skin care model essay

Skin Care Essay Example

When starting into the skin care  world, the one thing you should always remember is less is more. You don’t need a 9 step routine, especially in the morning. The 2 main purposes of a morning skin care  routine are 1. Wash off all nighttime products and 2. Protect your skin for the day. You should also remember that not one product suits everybody. The state of your skin will depend on the products you should use. Here are the key steps in a morning skin care  routine for someone with dry or oily skin.

The first step to a good skin care  routine is always a cleanser. In the morning a gentle cleanser is best, especially for dry skin. If your skin is extremely dry, cleansing with water would work fine. Follow the directions on the back of the bottle on how to apply the cleanser, then gently wash it off with water or a washcloth.

Next you should treat your skin, think of something you would like to change within your skin. As a person with dry skin you may want to add moisture to your skin. A great product for this is a hyaluronic acid serum. Hyaluronic takes moisture from the air and directs it to your skin. In a serum form, hyaluronic acid can be very gentle and effective.

Now you should use a moisturizer. Something light can suffice in the morning, especially if you're using sunscreen.

The final step is sunscreen. Now as I said this is a basic morning routine, but no matter how many products you use, you must always use sunscreen and it should always be the last step. No matter how good a skin care  routine is, it’s nothing without sunscreen.

Similar to a dry skin routine, the first step is a cleanser. However with oily skin, using only water is not as beneficial or effective to treating any problems that result from oiliness. A gentle or treatment cleanser is best for the morning, just always remember that the point of a morning cleanse is to take off night time products. 

Next you should use a treatment. A niacinamide or vitamin C serum is definitely the way to go. They get rid of any dark spots and reduce oiliness.

Now you should use a spot treatment. With oiliness usually comes clogged pores which cause acne. A spot treatment is an effective way to only treat a particular areo on the face. Look for products with salicylic acid or benzoyl peroxide for an effective treatment.

Finally, finish with sunscreen. Use one that is oil free and lightweight, this will decrease the feeling of oil on your face. You could also use a sunscreen, moisturizer combination product if that is what feels best.

As for normal skin, you have it lucky. You can basically use whatever products you want. You also have the luxury of treating other concerns such as fine lines and dark circles because you have no larger issues. Although you are more ‘free’ when it comes to choosing products, make sure you still use a minimalist routine, because when it comes to skin care , less is more.

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• 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.

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

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.

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).

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).

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).

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).

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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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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The Year That Skin Care Became a Coping Mechanism

By Jia Tolentino

Over the summer, in one of many small, ridiculous attempts to affirm to myself that I will outlive the Trump Administration, I decided to incorporate both retinol and sunscreen into my daily skin-care routine. Both were recommended to me last year by a dermatologist. Retinol is an anti-aging ingredient, and I flinched, a little, fancying myself too young, at twenty-eight, for the Sisyphean hobby of trying to halt the effects of time on one’s body. But I went home and did some research, clicking around various beauty publications while checking the news on my Twitter feed, which every few seconds loaded a fresh batch of disorientation and dread. The Web sites told me that I should have started retinol earlier . I thought about the moment, a few weeks after the election, when I found my first gray hair, and how, soul-wise, several thousand years had passed since then. Skin seemed like a nice controllable project. As it turned out, it both was and was not.

In recent years, the concept of skin care—specifically, of skin care as a phenomenon that invites unlimited expenditures of money, strategy, and time—has exploded kaleidoscopically. The Korean beauty industry has popularized, globally, the idea of a nightly ten-step program. (For example: cleanse, double cleanse, exfoliate, tone, spray yourself with “essence,” use an “ampoule,” apply a sheet mask, add eye cream, moisturize, moisturize again.) The invention of selfie-friendly sheet masks —individually packaged pieces of fabric that are soaked in serum and look ridiculous when applied—has ushered in a per-use price point. (They run from a few bucks to an astonishing twenty dollars each.) Before my recent deep dive, I’d thought of myself as fluent in beauty products: I am vain and from Texas, and also a former women’s-media editor. But the ingredients I knew about—vitamins, antioxidants, acids—now inhabit a climate of techno-surrealism: there are products with donkey milk, snail slime, placenta cream, pig collagen; there are face helmets that blast you with infrared light. I started lightly spiralling. I followed one tweet to a Sunday Riley lactic-acid serum that cost a hundred and sixty dollars, another to a Shiseido essence (a sort of very special water) that cost one-eighty. The New York home page recommended a cleanser that made your dead skin cells come off like eraser scraps. I bought it, along with a bunch of other stuff, unsure if I was buying skin care or a psychological safety blanket, or how much of a difference between the two there really is.

When my skin feels good, I feel happy: my skin is a miraculous six-pound organ that keeps my blood and muscle from spilling all over the C train, and I’d like to treat it well. At the same time, it’s impossible to ignore that the animating idea of the beauty industry is that women should always be working to look better, and that means, in our culture, that we should always be working to look as young as possible—shielding ourselves from what Susan Sontag , in her essay “The Double Standard of Aging,” calls the “humiliating process of gradual sexual disqualification.” The beauty industry functions partly by solving a “crisis of the imagination,” as Sontag puts it—the ambient fear that you will be less beautiful in the future, and that some obscure but awful consequences might result. This fear is both artificially imposed and pragmatic: as long as women are broadly objectified, beauty will function as value, and its absence as lack.

