Abstract
Pityriasis rosea (PR) represents a frequently occurring, acute, self-limiting papulosquamous disorder with complicated and undetermined pathogenesis, and there are no definite tissue markers or drug targets. Thus, the clinical treatment of this disease is challenging. Recent studies have partly unraveled correlations of trace elements with the onset and development of skin diseases. This study utilized the Mendelian randomization (MR) strategy to excavate the causality between trace elements and PR. A forward and multivariate MR analysis was implemented on trace elements and PR to infer the causal relationship between them, with inverse variance weighted (IVW) as the dominant analytical method, supplemented by other methods (heterogeneity, pleiotropy, and leave-one-out sensitivity tests) to ensure data stability. The results of MR analysis, mainly based on the IVW method, suggested a causation between vitamin A and PR and indicated vitamin A as a protective factor against PR (IVW: P = .023; odds ratio [OR] = 0.677; OR < 1; 95% confidence interval: 0.245–0.897). The multivariate MR analysis suggested that vitamin A could independently act on PR (P = .018; OR = 0.426; 95% confidence interval: 0.001–35.620). Vitamin A is causally associated with PR and serves as a protective factor against the disease, providing valuable insights for subsequent clinical diagnosis, treatment, and scientific research.
Keywords: causal inference, Mendelian randomization, pityriasis rosea, trace elements, vitamin A
1. Introduction
Pityriasis rosea (PR), a common acute self-limiting papular squamous disease, is usually initiated from a large erythematous squamous plaque, also known as herald patch or mother patch, appearing on the trunk or neck, along with multiple secondary minor erythematous squamous lesions, mainly positioned on the trunk and along the cleavage lines on the back.[1] Because of the absence of precise tissue markers or drug targets and complicated pathogenesis, the clinical treatment of this disease is challenging. PR can be regarded as an immune disease owing to the participation of cytokines and chemokines.[2,3] Moreover, with the development of modern medical research, trace elements and vitamins are considered to possess immunomodulatory effects. Increasing studies also suggest a certain correlation between the deficiency of trace elements and vitamins and the initiation of skin diseases.[4,5] However, the specific causal association between trace elements and PR is scarcely investigated. Therefore, this study boldly proposes a hypothesis that the onset of PR is partly correlated with trace elements. A forward and multivariate Mendelian randomization (MR) investigation is utilized to identify the causality between PR and trace elements.
2. Materials and methods
2.1. Study design
A multivariate MR analysis was implemented on trace element and PR data to explore the possible causal relationship. With trace elements (copper, calcium, iron, magnesium, selenium, potassium, vitamin A, vitamin B6, folic acid, carotenoids, vitamin B12, vitamin C, vitamin D, and vitamin E) as exposure factors and PR as the outcome factor, we investigated whether the causal relationship between trace elements and PR was established and further explore whether trace elements were influencing factors for PR.
Next, 3 major conditions were proposed: the establishment of MR research requires satisfying 3 core assumptions, including correlation, independence, and exclusivity. Correlation assumption: instrumental variable (IV) single-nucleotide polymorphisms (SNPs) are strongly linked to exposure factors; independence assumption: IV SNPs are irrelevant to any confounding factors; exclusivity assumption: IVs are only generated via exposure factors without direct impact on outcome factors.
2.2. Data sources
The exposure factor datasets in this research were all mined from the summary data of an independent European population provided by the Integrative Epidemiology Unit open genome‑wide association study (GWAS) database (https://gwas.mrcieu.ac.uk/). The summary statistical data of trace elements by GWAS were acquired from a Phesant derived variable study. In 2018, BenElsworth and his team conducted a GWAS analysis on the genetic effects of calcium, carotene, folate, iron, magnesium, potassium, vitamin A, vitamin B12, vitamin B6, vitamin C, vitamin D, and vitamin E using Phesant derived variables from the UK Biobank. The analysis involved 64,979 European participants and identified 9,851,867 SNPs, aiming to explore the linkage between trace element levels and genetic factors.[6]PR data were acquired from GWAS summary data of the Finnish population available at a Finnish database (https://www.finngen.fi/en), including 210 cases of PR and 397,564 control subjects. All GWAS data mentioned in the article had ethical approval from their respective institutions.
