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. 2026 Jun 16;19:607307. doi: 10.2147/CCID.S607307

Exploring the Role of Skin Microbiota in Autoimmune Skin Diseases from a Bidirectional Mendelian Randomization Perspective

Junlin Wang 1, Xuejun Wang 1,✉, Xuanjie Tao 2, Qianru Yang 1, Meng Zhang 1, Yimeng Wang 1, Shengquan Liu 1
PMCID: PMC13282993  PMID: 42328488

Abstract

Background

The etiologies of psoriasis, localized scleroderma (LoS), and systemic lupus erythematosus (SLE) remain incompletely understood. Although skin microbiota are implicated in cutaneous immune homeostasis, their causal relationships with autoimmune skin diseases are unclear.

Objective

To investigate bidirectional causal associations between skin microbiota and psoriasis, LoS, and SLE.

Methods

Summary-level genome-wide association study (GWAS) data for 1656 skin microbiome traits were obtained from public resources, and GWAS data for psoriasis, LoS, and SLE were obtained from FinnGen version 9. Two-sample Mendelian randomization (MR) was performed using inverse-variance weighting as the primary method, supplemented by MR-Egger regression, weighted median, simple mode, weighted mode, heterogeneity tests, pleiotropy assessment, MR-PRESSO, and leave-one-out analysis.

Results

Forward MR identified skin microbiota traits associated with the risk of psoriasis, LoS, and SLE. Specifically, 4, 5, and 5 microbiota traits were positively associated with these diseases, respectively, whereas 4, 3, and 8 traits were inversely associated. Reverse MR suggested that psoriasis, LoS, and SLE may also influence skin microbiota composition: psoriasis and LoS were associated with increased abundance of 4 and 1 microbiota traits, respectively, and psoriasis, LoS, and SLE were associated with reduced abundance of 8, 6, and 2 traits, respectively. Most significant associations showed no strong evidence of heterogeneity or horizontal pleiotropy.

Conclusion

This bidirectional MR study provides genetic evidence supporting reciprocal relationships between skin microbiota and autoimmune skin diseases. The findings are exploratory and require replication in larger multi-ancestry cohorts and functional validation before clinical translation.

Keywords: autoimmune skin diseases, skin microbiota, mendelian randomization, psoriasis, localized scleroderma, systemic lupus erythematosus

Introduction

The skin, the largest human organ, harbors a diverse microbial ecosystem composed of bacteria, fungi, viruses, and other microorganisms, collectively termed the skin microbiota. This ecosystem dynamically interacts with both the external environment and the host immune system. Disturbance of this microbial community, commonly referred to as dysbiosis, has been associated with the onset and progression of multiple dermatological disorders.1 Autoimmune skin diseases are chronic inflammatory conditions characterized by immune dysregulation and aberrant responses against self-tissues. Their etiology is complex and involves genetic predisposition, environmental exposures, barrier dysfunction, and immune abnormalities.2 In recent years, their increasing burden, persistent cutaneous and systemic manifestations, psychological impact, and limited therapeutic options have posed substantial challenges for clinical practice and research.3,4

This study focused on psoriasis, localized scleroderma (LoS), and systemic lupus erythematosus (SLE) because these conditions represent distinct but clinically important immune-mediated skin disease phenotypes: epidermal hyperproliferative inflammation, localized fibrotic skin disease, and systemic autoimmunity with prominent cutaneous involvement. All three disorders have been linked to microbial dysbiosis, yet the direction and causality of these associations remain uncertain. Psoriasis is a chronic inflammatory autoimmune skin condition characterized by hyperproliferation of epidermal cells and pronounced inflammatory responses.5 Previous investigations have reported dysbiosis in the skin microbiota of patients with psoriasis, including altered abundances of Streptococcus, Staphylococcus, Cutibacterium, and Corynebacterium species, suggesting that microbial metabolic activity and immunomodulatory functions may participate in disease pathogenesis.6,7 SLE is a complex multisystem autoimmune disorder with common cutaneous manifestations, and its pathogenesis is closely associated with inflammation triggered by autoantibodies and immune-complex deposition in skin vessels and basement membranes.8,9 LoS is a chronic autoimmune skin disorder mainly characterized by fibrosis, induration, and atrophy.10 Clinical studies suggest that Gram-negative anaerobic bacteria, such as members of the Bacteroidetes phylum, may be increased in the skin of scleroderma patients and may contribute to inflammatory and fibrotic processes.11 In skin microbiome studies, amplicon sequence variants (ASVs) are high-resolution sequence features inferred from 16S rRNA amplicon sequencing; they represent unique microbial DNA sequences and provide finer taxonomic discrimination than conventional operational taxonomic units.

Although previous studies have linked skin microbiota alterations to immune-mediated inflammatory skin diseases (IMIDs), observational designs are vulnerable to confounding and reverse causation.12 Mendelian randomization (MR) uses genetic variants as instrumental variables to strengthen causal inference and help distinguish directionality.13 Therefore, we applied bidirectional two-sample MR to examine putative causal relationships between genetically predicted skin microbiota traits, including taxonomic features and ASVs across different skin sites and microenvironments, and psoriasis, LoS, and SLE. This framework aimed to generate hypothesis-generating genetic evidence for the skin-immune axis and identify microbial features requiring further mechanistic and clinical validation.