As feminist discourse has gone mainstream, the beauty industry has tried to cover some of its tracks. At the Times Magazine, Amanda Hess recently wrote about how the term “anti-aging” is going out of fashion : instead of youthfulness, advertisers promise radiance. This is not a revision of beauty standards, Hess observed: it’s a rebranding, in which “young” is positioned as a synonym for “natural,” despite the fact that nothing is more natural than getting old. Something similar is going on today with a certain popular beauty look, which we might label “Instagram model.” The look evokes both nakedness and airbrushing and is made possible by technology. A lot of the work formerly performed by makeup has been redirected into products and procedures—eyelash extensions, micro-current facials, injections of all kinds—leading to, and prompted by, an aesthetic of militant naturalness surrounded by an unambiguous aura of money and work. It’s a regime posing as a regimen. “Rules of taste enforce structures of power,” Sontag wrote. The beauty industry runs on its ability to redefine “natural” at increasingly higher prices.

At the same time, the Internet’s destabilizing and democratizing tendencies have transformed the industry. I wrote to Alexis Swerdloff, the editor of New York’s The Strategist, which offers highly edited shopping guides; she pointed out that cheap, formerly hard-to-access Asian brands are now available online, and that women are increasingly looking to sources like Reddit for product recommendations, “which makes everything feel less force-fed to you by Big Beauty.” (A particularly popular post on The Strategist this year was written by Rio Viera-Newton, a nonprofessional enthusiast who detailed the Google doc she kept about her skin-care routine.) There’s also something perversely, unexpectedly hopeful about skin care in today’s political context. Traditionally, skin care represents an attempt to deny the inevitability of the future. For me, right now, it functions as part of a basic dream in which the future simply exists . I recently wrote about the embattled millennial generation , whose members overwhelmingly do not believe that we will receive the Social Security benefits that we are paying for, and for whom conversations about having children commonly invoke fears of climate destruction and violent nationalism and nuclear war. I wonder if women my age are less afraid of looking older than we are of the possibility that there will be no functional world to look old in. Sontag wrote, about anti-aging, “The collapse of the project is only a matter of time.” At the moment, that thought applies much more broadly.

The idea of beauty as a site of resistance rather than capitulation is often traced back to Audre Lorde, who, in 1988, wrote, “Caring for myself is not an act of self-indulgence, it is self-preservation, and that is an act of political warfare.” The context for these words is Lorde’s fight against liver cancer as well as the intersectional politics that she theorized as a black lesbian feminist. But her thought, in a much diluted iteration, has led to the popular idea of “ self-care ,” in which there is moral and political utility in relaxing with your sheet mask. And there can be—although it’s up to us to reframe beauty as the means to something, rather than, as the market would have it, an end in itself. “I think a lot about beauty as propaganda for a success story,” the writer Arabelle Sicardi wrote to me in an e-mail. “We want to be able to not have our suffering visible.” Beauty is a tool that tends to serve those in power, she wrote, and, at the same time, it fundamentally involves acts of witnessing the body, helping it to endure its conditions. This paradox becomes clearer to me each night, patting my face with serums while looking one-eyed at Twitter, using these apparatuses of self-loathing in an attempt to pronounce some form of love.

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A Qualitative Investigation of the Impact of Acne on Health-Related Quality of Life (HRQL): Development of a Conceptual Model

Gabriella fabbrocini.

Section of Dermatology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy

Sara Cacciapuoti

Giuseppe monfrecola, associated data.

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Introduction

The negative impact of acne on aspects of health-related quality of life (HRQL) has been demonstrated in many quantitative studies; however, there has been relatively little qualitative research exploring the impact of acne and the use of topical treatment. The study aimed to explore the impact of moderate–severe acne on HRQL in adolescents and adults with inflammatory and non-inflammatory lesions and to develop a conceptual model to illustrate the impact. In addition, the study aimed to identify the attributes of topical acne treatments that are most important for patients.

Thirty-four adolescents and 16 adults with moderate–severe acne who were currently/recently prescribed topical treatment were recruited in this cross-sectional qualitative study in the UK, Italy, and Germany. In-depth, semi-structured telephone interviews explored patients’ experiences of acne and the impact it has on their HRQL, and their experience of topical treatments for acne. Data were analyzed using thematic analysis and a conceptual model was developed.

The analysis identified seven main areas of HRQL that are affected by acne: emotional functioning, social functioning, relationships, leisure activities, daily activities, sleep, and school/work. Also common throughout the interviews was the perception and reaction to acne from others, which potentially had an impact on all areas of HRQL. The conceptual model illustrates the impact on HRQL and the links between HRQL domains. For both adolescents and adults, it was most important for acne treatments to be fast-acting, non-irritating, and non-bleaching.

The results of this qualitative study demonstrate that moderate–severe acne has an extensive impact on adolescents’ and adults’ HRQL. The conceptual model illustrates the many areas of HRQL that are affected and draws attention to the importance of effective treatments for acne. The study also highlights topical acne treatment attributes that are most important for patients.

Meda, a Mylan company.

Acne is a chronic, inflammatory disease of the pilosebaceous unit estimated to affect 9.4% of the global population [ 1 ]. By the age of 21 years, 80–90% of people are estimated to have had acne [ 2 ]. The primary pathogenic factors are increased sebum production by the sebaceous gland, alterations in the keratinization process, follicular colonization by Propionibacterium acnes , and activation of innate immunity followed by increased inflammation [ 3 ]. Signs of acne include non-inflammatory lesions (open and closed comedones), inflammatory lesions (papules and pustules), and seborrhea (oily skin). Typically, acne starts in early puberty and is a chronic condition that can last for many years [ 4 ].

