Metabolomics analysis of plasma samples from Mel compared to Con. (A, B) PCA score plots of Mel (green) and Con (red) in positive (A) and negative modes (B). (C, D) OPLS-DA score plots for Mel (green) and Con (red) in positive (C, R2Y=0.963, Q2=0.949) and negative modes (D, R2Y=0.351, Q2=0.102).
Mel: melasma patients, Con: healthy controls, PCA: principal component analysis, OPLS-DA: orthogonal partial least squares discriminate analysis.
Differential metabolites identification
According to the following screening criteria: 1) variable important for the projection of OPLS-DA model ≥1, 2) p-value <0.05, 125 differential metabolites were obtained, of which 74 were up-regulated, and 51 were down-regulated. To visualize the variation in metabolites between the two groups, hierarchical clustering heat maps and volcano maps were plotted (Fig. 3).
Fig. 3
Hierarchical clustering heat maps and volcano maps of differential metabolites. (A, B) Hierarchical clustering heat maps of the differential metabolites in Mel and Con groups in positive (A) and negative modes (B). The different colors in the heat map indicate the relative metabolite abundance of metabolites. (C, D) Volcano maps depict increased or decreased plasma metabolites of the Mel group versus the Con group in positive mode (C) and negative mode (D). The selection criteria for inclusion were p-value <0.05 and VIP >1.
Mel: melasma patients, Con: healthy controls, VIP: variable important for the projection.
Metabolite set enrichment analyses
The main metabolite differences between melasma patients and healthy controls were amino acids, lipids, and carbohydrate-related metabolites. Pathway analysis revealed notable enrichment in pathways such as tryptophan metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis (Fig. 4).
Fig. 4
Pathway enrichment of differentially expressed metabolites. (A) The KEGG classification of metabolites significantly altered in the Mel and Con groups. (B) Main metabolic pathways enriched based on the altered metabolites.
Mel: melasma patients, Con: healthy controls.
ROC analysis
ROC analysis was conducted using biologically meaningful metabolites to screen for potential biomarkers. Remarkably, the results of the ROC analysis indicated that some metabolites, such as (5R)-5-hydroxyhexanoic acid, 2-indolecarboxylic acid, urocanic acid, S-allylcysteine, 12-Keto-leukotriene B4, prostaglandin F1a, 2-arachidonoylglycerol, butyric acid, S-methylmethionine, O-adipoylcarnitine, butyrylcarnitine, neuromedin B, and aminoadipic acid, exhibited an area under the curve (AUC) value of 1. Additionally, metabolites including 6-hydroxymelatonin, L-phenylalanine, L-tryptophan, and isobutyryl-L-carnitine demonstrated an AUC greater than 0.85, suggesting their potential role as biomarkers for melasma (Fig. 4). Therefore, these metabolites were identified as main differential metabolites based on both pathway analysis and ROC analysis. The boxplot visually represents the changes in relative expression levels of these key metabolites in the melasma group compared to the control subjects, among which 9 were up-regulated, and 8 were down-regulated (Fig. 5 and Supplementary Table 1).
Fig. 5
*p<0.05, **p<0.01, ***p<0.001, analysed by t test.
ROC analysis, oxidative stress level, and correlation analysis. (A) The ROC analysis of 17 biological metabolites, with area under the curve greater than 0.85, indicative of robust predictive ability. (B) Box plot showing significantly altered metabolites in the plasma. The expressions of the metabolites were converted to log2(x + 1) values for better visualization. (C, D) Determination of SOD activity and MDA content in plasma of Mel and Con. (E) Correlation analysis of differential metabolite expression level with mMASI, SOD activity, and MDA content.
Mel: melasma patients, Con: healthy controls, mMASI: modified Melasma Area and Severity Index, SOD: superoxide dismutase, MDA: malondialdehyde, ROC: receiver operating characteristic curves, NS: not significant.
Oxidative stress level and correlation analysis
In addition, the superoxide dismutase activity was found to be decreased in patients with melasma. A slight increase in MDA content was noted; however, this difference did not attain statistical significance when compared to the control group. Importantly, the levels of the main metabolites were significantly correlated with the mMASI score and moderately correlated with MDA levels (Fig. 5).
DISCUSSION
Numerous skin disorders, including psoriasis, acne, and acanthosis nigricans have been linked to metabolic disorders10. Melasma is a complicated disease involving various pathological mechanisms, and the debate surrounding the association between melasma and metabolic disorders has been ongoing7. We believe that the onset of melasma may be associated with metabolism abnormality, mainly because the occurrence and development of melasma is related to the level of sex hormones in the body and thyroid function, and patients with melasma have abnormal lipid metabolism, increased inflammation, oxidative stress and so on2, 7, 11. Melasma is prone to relapse and difficult to treat, which may also be related to metabolic abnormal conditions. Therefore, in order to determine whether the disease is related to abnormal metabolism and to uncover abnormal metabolic substances that may be involved in the development of the disease, we conducted a metabolomics study.
Herein, the identified differential metabolites between melasma patients and healthy controls were primarily amino acids, lipids, and carbohydrate-related metabolites, which are directly related to basic metabolism and highlight the presence of abnormal metabolic patterns in melasma patients. Notably, the enrichment analysis of KEGG pathways pinpointed the perturbation of pathways like tryptophan metabolism and the biosynthesis of phenylalanine, tyrosine, and tryptophan in melasma. Tryptophan metabolism abnormalities have been previously reported in a patient with congenital dyschromia12. Tryptophan metabolism and the biosynthesis of phenylalanine, tyrosine, and tryptophan in melasma are the primary pathways involved in melanin synthesis and are essential for maintaining normal pigment distribution and pigmentation phenotype13, 14, 15. Tryptophan and phenylalanine are regarded as essential amino acids and the precursors of melanin production. The observed reduction in plasma levels of tryptophan and phenylalanine among melasma patients potentially hints at the increased metabolism of these amino acids into downstream molecules, contributing to the synthesis of melanin.