2.3. Selection of IVs
To ensure robust and comprehensive findings, this study selected SNPs strongly associated with vitamin A (P < 5 × 10−6; this threshold was determined based on sample size constraints) as IVs. To evaluate the biological validity of the selected IVs, functional annotations on SNPs significantly relevant to vitamin A levels were generated using the LDtrait tool (Division of Cancer Epidemiology and Genetics, National Cancer Institute, https://ldlink.nih.gov/tab‑ldtrait). These SNP biological annotations signified a high correlation with the immune regulatory function of vitamin A. To mitigate potential bias from linkage disequilibrium, SNPs exhibiting strong associations with the exposure factor were extracted from GWAS databases, with the genetic distance of the linkage disequilibrium region set as Kb = 10,000 and R2 = 0.001 for analysis. Furthermore, to minimize the influence of weak instrument bias, only SNPs with an F-statistic >10 were retained in the final analysis.
2.4. Statistical methods
The statistical analysis and plotting for this study were completed using R language 4.4.1 software (R Core Team) and the TwoSampleMR package (MRC Integrative Epidemiology Unit, University of Bristol). The multivariate MR investigation was implemented using 5 approaches: inverse variance weighted (IVW), MR-Egger regression, weighted median estimator, simple mode, and weighted mode. These approaches were used to further infer and explore the causation between PR and trace elements in the pathogenesis of the disease. Among them, IVW was chosen as the dominant analytical method, with other analytical methods as supplements. IVW integrated the MR effect estimates of various SNPs to yield population-weighted estimates of potential causal effects. If there is no horizontal pleiotropy regarding the IVs, the results of IVW analysis are most reliable.[7]
To ascertain whether the disparities in individual effect sizes among IVs may be related to pleiotropy rather than contingency, we conducted Cochran’s Q statistic.[8] If insignificant heterogeneity is observed (P > .05), ideal results are considered; otherwise, a random effects model is suggested. The MR-Egger intercept (intercept P < .05) was adopted here as an indicator to evaluate horizontal pleiotropy.[9]
In addition, no outlier SNP was detected using MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO), yielding stable results. Through a sensitivity test with the leave-one-out approach, we sequentially eliminated each SNP and then calculated the results of all remaining SNPs to determine whether the impact of a single SNP disproportionately affects the association. The absence/presence of publication bias was identified by visually assessing the symmetry of the funnel plot. The effect estimates between each variant trace element and PR were assessed using forest plots, and the integrated effect was calculated using MR-Egger regression and IVW.
Multivariate MR is an extension of standard univariate MR. To assess the causal impacts of diverse trace elements on the type of PR, multiple trace elements were taken into consideration, and the “Mendelian randomization” package (MRC Biostatistics Unit, University of Cambridge) was utilized to estimate the direct causal influence of single exposure in a single analysis,[10] thus determining whether a certain trace element can individually affect the exposure factor. In this model, we not only enrolled statistically significant trace elements from univariate MR results but also considered the relevant trace elements. By incorporating other trace elements into the model simultaneously, we could effectively “control” or “correct” their potential effects on the outcome (PR).
3. Results
3.1. The causal effect of trace elements on PR
Trace elements such as copper, calcium, carotenoids, folic acid, iron, magnesium, potassium, selenium, zinc, and vitamins A, B12, B6, C, D, and E were screened in compliance with the 3 set criteria to identify SNPs that met the criteria. Each SNP had an F-statistic >10, suggesting that the research was not affected by weak IVs. With P < .05 as the standard, the IVW approach was adopted to perform the dual-sample MR analysis of 15 trace elements mentioned above and PR.