Although acne is not life-threatening or physically disabling, the negative impact of acne on health-related quality of life (HRQL) has been demonstrated in many studies of adults and adolescents with acne [ 5 – 7 ]. In one study, pretreatment acne patients reported substantially more pain/discomfort and anxiety/depression than a population sample [ 5 ]. In another study, authors claimed that the HRQL of acne patients is comparable to patients with conditions such as chronic disabling asthma, epilepsy, back pain, and arthritis [ 6 ]; this is supported by a literature review that found the HRQL impact of acne was comparable to psoriasis, a condition that causes significant disability [ 8 ]. A small study of adults with acne found clinically significant anxiety and depression in 44% and 18% of the sample, respectively [ 9 ], and a large survey of 18-year-olds found that participants with acne had significantly more depressive symptoms, lower self-attitude and self-worth, more feelings of uselessness, and lower body satisfaction than those without acne [ 10 ]. Other psychological effects of facial acne, including embarrassment, impaired self-image, low self-esteem, self-consciousness, frustration, and anger, have been identified in qualitative research [ 11 ]. In addition, dermatological-related social anxiety has been shown to be negatively associated with intention to participate in sport/exercise, self-esteem, and dermatological HRQL [ 12 ]. A review of studies examining the relationship between HRQL and acne in adolescents concluded that acne has a negative impact on HRQL and improvements in acne are associated with improvement in individuals’ self-appraisals, thus highlighting the potential benefit of effective acne treatments on HRQL [ 13 ].

Many treatments for acne are currently available; guidelines recommend a combination of a topical retinoid and an antimicrobial agent for most patients with acne in order to target both inflammatory and non-inflammatory lesions [ 14 ]. Although acne typically requires prolonged treatment, poor adherence to acne therapies has been documented. One large study found poor adherence in 50% of participants prescribed treatment for acne; adherence rates reported in Europe were lower than those reported in the Americas or Asia [ 15 ]. Dissatisfaction with treatment has been closely associated with poor adherence [ 16 ]. Understanding more about what drives treatment satisfaction and what patients prefer in terms of their acne treatment may be important. A conjoint analysis assessing patient preferences for topical therapies evaluated five treatment attributes (form, storage, product life, method of application, and regimen). After using four topical treatments for a week each, participants preferred a gel formulation, room temperature storage, application with fingers, and a once-daily regimen [ 17 ].

Much of the previous research into the impact of acne on HRQL has used validated instruments such as the Acne Quality of Life (Acne-QOL) [ 18 ], Dermatology Quality of Life Index (DLQI) [ 19 ], or the Assessment of Quality of Life (A-QOL) [ 20 ]. Some qualitative studies have explored the psychological and social impact of acne [ 11 , 21 , 22 ]. Qualitative research methodologies allow an in-depth investigation into patients’ experiences and allow us to identify concepts of importance to patients, in terms of HRQL impact and experience of using treatment for acne. Qualitative data can be used to develop conceptual models to illustrate the impact of a condition and hypothesize links between the concepts. A conceptual model can be used to guide the choice of what to measure and how to measure it, and provide a context for the interpretation of findings [ 23 ].

The aims of this qualitative study were to explore the impact of moderate–severe acne on HRQL in adolescents and adults with inflammatory and non-inflammatory lesions, and to develop a conceptual model to illustrate this impact. The study also aimed to determine the attributes of topical treatments for acne that are the most important for patients.

Study Design

Qualitative methods were used to explore the impact of moderate–severe acne on adolescents’ and adults’ quality of life. Individual interviews were conducted with acne patients to inform the development of a conceptual model of patient-reported HRQL. The research was designed to comply with guidelines for conducting research with children and young people [ 24 ]. Ethical approval was provided by Salus IRB, an independent review board, and the study was conducted in accordance with the principles of the Declaration of Helsinki. All adult participants provided informed consent prior to participating in the study; parents or guardians of adolescent participants provided informed consent for their child to participate; adolescents also provided assent.

Participants

Interview participants were recruited through a specialist recruitment panel in the UK, Italy, and Germany. Participants were eligible if they had a self-reported diagnosis of acne vulgaris, were aged 12–17 years (adolescents) or 18 years or older (adults), were currently experiencing facial lesions including papules and/or pustules, and were currently or recently (in the last 6 months) prescribed a topical medication for acne. Recruitment aimed for a higher proportion of adolescents than adults in each country to reflect the higher incidence in younger people.

Data Collection Procedures

Semi-structured interview guides were developed following a literature review of HRQL in acne; separate versions were developed for adult and adolescent interviews. The interviews began with a series of sociodemographic and clinical questions followed by a semi-structured interview guide exploring patients’ experience of acne. The interview guide used open-ended questions to allow participants to spontaneously describe the ways in which acne affects them, e.g., “what is the most difficult part of having acne?”, followed by open-ended questions about different areas of HRQL. If not already reported, specific probe questions were also asked such as “how does acne affect your self-confidence or self-esteem?” Interviews also explored participants’ experiences of using topical treatment for acne and their views on different aspects of topical therapy. The interview guides were modified during the study to incorporate questions on topics mentioned in early interviews. Interviews were conducted by telephone; evidence suggests that telephone interviews can be used productively in qualitative research, including research with children, with no significant differences in the quality of the data obtained when compared with face-to-face interviews [ 25 , 26 ]. Telephone interviews are also particularly suitable for research on dermatological conditions as they allow participants to speak freely about their acne without feeling self-conscious that the interviewer can see their acne. The telephone interviews were conducted by experienced qualitative researchers following semi-structured adolescent or adult interview guides. Interviews lasted up to an hour and were audio recorded and transcribed verbatim.

