• Research article
  • Open access
  • Published: 12 June 2019

The impact of skin care products on skin chemistry and microbiome dynamics

  • Amina Bouslimani 1   na1 ,
  • Ricardo da Silva 1   na1 ,
  • Tomasz Kosciolek 2 ,
  • Stefan Janssen 2 , 3 ,
  • Chris Callewaert 2 , 4 ,
  • Amnon Amir 2 ,
  • Kathleen Dorrestein 1 ,
  • Alexey V. Melnik 1 ,
  • Livia S. Zaramela 2 ,
  • Ji-Nu Kim 2 ,
  • Gregory Humphrey 2 ,
  • Tara Schwartz 2 ,
  • Karenina Sanders 2 ,
  • Caitriona Brennan 2 ,
  • Tal Luzzatto-Knaan 1 ,
  • Gail Ackermann 2 ,
  • Daniel McDonald 2 ,
  • Karsten Zengler 2 , 5 , 6 ,
  • Rob Knight 2 , 5 , 6 , 7 &
  • Pieter C. Dorrestein 1 , 2 , 5 , 8  

BMC Biology volume  17 , Article number:  47 ( 2019 ) Cite this article

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Use of skin personal care products on a regular basis is nearly ubiquitous, but their effects on molecular and microbial diversity of the skin are unknown. We evaluated the impact of four beauty products (a facial lotion, a moisturizer, a foot powder, and a deodorant) on 11 volunteers over 9 weeks.

Mass spectrometry and 16S rRNA inventories of the skin revealed decreases in chemical as well as in bacterial and archaeal diversity on halting deodorant use. Specific compounds from beauty products used before the study remain detectable with half-lives of 0.5–1.9 weeks. The deodorant and foot powder increased molecular, bacterial, and archaeal diversity, while arm and face lotions had little effect on bacterial and archaeal but increased chemical diversity. Personal care product effects last for weeks and produce highly individualized responses, including alterations in steroid and pheromone levels and in bacterial and archaeal ecosystem structure and dynamics.

Conclusions

These findings may lead to next-generation precision beauty products and therapies for skin disorders.

The human skin is the most exposed organ to the external environment and represents the first line of defense against external chemical and microbial threats. It harbors a microbial habitat that is person-specific and varies considerably across the body surface [ 1 , 2 , 3 , 4 ]. Recent findings suggested an association between the use of antiperspirants or make-up and skin microbiota composition [ 5 , 6 , 7 ]. However, these studies were performed for a short period (7–10 days) and/or without washing out the volunteers original personal care products, leading to incomplete evaluation of microbial alterations because the process of skin turnover takes 21–28 days [ 5 , 6 , 7 , 8 , 9 ]. It is well-established that without intervention, most adult human microbiomes, skin or other microbiomes, remain stable compared to the differences between individuals [ 3 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].

Although the skin microbiome is stable for years [ 10 ], little is known about the molecules that reside on the skin surface or how skin care products influence this chemistry [ 17 , 18 ]. Mass spectrometry can be used to detect host molecules, personalized lifestyles including diet, medications, and personal care products [ 18 , 19 ]. However, although the impact of short-term dietary interventions on the gut microbiome has been assessed [ 20 , 21 ], no study has yet tested how susceptible the skin chemistry and Microbiome are to alterations in the subjects’ personal care product routine.

In our recent metabolomic/microbiome 3D cartography study [ 18 ], we observed altered microbial communities where specific skin care products were present. Therefore, we hypothesized that these products might shape specific skin microbial communities by changing their chemical environment. Some beauty product ingredients likely promote or inhibit the growth of specific bacteria: for example, lipid components of moisturizers could provide nutrients and promote the growth of lipophilic bacteria such as Staphylococcus and Propionibacterium [ 18 , 22 , 23 ]. Understanding both temporal variations of the skin microbiome and chemistry is crucial for testing whether alterations in personal habits can influence the human skin ecosystem and, perhaps, host health. To evaluate these variations, we used a multi-omics approach integrating metabolomics and microbiome data from skin samples of 11 healthy human individuals. Here, we show that many compounds from beauty products persist on the skin for weeks following their use, suggesting a long-term contribution to the chemical environment where skin microbes live. Metabolomics analysis reveals temporal trends correlated to discontinuing and resuming the use of beauty products and characteristic of variations in molecular composition of the skin. Although highly personalized, as seen with the microbiome, the chemistry, including hormones and pheromones such as androstenone and androsterone, were dramatically altered. Similarly, by experimentally manipulating the personal care regime of participants, bacterial and molecular diversity and structure are altered, particularly for the armpits and feet. Interestingly, a high person-to-person molecular and bacterial variability is maintained over time even though personal care regimes were modified in exactly the same way for all participants.

Skin care and hygiene products persist on the skin

Systematic strategies to influence both the skin chemistry and microbiome have not yet been investigated. The outermost layer of the skin turns over every 3 to 4 weeks [ 8 , 9 ]. How the microbiome and chemistry are influenced by altering personal care and how long the chemicals of personal care products persist on the skin are essentially uncharacterized. In this study, we collected samples from skin of 12 healthy individuals—six males and six females—over 9 weeks. One female volunteer had withdrawn due to skin irritations that developed, and therefore, we describe the remaining 11 volunteers. Samples were collected from each arm, armpit, foot, and face, including both the right and left sides of the body (Fig.  1 a). All participants were asked to adhere to the same daily personal care routine during the first 6 weeks of this study (Fig.  1 b). The volunteers were asked to refrain from using any personal care product for weeks 1–3 except a mild body wash (Fig.  1 b). During weeks 4–6, in addition to the body wash, participants were asked to apply selected commercial skin care products at specific body parts: a moisturizer on the arm, a sunscreen on the face, an antiperspirant on the armpits, and a soothing powder on the foot (Fig.  1 b). To monitor adherence of participants to the study protocol, molecular features found in the antiperspirant, facial lotion, moisturizer, and foot powder were directly tracked with mass spectrometry from the skin samples. For all participants, the mass spectrometry data revealed the accumulation of specific beauty product ingredients during weeks 4–6 (Additional file  1 : Figure S1A-I, Fig.  2 a orange arrows). Examples of compounds that were highly abundant during T4–T6 in skin samples are avobenzone (Additional file  1 : Figure S1A), dexpanthenol (Additional file  1 : Figure S1B), and benzalkonium chloride (Additional file  1 : Figure S1C) from the facial sunscreen; trehalose 6-phosphate (Additional file  1 : Figure S1D) and glycerol stearate (Additional file  1 : Figure S1E) from the moisturizer applied on arms; indolin (Additional file  1 : Figure S1F) and an unannotated compound ( m/z 233.9, rt 183.29 s) (Additional file  1 : Figure S1G) from the foot powder; and decapropylene glycol (Additional file  1 : Figure S1H) and nonapropylene glycol (Additional file  1 : Figure S1I) from the antiperspirant. These results suggest that there is likely a compliance of all individuals to study requirements and even if all participants confirmed using each product every day, the amount of product applied by each individual may vary. Finally, for weeks 7–9, the participants were asked to return to their normal routine by using the same personal care products they used prior to the study. In total, excluding all blanks and personal care products themselves, we analyzed 2192 skin samples for both metabolomics and microbiome analyses.

figure 1

Study design and representation of changes in personal care regime over the course of 9 weeks. a Six males and six females were recruited and sampled using swabs on two locations from each body part (face, armpits, front forearms, and between toes) on the right and left side. The locations sampled were the face—upper cheek bone and lower jaw, armpit—upper and lower area, arm—front of elbow (antecubitis) and forearm (antebrachium), and feet—in between the first and second toe and third and fourth toe. Volunteers were asked to follow specific instructions for the use of skin care products. b Following the use of their personal skin care products (brown circles), all volunteers used only the same head to toe shampoo during the first 3 weeks (week 1–week 3) and no other beauty product was applied (solid blue circle). The following 3 weeks (week 4–week 6), four selected commercial beauty products were applied daily by all volunteers on the specific body part (deodorant antiperspirant for the armpits, soothing foot powder for the feet between toes, sunscreen for the face, and moisturizer for the front forearm) (triangles) and continued to use the same shampoo. During the last 3 weeks (week 7–week 9), all volunteers went back to their normal routine and used their personal beauty products (circles). Samples were collected once a week (from day 0 to day 68—10 timepoints from T0 to T9) for volunteers 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, and 12, and on day 0 and day 6 for volunteer 8, who withdraw from the study after day 6. For 3 individuals (volunteers 4, 9, 10), samples were collected twice a week (19 timepoints total). Samples collected for 11 volunteers during 10 timepoints: 11 volunteers × 10 timepoints × 4 samples × 4 body sites = 1760. Samples collected from 3 selected volunteers during 9 additional timepoints: 3 volunteers × 9 timepoints × 4 samples × 4 body sites = 432. See also the “ Subject recruitment and sample collection ” section in the “ Methods ” section

figure 2

Monitoring the persistence of personal care product ingredients in the armpits over a 9-week period. a Heatmap representation of the most abundant molecular features detected in the armpits of all individuals during the four phases (0: initial, 1–3: no beauty products, 4–6: common products, and 7–9: personal products). Green color in the heatmap represents the highest molecular abundance and blue color the lowest one. Orange boxes with plain lines represent enlargement of cluster of molecules that persist on the armpits of volunteer 1 ( b ) and volunteer 3 ( c , d ). Orange clusters with dotted lines represent same clusters of molecules found on the armpits of other volunteers. Orange arrows represent the cluster of compounds characteristic of the antiperspirant used during T4–T6. b Polyethylene glycol (PEG) molecular clusters that persist on the armpits of individual 1. The molecular subnetwork, representing molecular families [ 24 ], is part of a molecular network ( http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=f5325c3b278a46b29e8860ec5791d5ad ) generated from MS/MS data collected from the armpits of volunteer 1 (T0–T3) MSV000081582 and MS/MS data collected from the deodorant used by volunteer 1 before the study started (T0) MSV000081580. c , d Polypropylene glycol (PPG) molecular families that persist on the armpits of individual 3, along with the corresponding molecular subnetwork that is part of the molecular network accessible here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=aaa1af68099d4c1a87e9a09f398fe253 . Subnetworks were generated from MS/MS data collected from the armpits of volunteer 3 (T0–T3) MSV000081582 and MS/MS data collected from the deodorant used by volunteer 3 at T0 MSV000081580. The network nodes were annotated with colors. Nodes represent MS/MS spectra found in armpit samples of individual 1 collected during T0, T1, T2, and T3 and in personal deodorant used by individual 1 (orange nodes); armpit samples of individual 1 collected during T0, T2, and T3 and personal deodorant used by individual 1 (green nodes); armpit samples of individual 3 collected during T0, T1, T2, and T3 and in personal deodorant used by individual 3 (red nodes); armpit samples of individual 3 collected during T0 and in personal deodorant used by individual 3 (blue nodes); and armpit samples of individual 3 collected during T0 and T2 and in personal deodorant used by individual 3 (purple nodes). Gray nodes represent everything else. Error bars represent standard error of the mean calculated at each timepoint from four armpit samples collected from the right and left side of each individual separately. See also Additional file  1 : Figure S1

To understand how long beauty products persist on the skin, we monitored compounds found in deodorants used by two volunteers—female 1 and female 3—before the study (T0), over the first 3 weeks (T1–T3) (Fig.  1 b). During this phase, all participants used exclusively the same body wash during showering, making it easier to track ingredients of their personal care products. The data in the first 3 weeks (T1–T3) revealed that many ingredients of deodorants used on armpits (Fig.  2 a) persist on the skin during this time and were still detected during the first 3 weeks or at least during the first week following the last day of use. Each of the compounds detected in the armpits of individuals exhibited its own unique half-life. For example, the polyethylene glycol (PEG)-derived compounds m/z 344.227, rt 143 s (Fig.  2 b, S1J); m/z 432.279, rt 158 s (Fig.  2 b, S1K); and m/z 388.253, rt 151 s (Fig.  2 b, S1L) detected on armpits of volunteer 1 have a calculated half-life of 0.5 weeks (Additional file  1 : Figure S1J-L, all p values < 1.81e−07), while polypropylene glycol (PPG)-derived molecules m/z 481.87, rt 501 s (Fig.  2 c, S1M); m/z 560.420, rt 538 s (Fig.  2 c, S1N); m/z 788.608, rt 459 s (Fig.  2 d, S1O); m/z 846.650, rt 473 s (Fig.  2 d, S1P); and m/z 444.338, rt 486 s (Fig.  2 d, S1Q) found on armpits of volunteers 3 and 1 (Fig.  2 a) have a calculated half-life ranging from 0.7 to 1.9 weeks (Additional file  1 : Figure S1M-Q, all p values < 0.02), even though they originate from the same deodorant used by each individual. For some ingredients of deodorant used by volunteer 3 on time 0 (Additional file  1 : Figure S1M, N), a decline was observed during the first week, then little to no traces of these ingredients were detected during weeks 4–6 (T4–T6), then finally these ingredients reappear again during the last 3 weeks of personal product use (T7–T9). This suggests that these ingredients are present exclusively in the personal deodorant used by volunteer 3 before the study. Because a similar deodorant (Additional file  1 : Figure S1O-Q) and a face lotion (Additional file  1 : Figure S1R) was used by volunteer 3 and volunteer 2, respectively, prior to the study, there was no decline or absence of their ingredients during weeks 4–6 (T4–T6).

Polyethylene glycol compounds (Additional file  1 : Figure S1J-L) wash out faster from the skin than polypropylene glycol (Additional file  1 : Figure S1M-Q)(HL ~ 0.5 weeks vs ~ 1.9 weeks) and faster than fatty acids used in lotions (HL ~ 1.2 weeks) (Additional file  1 : Figure S1R), consistent with their hydrophilic (PEG) and hydrophobic properties (PPG and fatty acids) [ 25 , 26 ]. This difference in hydrophobicity is also reflected in the retention time as detected by mass spectrometry. Following the linear decrease of two PPG compounds from T0 to T1, they accumulated noticeably during weeks 2 and 3 (Additional file  1 : Figure S1M, N). This accumulation might be due to other sources of PPG such as the body wash used during this period or the clothes worn by person 3. Although PPG compounds were not listed in the ingredient list of the shampoo, we manually inspected the LC-MS data collected from this product and confirmed the absence of PPG compounds in the shampoo. The data suggest that this trend is characteristic of accumulation of PPG from additional sources. These could be clothes, beds, or sheets, in agreement with the observation of these molecules found in human habitats [ 27 ] but also in the public GNPS mass spectrometry dataset MSV000079274 that investigated the chemicals from dust collected from 1053 mattresses of children.