The results of univariate MR analysis indicated that vitamin A was causally linked to PR and could reduce the risk of PR. It was suggested that vitamin A may be a protective agent for PR (IVW: P = .023; odds ratio [OR] = 0.677; 95% confidence interval = 0.245–0.897; Table 1). The MR analysis results of the remaining trace elements were positive (Fig. 1). MR-PRESSO outlier detection was employed to detect any biased SNPs. The MR-PRESSO test files showed P = .791 (P > .05), indicating no outliers for all SNPs.
Table 1.
The results of MR analysis on trace elements and PR.
| Outcome | Exposure | Method | pval | lo_ci | or | or_lci95 | or_uci95 |
|---|---|---|---|---|---|---|---|
| PR | Copper | IVW | .338 | −0.204 | 1.215 | 0.815 | 1.811 |
| PR | Selenium | IVW | .840 | −0.472 | 0.957 | 0.624 | 1.467 |
| PR | Zinc | IVW | .862 | −0.643 | 0.949 | 0.526 | 1.713 |
| PR | Folate | IVW | .934 | −2.052 | 0.920 | 0.128 | 6.584 |
| PR | Carotene | IVW | .178 | −0.571 | 3.495 | 0.565 | 21.615 |
| PR | Potassium | IVW | .131 | −4.095 | 0.168 | 0.017 | 1.696 |
| PR | Vitamin D | IVW | .202 | −0.750 | 4.046 | 0.473 | 34.645 |
| PR | Vitamin C | IVW | .738 | −3.351 | 0.614 | 0.035 | 10.748 |
| PR | Vitamin B12 | IVW | .935 | −2.389 | 0.910 | 0.092 | 9.021 |
| PR | Iron | IVW | .796 | −2.503 | 0.747 | 0.082 | 6.813 |
| PR | Vitamin E | IVW | .810 | −1.529 | 1.239 | 0.217 | 7.083 |
| PR | Magnesium | IVW | .886 | −1.790 | 1.152 | 0.167 | 7.947 |
| PR | Vitamin B6 | IVW | .471 | −2.422 | 0.521 | 0.089 | 3.063 |
| PR | Calcium | IVW | .964 | −1.783 | −0.960 | 0.168 | 5.483 |
| PR | Vitamin A | IVW | .023 | −105.413 | 0.677 | 0.245 | 0.897 |
IVW = inverse variance weighted, lo_ci = lower confidence interval, MR = Mendelian randomization, or_lci95 = odds ratio lower 95% confidence interval, or_uci95 = odds ratio upper 95% confidence interval, PR = pityriasis rosea.
Figure 1.
Causal relationship between trace elements and PR. IVW = inverse variance weighted, MR = Mendelian randomization, PR = pityriasis rosea.
The sensitivity test (leave-one-out) was adopted to remove a single SNP, and the remaining SNPs did not affect the overall comparison results of MR analytical data. The results demonstrated that the MR analytical results were robust (Fig. 2). As depicted in the funnel plot, the SNP dots were symmetrically distributed, and there was insignificant heterogeneity in the data (Fig. 3). Cochran’s Q test was applied to evaluate the heterogeneity among SNPs, and no remarkable heterogeneity was noted (P > .05; Table 2). The results of the MR-Egger intercept-based pleiotropy test displayed no remarkable horizontal pleiotropy (P > .05; Table 3).
Figure 2.
Leave-one-out sensitivity map. MR = Mendelian randomization.
Figure 3.
Multivariable MR analysis on trace elements and PR. MR = Mendelian randomization, PR = pityriasis rosea.
Table 2.
The heterogeneity among SNPs.