Data from the interviews were analyzed using thematic analysis, which uses inductive coding to identify themes across a dataset [ 27 ]. Transcripts were systematically coded using a qualitative software tool (MAXQDA). A coding framework was developed and discussed by the study team, which was then used throughout the analysis. Transcripts from adolescent and adult interviews were grouped and analyzed separately to allow for identification of concepts or themes occurring in only one group. Saturation, the point at which no new information is obtained from additional interviews [ 28 ], was assessed using saturation tables; the study continued until saturation was reached. The conceptual model was developed using the concepts and themes identified in the analysis. The model was reviewed by a dermatologist and revised until all authors were in agreement.

Sample Demographics

Fifty participants were recruited and interviewed; 34 adolescents and 16 adults with moderate–severe acne from the UK ( N  = 20 adolescents and N  = 10 adults), Italy ( N  = 7 adolescents and N  = 3 adults), and Germany ( N  = 7 adolescents and N  = 3 adults). The demographics of the sample are shown in Table  1 . Over half of the adolescent sample was female and three quarters identified themselves as white, with an mean age of 15 years. The adult sample had a mean age of 28 years; however, the UK sample was older on average (mean age 32 years) than the Italian (mean age 23 years) and German (mean age 19 years) samples. The adult sample had an equal number of male and female participants. Most of the sample rated their acne as at least moderate at the time of the interview and at least severe when at its worst.

Table 1

Sample demographics

Qualitative Results

For most participants, acne had an impact on many areas of their HRQL; however, one adolescent participant reported no impact on his HRQL. The analysis identified seven main areas of HRQL that are affected by acne: emotional functioning, social functioning, relationships, leisure activities, daily activities, and impact on sleep and school/work. The perception of acne from others and their reaction to it was also a common concept. Figure  1 shows the percentage of adolescents and adults reporting an impact on each of the domains, demonstrating that for almost all domains, a higher proportion of adult participants reported an impact compared with adolescents. Each of the domains and their inter-relationships are described in the sections below, with example quotations provided in Figs.  2 and ​ and3 3 .

Percentage of adolescent ( n  = 34) and adult ( n  = 16) participants reporting an impact of acne on each health-related quality of life (HRQL) concept. This figure was first presented at the 26th European Academy of Dermatology and Venereology Congress, September 13–17, 2017, Geneva, Switzerland

Example quotes related to the impact of acne on emotional functioning, social activities, social media, and relationships

Example quotes related to the impact of acne on leisure activities, daily activities, sleep, school/work, and reaction from others

Emotional Functioning

All except one adolescent reported that acne had a detrimental impact on some aspect of their emotional well-being, particularly on their self-confidence or self-esteem. For some participants, this was due to reaction from others. Having less confidence impacted other areas of participants’ lives, such as participation in school activities and socializing. Two-thirds of adolescents reported that acne made them feel down and almost half of participants felt lonely or isolated. Half of adolescents also felt frustrated by their acne because of having no control over it or treatments not working.

Adult participants reported similar emotional impacts; almost all felt that acne lowered their self-confidence or self-esteem, made them feel down or depressed, and self-conscious or embarrassed. Adults also reported feeling isolated or lonely because of their acne.

Social Functioning

Over two-thirds of adolescent and all adult participants reported that acne had an impact on their social activities. In particular, participants mentioned that interacting with strangers is difficult; they felt that people focused on their acne when speaking to them or judged them because of their acne. Participants did not want to socialize when their acne flared up and some participants did not attend activities if it meant that they would not be able to wear makeup.

Most participants reported that acne affected their use of social media. This included not posting pictures of themselves if their acne was visible, editing photos before posting them, asking people to remove photos of them, avoiding being in photos, or making sure they were in the background of group pictures.

Relationships

Almost half of adolescents and several adults reported that acne has an impact on them when talking to unfamiliar people. For some this was due to a lack of confidence, while others worried how new people would perceive their acne. Participants described feeling “discomfort”, “embarrassed”, “anxious”, or “intimidated” when talking to new people. Some adolescents reported that acne has an impact on forming relationships with girlfriends/boyfriends, for the same reasons. Two adult participants mentioned that their acne had caused relationships to end. Some adolescents also discussed problems acne caused with their friendships; this included friends making fun of their acne or friends not understanding when they complain about their acne. Almost half of the adolescent sample had friends with acne, which meant that they found it easier to cope with their own acne. In contrast, several adults did not have any friends with acne and therefore felt they did not have anyone to talk to who understood.

Leisure Activities

The leisure activities most commonly affected by acne were swimming and sports/exercise. Participants did not like to go swimming because other people would see more of their acne; some felt that chlorine would aggravate their acne, others did not want to be seen without makeup, and some adults did not want their acne scars to be visible. Taking part in sport was also affected by acne for several reasons; some participants found that their acne was made worse by sweat, while others did not want to join teams with strangers as they felt they would stare at their acne. Participants also reported feeling self-conscious about their acne when getting changed or when wearing an athletics vest. Four adolescents discussed acne having an impact on them taking part in activities such as acting or public speaking because they did not have the confidence to stand up in front of others.