Materials and Methods

Study Design

Our Mendelian randomization analysis operates under three crucial premises. The first, termed the relevance assumption, stipulates that robust genetic variants, specifically single-nucleotide polymorphisms (SNPs) with strong associations to the exposure, function as valid instrumental variables (IVs). The second, the independence assumption, posits that these IVs must exhibit no association with any pertinent confounding factors. Lastly, the exclusion restriction principle asserts that the IVs impact the outcome solely by influencing the exposure.14 A comprehensive outline of the present study’s design is provided in Figure 1.

Figure 1.

Flowchart detailing Mendelian Randomization study for genetic links in skin conditions. The flowchart illustrates the bidirectional Mendelian randomization study design and analysis. It begins with research data sources, including skin microbiota GWAS summary data and autoimmune skin diseases GWAS summary data. Genetic instrument variables were selected using P-value thresholds, LD clumping, and F-statistics. Exposure and outcome data were then harmonized. Forward MR analysis assessed causal effects from skin microbiota to autoimmune skin diseases, and reverse MR analysis assessed effects from autoimmune skin diseases to skin microbiota. Core causal effect estimation methods included IVW, MR-Egger, weighted median, simple mode, and weighted mode. Sensitivity analyses included Cochran’s Q, MR-Egger intercept, MR-PRESSO, and leave-one-out analysis. The study ultimately yielded robust causal association results.

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Flowchart of Mendelian Randomization Study Design and Analysis.

Abbreviations: LD, linkage disequilibrium; MR, Mendelian randomization; MR-PRESSO, MR Pleiotropy Residual Sum and Outlier test; SNPs, single-nucleotide polymorphisms; IVW, inverse variance weighting.

Data Sources

We obtained GWAS data on psoriasis (Ncase=9267, Ncontrol=364,071), localized scleroderma (Ncase=361, Ncontrol=353,088), and systemic lupus erythematosus (Ncase=1023, Ncontrol=281,127) from the FinnGen version 9 database (https://r9.risteys.finngen.fi/). GWAS data on the human skin microbiota were extracted from the EMBL-EBI GWAS Atlas database. These data originated from two cross-sectional cohorts from the general German population:15 KORA FF4 (n=324) and PopGen (n=273), with genetic data from a total of 597 individuals who provided 1656 skin microbiota samples for genetic analysis. In this dataset, microbial phenotypes included bacterial taxonomic abundances and ASVs derived from 16S rRNA amplicon sequencing. Samples were collected from diverse body sites, including the dorsal and palmar surfaces of the forearm, antecubital fossa, forehead, and retroauricular folds. Given the substantial variation in skin microbiota across dry, moist, and sebaceous microenvironments, site-specific traits were analyzed separately to reduce heterogeneity. Raw GWAS summary statistics are accessible via the GWAS Atlas (ebi.ac.uk; accession codes GCST90133164-GCST90133313). All datasets used in this study were obtained from publicly available GWAS platforms; therefore, additional ethical approval was not required.

Instrumental Variable Selection

To identify SNPs suitable as instrumental variables, we implemented the following screening criteria. First, SNPs associated with skin microbiota traits were selected using a significance threshold of P < 5 × 10−8 for MR analysis. In the reverse analysis, because localized scleroderma had a limited number of genome-wide significant SNPs, the threshold was relaxed to P < 5 × 10−5; findings based on this relaxed threshold were interpreted cautiously because of the increased possibility of false-positive results. Second, SNPs were clumped using a linkage disequilibrium (LD) threshold of r2 < 0.001 within a 10,000-kb window to ensure independence. Third, the strength of instrumental variables was assessed using the F-statistic, and weak instruments (F < 10) were excluded. This SNP-selection strategy is consistent with commonly used criteria in recent two-sample MR studies.16–18 Because the original skin microbiota GWAS may contain multiple independent genetic association signals for the same microbial trait at the same body site, such signals were analyzed separately and denoted as “Part 1” and “Part 2” in the tables and supplementary materials.

Mendelian Randomization Analysis

For causal inference, the inverse variance weighting (IVW) method was used as the primary analytical approach because it provides the most precise estimates when heterogeneity and horizontal pleiotropy are absent.19 Additional MR methods, including MR-Egger regression, weighted median (WME), simple mode (SM), and weighted mode (WM), were used as supplementary analyses under different methodological assumptions.20 Statistical heterogeneity was evaluated using Cochran’s Q test. If significant heterogeneity was detected (P < 0.05), MR-PRESSO was further applied to identify and exclude potentially influential outliers, followed by reanalysis.21 Horizontal pleiotropy was assessed using the MR-Egger intercept, with P > 0.05 indicating no strong evidence of directional pleiotropy. Leave-one-out (LOO) sensitivity analysis was conducted to assess whether any single genetic variant disproportionately influenced the overall causal estimate.22 Given the exploratory and hypothesis-generating nature of this study, formal multiple-testing correction was not applied; therefore, significant associations were interpreted cautiously and prioritized according to consistency across sensitivity analyses. All MR analyses were conducted using R software (version 4.4.2), TwoSampleMR (version 0.6.10), and MR-PRESSO using the MRPRESSO package (version 1.0).