| Microelement | MR-Egger | IVW | ||||
|---|---|---|---|---|---|---|
| Q | Q_df | Q_pval | Q | Q_df | Q_pval | |
| Copper | 9.844 | 4.000 | .043 | 11.556 | 5.000 | .041 |
| Selenium | 4.885 | 4.000 | .299 | 4.963 | 5.000 | .420 |
| Zinc | 9.029 | 5.000 | .108 | 9.669 | 6.000 | .139 |
| Folate | 2.972 | 10.000 | .982 | 3.933 | 11.000 | .972 |
| Carotene | 12.710 | 13.000 | .470 | 13.125 | 14.000 | .517 |
| Potassium | 15.790 | 12.000 | .201 | 16.420 | 13.000 | .227 |
| Vitamin D | 13.642 | 11.000 | .253 | 14.102 | 12.000 | .294 |
| Vitamin C | 15.992 | 8.000 | .042 | 16.054 | 9.000 | .066 |
| Vitamin B12 | 3.755 | 6.000 | .710 | 4.118 | 7.000 | .766 |
| Iron | 5.399 | 9.000 | .798 | 6.594 | 10.000 | .763 |
| Vitamin E | 8.237 | 10.000 | .606 | 8.584 | 11.000 | .660 |
| Magnesium | 17.335 | 15.000 | .299 | 17.336 | 16.000 | .364 |
| Vitamin B6 | 7.077 | 15.000 | .955 | 7.078 | 16.000 | .972 |
| Calcium | 18.033 | 17.000 | .387 | 18.519 | 18.000 | .422 |
| Vitamin A | 6.517 | 9.000 | .687 | 6.564 | 10.000 | .766 |
IVW = inverse variance weighted, PR = pityriasis rosea, SNP = single-nucleotide polymorphism.
Table 3.
Results of MR-Egger intercept-based pleiotropy test.
| Outcome | Exposure | Egger_intercept | SE | pval |
|---|---|---|---|---|
| PR | Copper | 0.087 | 0.105 | .451 |
| PR | Selenium | −0.027 | 0.108 | .813 |
| PR | Zinc | 0.125 | 0.211 | .577 |
| PR | Folate | 0.090 | 0.091 | .350 |
| PR | Carotene | 0.054 | 0.084 | .531 |
| PR | Potassium | −0.085 | 0.123 | .502 |
| PR | Vitamin D | 0.103 | 0.169 | .555 |
| PR | Vitamin C | −0.027 | 0.156 | .865 |
| PR | Vitamin B12 | −0.074 | 0.122 | .569 |
| PR | Iron | −0.121 | 0.110 | .303 |
| PR | Vitamin E | 0.049 | 0.083 | .569 |
| PR | Magnesium | −0.002 | 0.083 | .979 |
| PR | Vitamin B6 | −0.002 | 0.084 | .979 |
| PR | Calcium | −0.078 | 0.115 | .507 |
| PR | Vitamin A | 0.032 | 0.151 | .834 |
MR = Mendelian randomization, PR = pityriasis rosea, SE = standard error.
3.2. Results of multivariate MR analysis
R packages such as “remotes” (R‑Consortium) and “TwoSampleMR” (MRC Integrative Epidemiology Unit, University of Bristol, UK; GitHub repository: MRCIEU/TwoSampleMR) were applied for MR multivariate analyses. The results demonstrated a statistically significant causal relationship between vitamin A and PR (P = .018; OR = 0.426; 95% confidence interval: 0.001–35.620), with P < .05 indicating that vitamin A may independently influence PR, irrespective of other trace elements. An OR < 1 further supported vitamin A as a protective factor against the disease (Fig. 3). In addition, the MR-Egger intercept test revealed no evidence of horizontal pleiotropy, and heterogeneity tests confirmed negligible heterogeneity (P > .05; Tables 4 and 5).
Table 4.
Multivariate MR analysis of the pleiotropy effects of vitamin A on pityriasis rosea.
| Outcome | Exposure | Egger_intercept | SE | pval |
|---|---|---|---|---|
| Pityriasis rosea | Vitamin A | 0.032 | 0.151 | .834 |
MR = Mendelian randomization, SE = standard error.
Table 5.
Multivariate MR analysis of the heterogeneity effects of vitamin A on pityriasis rosea.
| Exposure | Outcome | SNPs | MR-Egger intercept | Inverse variance weighted | ||
|---|---|---|---|---|---|---|
| Egger intercept | p_value | Q | Q_pval | |||
| Vitamin A | Pityriasis rosea | 11 | 6.517 | .687 | 6.564 | .766 |
MR = Mendelian randomization, SNP = single-nucleotide polymorphism.