Temporal molecular and bacterial diversity in response to personal care use

To assess the effect of discontinuing and resuming the use of skin care products on molecular and microbiota dynamics, we first evaluated their temporal diversity. Skin sites varied markedly in their initial level (T0) of molecular and bacterial diversity, with higher molecular diversity at all sites for female participants compared to males (Fig.  3 a, b, Wilcoxon rank-sum-WR test, p values ranging from 0.01 to 0.0001, from foot to arm) and higher bacterial diversity in face (WR test, p  = 0.0009) and armpits (WR test, p  = 0.002) for females (Fig.  3 c, d). Temporal diversity was similar across the right and left sides of each body site of all individuals (WR test, molecular diversity: all p values > 0.05; bacterial diversity: all p values > 0.20). The data show that refraining from using beauty products (T1–T3) leads to a significant decrease in molecular diversity at all sites (Fig.  3 a, b, WR test, face: p  = 8.29e−07, arm: p  = 7.08e−09, armpit: p  = 1.13e−05, foot: p  = 0.002) and bacterial diversity mainly in armpits (WR test, p  = 0.03) and feet (WR test, p  = 0.04) (Fig.  3 c, d). While molecular diversity declined (Fig.  3 a, b) for arms and face, bacterial diversity (Fig.  3 c, d) was less affected in the face and arms when participants did not use skin care products (T1–T3). The molecular diversity remained stable in the arms and face of female participants during common beauty products use (T4–T6) to immediately increase as soon as the volunteers went back to their normal routines (T7–T9) (WR test, p  = 0.006 for the arms and face)(Fig.  3 a, b). A higher molecular (Additional file  1 : Figure S2A) and community (Additional file  1 : Figure S2B) diversity was observed for armpits and feet of all individuals during the use of antiperspirant and foot powder (T4–T6) (WR test, molecular diversity: armpit p  = 8.9e−33, foot p  = 1.03e−11; bacterial diversity: armpit p  = 2.14e−28, foot p  = 1.26e−11), followed by a molecular and bacterial diversity decrease in the armpits when their regular personal beauty product use was resumed (T7–T9) (bacterial diversity: WR test, p  = 4.780e−21, molecular diversity: WR test, p  = 2.159e−21). Overall, our data show that refraining from using beauty products leads to lower molecular and bacterial diversity, while resuming the use increases their diversity. Distinct variations between male and female molecular and community richness were perceived at distinct body parts (Fig.  3 a–d). Although the chemical diversity of personal beauty products does not explain these variations (Additional file  1 : Figure S2C), differences observed between males and females may be attributed to many environmental and lifestyle factors including different original skin care and different frequency of use of beauty products (Additional file  2 : Table S1), washing routines, and diet.

figure 3

Molecular and bacterial diversity over a 9-week period, comparing samples based on their molecular (UPLC-Q-TOF-MS) or bacterial (16S rRNA amplicon) profiles. Molecular and bacterial diversity using the Shannon index was calculated from samples collected from each body part at each timepoint, separately for female ( n  = 5) and male ( n  = 6) individuals. Error bars represent standard error of the mean calculated at each timepoint, from up to four samples collected from the right and left side of each body part, of females ( n  = 5) and males ( n  = 6) separately. a , b Molecular alpha diversity measured using the Shannon index from five females (left panel) and six males (right panel), over 9 weeks, from four distinct body parts (armpits, face, arms, feet). c , d Bacterial alpha diversity measured using the Shannon index, from skin samples collected from five female (left panel) and six male individuals (right panel), over 9 weeks, from four distinct body parts (armpits, face, arms, feet). See also Additional file  1 : Figure S2

Longitudinal variation of skin metabolomics signatures

To gain insights into temporal metabolomics variation associated with beauty product use, chemical inventories collected over 9 weeks were subjected to multivariate analysis using the widely used Bray–Curtis dissimilarity metric (Fig.  4 a–c, S3A). Throughout the 9-week period, distinct molecular signatures were associated to each specific body site: arm, armpit, face, and foot (Additional file  1 : Figure S3A, Adonis test, p  < 0.001, R 2 0.12391). Mass spectrometric signatures displayed distinct individual trends at each specific body site (arm, armpit, face, and foot) over time, supported by their distinct locations in PCoA (principal coordinate analysis) space (Fig.  4 a, b) and based on the Bray–Curtis distances between molecular profiles (Additional file  1 : Figure S3B, WR test, all p values < 0.0001 from T0 through T9). This suggests a high molecular inter-individual variability over time despite similar changes in personal care routines. Significant differences in molecular patterns associated to ceasing (T1–T3) (Fig.  4 b, Additional file 1 : Figure S3C, WR test, T0 vs T1–T3 p  < 0.001) and resuming the use of common beauty products (T4–T6) (Additional file  1 : Figure S3C) were observed in the arm, face, and foot (Fig.  4 b), although the armpit exhibited the most pronounced changes (Fig.  4 b, Additional file 1 : Figure S3D, E, random forest highlighting that 100% of samples from each phase were correctly predicted). Therefore, we focused our analysis on this region. Molecular changes were noticeable starting the first week (T1) of discontinuing beauty product use. As shown for armpits in Fig.  4 c, these changes at the chemical level are specific to each individual, possibly due to the extremely personalized lifestyles before the study and match their original use of deodorant. Based on the initial use of underarm products (T0) (Additional file  2 : Table S1), two groups of participants can be distinguished: a group of five volunteers who used stick deodorant as evidenced by the mass spectrometry data and another group of volunteers where we found few or no traces suggesting they never or infrequently used stick deodorants (Additional file  2 : Table S1). Based on this criterion, the chemical trends shown in Fig.  4 c highlight that individuals who used stick deodorant before the beginning of the study (volunteers 1, 2, 3, 9, and 12) displayed a more pronounced shift in their armpits’ chemistries as soon as they stopped using deodorant (T1–T3), compared to individuals who had low detectable levels of stick deodorant use (volunteers 4, 6, 7, and 10), or “rarely-to-never” (volunteers 5 and 11) use stick deodorants as confirmed by the volunteers (Additional file  1 : Figure S3F, WR test, T0 vs T1–T3 all p values < 0.0001, with greater distance for the group of volunteers 1, 2, 3, 9, and 12, compared to volunteers 4, 5, 6, 7, 10, and 11). The most drastic shift in chemical profiles was observed during the transition period, when all participants applied the common antiperspirant on a daily basis (T4–T6) (Additional file  1 : Figure S3D, E). Finally, the molecular profiles became gradually more similar to those collected before the experiment (T0) as soon as the participants resumed using their personal beauty products (T7–T9) (Additional file  1 : Figure S3C), although traces of skin care products did last through the entire T7–T9 period in people who do not routinely apply these products (Fig.  4 c).

figure 4

Individualized influence of beauty product application on skin metabolomics profiles over time. a Multivariate statistical analysis (principal coordinate analysis (PCoA)) comparing mass spectrometry data collected over 9 weeks from the skin of 11 individuals, all body parts, combined (first plot from the left) and then displayed separately (arm, armpits, face, feet). Color scale represents volunteer ID. The PCoA was calculated on all samples together, and subsets of the data are shown in this shared space and the other panels. b The molecular profiles collected over 9 weeks from all body parts, combined then separately (arm, armpits, face, feet). c Representative molecular profiles collected over 9 weeks from armpits of 11 individuals (volunteers 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12). Color gradient in b and c represents timepoints (time 0 to time 9), ranging from the lightest orange color to the darkest one that represent the earliest (time 0) to the latest (time 9) timepoint, respectively. 0.5 timepoints represent additional timepoints where three selected volunteers were samples (volunteers 4, 9, and 10). PCoA plots were generated using the Bray–Curtis dissimilarity matrix and visualized in Emperor [ 28 ]. See also Additional file  1 : Figure S3

Comparing chemistries detected in armpits at the end timepoints—when no products were used (T3) and during product use (T6)—revealed distinct molecular signatures characteristic of each phase (random forest highlighting that 100% of samples from each group were correctly predicted, see Additional file  1 : Figure S3D, E). Because volunteers used the same antiperspirant during T4–T6, molecular profiles converged during that time despite individual patterns at T3 (Fig.  4 b, c, Additional file  1 : Figure S3D). These distinct chemical patterns reflect the significant impact of beauty products on skin molecular composition. Although these differences may in part be driven by beauty product ingredients detected on the skin (Additional file  1 : Figure S1), we anticipated that additional host- and microbe-derived molecules may also be involved in these molecular changes.

To characterize the chemistries that vary over time, we used molecular networking, a MS visualization approach that evaluates the relationship between MS/MS spectra and compares them to reference MS/MS spectral libraries of known compounds [ 29 , 30 ]. We recently showed that molecular networking can successfully organize large-scale mass spectrometry data collected from the human skin surface [ 18 , 19 ]. Briefly, molecular networking uses the MScluster algorithm [ 31 ] to merge all identical spectra and then compares and aligns all unique pairs of MS/MS spectra based on their similarities where 1.0 indicates a perfect match. Similarities between MS/MS spectra are calculated using a similarity score, and are interpreted as molecular families [ 19 , 24 , 32 , 33 , 34 ]. Here, we used this method to compare and characterize chemistries found in armpits, arms, face, and foot of 11 participants. Based on MS/MS spectral similarities, chemistries highlighted through molecular networking (Additional file  1 : Figure S4A) were associated with each body region with 8% of spectra found exclusively in the arms, 12% in the face, 14% in the armpits, and 2% in the foot, while 18% of the nodes were shared between all four body parts and the rest of spectra were shared between two body sites or more (Additional file  1 : Figure S4B). Greater spectral similarities were highlighted between armpits, face, and arm (12%) followed by the arm and face (9%) (Additional file  1 : Figure S4B).

Molecules were annotated with Global Natural Products Social Molecular Networking (GNPS) libraries [ 29 ], using accurate parent mass and MS/MS fragmentation patterns, according to level 2 or 3 of annotation defined by the 2007 metabolomics standards initiative [ 35 ]. Through annotations, molecular networking revealed that many compounds derived from steroids (Fig.  5 a–d), bile acids (Additional file  1 : Figure S5A-D), and acylcarnitines (Additional file  1 : Figure S5E-F) were exclusively detected in the armpits. Using authentic standards, the identity of some pheromones and bile acids were validated to a level 1 identification with matched retention times (Additional file  1 : Figure S6B, S7A, C, D). Other steroids and bile acids were either annotated using standards with identical MS/MS spectra but slightly different retention times (Additional file  1 : Figure S6A) or annotated with MS/MS spectra match with reference MS/MS library spectra (Additional file  1 : Figure S6C, D, S7B, S6E-G). These compounds were therefore classified as level 3 [ 35 ]. Acylcarnitines were annotated to a family of possible acylcarnitines (we therefore classify as level 3), as the positions of double bonds or cis vs trans configurations are unknown (Additional file  1 : Figure S8A, B).

figure 5

Underarm steroids and their longitudinal abundance. a – d Steroid molecular families in the armpits and their relative abundance over a 9-week period. Molecular networking was applied to characterize chemistries from the skin of 11 healthy individuals. The full network is shown in Additional file  1 : Figure S4A, and networking parameters can be found here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=284fc383e4c44c4db48912f01905f9c5 for MS/MS datasets MSV000081582. Each node represents a consensus of a minimum of 3 identical MS/MS spectra. Yellow nodes represent MS/MS spectra detected in armpits samples. Hexagonal shape represents MS/MS spectra match between skin samples and chemical standards. Plots are representative of the relative abundance of each compound over time, calculated separately from LC-MS1 data collected from the armpits of each individual. Steroids detected in armpits are a , dehydroisoandrosterone sulfate ( m/z 369.190, rt 247 s), b androsterone sulfate ( m/z 371.189, rt 261 s), c 1-dehydroandrostenedione ( m/z 285.185, rt 273 s), and d dehydroandrosterone ( m/z 289.216, rt 303 s). Relative abundance over time of each steroid compound is represented. Error bars represent the standard error of the mean calculated at each timepoint from four armpit samples from the right and left side of each individual separately. See also Additional file  1 : Figures S4-S8

Among the steroid compounds, several molecular families were characterized: androsterone (Fig.  5 a, b, d), androstadienedione (Fig.  5 c), androstanedione (Additional file  1 : Figure S6E), androstanolone (Additional file  1 : Figure S6F), and androstenedione (Additional file  1 : Figure S6G). While some steroids were detected in the armpits of several individuals, such as dehydroisoandrosterone sulfate ( m/z 369.19, rt 247 s) (9 individuals) (Fig.  5 a, Additional file  1 : Figure S6A), androsterone sulfate ( m/z 371.189, rt 261 s) (9 individuals) (Fig.  5 b, Additional file  1 : Figure S6C), and 5-alpha-androstane-3,17-dione ( m/z 271.205, rt 249 s) (9 individuals) (Additional file  1 : Figure S6E), other steroids including 1-dehydroandrostenedione ( m/z 285.185, rt 273 s) (Fig.  5 c, Additional file  1 : Figure S6B), dehydroandrosterone ( m/z 289.216, rt 303 s) (Fig.  5 d, Additional file 1 : Figure S6D), and 5-alpha-androstan-17.beta-ol-3-one ( m/z 291.231, rt 318 s) (Additional file  1 : Figure S6F) were only found in the armpits of volunteer 11 and 4-androstene-3,17-dione ( m/z 287.200, rt 293 s) in the armpits of volunteer 11 and volunteer 5, both are male that never applied stick deodorants (Additional file  1 : Figure S6G). Each molecular species exhibited a unique pattern over the 9-week period. The abundance of dehydroisoandrosterone sulfate (Fig.  5 a, WR test, p  < 0.01 for 7 individuals) and dehydroandrosterone (Fig.  5 a, WR test, p  = 0.00025) significantly increased during the use of antiperspirant (T4–T6), while androsterone sulfate (Fig.  5 b) and 5-alpha-androstane-3,17-dione (Additional file  1 : Figure S6E) display little variation over time. Unlike dehydroisoandrosterone sulfate (Fig.  5 a) and dehydroandrosterone (Fig.  5 d), steroids including 1-dehydroandrostenedione (Fig.  5 c, WR test, p  = 0.00024) and 4-androstene-3,17-dione (Additional file  1 : Figure S6G, WR test, p  = 0.00012) decreased in abundance during the 3 weeks of antiperspirant application (T4–T6) in armpits of male 11, and their abundance increased again when resuming the use of his normal skin care routines (T7–T9). Interestingly, even within the same individual 11, steroids were differently impacted by antiperspirant use as seen for 1-dehydroandrostenedione that decreased in abundance during T4–T6 (Fig.  5 c, WR test, p  = 0.00024), while dehydroandrosterone increased in abundance (Fig.  5 d, WR test, p  = 0.00025), and this increase was maintained during the last 3 weeks of the study (T7–T9).