Results

Screening of Instrumental Variables

Based on significance thresholds and linkage disequilibrium thresholds, we screened SNPs associated with human skin microbiota phyla, classes, orders, families, genera, and autoimmune skin diseases as instrumental variables. All instrumental variables yielded F-statistics greater than 10, indicating no weak instrumental variables were included in this study.

Causal Effects of Skin Microbiota on Autoimmune Skin Diseases

As shown in Table 1 and Supplementary Figures 1–3, IVW analysis as the primary method identified four skin microbiota phenotypes positively associated with psoriasis risk: Neisseriaceae family in sebaceous skin on the forehead (OR = 1.036, 95% CI: 1.005–1.059, P = 0.01997), Neisseriaceae family on the dorsal dry skin of the forearm (OR = 1.037, 95% CI: 1.006–1.069, P = 0.01998), asv002 in moist skin of the antecubital fossa (OR = 1.032, 95% CI: 1.003–1.063, P = 0.0311), and Finegoldia genus in dry skin on the dorsal forearm (OR = 1.027, 95% CI: 1.000–1.054, P = 0.04653). Conversely, four skin microbiota phenotypes were negatively associated with psoriasis risk: asv053 in dry skin on the dorsal forearm (OR = 0.974, 95% CI: 0.950–0.998, P = 0.0341), asv070 in dry skin on the dorsal forearm (OR = 0.974, 95% CI: 0.952–0.997, P = 0.0290), asv016 in moist skin of the antecubital fossa (OR = 0.980, 95% CI: 0.962–0.999, P = 0.03909), and asv033 in sebaceous skin on the forehead (OR = 0.977, 95% CI: 0.958–0.996, P = 0.01795). Five skin microbiota phenotypes were positively associated with localized scleroderma risk: the genus Enhydrobacter in dry skin on the dorsal forearm (OR = 1.183, 95% CI: 1.029–1.361, P = 0.0183), asv070 in dry skin on the dorsal forearm (OR = 1.122, 95% CI: 1.013–1.242, P = 0.0268), asv013 in dry skin on the dorsal forearm (OR = 1.135, 95% CI: 1.030–1.250, P = 0.0106), asv072 in dry skin on the dorsal forearm (OR = 1.105, 95% CI: 1.001–1.221, P = 0.0484), and the family Neisseriaceae in moist skin of the antecubital fossa (OR = 1.151, 95% CI: 1.039–1.276, P = 0.0073). Three skin microbiota phenotypes were negatively associated with the risk of localized scleroderma: asv023 in dry skin on the dorsal forearm (OR = 0.907, 95% CI: 0.828–0.992, P = 0.0330), Rhodobacteraceae family in dry skin on the dorsal forearm (OR = 0.909, 95% CI: 0.834–0.991, P = 0.0305), and Staphylococcus genus in dry skin on the dorsal forearm (OR = 0.856, 95% CI: 0.734–0.999, P = 0.0491). Five skin microbiota phenotypes were positively associated with systemic lupus erythematosus risk: Proteobacteria phylum in dry skin on the dorsal forearm (OR = 1.091, 95% CI: 1.010–1.177, P = 0.0263), Actinomycetales order in dry skin on the dorsal forearm (OR = 1.065, 95% CI: 1.000–1.133, P = 0.0498), asv031 in dry skin on the dorsal forearm (OR = 1.049, 95% CI: 1.001–1.101, P = 0.0478), asv039 in moist skin of the antecubital fossa (OR = 1.093, 95% CI: 1.031–1.158, P = 0.0027), and asv001 in sebaceous skin on the forehead (OR = 1.074, 95% CI: 1.010–1.143, P = 0.0232). Eight skin microbiota phenotypes were negatively associated with systemic lupus erythematosus risk: Firmicutes phylum in sebaceous skin of the forehead (OR = 0.896, 95% CI: 0.803–1.000, P = 0.0489), genus Acinetobacter in dry skin on the dorsal forearm (OR = 0.935, 95% CI: 0.877–0.997, P = 0.0407), asv054 in dry skin on the dorsal forearm (Part 1, OR = 0.922, 95% CI: 0.859–0.989, P = 0.0240; Part 2, OR = 0.929, 95% CI: 0.877–0.985, P = 0.0136), asv008 in moist skin of the antecubital fossa (OR = 0.896, 95% CI: 0.829–0.969, P = 0.0057), asv092 in dry skin on the dorsal forearm (OR = 0.916, 95% CI: 0.846–0.991, P = 0.0299), Betaproteobacteria class in moist skin of the antecubital fossa (OR = 0.917, 95% CI: 0.852–0.987, P = 0.0213), asv003 in dry skin on the dorsal forearm (OR = 0.925, 95% CI: 0.869–0.985, P = 0.0152), and asv011 in dry skin on the dorsal forearm (OR = 0.941, 95% CI: 0.899–0.985, P = 0.0085).