Among female melasma patients, a significant alteration was observed in the levels of multiple lipids when compared to healthy controls. Notable changes included the up-regulation of lipids such as 2-arachidonylglycerol, prostaglandin F1a, and 12-Keto-leukotriene B4, along with the down-regulation of lipids like O-adipoylcarnitine, butyrylcarnitine, and isobutyryl-L-carnitine. 2-arachidonoylglycerol is one of the most abundant endocannabinoids that can bind and activate type 1 and 2 cannabinoid receptors16. The action of 2-arachidonoylglycerol in vivo may be induced by cannabinoid receptor activation, or mediated via 2-arachidonoylglycerol conversion to arachidonic acid, or oxidation of 2-arachidonoylglycerol by inflammatory enzymes such as cyclooxygenase or lipoxygenase17. On the other hand, Prostaglandin F1a and 12-Keto-leukotriene B4 are both arachidonic acid metabolites. Melasma patients with increased 2-arachidonoylglycerol levels may also have elevated arachidonic acid levels, further promoting inflammation18. Additionally, melanin synthesis can be stimulated by suitable concentration of 2-arachidonoylglycerol19.
In addition, urocanic acid was significantly elevated in the melasma patients, which may be attributed to the increase in ultraviolet radiation exposure20. In primary human keratinocytes, cis-urocanic acid has been demonstrated to up-regulate many oxidative stress-related genes and can cause human keratinocytes to produce reactive oxygen species21. Cis-urocanic acid significantly increases prostaglandin E2 production by upregulating cyclooxygenase 2, the rate-limiting enzyme in prostaglandin biosynthesis. This further increases melanogenesis, leading to or aggravating melasma. Carnitine and acylcarnitine molecules may alter the metabolic status of mitochondria, while the decline in O-adipoylcarnitine, butyrylcarnitine, and isobutyryl-L-carnitine results in mitochondrial metabolic abnormalities and are associated with aging22.
Patients with melasma were shown to have altered levels of several metabolites known to have antioxidant properties, such as S-allylcysteine, 6-hydroxymelatonin, L-tryptophan, and L-phenylalanine23, 24, 25. Among these, S-allylcysteine displayed an up-regulation, while 6-hydroxymelatonin, L-tryptophan, and L-phenylalanine exhibited down-regulation in patients with melasma. The intricate interplay between oxidative stress and melasma's development has been documented extensively26. Altered levels of antioxidant metabolites potentially reflect the elevated oxidative stress levels observed in melasma patients. In addition, we observed that SOD activities were significantly decreased in patients with melasma, consistent with previous studies27, 28. While melasma patients displayed slightly increased MDA contents, the difference compared to the control group was not statistically significant.
More importantly, metabolites including 5-hydroxyhexanoic acid, 2-indolecarboxylic acid, urocanic acid, S-allylcysteine, 12-keto-leukotriene B4, prostaglandin F1a, 2-arachidonoylglycerol, butyric acid, S-methylmethionine, O-adipoylcarnitine, butyrylcarnitine, neuromedin B, aminoadipic acid, 6-hydroxymelatonin, L-phenylalanine, L-tryptophan, isobutyryl-L-carnitine could emerge as key disease markers and therapeutic targets for melasma as determined by our ROC and correlation analyses.
In summary, this study marks an important effort to investigate and elucidate the connection between melasma and systemic metabolism, which contributes to a deeper understanding of melasma pathogenesis and emphasizes the importance of considering metabolic factors in the development and management of melasma. Despite yielding significant findings, several limitations merit careful consideration. The small sample size and lack of male patients with melasma restrict the reliability and generalizability of the findings. Additionally, there is a lack of tissue-level metabolomics and multi-omics analysis to validate the current findings. Comprehensive investigations into dynamic changes associated with the onset, amelioration, exacerbation and recurrence of the disease are imperative to unravel the temporal dynamics of metabolic perturbations in melasma pathogenesis.
To address these limitations and propel the field forward, future studies should take a multi-faceted approach. Firstly, recruiting a larger patient cohort and conducting longitudinal follow-up would enable a deeper comprehension of the metabolic perturbations associated with melasma and facilitate the identification of biomarkers predictive of disease progression and therapeutic response. Furthermore, metabolomics of tissue is desperately needed to delineate localized metabolic alterations within the skin microenvironment and to shed light on the molecular pathway mediating melanogenesis and pigmentary dysregulation in melasma. Integration of multi-omics approaches—genomics, transcriptomics, and metabolomics—may help clarify intricate molecular networks underlying melasma's pathophysiology, paving the way for precision medicine strategies tailored to individual patient profiles.
SUPPLEMENTARY MATERIALS
Statistical analysis results of the main metabolites changed in plasmaSupplementary Table 1
Partial least squares discriminant analysis score plots of Mel (green) and Con (red) in positive (A) and negative modes (B).Supplementary Fig. 1
FUNDING SOURCE:The work was supported by the CAMS Innovation Fund for Medical Sciences (CIFMS-2021-I2M-1-001; 2022-I2M-C&T-B-095) and the National Natural Science Foundation of China (Grant no. 82103705).
CONFLICTS OF INTEREST:The authors have nothing to disclose.
DATA SHARING STATEMENT:Data sharing is not applicable to this article as no new data were created or analyzed in this study.
ACKNOWLEDGMENT
The authors thank all the patients for participating in the study.
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ORCID IDs
Funding Information
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CAMS Innovation Fund for Medical Sciences
CIFMS-2021-I2M-1-0012022-I2M-C&T-B-095
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National Natural Science Foundation of China
82103705
