Association of Systemic Antibiotic Treatment of Acne With Skin Microbiota Characteristics

Abstract

Importance: Given the widespread use of systemic antibiotics for treatment of moderate to severe acne, it is important to understand the associations of such antibiotic use with changes not only in Cutibacterium acnes (formerly Propionibacterium acnes) but also in the complete bacterial community of the skin.

Objective: To examine the composition, diversity, and resilience of skin microbiota associated with systemic antibiotic perturbation in individuals with acne.

Design, setting, and participants: This longitudinal cohort study conducted at an academic referral center in Maryland from February 11 to September 23, 2014, included 4 female participants who had received a recent diagnosis of acne vulgaris, showed comedonal and inflammatory acne on the face, were at least 18 years old, and had no recent use of systemic or topical treatments for acne, including antibiotics and retinoids. Data analysis was performed between July 5, 2017, and November 7, 2018.

Interventions: Participants were prescribed oral minocycline, 100 mg, twice daily for 4 weeks. Skin areas on the forehead, cheek, and chin were sampled for 16S ribosomal RNA gene sequencing at baseline, 4 weeks after starting minocycline treatment, and then 1 week and 8 weeks after discontinuation of treatment.

Main outcomes and measures: Skin microbiota examined with respect to relative abundance of bacterial taxa, α diversity (represents within-sample microbial diversity), and β diversity (represents between-sample microbial diversity). Acne status evaluated with photography and lesion count.

Results: Of the 4 patients included in this study, 2 were 25 years old, 1 was 29 years old, and 1 was 35 years old; 2 were white women, 1 was an African American woman, and 1 was an Asian woman. Across all 4 patients, antibiotic treatment was associated with a 1.4-fold reduction in the level of C acnes (difference, -10.3%; 95% CI, -19.9% to -0.7%; P = .04) with recovery following cessation of treatment. Distinct patterns of change were identified in multiple bacterial genera, including a transient 5.6-fold increase in the relative abundance of Pseudomonas species (difference, 2.2%; 95% CI, 0.9%-3.4%; P < .001) immediately following antibiotic treatment, as well as a persistent 1.7-fold increase in the relative abundance of Streptococcus species (difference, 5.4%; 95% CI, 0.3%-10.6%; P = .04) and a 4.7-fold decrease in the relative abundance of Lactobacillus species (difference, -0.8%; 95% CI, -1.4% to -0.2%; P = .02) 8 weeks following antibiotic treatment withdrawal. In general, antibiotic administration was associated with an initial decrease from baseline of bacterial diversity followed by recovery. Principal coordinates analysis results showed moderate clustering of samples by patient (analysis of similarity, R = 0.424; P = .001) and significant clustering of samples by time in one participant (analysis of similarity, R = 0.733; P = .001).

Conclusions and relevance: In this study, systemic antibiotic treatment of acne was associated with changes in the composition and diversity of skin microbiota, with variable rates of recovery across individual patients and parallel changes in specific bacterial populations. Understanding the association between systemic antibiotic use and skin microbiota may help clinicians decrease the likelihood of skin comorbidities related to microbial dysbiosis.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Chien reported receiving grants from the American Acne and Rosacea Society during the conduct of the study; receiving grants from UniLever, L’Oréal, and Boots/Walgreens outside the submitted work; and serving on the advisory board of Galderma. Dr Kang reported receiving grants from the American Acne and Rosacea Society during the conduct of the study; receiving personal fees from Galderma and from Unilever outside the submitted work; and serving on the advisory boards for Galderma and for Unilever. No other disclosures were reported.

Figures

Figure 1.. Schematic Diagram of Study Design

A, Time course of antibiotic therapy and procedures performed at each study visit. B, Bilateral sites of the face sampled by sterile cotton swabs at each visit for 16S ribosomal RNA (rRNA) gene amplification and sequencing. aBilateral skin sampling (forehead, cheek, and chin), 16S rRNA gene sequencing, photography, and lesion count.

Figure 2.. Dynamics of Bacterial Populations in the Presence and After Withdrawal of Antibiotic Treatment

Relative abundance of Cutibacterium acnes (formerly Propionibacterium acnes) (A) and Staphylococcus epidermidis (B) at various times during and after antibiotic treatment. Horizontal bar within box plots represents the median; bottom and top of each box, first and third quartiles; lower error bar extends to the lowest data point within 1.5 times the interquartile range from the first quartile; upper error bar extends to the highest data point within 1.5 times the interquartile range from the third quartile. Data points beyond 1.5 times the interquartile range above the third quartile or below the first quartile are plotted individually as outliers. Relative abundance of selected bacterial genera at various times during and after antibiotic treatment (C). Error bars indicate 95% CIs. P values calculated using t tests with 1000 Monte Carlo permutations and 5% false-discovery rate adjustment. Sample sizes shown on x-axes indicate distinct skin samples obtained from all 4 participants. aP < .05 compared with baseline. bP < .01 compared with baseline.

Figure 3.. Trends in Acne Severity and Microbiota α Diversity

Numbers of inflamed lesions in all (A) or individual (B) patients across all sites at different times. Microbiota α diversity (represents within-sample microbial diversity), based on the whole tree phylogenetic diversity metric, of all (C) or individual (D) patients across all sites at various times and across all patients and times at different sites (E). Horizontal bar within box plots represents the median; bottom and top of each box, first and third quartiles; lower error bar extends to the lowest data point within 1.5 times the interquartile range from the first quartile; upper error bar extends to the highest data point within 1.5 times the interquartile range from the third quartile. Data points beyond 1.5 times the interquartile range above the third quartile or below the first quartile are plotted individually as outliers. P values were calculated using t tests with 999 Monte Carlo permutations and Bonferroni corrections. Sample sizes shown on x-axes of (C) and (E) indicate distinct skin samples obtained from all 4 patients. aP < .01 compared with baseline. bP < .05 compared with baseline.

Figure 4.. Microbiota β Diversity

Microbiota β diversity (between-sample microbial diversity) based on principal coordinates analysis (PCoA) of weighted UniFrac distances. A and B, PCoA plots show intersample distances by 2 principal coordinates (PC1 and PC2), with labeling of individual samples by patient, site, and time across all samples and by time for patients 1 to 4. Principal coordinates, calculated from a distance matrix of weighted Unifrac distances, and have no units. C, Hierarchical clustering of skin samples from patient 3 with the unweighted pair group method with arithmetic mean algorithm. Analysis of similarity (ANOSIM) test statistic R ranges from −1 to 1; values closer to 1 indicate more pronounced clustering of samples by the examined categorical variable (patient, site, or time).