Table 1.

Forward Mendelian Randomization Associations Between Skin Microbiota Traits and Autoimmune Skin Diseases

Disease Type Microbial Feature (Reported Trait) Number of SNPs Odds Ratio (95% CI) IVW P-value
Psoriasis Univariate microbial feature (family: neisseriaceae) at the forehead (sebaceous skin) 7 1.036 (1.005–1.059) 0.01997
Psoriasis Univariate microbial feature (family: neisseriaceae) at the dorsal forearm (dry skin) 4 1.037 (1.006–1.069) 0.01998
Psoriasis Univariate microbial feature (asv: asv002) at the antecubital fossa (moist skin) 7 1.032 (1.003–1.063) 0.0311
Psoriasis Univariate microbial feature (genus: finegoldia) at the dorsal forearm (dry skin) 9 1.027 (1.000–1.054) 0.04653
Psoriasis Univariate microbial feature (asv: asv053) at the dorsal forearm (dry skin) 3 0.974 (0.950–0.998) 0.0341
Psoriasis Univariate microbial feature (asv: asv070) at the dorsal forearm (dry skin) 7 0.974 (0.952–0.997) 0.029
Psoriasis Univariate microbial feature (asv: asv016) at the antecubital fossa (moist skin) 12 0.980 (0.9616–0.999) 0.03909
Psoriasis Univariate microbial feature (asv: asv033) at the forehead (sebaceous skin) 7 0.977 (0.9576–0.9959) 0.01795
Localized Scleroderma Univariate microbial feature (genus: enhydrobacter) at the dorsal forearm (dry skin) 6 1.183 (1.0289–1.361) 0.0183
Localized Scleroderma Univariate microbial feature (asv: asv070) at the dorsal forearm (dry skin) 7 1.122 (1.0133–1.2416) 0.0268
Localized Scleroderma Univariate microbial feature (asv: asv013) at the dorsal forearm (dry skin) 7 1.135 (1.0298–1.2504) 0.0106
Localized Scleroderma Univariate microbial feature (asv: asv072) at the dorsal forearm (dry skin) 9 1.105 (1.0007–1.2207) 0.0484
Localized Scleroderma Univariate microbial feature (family: neisseriaceae) at the antecubital fossa (moist skin) 9 1.151 (1.0387–1.2759) 0.0073
Localized Scleroderma Univariate microbial feature (asv: asv023) at the dorsal forearm (dry skin) 8 0.907 (0.8283–0.9921) 0.033
Localized Scleroderma Univariate microbial feature (family: rhodobacteraceae) at the dorsal forearm (dry skin) 15 0.909 (0.8337–0.9911) 0.0305
Localized Scleroderma Univariate microbial feature (genus: staphylococcus) at the dorsal forearm (dry skin) 5 0.856 (0.7337–0.9994) 0.0491
Systemic Lupus Erythematosus Univariate microbial feature (phylum: proteobacteria) at the dorsal forearm (dry skin) 8 1.091 (1.0103–1.1771) 0.0263
Systemic Lupus Erythematosus Univariate microbial feature (order: actinomycetales) at the dorsal forearm (dry skin) 12 1.065 (1.0001–1.1331) 0.0498
Systemic Lupus Erythematosus Univariate microbial feature (asv: asv031) at the dorsal forearm (dry skin) 8 1.049 (1.0005–1.1005) 0.0478
Systemic Lupus Erythematosus Univariate microbial feature (asv: asv039) at the antecubital fossa (moist skin) 8 1.093 (1.0311–1.1578) 0.0027
Systemic Lupus Erythematosus Univariate microbial feature (asv: asv001) at the forehead (sebaceous skin) 16 1.074 (1.0098–1.1428) 0.0232
Systemic Lupus Erythematosus Univariate microbial feature (phylum: firmicutes) at the forehead (sebaceous skin) 3 0.896 (0.8029–0.9995) 0.0489
Systemic Lupus Erythematosus Univariate microbial feature (genus: acinetobacter) at the dorsal forearm (dry skin) 9 0.935 (0.8765–0.9972) 0.0407
Systemic Lupus Erythematosus Univariate microbial feature (asv: asv054) at the dorsal forearm (dry skin) (Part 1) 8 0.922 (0.8589–0.9893) 0.024
Systemic Lupus Erythematosus Univariate microbial feature (asv: asv008) at the antecubital fossa (moist skin) 3 0.896 (0.829–0.9685) 0.0057
Systemic Lupus Erythematosus Univariate microbial feature (asv: asv054) at the dorsal forearm (dry skin)(Part 2) 7 0.929 (0.8766–0.985) 0.0136
Systemic Lupus Erythematosus Univariate microbial feature (asv: asv092) at the dorsal forearm (dry skin) 3 0.916 (0.8455–0.9914) 0.0299
Systemic Lupus Erythematosus Univariate microbial feature (class: Betaproteobacteria) at the antecubital fossa (moist skin) 11 0.917 (0.8518–0.9872) 0.0213
Systemic Lupus Erythematosus Univariate microbial feature (asv: asv003) at the dorsal forearm (dry skin) 9 0.925 (0.8693–0.9852) 0.0152
Systemic Lupus Erythematosus Univariate microbial feature (asv: asv011) at the dorsal forearm (dry skin) 10 0.941 (0.8988–0.9845) 0.0085
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Sensitivity analyses revealed no significant heterogeneity or horizontal pleiotropy in most associations, and LOO analyses indicated robust results (Supplementary Figures 4–6). For some associations, the MR-PRESSO global test could not be performed due to insufficient genetic instrumental variables, and these are denoted as “NA” in Supplementary Table S1. Detailed effect sizes, confidence intervals, and P-values for all significant associations are provided in Supplementary Table S1.