In addition to steroids, many bile acids (Additional file  1 : Figure S5A-D) and acylcarnitines (Additional file  1 : Figure S5E-F) were detected on the skin of several individuals through the 9-week period. Unlike taurocholic acid found only on the face (Additional file  1 : Figures S5A, S7A) and tauroursodeoxycholic acid detected in both armpits and arm samples (Additional file  1 : Figures S5B, S7B), other primary bile acids such as glycocholic (Additional file  1 : Figures S5C, S7C) and chenodeoxyglycocholic acid (Additional file  1 : Figures S5D, S7D) were exclusively detected in the armpits. Similarly, acylcarnitines were also found either exclusively in the armpits (hexadecanoyl carnitines) (Additional file  1 : Figures S5E, S8A) or in the armpits and face (tetradecenoyl carnitine) (Additional file  1 : Figures S5F, S8B) and, just like the bile acids, they were also stably detected during the whole 9-week period.

Bacterial communities and their variation over time

Having demonstrated the impact of beauty products on the chemical makeup of the skin, we next tested the extent to which skin microbes are affected by personal care products. We assessed temporal variation of bacterial communities detected on the skin of healthy individuals by evaluating dissimilarities of bacterial collections over time using unweighted UniFrac distance [ 36 ] and community variation at each body site in association to beauty product use [ 3 , 15 , 37 ]. Unweighted metrics are used for beta diversity calculations because we are primarily concerned with changes in community membership rather than relative abundance. The reason for this is that skin microbiomes can fluctuate dramatically in relative abundance on shorter timescales than that assessed here. Longitudinal variations were revealed for the armpits (Fig.  6 a) and feet microbiome by their overall trend in the PCoA plots (Fig.  6 b), while the arm (Fig.  6 c) and face (Fig.  6 d) displayed relatively stable bacterial profiles over time. As shown in Fig.  6 a–d, although the microbiome was site-specific, it varied more between individuals and this inter-individual variability was maintained over time despite same changes in personal care routine (WR test, all p values at all timepoints < 0.05, T5 p  = 0.07), in agreement with previous findings that individual differences in the microbiome are large and stable over time [ 3 , 4 , 10 , 37 ]. However, we show that shifts in the microbiome can be induced by changing hygiene routine and therefore skin chemistry. Changes associated with using beauty products (T4–T6) were more pronounced for the armpits (Fig.  6 a, WR test, p  = 1.61e−52) and feet (Fig.  6 b, WR test, p  = 6.15e−09), while little variations were observed for the face (Fig.  6 d, WR test, p  = 1.402.e−83) and none for the arms (Fig.  6 c, WR test, p  = 0.296).

figure 6

Longitudinal variation of skin bacterial communities in association with beauty product use. a - d Bacterial profiles collected from skin samples of 11 individuals, over 9 weeks, from four distinct body parts a) armpits, b) feet, c) arms and d) face, using multivariate statistical analysis (Principal Coordinates Analysis PCoA) and unweighted Unifrac metric. Each color represents bacterial samples collected from an individual. PCoA were calculated separately for each body part. e , f Representative Gram-negative (Gram -) bacteria collected from arms, armpits, face and feet of e) female and f) male participants. See also Additional file  1 : Figure S9A, B showing Gram-negative bacterial communities represented at the genus level

A significant increase in abundance of Gram-negative bacteria including the phyla Proteobacteria and Bacteroidetes was noticeable for the armpits and feet of both females (Fig.  6 e; Mann–Whitney U , p  = 8.458e−07) and males (Fig.  6 f; Mann–Whitney U , p  = 0.0004) during the use of antiperspirant (T4–T6), while their abundance remained stable for the arms and face during that time (Fig.  6 e, f; female arm p  = 0.231; female face p value = 0.475; male arm p = 0.523;male face p  = 6.848751e−07). These Gram-negative bacteria include Acinetobacter and Paracoccus genera that increased in abundance in both armpits and feet of females (Additional file  1 : Figure S9A), while a decrease in abundance of Enhydrobacter was observed in the armpits of males (Additional file  1 : Figure S9B). Cyanobacteria, potentially originating from plant material (Additional file  1 : Figure S9C) also increased during beauty product use (T4–T6) especially in males, in the armpits and face of females (Fig.  6 e) and males (Fig.  6 f). Interestingly, although chloroplast sequences (which group phylogenetically within the cyanobacteria [ 38 ]) were only found in the facial cream (Additional file  1 : Figure S9D), they were detected in other locations as well (Fig.  6 e, f. S9E, F), highlighting that the application of a product in one region will likely affect other regions of the body. For example, when showering, a face lotion will drip down along the body and may be detected on the feet. Indeed, not only did the plant material from the cream reveal this but also the shampoo used for the study for which molecular signatures were readily detected on the feet as well (Additional file  1 : Figure S10A). Minimal average changes were observed for Gram-positive organisms (Additional file  1 : Figure S10B, C), although in some individuals the variation was greater than others (Additional file  1 : Figure S10D, E) as discussed for specific Gram-positive taxa below.

At T0, the armpit’s microflora was dominated by Staphylococcus (26.24%, 25.11% of sequencing reads for females and 27.36% for males) and Corynebacterium genera (26.06%, 17.89% for females and 34.22% for males) (Fig.  7 a—first plot from left and Additional file  1 : Figure S10D, E). They are generally known as the dominant armpit microbiota and make up to 80% of the armpit microbiome [ 39 , 40 ]. When no deodorants were used (T1–T3), an overall increase in relative abundance of Staphylococcus (37.71%, 46.78% for females and 30.47% for males) and Corynebacterium (31.88%, 16.50% for females and 44.15% for males) genera was noticeable (WR test, p  < 3.071e−05) (Fig.  7 a—first plot from left), while the genera Anaerococcus and Peptoniphilus decreased in relative abundance (WR test, p  < 0.03644) (Fig.  7 a—first plot from left and Additional file  1 : Figure S10D, E). When volunteers started using antiperspirants (T4–T6), the relative abundance of Staphylococcus (37.71%, 46.78% females and 30.47% males, to 21.71%, 25.02% females and 19.25% males) and Corynebacterium (31.88%, 16.50% females and 44.15% males, to 15.83%, 10.76% females and 19.60% males) decreased (WR test, p  < 3.071e−05) (Fig.  7 a, Additional file  1 : Figure S10D, E) and at the same time, the overall alpha diversity increased significantly (WR test, p  = 3.47e−11) (Fig.  3 c, d). The microbiota Anaerococcus (WR test, p  = 0.0006018) , Peptoniphilus (WR test, p  = 0.008639), and Micrococcus (WR test, p  = 0.0377) increased significantly in relative abundance, together with a lot of additional low-abundant species that lead to an increase in Shannon alpha diversity (Fig.  3 c, d). When participants went back to normal personal care products (T7–T9), the underarm microbiome resembled the original underarm community of T0 (WR test, p  = 0.7274) (Fig.  7 a). Because armpit bacterial communities are person-specific (inter-individual variability: WR test, all p values at all timepoints < 0.05, besides T5 p n.s), variation in bacterial abundance upon antiperspirant use (T4–T6) differ between individuals and during the whole 9-week period (Fig.  7a —taxonomic plots per individual). For example, the underarm microbiome of male 5 exhibited a unique pattern, where Corynebacterium abundance decreased drastically during the use of antiperspirant (82.74 to 11.71%, WR test, p  = 3.518e−05) while in the armpits of female 9 a huge decrease in Staphylococcus abundance was observed (Fig.  7 a) (65.19 to 14.85%, WR test, p  = 0.000113). Unlike other participants, during T0–T3, the armpits of individual 11 were uniquely characterized by the dominance of a sequence that matched most closely to the Enhydrobacter genera . The transition to antiperspirant use (T4–T6) induces the absence of Enhydrobacter (30.77 to 0.48%, WR test, p  = 0.01528) along with an increase of Corynebacterium abundance (26.87 to 49.74%, WR test, p  = 0.1123) (Fig.  7 a—male 11).

figure 7

Person-to-person bacterial variabilities over time in the armpits and feet. a Armpit microbiome changes when stopping personal care product use, then resuming. Armpit bacterial composition of the 11 volunteers combined, then separately, (female 1, female 2, female 3, male 4, male 5, male 6, male 7, female 9, male 10, male 11, female 12) according to the four periods within the experiment. b Feet bacterial variation over time of the 12 volunteers combined, then separately (female 1, female 2, female 3, male 4, male 5, male 6, male 7, female 9, male 10, male 11, female 12) according to the four periods within the experiment. See also Additional file  1 : Figure S9-S13

In addition to the armpits, a decline in abundance of Staphylococcus and Corynebacterium was perceived during the use of the foot powder (46.93% and 17.36%, respectively) compared to when no beauty product was used (58.35% and 22.99%, respectively) (WR test, p  = 9.653e−06 and p  = 0.02032, respectively), while the abundance of low-abundant foot bacteria significantly increased such as Micrococcus (WR test, p  = 1.552e−08), Anaerococcus (WR test, p  = 3.522e−13), Streptococcus (WR test, p  = 1.463e−06), Brevibacterium (WR test, p  = 6.561e−05), Moraxellaceae (WR test, p  = 0.0006719), and Acinetobacter (WR test, p  = 0.001487), leading to a greater bacterial diversity compared to other phases of the study (Fig.  7 b first plot from left, Additional file  1 : Figure S10D, E, Fig.  3 c, d).

We further evaluated the relationship between the two omics datasets by superimposing the principal coordinates calculated from metabolome and microbiome data (Procrustes analysis) (Additional file  1 : Figure S11) [ 34 , 41 , 42 ]. Metabolomics data were more correlated with patterns observed in microbiome data in individual 3 (Additional file  1 : Figure S11C, Mantel test, r  = 0.23, p  < 0.001), individual 5 (Additional file  1 : Figure S11E, r  = 0.42, p  < 0.001), individual 9 (Additional file  1 : Figure S11H, r  = 0.24, p  < 0.001), individual 10 (Additional file  1 : Figure S11I, r  = 0.38, p  < 0.001), and individual 11 (Additional file  1 : Figure S11J, r  = 0.35, p  < 0.001) when compared to other individuals 1, 2, 4, 6, 7, and 12 (Additional file  1 : Figure S11A, B, D, F, G, K, respectively) (Mantel test, all r  < 0.2, all p values < 0.002, for volunteer 2 p n.s). Furthermore, these correlations were individually affected by ceasing (T1–T3) or resuming the use of beauty products (T4–T6 and T7–T9) (Additional file  1 : Figure S11A-K).

Overall, metabolomics–microbiome correlations were consistent over time for the arms, face, and feet although alterations were observed in the arms of volunteers 7 (Additional file  1 : Figure S11G) and 10 (Additional file  1 : Figure S11I) and the face of volunteer 7 (Additional file  1 : Figure S11G) during product use (T4–T6). Molecular–bacterial correlations were mostly affected in the armpits during antiperspirant use (T4–T6), as seen for volunteers male 7 (Additional file  1 : Figure S11G) and 11 (Additional file  1 : Figure S11J) and females 2 (Additional file  1 : Figure S11B), 9 (Additional file  1 : Figure S11H), and 12 (Additional file  1 : Figure S11K). This perturbation either persisted during the last 3 weeks (Additional file  1 : Figure S11D, E, H, I, K) when individuals went back to their normal routine (T7–T9) or resembled the initial molecular–microbial correlation observed in T0 (Additional file  1 : Figure S11C, G, J). These alterations in molecular–bacterial correlation are driven by metabolomics changes during antiperspirant use as revealed by metabolomics shifts on the PCoA space (Additional file  1 : Figure S11), partially due to the deodorant’s chemicals (Additional file  1 : Figure S1J, K) but also to changes observed in steroid levels in the armpits (Fig.  5A, C, D , Additional file 1 : Figure S6G), suggesting metabolome-dependant changes of the skin microbiome. In agreement with previous findings that showed efficient biotransformation of steroids by Corynebacterium [ 43 , 44 ], our correlation analysis associates specific steroids that were affected by antiperspirant use in the armpits of volunteer 11 (Fig.  5 c, d, Additional file 1 : Figure S6G) with microbes that may produce or process them: 1-dehydroandrostenedione, androstenedione, and dehydrosterone with Corynebacterium ( r  = − 0.674, p  = 6e−05; r  = 0.671, p  = 7e−05; r  = 0.834, p  < 1e−05, respectively) (Additional file  1 : Figure S12A, B, C, respectively) and Enhydrobacter ( r  = 0.683, p  = 4e−05; r  = 0.581, p  = 0.00095; r  = 0.755, p  < 1e−05 respectively) (Additional file  1 : Figure S12D, E, F, respectively).

Despite the widespread use of skin care and hygiene products, their impact on the molecular and microbial composition of the skin is poorly studied. We established a workflow that examines individuals to systematically study the impact of such lifestyle characteristics on the skin by taking a broad look at temporal molecular and bacterial inventories and linking them to personal skin care product use. Our study reveals that when the hygiene routine is modified, the skin metabolome and microbiome can be altered, but that this alteration depends on product use and location on the body. We also show that like gut microbiome responses to dietary changes [ 20 , 21 ], the responses are individual-specific.