Daily Activities

Both adolescents and adults discussed the time-consuming aspect of acne, having to allow time for their skin care and treatment routine each day. All female participants discussed makeup; a few did not wear cosmetics as they felt it would make their acne worse, others used makeup to cover up their acne and make it less noticeable. Some would not leave their house without makeup on. Some participants did not wear certain clothes because of their acne, as they did not want their shoulders or chest to be visible or they did not want new clothes to be bleached by their acne medication. Participants would feel anxious or embarrassed and feel like people were staring at them; therefore, they avoided going out when their acne was at its worst.

Impact on Sleep

Acne had an impact on sleep for a third of adolescent and half of adult participants. For several this was due to pain or soreness caused by their acne, which would make it difficult to get to sleep; for others itchiness made it difficult to sleep. Three participants sometimes found it difficult to sleep because they worried about their acne or felt depressed about it.

Impact on School or Work

All of the adolescent participants were at school or college; although most felt that their school work was not affected by acne, some mentioned feeling distracted by their acne when it was at its worst. Three participants contributed less to class activities because of the impact of acne on their self-confidence. A few participants were sometimes bullied or picked on at school about their acne. For two participants, acne affected them to the extent that they took days off school when their acne was at its worst.

Acne also affected some adult participants’ work. Two participants felt they were less likely to get jobs because they would be judged on their acne, while another opted to work at home when his acne flared up under the pretense of having chickenpox. One participant had lost her job because she would not go to work when her acne was bad, another participant had resigned from her job because she felt people were laughing at her acne. A student nurse felt patients thought he had an infection when his acne flared up and therefore did not want him to treat them.

Perception of Acne/Reaction of Others

Most participants had experienced people being mean or insensitive to them about their acne. For adults this was more common when they were younger and people were generally now more sympathetic. Many participants felt that people stared at them because of their acne; this made them feel self-conscious or nervous and affected their self-confidence. Some participants felt that they are judged on the basis of their acne and are viewed in a negative way because of it.

Five adolescents and half of adults felt that people thought their acne must be their fault in some way; either caused by a lack of hygiene and not looking after their skin properly, a bad diet, or drinking alcohol.

Participants were asked whether they felt their acne is taken seriously by other people. Adolescents generally felt their acne is taken seriously, particularly by their family and their doctor. Over half of adults felt their acne is not taken seriously by others, including their general practitioner (GP); some had to visit their GP several times before they were referred to a dermatologist.

Conceptual Model

A conceptual model was developed from the qualitative data, which illustrates the impact of acne on HRQL and the links between concepts (Fig.  4 ). Emotional functioning is central to the conceptual model, as this appeared to have a subsequent impact on all other areas of HRQL. The arrows in the model are based on the qualitative data where participants indicated that concepts are linked and indicate the direction of influence. The dotted arrows indicate a potential moderator that can have a positive or negative influence on the extent to which HRQL is impacted. A concept that was common throughout adolescent and adult interviews was the perception of acne from others and their reaction to it, which potentially had an impact on all areas of HRQL.

Conceptual model of the impact of acne on health-related quality of life (HRQL). Arrows indicate direction of influence; dotted arrows indicate a potential moderator that can have a positive or negative influence on the extent of impact on HRQL.

This figure was first presented at the 26th European Academy of Dermatology and Venereology Congress, September 13–17, 2017, Geneva, Switzerland

Attributes of Topical Acne Treatments

Participants were asked to rate how important several different acne treatment attributes are to them on a scale from “not important at all” to “critical/essential”. The same three attributes were rated most highly by adolescents and adults: treatment works quickly, does not cause irritation, and does not bleach. Some example quotations about the treatment attributes are displayed in Fig.  5 . For adolescents it was important that an acne treatment does not contain alcohol; several felt that alcohol might sting or irritate their skin and cause a burning sensation. This attribute was generally less important for adult participants who felt that the treatment ingredients were not important as long as the treatment was effective. Some participants felt it was very important for a treatment to only require application once a day; however, some participants would apply it as many times as necessary if it was effective. For some participants it was important that the treatment could be applied with their fingers rather than an applicator or pad as they felt it would be more hygienic and convenient. Similarly, for convenience, some participants preferred a treatment that did not need to be kept refrigerated.

Example quotes about the attributes of topical acne treatments

This qualitative study explored the impact of acne and its treatment on adolescents’ and adults’ HRQL and found that HRQL was affected by acne for all except one participant. The conceptual model illustrates the impact on HRQL and demonstrates the importance of an effective treatment for acne to reduce this. Although the effect of acne on adolescents and adults is similar, there are some important differences. For adolescents, acne is more common among people the same age; therefore, many adolescents had friends with acne or other people at school had acne and thus they had some support from others who understood how it felt. For adults, some commented that they do not know anyone else their age who has acne and therefore felt alone and suggested that others do not understand how it feels. Adults also commented that people view acne as something that affects teenagers and therefore felt that people believed it might be due to them not looking after their skin, their diet, or another skin condition.

One difference between male and female participants was evident in the use of makeup to conceal acne, which reinforces the use of adjuvant therapies to improve acne treatment outcomes [ 29 , 30 ]. A small number of male participants had tried to conceal their acne with cosmetics in the past; however, this topic was largely discussed by female participants and for some the ability to conceal their acne made it easier to cope with.

