Microbial Dynamics: Assessing Skincare Regimens' Impact on the Facial Skin Microbiome and Skin Health Parameters

Abstract

The human skin microbiome, a complex ecosystem of microbes, plays a pivotal role in skin health. This study aimed to investigate the impact of two skincare regimens, with preservatives (CSPs) and preservative-free (PFPs), on the skin microbiome in correlation to skin quality. double-blind randomized cosmetic studywith a split-face design was conducted on 26 female participants. Microbial diversity and abundance were analyzed using 16S rRNA amplicon sequence data and skin quality utilizing the Antera 3D skin camera. We confirmed earlier studies on the identification of major skin microbial taxa at the genus level, including Cutibacterium acnes, Corynebacterium, and Neisseriaceae as a predominant part of the facial skin microbiome. Furthermore, microbiome profile-based subgrouping was employed, which revealed that the cluster, characterized by the Neisseriaceae family as its predominant organism, exhibited significant reduction in folds count, fine lines, and redness after application of PFP compared to CSP. A Spearman correlation analysis highlighted the correlation between changes in specific bacteria and skin quality parameters such as redness, pores, and texture in the context of comparing PFP and CSP. Overall, the PFP treatment demonstrated a greater number of significant correlations between bacterial changes and skin quality compared to the CSP treatment, suggesting a distinct impact of the preservative-free skincare regimen on the skin microbiome and skin quality. Our study provides insights into different microbiome-centered approaches to improve our understanding of the skin microbiome's interplay with skin quality but also highlights the need for larger, comprehensive research to further understand the microbiome's role in dermatology.

Keywords: 16S rRNA; dermatology; effects of skincare; microbiology; microbiome analysis; skin microbiome.

Figure 1

Taxonomic profile of all samples, including the top 20 samples, with the remaining being denoted as “other species”.

Figure 2

NMDS profile of the different clusters of samples showing that the separation can be seen clearly with cluster D, representing sample 23 as being farthest from the other samples.

Figure 3

Taxonomic profile of the top 20 species, along with “other species” in each cluster.

Figure 4

Alpha diversity between time point 1 and time point 2 for each treatment.

Figure 5

Shannon diversity between clusters at different points in the treatment.

Figure 6

Shannon diversity difference values for each of the samples.

Figure 7

Heatmaps depicting the delta CLR (the lighter color indicates a decrease after treatment, and the darker color indicates an increase after treatment).

Figure 8

Jensen–Shannon Distance between the samples.

Figure 9

ANCOMBC2 volcano plot indicating the log-fold change on the x-axis and log10 of FDR-corrected p-value (q-value) on the y-axis. Black dots represent organisms with a significantly different log fold change of value greater than absolute value of 2.

Figure 10

Significant changes observed in cluster 2 between the amounts of change in the two treatments for the variables.

Figure 11

Significant Spearman correlation between taxa and Antera variables for the PFP treatment. Purple dots signify a positive correlation, while blue ones signify a negative correlation.

Figure 12

Significant Spearman correlation between taxa and Antera variables for the CSP treatment. Purple dots signify a positive correlation, while blue ones signify a negative correlation.