We recently reported that traces of our lifestyle molecules can be detected on the skin days and months after the original application [ 18 , 19 ]. Here, we show that many of the molecules associated with our personal skin and hygiene products had a half-life of 0.5 to 1.9 weeks even though the volunteers regularly showered, swam, or spent time in the ocean. Thus, a single application of some of these products has the potential to alter the microbiome and skin chemistry for extensive periods of time. Our data suggests that although host genetics and diet may play a role, a significant part of the resilience of the microbiome that has been reported [ 10 , 45 ] is due to the resilience of the skin chemistry associated with personal skin and hygiene routines, or perhaps even continuous re-exposure to chemicals from our personal care routines that are found on mattresses, furniture, and other personal objects [ 19 , 27 , 46 ] that are in constant contact. Consistent with this observation is that individuals in tribal regions and remote villages that are infrequently exposed to the types of products used in this study have very different skin microbial communities [ 47 , 48 ] and that the individuals in this study who rarely apply personal care products had a different starting metabolome. We observed that both the microbiome and skin chemistry of these individuals were most significantly affected by these products. This effect by the use of products at T4–T6 on the volunteers that infrequently used them lasted to the end phase of the study even though they went back to infrequent use of personal care products. What was notable and opposite to what the authors originally hypothesized is that the use of the foot powder and antiperspirant increased the diversity of microbes and that some of this diversity continued in the T7–T9 phase when people went back to their normal skin and hygiene routines. It is likely that this is due to the alteration in the nutrient availability such as fatty acids and moisture requirements, or alteration of microbes that control the colonization via secreted small molecules, including antibiotics made by microbes commonly found on the skin [ 49 , 50 ].

We detected specific molecules on the skin that originated from personal care products or from the host. One ingredient that lasts on the skin is propylene glycol, which is commonly used in deodorants and antiperspirants and added in relatively large amounts as a humectant to create a soft and sleek consistency [ 51 ]. As shown, daily use of personal care products is leading to high levels of exposure to these polymers. Such polymers cause contact dermatitis in a subset of the population [ 51 , 52 ]. Our data reveal a lasting accumulation of these compounds on the skin, suggesting that it may be possible to reduce their dose in deodorants or frequency of application and consequently decrease the degree of exposure to such compounds. Formulation design of personal care products may be influenced by performing detailed outcome studies. In addition, longer term impact studies are needed, perhaps in multiple year follow-up studies, to assess if the changes we observed are permanent or if they will recover to the original state.

Some of the host- and microbiome-modified molecules were also detected consistently, such as acylcarnitines, bile acids, and certain steroids. This means that a portion of the molecular composition of a person’s skin is not influenced by the beauty products applied to the skin, perhaps reflecting the level of exercise for acylcarnitines [ 53 , 54 ] or the liver (dominant location where they are made) or gallbladder (where they are stored) function for bile acids. The bile acid levels are not related to sex and do not change in amount during the course of this study. While bile acids are typically associated with the human gut microbiome [ 34 , 55 , 56 , 57 , 58 ], it is unclear what their role is on the skin and how they get there. One hypothesis is that they are present in the sweat that is excreted through the skin, as this is the case for several food-derived molecules such as caffeine or drugs and medications that have been previously reported on the human skin [ 19 ] or that microbes synthesize them de novo [ 55 ]. The only reports we could find on bile acids being associated with the skin describe cholestasis and pruritus diseases. Cholestasis and pruritus in hepatobiliary disease have symptoms of skin bile acid accumulation that are thought to be responsible for severe skin itching [ 59 , 60 ]. However, since bile acids were found in over 50% of the healthy volunteers, their detection on the skin is likely a common phenotype among the general population and not only reflective of disease, consistent with recent reports challenging these molecules as biomarkers of disease [ 59 ]. Other molecules that were detected consistently came from personal care products.

Aside from molecules that are person-specific and those that do not vary, there are others that can be modified via personal care routines. Most striking is how the personal care routines influenced changes in hormones and pheromones in a personalized manner. This suggests that there may be personalized recipes that make it possible to make someone more or less attractive to others via adjustments of hormonal and pheromonal levels through alterations in skin care.

Here, we describe the utilization of an approach that combines metabolomics and microbiome analysis to assess the effect of modifying personal care regime on skin chemistry and microbes. The key findings are as follows: (1) Compounds from beauty products last on the skin for weeks after their first use despite daily showering. (2) Beauty products alter molecular and bacterial diversity as well as the dynamic and structure of molecules and bacteria on the skin. (3) Molecular and bacterial temporal variability is product-, site-, and person-specific, and changes are observed starting the first week of beauty product use. This study provides a framework for future investigations to understand how lifestyle characteristics such as diet, outdoor activities, exercise, and medications shape the molecular and microbial composition of the skin. These factors have been studied far more in their impact on the gut microbiome and chemistry than in the skin. Revealing how such factors can affect skin microbes and their associated metabolites may be essential to define long-term skin health by restoring the appropriate microbes particularly in the context of skin aging [ 61 ] and skin diseases [ 49 ] as has shown to be necessary for amphibian health [ 62 , 63 ], or perhaps even create a precision skin care approach that utilizes the proper care ingredients based on the microbial and chemical signatures that could act as key players in host defense [ 49 , 64 , 65 ].

Subject recruitment and sample collection

Twelve individuals between 25 and 40 years old were recruited to participate in this study, six females and six males. Female volunteer 8 dropped out of the study as she developed a skin irritation during the T1–T3 phase. All volunteers signed a written informed consent in accordance with the sampling procedure approved by the UCSD Institutional Review Board (Approval Number 161730). Volunteers were required to follow specific instructions during 9 weeks. They were asked to bring in samples of their personal care products they used prior to T0 so they could be sampled as well. Following the initial timepoint time 0 and during the first 3 weeks (week 1–week 3), volunteers were asked not to use any beauty products (Fig.  1 b). During the next 3 weeks (week 4–week 6), four selected commercial beauty products provided to all volunteers were applied once a day at specific body part (deodorant for the armpits, soothing foot powder between the toes, sunscreen for the face, and moisturizer for front forearms) (Fig.  1 b, Additional file  3 : Table S2 Ingredient list of beauty products). During the first 6 weeks, volunteers were asked to shower with a head to toe shampoo. During the last 3 weeks (week 7–week 9), all volunteers went back to their normal routine and used the personal care products used before the beginning of the study (Fig.  1 b). Volunteers were asked not to shower the day before sampling. Samples were collected by the same three researchers to ensure consistency in sampling and the area sampled. Researchers examined every subject together and collected metabolomics and microbiome samples from each location together. Samples were collected once a week (from day 0 to day 68—10 timepoints total) for volunteers 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, and 12, and on day 0 and day 6 for volunteer 8. For individuals 4, 9, and 10, samples were collected twice a week. Samples collected for 11 volunteers during 10 timepoints: 11 volunteers × 10 timepoints × 4 samples × 4 body sites = 1760. Samples collected from 3 selected volunteers during 9 additional timepoints: 3 volunteers × 9 timepoints × 4 samples × 4 body sites = 432. All samples were collected following the same protocol described in [ 18 ]. Briefly, samples were collected over an area of 2 × 2 cm, using pre-moistened swabs in 50:50 ethanol/water solution for metabolomics analysis or in Tris-EDTA buffer for 16S rRNA sequencing. Four samples were collected from each body part right and left side. The locations sampled were the face—upper cheek bone and lower jaw, armpit—upper and lower area, arm—front of the elbow (antecubitis) and forearm (antebrachium), and feet—in between the first and second toe and third and fourth toe. Including personal care product references, a total of 2275 samples were collected over 9 weeks and were submitted to both metabolomics and microbial inventories.

Metabolite extraction and UPLC-Q-TOF mass spectrometry analysis

Skin swabs were extracted and analyzed using a previously validated workflow described in [ 18 , 19 ]. All samples were extracted in 200 μl of 50:50 ethanol/water solution for 2 h on ice then overnight at − 20 °C. Swab sample extractions were dried down in a centrifugal evaporator then resuspended by vortexing and sonication in a 100 μl 50:50 ethanol/water solution containing two internal standards (fluconazole 1 μM and amitriptyline 1 μM). The ethanol/water extracts were then analyzed using a previously validated UPLC-MS/MS method [ 18 , 19 ]. We used a ThermoScientific UltiMate 3000 UPLC system for liquid chromatography and a Maxis Q-TOF (Quadrupole-Time-of-Flight) mass spectrometer (Bruker Daltonics), controlled by the Otof Control and Hystar software packages (Bruker Daltonics) and equipped with ESI source. UPLC conditions of analysis are 1.7 μm C18 (50 × 2.1 mm) UHPLC Column (Phenomenex), column temperature 40 °C, flow rate 0.5 ml/min, mobile phase A 98% water/2% acetonitrile/0.1% formic acid ( v / v ), mobile phase B 98% acetonitrile/2% water/0.1% formic acid ( v / v ). A linear gradient was used for the chromatographic separation: 0–2 min 0–20% B, 2–8 min 20–99% B, 8–9 min 99–99% B, 9–10 min 0% B. Full-scan MS spectra ( m/z 80–2000) were acquired in a data-dependant positive ion mode. Instrument parameters were set as follows: nebulizer gas (nitrogen) pressure 2 Bar, capillary voltage 4500 V, ion source temperature 180 °C, dry gas flow 9 l/min, and spectra rate acquisition 10 spectra/s. MS/MS fragmentation of 10 most intense selected ions per spectrum was performed using ramped collision induced dissociation energy, ranged from 10 to 50 eV to get diverse fragmentation patterns. MS/MS active exclusion was set after 4 spectra and released after 30 s.

Mass spectrometry data collected from the skin of 12 individuals can be found here MSV000081582.

LC-MS data processing

LC-MS raw data files were converted to mzXML format using Compass Data analysis software (Bruker Daltonics). MS1 features were selected for all LC-MS datasets collected from the skin of 12 individuals and blank samples (total 2275) using the open-source software MZmine [ 66 ]—see Additional file  4 : Table S3 for parameters. Subsequent blank filtering, total ion current, and internal standard normalization were performed (Additional file  5 : Table S4) for representation of relative abundance of molecular features (Fig.  2 , Additional file  1 : Figure S1), principal coordinate analysis (PCoA) (Fig.  4 ). For steroid compounds in Fig.  5 a–d, bile acids (Additional file  1 : Figure S5A-D), and acylcarnitines (Additional file  1 : Figure S5E, F) compounds, crop filtering feature available in MZmine [ 66 ] was used to identify each feature separately in all LC-MS data collected from the skin of 12 individuals (see Additional file  4 : Table S3 for crop filtering parameters and feature finding in Additional file  6 : Table S5).

Heatmap in Fig.  2 was constructed from the bucket table generated from LC-MS1 features (Additional file  7 : Table S6) and associated metadata (Additional file  8 : Table S7) using the Calour command line available here: https://github.com/biocore/calour . Calour parameters were as follows: normalized read per sample 5000 and cluster feature minimum reads 50. Procrustes and Pearson correlation analyses in Additional file  1 : Figures S10 and S11 were performed using the feature table in Additional file  9 : Table S8, normalized using the probabilistic quotient normalization method [ 67 ].

16S rRNA amplicon sequencing

16S rRNA sequencing was performed following the Earth Microbiome Project protocols [ 68 , 69 ], as described before [ 18 ]. Briefly, DNA was extracted using MoBio PowerMag Soil DNA Isolation Kit and the V4 region of the 16S rRNA gene was amplified using barcoded primers [ 70 ]. PCR was performed in triplicate for each sample, and V4 paired-end sequencing [ 70 ] was performed using Illumina HiSeq (La Jolla, CA). Raw sequence reads were demultiplexed and quality controlled using the defaults, as provided by QIIME 1.9.1 [ 71 ]. The primary OTU table was generated using Qiita ( https://qiita.ucsd.edu/ ), using UCLUST ( https://academic.oup.com/bioinformatics/article/26/19/2460/230188 ) closed-reference OTU picking method against GreenGenes 13.5 database [ 72 ]. Sequences can be found in EBI under accession number EBI: ERP104625 or in Qiita ( qiita.ucsd.edu ) under Study ID 10370. Resulting OTU tables were then rarefied to 10,000 sequences/sample for downstream analyses (Additional file  10 Table S9). See Additional file  11 : Table S10 for read count per sample and Additional file  1 : Figure S13 representing the samples that fall out with rarefaction at 10,000 threshold. The dataset includes 35 blank swab controls and 699 empty controls. The blank samples can be accessed through Qiita ( qiita.ucsd.edu ) as study ID 10370 and in EBI with accession number EBI: ERP104625. Blank samples can be found under the metadata category “sample_type” with the name “empty control” and “Swabblank.” These samples fell below the rarefaction threshold at 10,000 (Additional file  11 : Table S10).

To rule out the possibility that personal care products themselves contained the microbes that induced the changes in the armpit and foot microbiomes that were observed in this study (Fig.  7 ), we subjected the common personal care products that were used in this study during T4–T6 also to 16S rRNA sequencing. The data revealed that within the limit of detectability of the current experiment, few 16S signatures were detected. One notable exception was the most dominant plant-originated bacteria chloroplast detected in the sunscreen lotion applied on the face (Additional file  1 : Figure S9D), that was also detected on the face of individuals and at a lower level on their arms, sites where stable microbial communities were observed over time (Additional file  1 : Figure S9E, F). This finding is in agreement with our previous data from the 3D cartographical skin maps that revealed the presence of co-localized chloroplast and lotion molecules [ 18 ]. Other low-abundant microbial signatures found in the sunscreen lotion include additional plant-associated bacteria: mitochondria [ 73 ], Bacillaceae [ 74 , 75 ], Planococcaceae [ 76 ], and Ruminococcaceae family [ 77 ], but all these bacteria are not responsible for microbial changes associated to beauty product use, as they were poorly detected in the armpits and feet (Fig.  7 ).