This study also explored the importance of different attributes of topical acne treatments. For both adolescents and adults the most important attributes were fast-acting treatments that do not cause irritation and do not bleach clothing. Adolescents felt it was important for a treatment to not contain alcohol; however, this was less important for adults. Adults placed more importance on a treatment that had no specific storage requirements. Given the low adherence to acne therapies [ 15 ] (and the link between dissatisfaction with treatment and non-adherence [ 16 ]), when prescribing treatments it is important to consider the attributes that may lead to increased satisfaction with treatment.

The results of this study support the many other studies that demonstrate the negative impact of acne on HRQL [ 5 – 7 ]. A previous study found that for adults with acne, perceived stigma is a significant predictor of acne-related HRQL, contributing more than factors such as severity, gender, or age [ 31 ]. The findings of the current study provide qualitative support to this; although the term “stigma” was not used by participants, the negative impact of feeling judged by their acne or feeling as though people are staring at their acne was discussed by most participants.

Participants in the current study reported ways in which acne affects their use of social media. Although many participants had social media accounts, they did not post pictures of themselves if their acne was visible and avoided having pictures taken as they did not want them to be posted online by others. Previous studies have explored social media in relation to dermatology in terms of it being a source of information or support [ 32 , 33 ]; however, to our knowledge, this is the first study to explore the impact of acne on use of social media.

This study illustrates the impact of acne through the development of a conceptual model from qualitative data. Conceptual models can be useful tools that provide a visual representation of the impact of a condition, allowing the links between concepts to be identified. In addition to highlighting the many areas of HRQL that are affected by acne, the model can be used to identify concepts of interest for future studies, to guide selection of an appropriate instrument to measure the impact on HRQL [ 23 ], or as the basis for the development of a new instrument to assess HRQL in acne. While existing acne-specific HRQL instruments assess several of the concepts identified in the conceptual model, there were concepts identified as important to participants in the current study that are not assessed by such instruments. For example, most instruments do not capture the impact of acne on sleep, daily activities, or work/school; therefore existing instruments may not fully capture the impact of acne on HRQL.

Participants discussed some ways in which their relationships are affected by acne. As reported in the results, some participants discussed feeling anxious or embarrassed when talking to new people or feeling like they were judged on their acne, which caused problems with forming relationships with girlfriends/boyfriends. Although some participants discussed this, many did not report an impact on this area of their HRQL. It may be that adolescents in particular did not feel comfortable speaking about this or that a parent or family member was in the room during the interview, which made participants reluctant to discuss it. This is a possible limitation of the study, as qualitative telephone interviews may not have obtained the depth of data on this topic that might be expected. Items on relationships are included in several acne-specific instruments (Acne-QOL, AQOL, DLQI, Skindex-29) and it may be that using quantitative methods is a more appropriate way to explore this concept.

Some limitations should be considered when interpreting the findings of this study. The extent to which the findings can be generalized to all adolescents and adults with acne is limited by the small sample size; however, data saturation was reached for both adolescent and adult samples, suggesting that additional interviews may not have introduced new concepts. The recruitment also relied on self-reported severity in order to identify people with moderate–severe acne; however, all participants were currently or recently prescribed topical treatment and had facial papules and/or pustules at the time of the interview. Although some participants experienced acne on several areas of their body, the inclusion criteria only required participants to have facial acne; therefore, the results can only be assumed to be relevant to facial acne. In addition, it is possible that experiences of older adolescents and young adults are similar and any differences noted between the adolescent and adult samples are really due to the older adults in the sample. The German adult sample consisted of teenagers; therefore, the experience of older adults in Germany may not be represented.

Conclusions

The results of this qualitative study demonstrate that moderate–severe acne has an extensive impact on adolescents’ and adults’ HRQL. The conceptual model illustrates the many areas of HRQL that are affected and draws attention to the importance of effective treatments for acne. The study highlights the importance of measuring HRQL in future interventional studies of acne treatments and identifies treatment attributes that are most important to patients.

Acknowledgements

This study and article processing charges were funded by Meda, a Mylan company. All authors had full access to all of the data in this study and take complete responsibility for the integrity of the data and accuracy of the data analysis.

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this manuscript and have given final approval of the version to be published.

Medical Writing and/or Editorial Assistance

Medical writing assistance in the preparation of this manuscript was provided by Jane Murphy (CircleScience, an Ashfield Company, part of UDG Healthcare plc) and funded by Meda, a Mylan company.

Thanking Patient Participants

Thank you to study participants for their involvement in this study.

Disclosures

Gabriella Fabbrocini receives research fees from Meda, a Mylan Company. Giuseppe Monfrecola receives research fees from Meda, a Mylan company. Sara Cacciapuoti has nothing to disclose.

Compliance with Ethics Guidelines

The research was designed to comply with guidelines for conducting research with children and young people [ 24 ]. Ethical approval was provided by Salus IRB, an independent review board, and the study was conducted in accordance with the principles of the Declaration of Helsinki. All adult participants provided informed consent prior to participating in the study; parents or guardians of adolescent participants provided informed consent for their child to participate; adolescents also provided assent.

Data Availability

Open access.

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( http://creativecommons.org/licenses/by-nc/4.0/ ), which permits any noncommercial 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.

Enhanced content

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115 Cosmetology Essay Topic Ideas & Examples

🏆 best cosmetology topic ideas & essay examples, 👍 good essay topics on cosmetology, 🥇 most interesting cosmetology topics to write about, 📌 simple & easy cosmetology essay titles, ❓ research questions about skin care.