Causal Effects of Autoimmune Skin Diseases on Skin Microbiota

In reverse causality analysis, we assessed the impact of autoimmune skin diseases on skin microbiota abundance (Table 2 and Supplementary Figures 7–9). Psoriasis was associated with increased abundance of asv012 in moist skin of the antecubital fossa (OR = 1.462, 95% CI: 1.064–1.998, P = 0.01888), Anaerococcus genus in dry skin on the dorsal forearm (OR = 1.411, 95% CI: 1.027–1.939, P = 0.03382), asv007 in dry skin on the dorsal forearm (OR = 1.582, 95% CI: 1.065–2.349, P = 0.02308), and Actinomycetales order in moist skin of the antecubital fossa (OR = 1.444, 95% CI: 1.121–1.858, P = 0.00440). Conversely, psoriasis was associated with lower abundance of Neisseriaceae family in dry skin on the dorsal forearm (OR = 0.718, 95% CI: 0.555–0.931, P = 0.01234), Lactobacillales order in dry skin on the dorsal forearm (OR = 0.734, 95% CI: 0.564–0.954, P = 0.02090), Haemophilus genus in dry skin on the dorsal forearm (Part 1, OR = 0.675, 95% CI: 0.491–0.929, P = 0.01568; Part 2, OR = 0.675, 95% CI: 0.484–0.940, P = 0.02018), Streptococcaceae family in dry skin on the dorsal forearm (OR = 0.758, 95% CI: 0.583–0.988, P = 0.04005), asv065 in dry skin on the dorsal forearm (OR = 0.675, 95% CI: 0.456–0.997, P = 0.04823), Betaproteobacteria class in dry skin on the dorsal forearm (OR = 0.675, 95% CI: 0.484–0.940, P = 0.02018), Lactobacillales order in moist skin of the antecubital fossa (OR = 0.754, 95% CI: 0.570–0.998, P = 0.04810), and asv013 in sebaceous skin on the forehead (OR = 0.701, 95% CI: 0.521–0.942, P = 0.01850). Localized scleroderma increased the abundance of asv021 in the moist skin of the antecubital fossa (OR = 1.462, 95% CI: 1.027–2.082, P = 0.03509). Simultaneously, it reduced the abundance of asv005 in sebaceous skin on the forehead (OR = 0.688, 95% CI: 0.525–0.902, P = 0.00672), Kocuria genus in dry skin on the dorsal forearm (OR = 0.683, 95% CI: 0.474–0.984, P = 0.04076), asv008 in dry skin on the dorsal forearm (OR = 0.533, 95% CI: 0.355–0.799, P = 0.00231), asv042 in dry skin on the dorsal forearm (OR = 0.603, 95% CI: 0.393–0.925, P = 0.02063), asv059 in dry skin on the dorsal forearm (OR = 0.504, 95% CI: 0.315–0.804, P = 0.00408), and Bacteroides genus in moist skin of the antecubital fossa (OR = 0.640, 95% CI: 0.436–0.939, P = 0.02264). Systemic lupus erythematosus reduced the abundance of asv008 in moist skin of the antecubital fossa (OR = 0.630, 95% CI: 0.435–0.913, P = 0.01463) and asv045 in dry skin on the dorsal forearm (OR = 0.608, 95% CI: 0.427–0.865, P = 0.00562).

Table 2.

Reverse Mendelian Randomization Associations Between Autoimmune Skin Diseases and Skin Microbiota Traits