To assess the origin of Cyanobacteria detected in skin samples, each Greengenes [ 72 ] 13_8 97% OTU table (per lane; obtained from Qiita [ 78 ] study 10,370) was filtered to only features with a p__Cyanobacteria phylum. The OTU maps for these tables—which relate each raw sequence to an OTU ID—were then filtered to only those observed p__Cyanobacteria OTU IDs. The filtered OTU map was used to extract the raw sequences into a single file. Separately, the unaligned Greengenes 13_8 99% representative sequences were filtered into two sets, first the set of representatives associated with c__Chloroplast (our interest database), and second the set of sequences associated with p__Cyanobacteria without the c__Chloroplast sequences (our background database). Platypus Conquistador [ 79 ] was then used to determine what reads were observed exclusively in the interest database and not in the background database. Of the 4,926,465 raw sequences associated with a p__Cyanobacteria classification (out of 318,686,615 total sequences), at the 95% sequence identity level with 100% alignment, 4,860,258 sequences exclusively recruit to full-length chloroplast 16S by BLAST [ 80 ] with the bulk recruiting to streptophytes (with Chlorophyta and Stramenopiles to a lesser extent). These sequences do not recruit non-chloroplast Cyanobacteria full length 16S.

Half-life calculation for metabolomics data

In order to estimate the biological half-life of molecules detected in the skin, the first four timepoints of the study (T0, T1, T2, T3) were considered for the calculation to allow the monitoring of personal beauty products used at T0. The IUPAC’s definition of biological half-life as the time required to a substance in a biological system to be reduced to half of its value, assuming an approximately exponential removal [ 81 ] was used. The exponential removal can be described as C ( t )  =  C 0 e − tλ where t represents the time in weeks, C 0 represents the initial concentration of the molecule, C ( t ) represents the concentration of the molecule at time t , and λ is the rate of removal [ http://onlinelibrary.wiley.com/doi/10.1002/9780470140451.ch2/summary ]. The parameter λ was estimated by a mixed linear effects model in order to account for the paired sample structure. The regression model tests the null hypothesis that λ is equal to zero and only the significant ( p value < 0.05) parameters were considered.

Principal coordinate analysis

We performed principal coordinate analysis (PCoA) on both metabolomics and microbiome data. For metabolomics, we used MS1 features (Additional file  5 : Table S4) and calculated Bray–Curtis dissimilarity metric using ClusterApp ( https://github.com/mwang87/q2_metabolomics ).

For microbiome data, we used rarefied OTU table (Additional file 10 : Table S9) and used unweighted UniFrac metric [ 36 ] to calculate beta diversity distance matrix using QIIME2 (https://qiime2.org). Results from both data sources were visualized using Emperor ( https://biocore.github.io/emperor/ ) [ 28 ].

Molecular networking

Molecular networking was generated from LC-MS/MS data collected from skin samples of 11 individuals MSV000081582, using the Global Natural Products Social Molecular Networking platform (GNPS) [ 29 ]. Molecular network parameters for MS/MS data collected from all body parts of 11 individuals during T0–T9 MSV000081582 are accessible here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=284fc383e4c44c4db48912f01905f9c5 . Molecular network parameters for MS/MS data collected from armpits T0–T3 MSV000081582 and deodorant used by individual 1 and 3 MSV000081580 can be found here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=f5325c3b278a46b29e8860ec57915ad and here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=aaa1af68099d4c1a87e9a09f398fe253 , respectively. Molecular networks were exported and visualized in Cytoscape 3.4.0. [ 82 ]. Molecular networking parameters were set as follows: parent mass tolerance 1 Da, MS/MS fragment ion tolerance 0.5 Da, and cosine threshold 0.65 or greater, and only MS/MS spectral pairs with at least 4 matched fragment ions were included. Each MS/MS spectrum was only allowed to connect to its top 10 scoring matches, resulting in a maximum of 10 connections per node. The maximum size of connected components allowed in the network was 600, and the minimum number of spectra required in a cluster was 3. Venn diagrams were generated from Cytoscape data http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=284fc383e4c44c4db48912f01905f9c5 using Cytoscape [ 82 ] Venn diagram app available here http://apps.cytoscape.org/apps/all .

Shannon molecular and bacterial diversity

The diversity analysis was performed separately for 16S rRNA data and LC-MS data. For each sample in each feature table (LC-MS data and microbiome data), we calculated the value of the Shannon diversity index. For LC-MS data, we used the full MZmine feature table (Additional file  5 : Table S4). For microbiome data, we used the closed-reference BIOM table rarefied to 10,000 sequences/sample. For diversity changes between timepoints, we aggregated Shannon diversity values across groups of individuals (all, females, males) and calculated mean values and standard errors. All successfully processed samples (detected features in LC-MS or successful sequencing with 10,000 or more sequences/sample) were considered.

Beauty products and chemical standards

Samples (10 mg) from personal care products used during T0 and T7–T9 MSV000081580 (Additional file  2 : Table S1) and common beauty products used during T4–T6 MSV000081581 (Additional file  3 : Table S2) were extracted in 1 ml 50:50 ethanol/water. Sample extractions were subjected to the same UPLC-Q-TOF MS method used to analyze skin samples and described above in the section “ Metabolite extraction and UPLC-Q-TOF mass spectrometry analysis .” Authentic chemical standards MSV000081583 including 1-dehydroandrostenedion (5 μM), chenodeoxyglycocholic acid (5 μM), dehydroisoandrosterone sulfate (100 μM), glycocholic acid (5 μM), and taurocholic acid (5 μM) were analyzed using the same mass spectrometry workflow used to run skin and beauty product samples.

Monitoring beauty product ingredients in skin samples

In order to monitor beauty product ingredients used during T4–T6, we selected only molecular features present in each beauty product sample (antiperspirant, facial lotion, body moisturizer, soothing powder) and then filtered the aligned MZmine feature table (Additional file  5 : Table S4) for the specific feature in specific body part samples. After feature filtering, we selected all features that had a higher average intensity on beauty product phase (T4–T6) compared to non-beauty product phase (T1–T3). The selected features were annotated using GNPS dereplication output http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=69319caf219642a5a6748a3aba8914df , plotted using R package ggplot2 ( https://cran.r-project.org/web/packages/ggplot2/index.html ) and visually inspected for meaningful patterns.

Random forest analysis

Random forest analysis was performed in MetaboAnalyst 3.0 online platform http://www.metaboanalyst.ca/faces/home.xhtml . Using LC-MS1 features found in armpit samples collected on T3 and T6. Random forest parameters were set as follows: top 1000 most abundant features, number of predictors to try for each node 7, estimate of error rate (0.0%).

BugBase analysis

To determine the functional potential of microbial communities within our samples, we used BugBase [ 83 ]. Because we do not have direct access to all of the gene information due to the use of 16S rRNA marker gene sequencing, we can only rely on phylogenetic information inferred from OTUs. BugBase takes advantage of this information to predict microbial phenotypes by associating OTUs with gene content using PICRUSt [ 84 ]. Thus, using BugBase, we can predict such phenotypes as Gram staining, or oxidative stress tolerance at each timepoint or each phase. All statistical analyses in BugBase are performed using non-parametric differentiation tests (Mann–Whitney U ).

Taxonomic plots

Rarefied OTU counts were collapsed according to the OTU’s assigned family and genus name per sample, with a single exception for the class of chloroplasts. Relative abundances of each family-genus group are obtained by dividing by overall reads per sample, i.e., 10,000. Samples are grouped by volunteer, body site, and time/phase. Abundances are aggregated by taking the mean overall samples, and resulting abundances are again normalized to add up to 1. Low-abundant taxa are not listed in the legend and plotted in grayscale. Open-source code is available at https://github.com/sjanssen2/ggmap/blob/master/ggmap/snippets.py

Dissimilarity-based analysis

Pairwise dissimilarity matrices were generated for metabolomics and 16S metagenomics quantification tables, described above, using Bray–Curtis dissimilarity through QIIME 1.9.1 [ 71 ]. Those distance matrices were used to perform Procrustes analysis (QIIME 1.9.1), and Mantel test (scikit-bio version 0.5.1) to measure the correlation between the metabolome and microbiome over time. The metabolomics dissimilarities were used to perform the PERMANOVA test to assess the significance of body part grouping. The PCoA and Procrustes plots were visualized in EMPeror. The dissimilarity matrices were also used to perform distance tests, comparing the distances within and between individuals and distances from time 0 to times 1, 2, and 3 using Wilcoxon rank-sum tests (SciPy version 0.19.1) [ 19 ].

Statistical analysis for molecular and microbial data

Statistical analyses were performed in R and Python (R Core Team 2018). Monotonic relationships between two variables were tested using non-parametric Spearman correlation tests. The p values for correlation significance were subsequently corrected using Benjamini and Hochberg false discovery rate control method. The relationship between two groups was tested using non-parametric Wilcoxon rank-sum tests. The relationship between multiple groups was tested using non-parametric Kruskal–Wallis test. The significance level was set to 5%, unless otherwise mentioned, and all tests were performed as two-sided tests.

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Acknowledgements

We thank all volunteers who were recruited in this study for their participation and Carla Porto for discussions regarding beauty products selected in this study. We further acknowledge Bruker for the support of the shared instrumentation infrastructure that enabled this work.

This work was partially supported by US National Institutes of Health (NIH) Grant. P.C.D. acknowledges funding from the European Union’s Horizon 2020 Programme (Grant 634402). A.B was supported by the National Institute of Justice Award 2015-DN-BX-K047. C.C. was supported by a fellowship of the Belgian American Educational Foundation and the Research Foundation Flanders. L.Z., J.K, and K.Z. acknowledge funding from the US National Institutes of Health under Grant No. AR071731. TLK was supported by Vaadia-BARD Postdoctoral Fellowship Award No. FI-494-13.

Availability of data and materials

The mass spectrometry data have been deposited in the MassIVE database (MSV000081582, MSV000081580 and MSV000081581). Molecular network parameters for MS/MS data collected from all body parts of 11 individuals during T0-T9 MSV000081582 are accessible here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=284fc383e4c44c4db48912f01905f9c5 . Molecular network parameters for MS/MS data collected from armpits T0–T3 MSV000081582 and deodorant used by individual 1 and 3 MSV000081580 can be found here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=f5325c3b278a46b29e8860ec5791d5ad and here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=aaa1af68099d4c1a87e9a09f398fe253 , respectively. OTU tables can be found in Qiita ( qiita.ucsd.edu ) as study ID 10370, and sequences can be found in EBI under accession number EBI: ERP104625.

Author information

Amina Bouslimani and Ricardo da Silva contributed equally to this work.

Authors and Affiliations

Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, San Diego, USA

Amina Bouslimani, Ricardo da Silva, Kathleen Dorrestein, Alexey V. Melnik, Tal Luzzatto-Knaan & Pieter C. Dorrestein

Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92037, USA

Tomasz Kosciolek, Stefan Janssen, Chris Callewaert, Amnon Amir, Livia S. Zaramela, Ji-Nu Kim, Gregory Humphrey, Tara Schwartz, Karenina Sanders, Caitriona Brennan, Gail Ackermann, Daniel McDonald, Karsten Zengler, Rob Knight & Pieter C. Dorrestein

Department for Pediatric Oncology, Hematology and Clinical Immunology, University Children’s Hospital, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany

Stefan Janssen

Center for Microbial Ecology and Technology, Ghent University, 9000, Ghent, Belgium

Chris Callewaert

Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, 92307, USA

Karsten Zengler, Rob Knight & Pieter C. Dorrestein

Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA

Karsten Zengler & Rob Knight

Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, 92093, USA

Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92037, USA

Pieter C. Dorrestein

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Contributions

AB and PCD contributed to the study and experimental design. AB, KD, and TLK contributed to the metabolite and microbial sample collection. AB contributed to the mass spectrometry data collection. AB, RS, and AVM contributed to the mass spectrometry data analysis. RS contributed to the metabolomics statistical analysis and microbial–molecular correlations. GH, TS, KS, and CB contributed to the 16S rRNA sequencing. AB and GA contributed to the metadata organization. TK, SJ, CC, AA, and DMD contributed to the microbial data analysis and statistics. LZ, JK, and KZ contributed to the additional data analysis. AB, PCD, and RK wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Rob Knight or Pieter C. Dorrestein .

Ethics declarations

Ethics approval and consent to participate.

All participants signed a written informed consent in accordance with the sampling procedure approved by the UCSD Institutional Review Board (Approval Number 161730).

Competing interests

Dorrestein is on the advisory board for SIRENAS, a company that aims to find therapeutics from ocean environments. There is no overlap between this research and the company. The other authors declare that they have no competing interests.

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Additional files

Additional file 1:.

Figure S1. Beauty products ingredients persist on skin of participants. Figure S2. Beauty product application impacts the molecular and bacterial diversity on skin of 11 individuals while the chemical diversity from personal beauty products used by males and females on T0 is similar. Figure S3. Longitudinal impact of ceasing and resuming the use of beauty products on the molecular composition of the skin over time. Figure S4. Molecular networking to highlight MS/MS spectra found in each body part. Figure S5. Longitudinal abundance of bile acids and acylcarnitines in skin samples. Figure S6. Characterization of steroids in armpits samples. Figure S7. Characterization of bile acids in armpit samples. Figure S8. Characterization of Acylcarnitine family members in skin samples. Figure S9. Beauty products applied at one body part might affect other areas of the body, while specific products determine stability versus variability of microflora at each body site. Figure S10. Representation of Gram-positive bacteria over time and the molecular features from the shampoo detected on feet. Figure S11. Procrustes analysis to correlate the skin microbiome and metabolome over time. Figure S12. Correlation between specific molecules and bacteria that change over time in armpits of individual 11. Figure S13. Representation of the number of samples that were removed (gray) and those retained (blue) after rarefaction at 10,000 threshold. (DOCX 1140 kb)

Additional file 2:

Table S1. List of personal (T0 and T7–9) beauty products and their frequency of use. (XLSX 30 kb)

Additional file 3:

Table S2. List of ingredients of common beauty products used during T4–T6. (PDF 207 kb)

Additional file 4:

Table S3. Mzmine feature finding and crop filtering parameters. (XLSX 4 kb)

Additional file 5:

Table S4. Feature table for statistical analysis with blank filtering and total ion current normalization. (CSV 150242 kb)

Additional file 6:

Table S5. Feature table for individual feature abundance in armpits. (XLSX 379 kb)

Additional file 7:

Table S6. Feature table for Calour analysis. (CSV 91651 kb)

Additional file 8:

Table S7. Metadata for Calour analysis. (TXT 129 kb)

Additional file 9:

Table S8. feature table with Probabilistic quotient normalization for molecular–microbial analysis. (ZIP 29557 kb)

Additional file 10:

Table S9. OTU table rarefied to 10,000 sequences per sample. (BIOM 9493 kb)

Additional file 11:

Table S10. 16S rRNA sequencing read counts per sample. (TSV 2949 kb)

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

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Bouslimani, A., da Silva, R., Kosciolek, T. et al. The impact of skin care products on skin chemistry and microbiome dynamics. BMC Biol 17 , 47 (2019). https://doi.org/10.1186/s12915-019-0660-6

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Cosmetic scientists strive to unpack skin regeneration

Produced by

skin care research

Skin can be treated with laser therapy or microneedling, which are thought to work by stimulating skin regeneration. Credit: L’Oréal

When it comes to treating sun damage, acne scars, wrinkles and abnormal pigmentation, two procedures are increasingly popular. Microneedling involves making tiny holes in the outermost layer of skin, while laser therapy uses intensely focused beams of light to heat the skin.