• Cosmetic Industry Five Forces Analysis For the cosmetic industry, the most important barriers are the exclusive rights and economies of scale. Potential Development of Substitute Products Ease of substitution Buyer inclination to substitute Buyer switching costs Relative price performance of […]
• Cosmetic Testing on Animals The surface of the skin or near the eyes of such animals is meant to simulate that of the average human and, as such, is one of easiest methods of determining whether are particular type […] We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
• Lush Fresh Handmade Cosmetics: Brand Image Thesis: Lush Fresh Handmade Cosmetics maintains the consistency of their brand image of a sustainable, natural, and eco-friendly beauty product by encouraging recycling, using package-less practices, choosing natural and vegan materials to produce their cosmetics, […]
• Bright Cosmetic Firm’s Contingency Planning The company is located in South Korea due to the increased prevalence of the cosmetic industry in South Asia, with a revenue of approximately 50% of the global market.
• La Roche-Posay Cosmetics in European Market It is within this context that the essay examines the European market of LRP by analyzing the beauty market, competition, positioning strategy, identifying brand consumers, examining market trends, and the type of innovations evident in […]
• Multimodal Analysis of Cosmetic Surgery Advertising The analysis has considered the textual and visual elements used to pass the messages to the targeted audience. The essence of this section, therefore, is to provide an in-depth understanding of the issue under investigation […]
• Catholic Church View on Cosmetic and Reconstructive Surgery Therefore, the authors had a negative stance on cosmetic surgery performed on women, stating that it was a betrayal of the “truth of the feminine self” and a contribution to the exploitation of the female […]
• Factors Affecting the Consumption of Men’s Cosmetic Products The main aim of this study was to determine the factors that affect the consumption of cosmetic products in the male population.
• Luxury Cosmetics Branding and Pricing It is considered that “beauty products appeal to the emotions and customers tend to choose based on the product image,” yet luxury brands, such as Chanel, usually emphasize the quality of cosmetics and the technology […]
• Chanel Cosmetics Competition Analysis The management of this firm has been keen on adopting new market trends, which has ensured that the firm remains relevant in this industry.
• Cosmetics as a Decorative Technique Used by Women At the beginning of the 20th century, makeup was used to protect and project a sense of self. For me, makeup means the possibility to create and underline a unique identity and the self.
• New Product Campaign Pitch: Women Cosmetic The target market for the marketing campaign of the new women’s cosmetics is mainly women aging from 15 to 45 in Australia.
• Natural Cosmetic Skincare Products Market The trends in the UK market are quite similar to the trends in the European market. There is fierce competition for the market share of natural skincare products.
• Racial Discrimination Through the Cosmetics Industry The variety of preconceptions such as the hypersexuality of black women and the perception of their beauty as an unideal version of whites’ one also indicates racism.
• Black Women and the Cosmetics Industry While the industry experienced a significant increase in revenue and scale, the source of this process was the racial discrimination of black women since the marketing campaign emphasized that black women are beautiful.
• Cosmetic Surgery: Dangers and Alternatives These data are consistent with the prevailing view of cosmetic surgery as a tool that primarily applies changes to faces and breasts or reduces the weight of the patient.
• Analysis of Cosmetics as a Consumer Product Improvement of communication and information technology has contributed to advanced forms of advertisement that promote the consumption and exploitation of markets down to the most localized places within the global village.
• Kiara Sky Cosmetics Company Analysis The application of analysis skills coupled with specific ways of researching a company allows an individual to evaluate and determine the success of a particular business.
• Osborne Cosmetic Surgery’s Market Viability As a result, of Austin’s maturing population and the lucrative location with the most prevalent cosmetic surgery rates, the city is appropriate for the surgery services business.
• Cheek Dimples: Cosmetic Surgery Cheek dimples are frequently referred to as a hereditary dominant characteristic. The data collected was evaluated, and the findings showed that the phenomenon of cheek dimples is mostly a dominant trait.
• Isopropyl Alcohol in Cosmetics and Medicine Isopropyl is synthesized in two steps: through the reaction of propylene with sulfuric acid and the consequent hydrolysis. In the context of isopropyl alternatives as sanitizers, ethyl alcohol serves as a solid option.
• Aloe Vera: The Use in Cosmetics and in Food After consulting a doctor, to improve the general condition, a person can use aloe juice and gel as a food supplement.
• Female Consumers on Luxury Brand Clothing Over Cosmetics Consumption The findings of this study suggests that the majority of the respondents preferred to spend more on clothing, although luxury brand clothing preference was notable only to a minority of the respondents.
• AG Hair Cosmetics: Style With Substance We are a company that’s driven by the passion, and determination of our employees and artists spreading the infectious AG philosophy.
• Cosmetic Surgery in Thailand and Cultural Implications Following the current environmental changes and the exposure of the ozone layer, many skin complications have been reported in which many of the victims end up undertaking plastic surgery.the provision of medical services in contemporary […]
• Cosmetic Surgery Implications A good evaluation of the pros and cons of cosmetic surgery should be considered because after all, this is surgery and there are high possibilities of complications that arise later on after surgery.
• Advertisement Impact on Potential Buyers in the Cosmetic Industry Thus, looking at how various products are being advertised, one cannot fail to note the seriousness of the product owners in as far as convincing the consumers to fall for the given product.