Disease Type Microbial Feature (Reported Trait) Number of SNPs Odds Ratio (95% CI) IVW P-value
Psoriasis Univariate microbial feature (asv: asv012) at the antecubital fossa (moist skin) 17 1.46 (1.06–2.00) 0.019
Psoriasis Univariate microbial feature (family: neisseriaceae) at the dorsal forearm (dry skin) 17 0.72 (0.55–0.93) 0.012
Psoriasis Univariate microbial feature (order: lactobacillales) at the dorsal forearm (dry skin) 17 0.73 (0.56–0.95) 0.021
Psoriasis Univariate microbial feature (genus: Haemophilus) at the dorsal forearm (dry skin)(Part 1) 17 0.68 (0.49–0.93) 0.016
Psoriasis Univariate microbial feature (family: Streptococcaceae) at the dorsal forearm (dry skin) 17 0.76 (0.58–0.99) 0.04
Psoriasis Univariate microbial feature (asv: asv065) at the dorsal forearm (dry skin) 17 0.67 (0.46–1.00) 0.048
Psoriasis Univariate microbial feature (class: Betaproteobacteria) at the dorsal forearm (dry skin) 17 0.67 (0.48–0.94) 0.02
Psoriasis Univariate microbial feature (order: lactobacillales) at the antecubital fossa (moist skin) 17 0.75 (0.57–1.00) 0.048
Psoriasis Univariate microbial feature (asv: asv013) at the forehead (sebaceous skin) 17 0.70 (0.52–0.94) 0.019
Psoriasis Univariate microbial feature (genus: Anaerococcus) at the dorsal forearm (dry skin) 17 1.41 (1.03–1.94) 0.034
Psoriasis Univariate microbial feature (asv: asv007) at the dorsal forearm (dry skin) 17 1.58 (1.07–2.35) 0.023
Psoriasis Univariate microbial feature (order: actinomycetales) at the antecubital fossa (moist skin) 17 1.44 (1.12–1.86) 0.004
Psoriasis Univariate microbial feature (genus: haemophilus) at the dorsal forearm (dry skin)(Part 2) 17 0.67 (0.48–0.94) 0.020
Localized Scleroderma Univariate microbial feature (asv: asv021) at the antecubital fossa (moist skin) 3 1.46 (1.03–2.08) 0.035
Localized Scleroderma Univariate microbial feature (asv: asv005) at the forehead (sebaceous skin) 3 0.69 (0.52–0.90) 0.007
Localized Scleroderma Univariate microbial feature (genus: kocuria) at the dorsal forearm (dry skin) 3 0.68 (0.47–0.98) 0.041
Localized Scleroderma Univariate microbial feature (asv: asv008) at the dorsal forearm (dry skin) 3 0.53 (0.36–0.80) 0.002
Localized Scleroderma Univariate microbial feature (asv: asv042) at the dorsal forearm (dry skin) 3 0.60 (0.39–0.93) 0.021
Localized Scleroderma Univariate microbial feature (asv: asv059) at the dorsal forearm (dry skin) 3 0.50 (0.32–0.80) 0.004
Localized Scleroderma Univariate microbial feature (genus: Bacteroides) at the antecubital fossa (moist skin) 3 0.64 (0.44–0.94) 0.023
Systemic Lupus Erythematosus Univariate microbial feature (asv: asv008) at the antecubital fossa (moist skin) 3 0.63 (0.44–0.91) 0.015
Systemic Lupus Erythematosus Univariate microbial feature (asv: asv045) at the dorsal forearm (dry skin) 4 0.61 (0.43–0.86) 0.006
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Sensitivity analyses revealed no substantial evidence of heterogeneity or horizontal pleiotropy in most associations, and LOO analyses indicated robust results (Supplementary Figures 10–12). It should be noted that some MR-PRESSO global test results were marked as “NA” due to insufficient genetic instrumental variables for the relevant associations to perform this test. Detailed effect sizes, confidence intervals, and P-values for all significant associations are provided in Supplementary Table S2.

This study constructed a bidirectional Mendelian randomization association ring diagram (Figure 2), integrating effect estimates and key methodological validation metrics for all significant associations. A causal network diagram (Figure 3) visualized causal pathways, where nodes represent microbiota or diseases and directed edges (colored and weighted to indicate direction and strength) denote causal relationships, providing a macro-level overview of host-microbiota interaction patterns.

Figure 2.

A diagram of bidirectional MR associations between skin diseases and microbiota traits. The diagram illustrates bidirectional Mendelian randomization associations between autoimmune skin diseases and skin microbiota traits. It features a circular layout with various microbiota and diseases labeled around the perimeter. The inner rings display p-values for different statistical tests: IVW, Simple Mode, Weighted Mode, Heterogeneity Q, Egger Intercept and MR-PRESSO Global. Analysis direction is indicated as Forward MR or Reverse MR. The diagram provides a visual summary of causal relationships, with nodes representing microbiota or diseases and directed edges denoting causal pathways. The legend explains the p-value ranges and analysis directions.

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Bidirectional Mendelian Randomization Association Ring Diagram. This diagram summarizes all significant bidirectional MR associations between autoimmune skin diseases and skin microbiota traits. Direction indicates the inferred causal direction, and color indicates the direction of effect: red represents a positive association and blue represents a negative association.

Abbreviations: OR, odds ratio; CI, confidence interval; P-value, probability value; IVW, inverse variance weighting; MR-Egger, MR-Egger regression; WME, weighted median; SM, simple mode; WM, weighted mode; MR-PRESSO, MR Pleiotropy Residual Sum and Outlier test; NA, not available because the number of available instrumental variables was insufficient for the corresponding test.

Figure 3.