Both treatments are thought to work by stimulating skin regeneration as part of normal healing, but the precise mechanisms remain unclear. To further optimize treatments, cosmetic researchers need a deeper understanding of what’s happening.

With that goal, researchers at L’Oréal have developed human-skin models to allow standardized testing of cosmetic products. They have also clinically tested a variety of ingredients designed to improve recovery from microneedling and lasering. “There is still a significant gap in understanding how these procedures drive skin regeneration, what the side effects are, and how to enhance the effects of these procedures,” says skin scientist, Charbel Bouez, Global Head of Regenerative Beauty, Research and Innovation, L’Oréal, in the United States.

Regenerative dermatology is a key research area at L’Oréal. Founded in 1909, the company has a presence in 150 countries and a workforce of more than 88,000, including approximately 4,000 people dedicated to research. Annually, L’Oréal invests more than $1.2 billion in research and innovation, leading to 561 international and national patents in 2022 alone.

Several significant patents have come from the La Roche Posay skincare laboratory in Paris, France, which is part of L’Oréal’s Dermatological Beauty Division.

“Developing scientifically relevant models helps us understand the effects of aesthetic procedures like lasers and microneedling on the skin,” says I-Chien Liao, director of Advanced Research, North America, L’Oréal.

Aesthetic models

In one model, L’Oréal researchers used skin removed during abdominoplasty or ‘tummy tucks’ 1 and kept alive in the lab. Unlike earlier models that used reconstructed skin or lab animals, such ‘real-world skin’ includes all skin cell types and immune cells, Bouez says.

L’Oréal studies using the model were among the first to show tissue regeneration in microneedled skin in the lab. “We demonstrated that skin goes through the hallmark signs of epidermal regeneration through morphological and molecular changes after the treatment,” says Bouez.

Microneedling of the skin model was found to stimulate the production of keratinocytes, the primary cell type found in the outermost layer of the skin. The cells migrated to the skin surface. Proteins such as vascular endothelial growth factor, platelet-derived growth factor, fibroblast growth factor and insulin were also activated. That is typical of the early stages of tissue repair.

A second skin model has been used to study healing after laser resurfacing. It is constructed from layers of human fibroblasts, the primary cell type in connective tissue, and keratinocytes 2 .

“To our knowledge, this is the first work in vitro to cover the dynamic changes to the skin following aesthetic laser treatment,” Liao says. “The reconstructed skin behaved very similarly to skin undergoing the key stages of wound healing.”

This included inflammation, characterized by an increase in cytokines. There was also an increase in the skin regeneration biomarkers Ki67, a protein associated with cell proliferation, and filaggrin, a protein that binds to keratin fibres in epithelial cells.

skin care research

Frédérique Labatut of the La Roche-Posay skincare laboratory in Paris, France, part of L’Oréal’s Dermatological Beauty Division. Credit: L’Oréal

As wounds heal following microneedling and laser treatments, people can experience temporary redness, swelling, itching and burning — and sometimes permanent unwanted pigmentation and even scarring.

“The management of barrier restoration and inflammation likely helps the skin to recover from procedures,” Liao says. With that in mind, L’Oréal’s research also focuses on post-procedure care that could reduce recovery times and improve aesthetic outcomes.

For example, in one double-blinded, randomized controlled trial, a cream developed by La Roche-Posay and L’Oréal China that contains 5% panthenol was used to treat one side of the face of 43 women who had received a type of laser treatment called fractional CO 2 laser resurfacing. The other side was treated with a hospital-made wound healing cream 3 .

Skin recovery

Three days after treatment, the researchers measured redness and swelling, comparing the two sides of the face. A week later, they measured loss of moisture from the skin as an indicator of the integrity of the skin.

In a second trial, L’Oréal researchers monitored redness and swelling following laser treatment in people with acne scars on both cheeks. Twenty participants received L’Oréal’s 5% panthenol cream on one side of their face, and a cream containing 0.02% triamcinolone acetonide (a synthetic corticosteroid medication) on the other side 4 .

L’Oréal scientists are now conducting research to understand better why people with deeper skin tones are more prone to developing of post-inflammatory hyperpigmentation after aesthetic procedures such as microneedling and lasering.

skin care research

L’Oréal’s I-Chien Liao (at left) and Charbel Bouez (at centre) discuss research results with a colleague. Credit: L’Oréal

Such trials, and the insights gained from them, help L’Oréal’s researchers optimize their product formulations.

“Understanding the biological pathways induced after a procedure, and the efficacy of core ingredients, allow us to design the optimal approach to targeting regenerative pathways, including effective delivery systems for those active ingredients,” says Frédérique Labatut, research director of the La Roche-Posay skincare laboratory in Paris, France.

Global team

L’Oréal’s science-led approach is evident in a wound-healing cream, which combines a proprietary xylose-derived sugar protein with niacinamide and panthenol. The formula is trademarked as REGENAMIDE-22. It could, the scientists say, help to heal and enhance the regenerative effects after aesthetic procedures. According to Bouez, the product was made possible by more than 50 lab studies and four clinical studies performed in China, France, and the United States.

During that research, L’Oréal examined the ability of its sugar protein to help skin retain water and support the skin structural proteins collagen and elastin. They also examined keratinocyte migration and the strengthening of the dermal-epidermal junction.

Guided by studies, L’Oréal researchers married their formula with a proprietary delivery system designed to trap moisture and encourage wound healing.

skin care research

Aesthetic procedures such as laser treatments (at far left) may stimulate skin regeneration as part of natural wound healing, as depicted in this conceptual illustration from L’Oréal. Credit: L’Oréal

Bouez argues that the scientific grounding has allowed L’Oréal to innovate around “skin wound healing, and as a result, enhance the regenerative effects of aesthetic procedures.”

The xylose-derived sugar protein, niacinamide and panthenol formula has also been incorporated into another cream that includes a swelling gel and ‘multilamellar vesicles’ — tiny spheres made of multiple layers like an onion — which are intended to store water and active compounds and enhance penetration within the skin. This cream is designed to mimic a medical wound dressing that traps moisture, Bouez says.

Results of research on the formula and laser technology related to wrinkles, fine lines, and skin smoothness were presented at the 25th World Conference of Dermatology in Singapore in July 2023.

“L’Oréal’s research group has a strong interest in leveraging regenerative medicine concepts and advances to create the future of beauty, which we believe will be regenerative,” Bouez says. “Aesthetic procedures like microneedling and laser therapy are the perfect examples of regenerative medicine being applied for skin beauty.”

Contact details:

www.lorealchina.com

Liu, X. et al. Sci. Reports 12 , 18115 (2022).

Article   Google Scholar  

Chunyan, H. et al. J. Cosmet. Dermatol. 22 , 1495-1506 (2023).

Article   PubMed   Google Scholar  

Li, W., et al. Dermatol. Ther. 33 , 13533 (2020).

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Lueangarun, S. et al. J. Cosmet. Dermatol. 18 , 1751-1757 (2019).

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Evidence-Based Skin Care: A Systematic Literature Review and the Development of a Basic Skin Care Algorithm

Affiliation.

  • 1 Andrea Lichterfeld, MA, Clinical Research Center for Hair and Skin Science, Department of Dermatology and Allergy, Charité-Universitätsmedizin, Berlin, Germany. Armin Hauss, MSc, Clinical Quality and Risk Management, Charité - Universitätsmedizin Berlin, Germany Christian Surber, PhD, Department of Dermatology, University of Basel and Zurich, Switzerland. Tina Peters, MSc, Clinical Research Center for Hair and Skin Science, Department of Dermatology and Allergy, Charité-Universitätsmedizin Berlin, Germany. Ulrike Blume-Peytavi, MD, PhD, Clinical Research Center for Hair and Skin Science, Department of Dermatology and Allergy, Charité-Universitätsmedizin Berlin, Germany. Jan Kottner, PhD, Clinical Research Center for Hair and Skin Science, Department of Dermatology and Allergy, Charité-Universitätsmedizin Berlin, Germany.
  • PMID: 26165590
  • DOI: 10.1097/WON.0000000000000162

Patients in acute and long-term care settings receive daily routine skin care, including washing, bathing, and showering, often followed by application of lotions, creams, and/or ointments. These personal hygiene and skin care activities are integral parts of nursing practice, but little is known about their benefits or clinical efficacy. The aim of this article was to summarize the empirical evidence supporting basic skin care procedures and interventions and to develop a clinical algorithm for basic skin care. Electronic databases MEDLINE, EMBASE, and CINAHL were searched and afterward a forward search was conducted using Scopus and Web of Science. In order to evaluate a broad range of basic skin care interventions systematic reviews, intervention studies, and guidelines, consensus statements and best practice standards also were included in the analysis. One hundred twenty-one articles were read in full text; 41documents were included in this report about skin care for prevention of dry skin, prevention of incontinence-associated dermatitis and prevention of skin injuries. The methodological quality of the included publications was variable. Review results and expert input were used to create a clinical algorithm for basic skin care. A 2-step approach is proposed including general and special skin care. Interventions focus primarily on skin that is either too dry or too moist. The target groups for the algorithm are adult patients or residents with intact or preclinical damaged skin in care settings. The goal of the skin care algorithm is a first attempt to provide guidance for practitioners to improve basic skin care in clinical settings in order to maintain or increase skin health.

Publication types

  • Research Support, Non-U.S. Gov't
  • Systematic Review
  • Dermatitis / nursing
  • Dermatitis / prevention & control
  • Dermatitis / therapy
  • Evidence-Based Nursing
  • Long-Term Care / methods*
  • Long-Term Care / standards
  • Skin Care / methods*
  • Skin Care / nursing*
  • Skin Care / standards

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  • Skin Regeneration and Rejuvenation

Whether through injury or simple wear and tear, the skin’s integrity and function can be easily compromised. Although this impacts billions of people worldwide, little is known about how to prevent skin degeneration.

The Harvard Stem Cell Institute (HSCI) Skin Program is committed to understanding why skin sometimes fails to heal or forms scars, as well as why skin inevitably becomes thin, fragile, and wrinkled with age. The Skin Program’s ultimate goal is to identify new therapies for skin regeneration and rejuvenation.

How We Heal

Wound healing is a major problem for many older individuals. Furthermore, chronic, non-healing skin ulcers are a major source of health care costs and patient morbidity and mortality.

Human skin repairs itself slowly, via the formation of contractile scars which may cause dysfunction. In contrast, the axolotl salamander can readily regrow a severed limb, the spiny mouse has densely haired skin that heals with remarkable speed, and the skin of the growing human embryo can regenerate after trauma without the need for any scar formation. By studying these examples, scientists are finding clues for how to enhance skin healing through a more regenerative response.

Promoting Skin Regeneration

During normal wound healing, scars form from dermal cells that align in parallel. But when this alignment is disrupted by a biodegradable scaffold that directs cells to grow in a random orientation, the cells follow the diverse differentiation program necessary for true regeneration.

HSCI scientists have also identified biomarkers for the key cells involved in skin regeneration, and are developing therapeutic strategies for their enrichment and activation. Ongoing clinical trials are using skin stem cells to treat chronic, non-healing ulcers, and early results are promising.

Additional approaches include 3D bioprinting, where skin stem cells are layered into a complex structure that mimics skin and could be potentially used for transplantation.       

Beyond Wound Healing

Skin aging can be thought of as a form of wounding, in which stem cells no longer maintain normal skin thickness, strength, function, and hair density. Understanding how to harness stem cells for scarless wound healing will also provide key insights into regenerating aged skin, a process termed rejuvenation. Multidisciplinary collaborators in the HSCI Skin Program are investigating the biological basis for how the skin ages over time and when exposed to ultraviolet radiation.

In addition to aging, skin stem cells also may mistake normal regions of the skin as wounds, then erroneously attempt to fill them. HSCI investigators are exploring whether this may be one of the underpinnings of psoriasis, a common and devastating disorder.

These areas of investigation are just the beginning. Skin stem cell biology has the potential to provide key insights into the mechanisms of regeneration for other organs in the body.

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Cost-of-Illness of Skin Cancer: A Systematic Review

  • Systematic Review
  • Published: 16 May 2024

Cite this article

skin care research

  • Annick Meertens   ORCID: orcid.org/0000-0003-2035-6277 1 , 2 , 3 ,
  • Laura Van Coile   ORCID: orcid.org/0000-0002-8072-6041 1 , 2 ,
  • Tijs Van Iseghem   ORCID: orcid.org/0000-0003-2728-4878 3 ,
  • Lieve Brochez   ORCID: orcid.org/0000-0001-6105-6719 1 , 2 ,
  • Nick Verhaeghe   ORCID: orcid.org/0000-0001-9956-4970 3 , 4   na1 &
  • Isabelle Hoorens   ORCID: orcid.org/0000-0002-2513-035X 1 , 2   na1  

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Skin cancer’s rising incidence demands understanding of its economic impact. The current understanding is fragmented because of the various methodological approaches applied in skin cancer cost-of-illness studies.

This study systematically reviews melanoma and keratinocyte carcinoma cost-of-illness studies to provide an overview of the applied methodological approaches and to identify the main cost drivers.