3.3. Reverse MR analysis results
Reverse MR analysis was performed with PR as the exposure and vitamin A as the outcome. The results indicated no causal relationship between PR and vitamin A levels (P > .05), suggesting that PR does not influence vitamin A levels (Fig. 4).
Figure 4.
Reverse MR analysis of pityriasis rosea and vitamin A. MR = Mendelian randomization.
4. Discussion
PR is an acute self-limiting rash characterized by oval erythema and scaly lesions on the trunk and proximal limbs.[11] The common manifestations include diffuse redness, surrounding connective tissue scales, and peripheral blood vessels under a dermatoscope. Modern medicine believes that the etiology of PR is complicated, but most scholars support the theory of autoimmunity.[12] Trace elements exert critical roles in skin barrier protection and immune response, including antioxidant, anti-inflammatory, and antimicrobial roles.[13,14] Therefore, their roles in skin diseases have attracted much attention in recent years.
Through an MR analysis based on large-scale genetic data, this study systematically discussed the causal relationship between 15 trace elements and the risk of PR. The study integrated the results of univariate, reverse, and multivariate analyses and finally found that vitamin A could serve as an independent protector for PR, without any reverse causal association. The results obtained using the IVW method were consistent with other supplementary analytical methods and do not indicate significant pleiotropy or heterogeneity.
In previous reports, trace elements showed significant impacts on immune-mediated inflammatory skin diseases. Systematic reviews and meta-analyses have revealed significant differences in the levels of zinc, copper, selenium, and iron in the serum of patients with psoriasis, vitiligo, and atopic dermatitis as compared with healthy controls. These differences may be related to the deficiency or excess of these elements, which in turn affects the initiation, development, and prognosis of the disease.[15] In addition, copper and iron participate in the activation of immune cells and the production of cytokines, exerting a therapeutic role in lupus erythematosus by regulating Th1/Th2 balance and inhibiting excessive immune responses.[16] The deficiency of vitamin A, a fat-soluble vitamin, is linked to impaired immune response and increased susceptibility to skin infections and inflammatory skin diseases. Vitamin A and its derivatives can act as innate immune modulators by strengthening skin immunity and maintaining skin microbiota.[13] Therefore, regulating trace element levels may become a novel therapeutic strategy for skin diseases. To substantiate the experimental hypothesis, this study employed methods different from the abovementioned studies. Herein, the MR method was utilized, and the results unveiled that copper, calcium, carotene, folate, iron, magnesium, potassium, selenium, vitamin B12, vitamin B6, vitamin C, vitamin D, vitamin E, and zinc had no causal relationship with PR. This result differed from the theoretical derivation of the aforementioned studies. This difference is likely attributed to the unique properties of MR methods. MR evaluates the potential causal effects of genetically determined, lifelong stable levels of trace elements, which largely avoids the influence of short-term measurements, dietary fluctuations, reverse causality, and confounding bias in traditional observational studies. Meanwhile, the preliminary analytical results of this study supported the protective effect of vitamin A on PR.
A number of studies suggest that the beneficial regulatory effect of vitamin A on PR may be achieved by mediating human immune function. It has been reported that the interleukin (IL)-4 level is reduced while IL-12 and interferon gamma (IFN-γ) levels are increased in the serum of patients with PR during the acute phase, indicating an imbalance in Th1/Th2 immune response. These cytokines are involved in immune regulation. In addition, the CD4+/CD8+ ratio increased in the skin lesions of patients, dominated by CD4+ T cell infiltration, accompanied by an increase in serum IFN-γ levels.[17] IL-12 promotes T-bet expression, drives the Th1 immune response, and upregulates IFN-γ, indicating that cellular immune imbalance participates in the onset and progression of PR.[18,19] Vitamin A, as a key factor in immune regulation,[20] accelerates the development and differentiation of immune cells such as T cells, B cells, and natural killer cells.[21] In a vitamin A-sufficient environment, natural killer cells may acquire immunosuppressive function,[22] which reduces the CD4+/CD8+ ratio, regulates T cell subsets, and decreases IFN-γ expression. The aforementioned mechanism indicates that vitamin A is not only extensively applied in the treatment of immune-relevant skin diseases but also has the potential to be applied in the treatment of PR by limiting IFN-γ secretion and modulating T cell balance (reducing the CD4+/CD8+ ratio) to restrain proinflammatory responses.