Diagrams show causal links between skin diseases and microbiome features. The diagrams illustrate causal associations between skin diseases and microbiome features. Each disease is represented in two diagrams: 'Microbiome to Disease' and 'Disease to Microbiome'. For Localized Scleroderma, Psoriasis and Systemic Lupus Erythematosus, nodes represent microbial features at different taxonomic levels and skin sites. Arrows indicate causal directions, with beta values shown on a scale. Microbial levels are categorized as phylum, class, order, family, genus and ASV. Skin sites are marked as sebaceous (square), dry (circle) and moist (diamond). The diagrams visualize bidirectional relationships, highlighting the interaction patterns between diseases and microbiome features.

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Causal Association Networks between Autoimmune Skin Diseases and Skin Microbiome Features. This network visualizes bidirectional causal relationships between autoimmune skin diseases and skin microbiome features. Nodes represent diseases or microbial features at different taxonomic/sequence levels (phylum, class, order, family, genus, and ASV) and/or skin microenvironments (sebaceous, dry, and moist sites), as indicated in the legend. Directed arrows indicate the inferred causal direction. Arrow color represents the beta coefficient: blue indicates a negative beta value and inverse association, whereas red indicates a positive beta value and positive association. Arrow thickness represents the absolute beta value, with thicker arrows indicating stronger effect estimates.

Abbreviations: ASV, amplicon sequence variant; MR, Mendelian randomization.

Discussion

Leveraging bidirectional Mendelian randomization analysis, this study provides a comprehensive investigation into the causal relationships linking the cutaneous microbiota with three specific autoimmune dermatological conditions, namely psoriasis, localized scleroderma, and systemic lupus erythematosus.

MR analysis revealed that genetically predicted abundance of specific skin microbiota, such as the Neisseriaceae family and Finegoldia genus, positively correlated with psoriasis risk, while ASV016 and ASV033 exhibited protective effects—findings consistent with observational studies. Kayıran et al23 similarly observed significant enrichment of the Finegoldia genus in psoriatic lesions, corroborating the positive association between the Neisseriaceae family and psoriasis risk in this study. Previous studies24–26 have identified increased abundances of pathogenic bacteria such as Streptococcus genus, Anaerococcus genus, and Staphylococcus aureus (the latter inducing Th17 inflammation), alongside suppression of the Neisseriaceae family, Lactobacillales order, Betaproteobacteria class, and immunomodulatory bacteria like Staphylococcus epidermidis and Cutibacterium acnes. This imbalance is accompanied by increased diversity and reduced stability of the microbiota, potentially forming distinct “skin microbiota types”.

Genetic susceptibility to psoriasis can conversely reshape the microbiome: it promotes the abundance of microbial groups such as ASV012, Anaerococcus genus, ASV007, and Actinomycetales order; concurrently, it suppresses the abundance of groups including Neisseriaceae family, Lactobacillales order, Haemophilus genus, Streptococcaceae family, ASV065, Betaproteobacteria class, and ASV013.Two points warrant attention: First, this study found that psoriasis exhibits an inhibitory effect on the Streptococcaceae family and a promoting effect on the Actinomycetales order, which contradicts previous research conclusions. Second, the Neisseriaceae family exhibits an apparently contradictory dual-action pattern in psoriasis: while increased abundance in the sebaceous skin on the forehead and dry skin on the dorsal forearm elevates disease risk, reverse MR analysis conversely reveals that psoriasis suppresses Neisseriaceae family abundance in the dry skin on the dorsal forearm.

This phenomenon may reflect compensatory regulatory mechanisms employed by the host to maintain immune homeostasis. Initial Neisseriaceae family proliferation may contribute to disease onset through pathways such as Th17 activation,27 whereas subsequent changes in the inflammatory microenvironment (eg, pH shifts, antimicrobial peptides, barrier disruption) limit its further colonization.28,29 Reverse MR analysis indicates the presence of this negative feedback regulation, though its effect is limited during disease progression. These findings suggest host-microbiome interactions jointly influence psoriasis pathogenesis. This bidirectional causality and negative feedback loop are crucial for elucidating chronic disease mechanisms and developing interventions targeting microbiome dynamic equilibrium. Concerning localized scleroderma, analyses identified notable associations between specific skin microbiota characteristics and the risk of disease. Specifically, elevated abundances of Enhydrobacter genus, ASV070, ASV013, ASV072, and Neisseriaceae family are linked to a heightened risk of the condition; conversely, ASV023, the Rhodobacteraceae family, and the Staphylococcus genus demonstrated potential protective effects against localized scleroderma.While specific studies on the skin microbiota in localized scleroderma (LoS) are limited, previous investigations in systemic sclerosis (SSc) patients indicate increased proportions of Gram-negative anaerobic bacteria (eg, Bacteroidetes phylum) in their skin.These Gram-negative anaerobes may activate the Toll-like receptor 4 (TLR4) pathway in the host immune system by secreting membrane vesicles or their metabolites (eg, lipopolysaccharides, LPS), thereby triggering pro-inflammatory cascades and inducing collagen deposition, exacerbating skin fibrosis and inflammation.11,30 Concurrently, protective microbiota such as the Staphylococcus genus may regulate local immune balance by maintaining skin barrier integrity, producing anti-inflammatory metabolites, or secreting antimicrobial peptides (eg, PSMα), collectively counteracting scleroderma progression.31,32 This study indicates that a specific microbiota can perform heterogeneous roles.