This systematic review was conducted adhering to the 2020 PRISMA guidelines. PubMed, Embase, and Web of Science were searched from December 2022 until December 2023 using a search strategy with entry terms related to the concepts of skin cancer and cost of illness. The records were screened on the basis of the title and abstract and subsequently on full text against predetermined eligibility criteria. Articles published before 2012 were excluded. A nine-item checklist adapted for cost-of-illness studies was used to assess the methodological quality of the articles.

This review included a total of 45 studies, together evaluating more than half a million patients. The majority of the studies ( n = 36) focused on melanoma skin cancer, a few ( n = 3) focused on keratinocyte carcinomas, and 6 studies examined both. Direct costs were estimated in all studies, while indirect costs were only estimated in nine studies. Considerable heterogeneity was observed across studies, mainly owing to disparities in study population, methodological approaches, included cost categories, and differences in healthcare systems. In melanoma skin cancer, both direct and indirect costs increased with progressing tumor stage. In advanced stage melanoma, systemic therapy emerged as the main cost driver. In contrast, for keratinocyte carcinoma no obvious cost drivers were identified.

Conclusions

A homogeneous skin cancer cost-of-illness study design would be beneficial to enhance between-studies comparability, identification of cost drivers, and support evidence-based decision-making for skin cancer.

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Nick Verhaeghe and Isabelle Hoorens contributed equally.

Authors and Affiliations

Department of Dermatology, University Hospital Ghent, Corneel Heymanslaan 10, 9000, Ghent, Belgium

Annick Meertens, Laura Van Coile, Lieve Brochez & Isabelle Hoorens

Cancer Research Institute Ghent (CRIG), Ghent, Belgium

Department of Public Health and Primary Care, Interuniversity Centre for Health Economics Research (I-CHER), Ghent University, Ghent, Belgium

Annick Meertens, Tijs Van Iseghem & Nick Verhaeghe

Department of Public Health, Interuniversity Centre for Health Economics Research (I-CHER), Vrije Universiteit Brussel, Brussels, Belgium

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Correspondence to Isabelle Hoorens .

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The research activities of A. Meertens are supported by a research grant from the Innovation and Clinical Research Fund of the Ghent University Hospital. The research activities of I.H. are supported by a postdoctoral fellowship from the Scientific Research Foundation-Flanders (number: 12Y2420N). The research activities of L.V.C.(MD) are supported by a predoctoral fellowship from the Scientific Research Foundation-Flanders (number 1S69921N). The research activities of T.V.I. are supported by a Strategic Basic Research project from the Scientific Research Foundation-Flanders (number S006123N). The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. They, therefore, accept no responsibility for the contents.

Conflict of interest

A.M. received an unrestricted travel grant from the Scientific Research Foundation-Flanders and from the Mobility and Sabbatical Fund of Ghent University.

Data availability statement

This study is a systematic review; data are from published studies.

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Consent for publication, code availability.

Search strategy was added to Online Resource 2.

Author contributions

The research aim was conceptualized by A.M., L.B., N.V., and I.H. A. Meertens conducted the literature search. Screening of articles was conducted by A.M, L.V.C., and T.V.I.. Data analysis was performed by A.M.. Quality assessment was performed by A.M. and T.V.I.. A.M. wrote the manuscript draft. All authors critically revised the manuscript.

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Meertens, A., Van Coile, L., Van Iseghem, T. et al. Cost-of-Illness of Skin Cancer: A Systematic Review. PharmacoEconomics (2024). https://doi.org/10.1007/s40273-024-01389-5

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DOI : https://doi.org/10.1007/s40273-024-01389-5

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Perceptions and Behavior Regarding Skin Health and Skin Care Products: Analysis of the Questionnaires for the Visitors of Skin Health Expo 2018

Young bok lee.

Department of Dermatology, College of Medicine, The Catholic University of Korea, Seoul, Korea.

Min Kyung Shin

1 Department of Dermatology, College of Medicine, Kyunghee University, Seoul, Korea.

Joung Soo Kim

2 Department of Dermatology, Hanyang University College of Medicine, Seoul, Korea.

Young Lip Park

3 Department of Dermatology, Soonchunhyang University College of Medicine, Bucheon, Korea.

4 Department of Dermatology and Cutaneous Biology Research Institute, Yonsei University College of Medicine, Seoul, Korea.

Dong Hyun Kim

5 Department of Dermatology, CHA University College of Medicine, Seongnam, Korea.

Ji Young Ahn

6 Department of Dermatology, National Medical Center, Seoul, Korea.

Sang Ju Lee

7 Yonsei Star Skin and Laser Clinic, Seoul, Korea.

Hye One Kim

8 Department of Dermatology, Hallym University College of Medicine, Seoul, Korea.

Sang Seok Kim

Hyun jeong lee.

9 CNP Skin Clinic, Seongnam, Korea.

10 Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Yang Won Lee

11 Department of Dermatology, Konkuk University School of Medicine, Seoul, Korea.

Mi Youn Park

Associated data.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Social standards for skin health have changed and have led to a rise in the general population's interest to achieve flawless skin.

To survey the Korean public's perception of skin health and dermatologists and skin-health-related behaviors.

A total of 355 participants of the Skin Health Expo 2018, held by the Dermatological Research Foundation of the Korean Dermatological Association from September 14 to 16, 2018, completed a questionnaire that assessed demographics, status, and behaviors regarding skin health. The questionnaire also included lifestyle characteristics, satisfaction, behavioral factors, and perceptions regarding skin-care products.

Participants were using various skin-care products including toners, lotions, hydrating or nutrition creams, sunscreens, essences, serums, facial masks and packs, body, hand, and foot moisturizers, eye creams, and hair products. The participants believed that the most reliable source of information on skin-care product recommendations was the dermatologist (41.55%). However, purchases were also being made at skin-care product stores (27.64%) based on the recommendation of a friend or acquaintance (40.11%). The results also revealed that 23.8% of participants experienced side effects from using skin-care and cosmetic products, and about 40% of participants could not yet distinguish hospitals run by dermatologists.

Demand for skin-care products has been increasing, and dermatologists should provide proper information about skin health to prevent adverse effects from skin-care products. Educational and informative activities, such as the expo planned by dermatologists, are necessary to improve skin health in the general population.

INTRODUCTION

As flawless skin emerges as a defining feature of beauty and status, there is no longer satisfaction in thinking that healthy skin only implies a lack of underlying medical conditions. As such, the demand for wrinkle-reducing, scar-eliminating, and antiaging skin-care treatments and procedures continues to grow rapidly 1 . Nowadays, the market is flooded with unverified advice to achieve flawless and beautiful skin, tempting countless consumers to seek knowledge and perform various home remedies on themselves. This growing interest in healthy and flawless skin indicates the need to understand consumers' perceptions and behavior regarding skin health. However, the amount of research on this subject in Korea remains limited.

The authors of this study gave questionnaires to the visitors of Skin Health Expo 2018 to identify their perceptions regarding skin health and the expo. Afterward, the findings were analyzed to reveal useful insights for dermatologists and dermatological societies to determine their possible actions in response to the public's demand for better skin appearance and health.

MATERIALS AND METHODS

The poll focused on the opinions of willing participants who visited Skin Health Expo 2018, held by the Dermatological Research Foundation of the Korean Dermatological Association at Seoul Trade Exhibition & Convention (SETEC) from September 14 to 16, 2018. A total of 355 visitors answered the questionnaires for data analysis, forming the final analysis sample. Moreover, the research design was authorized by the Institutional Review Board overseeing Uijeongbu St. Mary's Hospital (IRB No. UC19ECSI0118).

Questionnaire

The authors developed the questionnaire, which includes 10 questions on the respondents' demographic information, 24 on their perceptions and behavior regarding skin-care products, and 9 on their perceptions of and satisfaction with the Skin Health Expo. See Supplementary Material for information.

Demographic and lifestyle characteristics

1) sex and age distribution.

The answers provided by a total of 355 visitors, comprised of 99 male (27.89%) and 256 female (72.11%), were analyzed. Among them, 20.0% (71 persons) were in their 20s, another 20.0% (71) were in their 30s, 24.2% (86) were in their 40s, 17.2% (61) were in their 50s, 11.3% (40) were in their 60s, and 7.3% (26) were in their 70s or older.

2) Regions and occupations represented

Among the participants, 68.45% (243 persons) lived in Seoul, 23.1% (82) in Gyeonggi, and 3.1% (11) in Incheon. Thus, the majority had come from the Seoul–Gyeonggi–Incheon region.

For occupations, 29.3% (104 persons) were housewives, 21.7% (77) were office workers, 13.5% (48) were self-employed, and 12.1% (43) were students. Moreover, there were service workers, people involved in technical trades, and unemployed participants.

3) Sleeping hours and stress

Among the 351 participants who had answered the question about sleeping hours, 191 (54.3%) answered that they slept for relatively sufficient hours, while 121 (34.4%) slept too little. Furthermore, another 44 (12.5%) slept for sufficiently long hours, while the remaining 5 (1.42%) slept for extremely short hours.

As for health-related lifestyle factors, 258 (73.3%) of the participants answered that they had been lifelong nonsmokers, and only 23 (6.53%) were current smokers.

Less than half of the participants (161 persons or 45.35%) indicated that they exercised regularly.

As for how they would rate their stress levels, 204 participants (57.95%) said they were a little stressed, and 96 (27.27%) said they were quite stressed. Moreover, 41 participants (11.65%) indicated that they hardly felt stressed, while 11 (3.1%) indicated that they were extremely stressed out.

4) Satisfaction with skin health

When asked to rate their satisfaction with their skin conditions (from “1” indicating “not satisfied at all” to “10” indicating “very satisfied”), the participants gave an average rating of 4.9 points. Men had an average rating of 5.3 points, while women had a lower rating of 4.87%. Moreover, the self-rated skin satisfaction score showed differences among age groups, with participants in their 20s giving themselves an average rating of 4.42 points, 30s with 4.97 points, 40s with 5.12 points, 50s with 5.20 points, 60s with 4.95 points, and 70s or older with 4.62 points.

5) Related behavioral factors for skin health

The participants were asked to select multiple options among the listed behavioral factors necessary for skin health. Drinking enough water garnered the most votes, which is 146 (41.9%) out of 348 participants, followed by getting complete sleep (129 persons or 37.1%), applying moisturizers (26 persons or 7.47%), abstaining from smoking and drinking (14 persons or 4.02%), applying sunblock products (14 persons or 4.02%), and taking vitamins (9 persons or 2.59%).

Perceptions and behavior regarding skin-care products

1) self-assessed skin types.

When the participants were asked to assess their own skin types, 140 (39.44%) of 355 chose the mixed type, 113 (31.8%) chose dry, 56 (15.77%) chose oily, and 24 (6.76%) chose sensitive.

2) Skin-care products used

As for the types of skin-care products the participants were using, 252 of the 355 (70.79%) indicated that they use toners; 223 (62.64%) use lotions; 169 (47.47%) use hydrating or nutrition creams; 168 (47.19%) use sunscreens; 115 (32.3%) use essences; 106 (29.78%) use serums; 99 (27.81%) use facial masks and packs; 94 (26.4%) use body, hand, and foot moisturizers; 91 (25.56%) use eye creams; and 73 (20.51%) use hair products.

3) Channels of purchases

When asked to identify the major channels where the participants purchase skin-care products, 97 of the 351 (27.64%) chose specialized cosmetic and skin-care shops (known as “drug stores” in Korea), 63 (17.95%) chose department stores, 42 (11.97%) chose single cosmetic brand shops, 33 (9.40%) chose hospitals and clinics other than dermatologists' clinics, 31 (8.83%) chose online shopping sites, 30 (8.55%) chose large grocery store chains, and 26 (7.41%) chose dermatologists' clinics ( Table 1 ).

4) Important factors of purchase

As for the factors they considered as important in purchasing skin-care products, 108 of the 350 participants (30.86%) picked price, 68 (19.43%) picked brand or company names, 66 (18.85%) picked ingredients (no parabens, all-natural ingredients, etc.), 56 (16.0%) picked stated functions (brightening, antiaging, acne care, etc.), and 24 (6.86%) picked prior experience (with sample products, etc.). Moreover, quantity (chosen by 12 persons or 3.43%), packaging design (7 persons or 1.71%), and brand ambassadors and celebrities (1 person or 0.29%) were factors not regarded as important by the majority of the participants ( Table 1 ).

5) Trusted sources of information on skin-care products

As for the most trusted sources of information on skin-care products, 145 of 349 participants (41.55%) picked dermatologists, 98 (28.08%) picked friends and acquaintances, 38 (10.89%) picked sales representatives, 32 (9.17%) picked online reviews, and 23 (6.59%) picked pharmacists ( Table 1 ). On the other hand, as for the actual sources of information that most influenced participants' purchases, 140 (40.11%) chose friends or acquaintances, 74 (20.20%) picked dermatologists, and 58 (16.60%) selected online reviews ( Table 1 ). When asked whether they thought their dermatologists were aware of skin-care products, 138 (39.54%) participants gave an affirmative answer, 113 (32.38%) expressed doubt, and 98 (28.08%) indicated that they had not given the subject much thought.

6) Past dermatological conditions

Out of the 355 participants, 85 (23.88%) indicated that they had never suffered from dermatological conditions in the past. On the other hand, the other 270 had suffered at least 1 condition each. Freckles were the most commonly recognized condition (32.3%), followed by acne (15.73%), contact dermatitis (12.64%), psoriasis (12.08%), atopic dermatitis (10.11%), urticaria (8.43%), alopecia (7.58%), verruca vulgaris (6.74%), and dermatomycosis (6.74%).

Out of the 270 participants who indicated having suffered from at least 1 dermatological condition, 53.24% obtained their diagnoses at dermatologists, 21.58% diagnosed themselves, 19.42% were diagnosed by general or primary practitioners, 9.71% were assessed by pharmacists, 9.35% were examined by skin-care specialists, 8.63% got diagnoses from their family, friends, and acquaintances, and 5.4% were assessed by traditional Korean traditional medicine doctors.

Out of the 347 participants who answered the relevant question, 207 persons (59.65%) indicated that they were able to identify clinics or hospitals run by dermatologists, while the remaining 140 (40.35%) said they were not able to do so ( Table 2 ).

Values are presented as percentage (%) or mean±standard deviation.