However, existing evidence cannot directly indicate a direct association between PR and vitamin A. To validate its causal effect on human diseases, this study adopted MR analysis to simulate lifelong exposure levels of vitamin A based on genetic IVs and systematically evaluate its causal effect on PR. IVW univariate analysis revealed vitamin A as a protective factor for the disease. The multivariate analysis further substantiated its independence without interference from other trace elements, suggesting that vitamin A may become a promising drug delivery target in the treatment of PR.
This study offered a new approach for the diagnosis and treatment of PR. However, it should be noted that its genomic data were mainly sourced from European populations, which limited the applicability of the results to other ethnic groups owing to differences in genetic structure, environmental exposure, and sociocultural factors. To verify the universality of the results, it is recommended to conduct multiethnic cohort studies based on relevant databases and collaborate with institutions in Asia, Africa, and South America to conduct cross-ethnic analysis and multiethnic data collection, so as to reveal the universality or population-specific characteristics of vitamin A intervention in the disease development mechanism of PR. At present, although there are no direct reports confirming the direct association between PR and vitamin A, the results of this study still provide an important reference for the clinical treatment of PR.
Acknowledgments
Over the course of my researching and writing this paper. I would like to express my thanks to all those who have helped me.
First, I would like to express my gratitude to all those who helped me during the writing of this thesis. A special acknowledgement should be shown to Professor Jing Xu, from whose education I benefited greatly. I am particularly indebted to Ms. Xu, who gave me kind encouragement and useful instruction throughout my writing.
Sincere gratitude should also go to all my learned professors and warm-hearted teachers who have greatly helped me in my study as well as in my life.
And my warm gratitude also goes to my friends and family who gave me much encouragement and financial support, respectively. Moreover, I wish to extend my thanks to the library and the GWAS dataspace for providing much useful information for my thesis.
Author contributions
Writing – original draft: Tingting Yu.
Writing – review & editing: Tingting Yu.
Methodology: Xinyue Mou, Yi Luo, Liping Cao, Peixin Chen, Hai Yu, Chun Yang.
Supervision: Jing Xu.
Abbreviations:
- GWAS
- genome‑wide association study
- GWAS
- genome‑wide association study
- IFN-γ
- = interferon gamma
- IL
- = interleukin
- IV
- instrumental variable
- IVW
- inverse variance weighted
- MR
- Mendelian randomization
- MR-PRESSO
- MR-Pleiotropy Residual Sum and Outlier
- OR
- odds ratio
- MR-PRESSO
- MR-Pleiotropy Residual Sum and Outlier
- OR
- odds ratio
- PR
- pityriasis rosea
- SNPs
- single-nucleotide polymorphisms
The authors have no funding and conflicts of interest to declare.
The datasets generated during and/or analyzed during the current study are publicly available.
How to cite this article: Yu T, Mou X, Luo Y, Cao L, Chen P, Yu H, Yang C, Xu J. Causal relationship between trace elements and pityriasis rosea: A Mendelian randomization analysis. Medicine 2026;105:36(e50519).
We declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere.
We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.
We understand that the corresponding author is the sole contact for the editorial process. She is responsible for communicating with the other authors about progress, submissions of revisions, and final approval of proofs.
Contributor Information
Tingting Yu, Email: 645967056@qq.com.
Xinyue Mou, Email: 13225357907@163.com.
Yi Luo, Email: 372602071@qq.com.
Liping Cao, Email: 1321877921@qq.com.
Peixin Chen, Email: 862955300@qq.com.
Hai Yu, Email: 645967056@qq.com.
Chun Yang, Email: plxyc123456@qq.com.
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