These roles vary across different autoimmune skin diseases. For instance, ASV070 acts as a protective factor in psoriasis. Nevertheless, it transforms into a risk factor in localized scleroderma. This diametrically opposed effect pattern likely originates from disease-specific pathophysiological mechanisms. It may also result from functional shifts of this microbiota. These shifts occur within distinct disease microenvironments. This context-dependent causal relationship provides crucial insights. These insights are valuable for future precision interventions.

Regarding systemic lupus erythematosus (SLE), our analysis identified multiple skin microbiota features. These features are the Proteobacteria phylum and the Actinomycetales order. They were significantly associated with an elevated SLE risk. Conversely, the Firmicutes phylum exhibited protective effects. The Acinetobacter genus also demonstrated protective effects. Additionally, ASV054 showed protective effects against SLE development.Previous microbiome studies concerning systemic lupus erythematosus (SLE) have primarily focused on the gut.33–37 They have also focused on the oral cavity. Research on skin microbiota in SLE has been relatively limited. Our investigation utilized genomic methodologies. We aimed to examine the contribution of the dermal microbiome to SLE development. This work also further highlighted shared structural attributes. These attributes are evident in the gut and oral microbial communities.Both the gut microbiomes and the oral microbiomes of SLE patients showed enrichment of Proteobacteria. For example, Escherichia-Shigella and Enterobacteriaceae were found in the gut. Pseudomonas was present in the oral cavity. These specific bacteria may contribute to SLE progression. They achieve this via LPS-mediated TLR4 activation and bile acid metabolism disruption.Increased cutaneous Proteobacteria may similarly contribute to SLE progression through pro-inflammatory mechanisms. Gut microbiomes often exhibited reduced Firmicutes. Firmicutes (eg, Clostridiales) produce short-chain fatty acids (SCFAs), which contribute significantly to upholding intestinal barrier integrity and regulating immune function.38–40 Similarly, a reduction of Firmicutes in the skin may thus impair barrier function and immune metabolism, diminishing protective effects. This cross-habitat similarity suggests specific phyla may influence systemic immune dysregulation in SLE through shared pathological mechanisms (eg, LPS-triggered systemic inflammation, barrier dysfunction).

This study represents one of the first applications of bidirectional MR to explore causal relationships between the skin microbiota and autoimmune cutaneous disorders. Several limitations should be considered. First, the skin microbiota GWAS had a relatively small sample size and was based on individuals of European ancestry, whereas the outcome data were derived from FinnGen. Although both datasets were European, differences between the German exposure cohorts and the Finnish outcome cohort may introduce residual population-structure bias and limit generalizability to non-European populations. Second, the number of localized scleroderma cases was limited, resulting in lower statistical power; the relaxed SNP threshold used in reverse analyses may therefore increase the possibility of false-positive findings. Third, no formal multiple-testing correction was applied; consequently, the results should be interpreted as suggestive and hypothesis-generating rather than definitive causal proof. Fourth, 16S rRNA sequencing and ASV-level inference cannot always provide reliable species-level resolution, which may limit biological specificity. Finally, most identified associations have not yet been functionally validated, and residual pleiotropy or gene-environment interactions cannot be completely excluded. Future studies using larger multi-ancestry cohorts, together with experimental and clinical validation, are needed to confirm these findings and clarify the underlying mechanisms.

Conclusion

This bidirectional MR analysis provides genetic evidence suggesting reciprocal relationships between skin microbiota and psoriasis, LoS, and SLE. These findings support the potential involvement of the skin-immune axis in autoimmune skin disease pathogenesis and highlight microbial traits that warrant further investigation. However, the observed effects were generally modest and should be interpreted cautiously because of limited sample sizes, absence of formal multiple-testing correction, and lack of functional validation. Replication in larger multi-ancestry datasets and mechanistic studies is required before these findings can inform microbiota-based prevention or treatment strategies.

Acknowledgments

This work benefited from the publicly available statistics of GWAS. We thank the contributors to the original GWAS database.

Funding Statement

No funding was received for this manuscript.

Abbreviations

ASV, Amplicon sequence variant; CI, Confidence interval; GWAS, Genome-wide association study; IMID, Immune-mediated inflammatory skin disease; IV, Instrumental variable; IVW, Inverse variance weighting; LD, Linkage disequilibrium; LOO, Leave-one-out; LoS, Localized scleroderma; LPS, Lipopolysaccharide; MR, Mendelian randomization; MR-Egger, Mendelian randomization-Egger regression; MR-PRESSO, MR Pleiotropy Residual Sum and Outlier test; OR, Odds ratio; SCFA, Short-chain fatty acid; SLE, Systemic lupus erythematosus; SM, Simple mode; SNP, Single-nucleotide polymorphism; SSc, Systemic sclerosis; TLR4, Toll-like receptor 4; WME, Weighted median; WM, Weighted mode.

Data Sharing Statement

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Associated Data

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Data Availability Statement

All data generated or analyzed during this study are included in this published article and its supplementary information files.

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