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. 2021 Oct;10(5):e1236.
doi: 10.1002/mbo3.1236.

Effect of the skincare product on facial skin microbial structure and biophysical parameters: A pilot study

Bo Kyoung Hwang  1 , Sado Lee  1 , Joonoh Myoung  1 , Seung Jin Hwang  1 , Jun Man Lim  1 , Eui Taek Jeong  1 , Sun Gyoo Park  1 , Sung Hun Youn  1
Affiliations

Effect of the skincare product on facial skin microbial structure and biophysical parameters: A pilot study

Bo Kyoung Hwang et al. Microbiologyopen. 2021 Oct.

Abstract

Daily use of cosmetics is known to affect the skin microbiome. This study aimed to determine the bacterial community structure and skin biophysical parameters following the daily application of a skincare product on the face. Twenty-five Korean women, who used the same skincare product for four weeks participated in the study. During this period, skin hydration, texture, sebum content, and pH were measured, and skin swab samples were collected on the cheeks. The microbiota was analyzed using the MiSeq system. Through these experiments, bacterial diversity in facial skin increased and the microbial community changed after four weeks of skincare product application. The relative abundance of Cutibacterium and Staphylococcus increased, significant changes in specific bacterial modules of the skin microbial network were observed, and skin hydration and texture improved. It was suggested that daily use of skincare products could affect the microbial structure of facial skin as well as the biophysical properties of the facial skin. These findings expand our understanding of the role of skincare products on the skin environment.

Keywords: skin environment; skin microbiome; skin physiology; skincare product.

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Conflict of interest statement

All authors are employees of LG Household and Health Care.

Figures

FIGURE 1
FIGURE 1
Comparison of skin microbiome composition following use of the skincare product. (a) The number of observed OTUs. (b) Shannon diversity index. Relative abundance of bacteria at (c) order level and (d) genus level. Relative abundance of (e) Cutibacterium and (f) Staphylococcus in skin sample. *p < 0.05, **p < 0.01, ***p < 0.005
FIGURE 2
FIGURE 2
Changes to the bacterial network after use of the skincare product. (a) Bacterial co‐occurrence network. The size of each node is proportional to the relative abundance, node color represents bacterial module identity, and edge color indicates Spearman's correlation coefficient. (b) Correlation heatmap showing co‐occurrence patterns between bacteria
FIGURE 3
FIGURE 3
Comparison of skin biophysical parameters measured on the cheek. (a) Hydration level. (b) Texture level. (c) Sebum amount. (d) pH. **p < 0.01, ***p < 0.005
FIGURE 4
FIGURE 4
Dissimilarity of skin microbiota. (a) Canonical correspondence analysis (CCA) plot of skin microbiota. The points and arrows indicate each sample and skin biophysical parameters, respectively. (b) Clustering dendrogram of microbiota based on the Bray‐Curtis distance
FIGURE 5
FIGURE 5
Correlation between skin microbiota and skin biophysical parameters. (a) Order level. (b) Genus level. Node colors in each network correspond to (a) phylum and (b) order level. Edge colors indicate Spearman's correlation coefficient
FIGURE A1
FIGURE A1
Temperature and relative humidity in Daejeon, South Korea during this study. Data were obtained from KMA (Korea Meteorological Administration) National Climate Data Center (https://data.kma.go.kr/cmmn/main.do).
FIGURE A2
FIGURE A2
Linear discriminant analysis effect size (LEfSe) analysis of bacteria among sampling time points.
See this image and copyright information in PMC

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