7) Experiences with side effects from skin-care products

Out of the 332 participants who answered the relevant question, 79 (23.8%) indicated that they experienced side effects from using skin-care and cosmetic products. From these, however, only 26 (31.91%) sought treatment from dermatologists (including those at general hospitals), 20 (25.32%) went to skin-care specialists, another 20 (25.32%) went to pharmacists, and 11 (13.92%) went to doctors other than dermatologists. When asked why they did not seek treatment from dermatologists, 47.1% answered that their symptoms were not severe, and 23.93% answered that their conditions improved shortly after they stopped using the products.

A total of 345 participants answered questions regarding complications after using skin-care and cosmetic products. From them, 90 (26.09%) reported that they experienced reddening, 87 (24.35%) experienced itchiness, 57 (16.52%) experienced prickly or burning sensations, and 35 (10.14%) experienced extreme dryness.

8) Dermatologists' role in consumers' choice of skin-care and cosmetic products

When asked to rate how influential consultations with dermatologists would be on their choice of skin-care and cosmetic products on a 10-point scale (with “1” indicating “not helpful at all” and “10” indicating “very helpful”), the participants gave dermatologists' role an average rating of 5.93 points. In particular, 129 of the 349 participants (36. 96%) expected dermatologists to recommend suitable products, 123 (35.24%) expected dermatologists to provide guidance on the appropriate use of products, 53 (15.19%) expected dermatologists to provide care and treatment for symptoms because of complications, and 18 (5.16%) expected dermatologists to develop functional skin-care and cosmetic products ( Table 2 ).

9) Satisfaction with moisturizers, whitening products, acne care products, sunblock products, and hair products purchased

The satisfaction score of skin care products were not statistically different between the products on the market and the prescribed ones (moisturizer: 5.71±2.17 vs. 5.64±2.40, whitening cosmetics: 5.30±2.03 vs. 5.37±2.15, acne cosmetics: 5.16±2.29 vs. 5.22±2.21, sunscreeens: 6.14±2.02 vs. 5.53±2.39, and hair products: 5.56±2.00 vs. 5.12±2.45, respectively). However, majority of the participants thought that it was necessary to use the prescribed skin care products when there is a skin problems ( Table 3 ).

Values are presented as mean±standard deviation or percentage (%).

Experience in the skin health expo

1) types of visits.

Out of the 355 participants, 82.25% were members of the general public, while 17.75% were visitors with special purposes (industry experts, buyers, etc.). Meanwhile, 35.01% were unaccompanied, 28.85% were accompanied by family members, 26.61% were accompanied by friends or acquaintances, and 7% were accompanied by colleagues or school peers.

In particular, 73.95% arrived at the expo by public transportation (metro and/or buses), 10% by foot, 8.96% by car, 2.24% by bicycle, and 2.24% by regional buses. Moreover, word-of-mouth recommendation from acquaintances, etc., was the most common channel through which they discovered the expo (31.37%), followed by the Internet (16.25%), social media (14.57%), TV and radio (12.04%), and letters of invitation (9.52%). The percentage of visitors who discovered the expo via social or popular media had increased from the previous year.

2) Satisfaction with the expo

The participants were allowed to make multiple selections of the topics of the expo they found most engaging. As such, dry skin care and moisturization emerged as the most popular topic (43.34%), followed by antiaging care (22.95%), and sensitive skin care (20.68%). Furthermore, various product trials and demonstration sessions emerged as the most satisfying activities of the expo (39.02%), followed by the tour of booths on display (37.28%), and product consultation and purchase (10.69%).

On the other hand, lectures by dermatologists (5.49%), discussions with beauty creators (2.6%), and one-on-one consultations with dermatologists (1.45%) generally had low satisfaction ratings. Moreover, the majority (53.74%) of the participants said that they spent two to three hours touring the expo, 22.13% toured up to one hour, 17.24% toured four to five hours, and 4.31% toured for six hours or more.

Among the participants, 73.81% expressed their willingness to attend to the next skin health expo, 21.27% were undecided, and 4.92% were not willing to attend the next skin health expo.

This study presented the results of the questionnaires given to the visitors of Skin Health Expo 2018, in relation to their perceptions and behavior regarding skin health. This event was hosted by the Dermatological Research Foundation of the Korean Dermatological Association for the second time to provide proper information on skin health and introduce excellent skin-care products and skin-care services. The visitors' satisfaction with the expo revealed that it was a successful expo, with three out of four visitors expressing their desire to participate again.

The rising social and economic costs of treating dermatological and other related conditions have increased. The educational and preventive approach to dermatological care has been considered to be more important than the treatment of identified and existing skin problems. Education, an important element of public health along with public medicine and health policy, can efficiently reduce the socioeconomic costs of dermatological conditions. Preferred knowledge acquisition methods were studied according to the characteristics of patients and the public in order to efficiently communicate dermatological knowledge in several countries 2 , 3 , 4 , 5 . The authors of this study believed that understanding the general public's perceptions and behavior regarding skin health was the first crucial step toward achieving that goal and sought to ascertain conditions necessary to educate the Korean public on how to take care of their skin better.

There has been a growing social trend in Korea that promotes interest in dermatological conditions and skin care, while the general public's demand for advice and treatment from dermatologists is also rising. Nevertheless, the Korean public is bombarded with unverified and promotional claims, as well as services and explanations from nonspecialists on popular and social media.

In this study, the participants revealed that they used various skin-care products and purchased them at a skin-care store based on the recommendation of a friend or acquaintance. Here, 23.8% of participants experienced side effects from using skin-care and cosmetic products. The participants thought that the most reliable source of information on skin-care products recommendations was the dermatologist. Therefore, Korean dermatologists now need to consider and explore the most effective ways to deliver accurate knowledge on dermatological science and correct consumers' misguided behavior. As such, the first step toward achieving this is to ascertain the specific channels or methods through which the general public gains information on skin care and dermatology.

In reality, Korean dermatologists are compelled to treat as many patients as possible in short periods. They find it nearly impossible to spend adequate time on consulting patients and general consumers to satisfactory extents. The need among patients for continued access to the information after their treatment ends also persists. As such, dermatological societies and the Korean government should explore ways to enhance dermatologists' expertise, keep the general public correctly informed on dermatological matters, and cater better to consumers' rising demand for skin care. Thus, the development of a more comprehensive and newer approach is required.

On the other hand, the current overflow of misinformation suggests that insufficient efforts or opportunities have been made to inform the Korean public of the diverse ways in which dermatologists' expertise and services contribute to national well-being and quality of life. Consequently, the general public's perception of dermatological science influences future health policy and related research. Moreover, Brezinski et al. 6 stress the importance of educational policy, campaigns, and activities to inform the public of the value of dermatologists' expertise in improving overall public health.

In this study, more than 40% of participants who attended the Skin Health Expo could not yet distinguish hospitals run by dermatologists. In practice, there are many clinics that treat skin beauty run by non-dermatological doctors in Korea, so it is difficult for people to recognize the hospitals run by dermatologists. Therefore, dermatologists need to continue to publicize the hospital run by dermatologists. The percentage of participants who were satisfied with the skin care products on the recommendation of a dermatologist was less than 25%. The expectations were not high for the role of a dermatologist in choosing skin care products ( Table 3 ). Therefore, a lot of efforts from dermatologists are required to meet the needs of skin care products. Therefore, education is necessary to better inform public understanding and perception of dermatologists' expertise. The public's perception is critical because these perceptions represent the importance of dermatologists' contribution to skin health care. Furthermore, the public's perception also may influence policy decisions regarding resource allocation for health care and research 6 . Although the Skin Health Expo 2018 was considered to be held successfully, the lectures by dermatologists, discussions with beauty creators, and one-on-one consultations with dermatologists had low satisfaction ratings. Various ways or channels suitable for participants will be needed for public education.

This study was limited to analyzing only the findings of a single survey of visitors at a particular event. However, it is still substantial as it surveys the opinion of the general public rather than patients. As such, the findings of popular perceptions and behavior provided herein may be more representative of the Korean population.

Institutional measures and dermatologists' attention are needed for the Korean public to obtain appropriate dermatological care and information. Moreover, regional and national programs of education should be introduced to emphasize the importance of dermatologists' expertise and services to policymakers, public health specialists, and medical professionals. In other words, campaigns, expos, and such programs of major and national scales are needed, along with dermatologists' own commitment and greater cooperation from the society and policymakers, to provide correct information for the public.

CONFLICTS OF INTEREST: The authors have nothing to disclose.

FUNDING SOURCE: None.

DATA SHARING STATEMENT

Supplementary materials.

Supplementary data can be found via http://anndermatol.org/src/sm/ad-32-375-s001.pdf .

Welcome to Skin Care Research!

We are an affiliate of Dermatologists of Central States , a practice group with over one hundred and ninety (190) providers. Our state-of-the-art research site is located in Clinton Township in the same building as our Dermatology office, Midwest Center for Dermatology & Cosmetic Surgery . We research a variety of skin conditions (see Skin Conditions & Diseases ) with the purpose of improving your quality of life. Interested in one of our studies? Sign up using our Clinical Trial Request Form and we will be sure to reach out to you as soon as possible. We do not want you to miss this opportunity!

5 Common Places Dust Mites are Lurking

skin care research

Allergies to microscopic creatures living in dust, also known as dust mites, are very common.1 Roughly 20 million people in the U.S. have a dust mite allergy. Since allergies often go hand-in-hand with eczema, dust mites can be an issue for many in this community. “When dust mite allergens penetrate the skin barrier of patients with eczema, it can lead to allergic and inflammatory responses in the skin that cause further redness, scaling and itching,” said Dr. Ari Zelig, an allergist and immunologist with Charleston ENT and Allergy in Charleston, South Carolina. . According to Dr. Zelig, other symptoms can include itchy eyes, a stuffy nose and sneezing. Dr. Zelig explained that dust mites can also exacerbate asthma symptoms. “For those with asthma triggered by dust mites, it may lead to cough, shortness of breath, chest tightness and wheezing,” he said. This is why taking steps to minimize dust mite exposure is often recommended for people with eczema. Knowing where dust mites live is key to keeping them — and the symptoms they can cause — in check. Below are five common places you might find dust mites.

1. Areas with high humidity : Dust mites require humidity to survive. Dr. Michael Nevid, a pediatric allergist and immunologist at National Jewish Health in Denver, Colorado, explained that dust mites “don’t drink water; they actually absorb moisture from ambient air.” As a result, they can’t live when indoor relative humidity levels are consistently below 50%. If you live in a drier climate, dust mites likely won’t be an issue for you. For people living in more humid climates, dehumidifiers can be an important first intervention. Some research found that even when indoor relative humidity levels rose above 50% for two to eight hours each day, dust mite populations were still controlled, as long as humidity levels stayed below 50% the rest of the time.

2. Beds : Beds are a favorite dust mite hangout. There is ample soft fabric to hide in and easy access to dust mites’ primary food source — dead skin cells. Tightly woven dust mite covers that encase pillowcases and mattresses, trapping dust mites inside, are sometimes recommended. However, Dr. Nevid cautioned that dust mite covers are only part of the solution. “Even though they might decrease the amount of dust mites, or the allergenic material that’s found outside of those coverings, that on its own often may not be enough,” he said. Dr. Nevid recommends washing bedding such as pillowcases, sheets, blankets and duvet covers in hot water weekly. A temperature of 130-140 degrees Fahrenheit is ideal.2 Putting the laundry through a hot dryer also kills dust mites.5 If you do invest in dust mite covers, look for products made of breathable fabrics. Plastic materials on mattresses or pillows may cause sweating, which can irritate the skin or cause flares. Soft, stuffed toys, often an essential fixture on children’s beds, are also known to harbor dust mites.One study found that putting them in the dryer for an hour, freezing them overnight or washing them in water mixed with eucalyptus oil were effective ways to reduce dust mite numbers.6 Purifying the air with a high-efficiency particulate air (HEPA) filter is another recommended strategy to diminish dust mite allergens in the bedroom.2

3. Carpets : With their many soft fibers and crannies for dust to settle in, carpets are another popular dust mite habitat. Dr. Nevid recommends removing carpets, if possible, especially in bedrooms. He also advises people with dust mite allergies to clean carpets regularly with a HEPA filter vacuum. Some research also found that steam cleaning carpets effectively reduced dust mite populations.7

4.Fabric Furniture : Dr. Nevid explained that upholstered furniture can be another common dust mite breeding ground. These can be a little trickier to deal with since the cushion covers can’t typically be washed as often using high heat. Leather or vinyl furniture is considered to be preferable to fabric for dust mite control,8 but these may increase sweating, which is not ideal for people with eczema. Opting for furniture with easily washable covers may be helpful. Steam cleaning has the potential to reduce dust mites in fabric furniture. However, not all upholstered furniture can withstand steam cleaning. Check the labels on your furniture or the manufacturer’s website before trying it.

5. Basements : According to Dr. Nevid, areas with poor ventilation that tend towards dampness, like basements or the first floor of a building, can also have high dust mite numbers. Reducing humidity in these areas, either by using a dehumidifier or opening windows periodically, could be helpful. Be sure to follow other recommendations like removing carpeting or minimizing upholstered furniture here. Also avoid bedrooms in a basement or on the first floor if you have a dust mite allergy. Bedrooms located on the second floor or above are more ideal.5

Seek Medical Advice : Although doing what you can to minimize dust mites at home can help manage eczema flares or other dust mite allergy symptoms, it’s also important to seek personalized advice from medical professionals. Allergists can not only diagnose dust mite allergies but also help treat them. Immunotherapy is often recommended for dust mite allergy. “Allergen immunotherapy, which most people might know as allergy shots, is typically given as injections, although there are sublingual options for dust mite allergy under the tongue,” said Dr. Nevid. According to Dr. Nevid, immunotherapy “has been shown to be effective for dust mite allergy and patients who have eczema.” Talking to an allergist is a great way to get personalized advice and treatment for your unique circumstances.

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    Welcome to Skin Care Research! We are an affiliate of Dermatologists of Central States, a practice group with over one hundred and ninety (190) providers. Our state-of-the-art research site is located in Clinton Township in the same building as our Dermatology office, Midwest Center for Dermatology & Cosmetic Surgery.