• Cosmetics Industry and Female Identity While many are willing to pin the blame for this attitude on the women themselves, there is plenty of evidence suggesting it is a concept perpetuated and emphasized by the cosmetics industries through the medium […]
• The Interface of Subcultures and the Use of Cosmetics There are numerous factors that influence the process a consumer goes through from the time he or she conceptualizes the purchase of the product through to the actual buying of the product.
• “Clearing Up Cosmetic Confusion” and “The Great Pretenders?” The author uses a method of observations and personal experience to describe the current state of the corporate regulations within the cosmetics industry.
• Cosmetic Surgery Marketing for Young Irish Women The authors of the study emphasize that before the surgery, self-esteem and the level of self-sufficiency in women from the first group were significantly lower than in the representatives of the general population, and, later, […]
• Competition in the U.S. Cosmetics Industry At the moment, the cosmetics market in the U. The high level of rivalry peculiar to the beauty and personal care market demands an improved understanding of the competitive landscape.
• Media Influence, Cosmetic Surgery and Women’s Health The scope of the research is to provide a comprehensive and well-grounded answer to the following question: Does the image of women promoted by the media cause them to undertake cosmetic surgery?
• Media Persuasion to Undertake Cosmetic Surgery One of the downsides includes the pressure on women that is applied to them by the image that the media has promoted over the years.
• Organic Cosmetic Online Store’s Financial Risks Further, a more elaborate way of discussing financial risk as an influential aspect of the success of the online organic cosmetic store is by discussing security and privacy issues.
• Biofilm Prevention After Cosmetic Injection The concept of biofilm remains relatively new to dermatology, with few studies available on the formation of biofilm post-cosmetic injections; however, it is needed to explore the ways of preventing biofilm formation from reducing the […]
• Cosmetic Surgery: Are Black People Emulating White Culture? In conclusion, cosmetic surgery is not all about the blacks imitating the white culture but about personal choice and preference and the affordability, it all about what one wants to do with his or her […]
• Gender and Sexuality in Cosmetic Advertising It also assesses the correctness of truths conveyed to and the effect of these advertising images to the audience. The woman’s position to the back could be interpreted as a sign of feminine subordination.
• Business in the US Cosmetic Retailing Industry US cosmetics retailing industry tends to expand within the domestic and international markets because of the rising level of discretionary income in the developed and developing countries.
• Social Issues of Genital Cosmetic Surgery for Women The labia minora is the main target, and it aims at reducing the size of these lips to avoid protruding beyond the labia majora.
• Entrepreneurship in the Organic Cosmetics Sphere According to the article, customers want to be green, and they want to contribute to making the world better. In conclusion, it is possible to note that the article in question provides particular points in […]
• The Peculiar Features of Organic Cosmetics’ Business Gewirtz’s “Organics of Scale” and Traber’s “Experts Sound Alarm on ‘Dirty Dozen’” are the two articles that can be used by people, who want to run a company on organic cosmetics, as these sources provide […]
• Addressing Cosmetic Surgery Concerns Hence, cosmetic surgeon patients should understand that the training, experience, and education for becoming competent in cosmetic surgery are not the same as that required to become knowledgeable in plastic surgery.
• Organic Cosmetics: Shaping Consumer Behavior This will lead to an increase in the market share enjoyed by individual organic cosmetic companies and individual brand owners of organic cosmetic products.
• Characteristics of the Skin Care and Cosmetic Industry in China L’Oreal is the market leader in Chinese cosmetic and skin care industry. The success of cosmetic and skin care products companies depends on women consumers.
• Skin Care and Cosmetic Industries in China The skin care and cosmetic industries in China are divided into male and female ones and it is possible to notice the tendency of the men’s skin care industry development increase.
• Ethical Issues and Considerations in Cosmetic Surgery The effects of war in the 20th century saw the widening of the scale of reconstructive surgery due to the increase in the number of people with intense injuries that required intense reconstructive practices.
• Cosmetic Surgery: A Symbolic Damage to All Women In the twenty first century, thinner is preferred by most men in the globe, and this has driven the women to modify their bodies by going for plastic surgery. Most of the modern women have […]
• The Extent of the Cosmetic Industry in Australia The use of advertising in the promotion of the marketability of this product is a strategy that Procter & Gamble has effectively developed across Australia and the rest of the international market.
• Excessive Cosmetic Surgery Modifications come in the form of socially acceptable ones and those that are shunned by the majority of the community mostly due to their profane nature.
• From the Perspective of Female College Students Majoring in Cosmetology Factor Analysis on Femininity
• Language Socialization in a Southern, African American Cosmetology School
• African-American Cosmetology as a Link Between Computing Education and Community Wealth
• The Demand for Medical Cosmetology: Evidence From China
• Hydrogel-Based Active Substance Release Systems for Cosmetology and Dermatology Application
• Cosmetology, Cosmetics, Cosmeceuticals: Definitions and Regulations
• Cosmetics and Cosmetology at Shahr-i Sokhta
• Development of Hardware Cosmetology in Kazakhstan
• Occupational Licensing in a “Competitive” Labor Market: The Case of Cosmetology
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• Methods for the Improvement of Acne Scars Used in Dermatology and Cosmetology
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• High-Energy Laser Exposure in Dermatology and Cosmetology
• The Properties and Application of Argan Oil in Cosmetology
• Breaking the Frontiers of Cosmetology With Antimicrobial Peptides
• Service Quality, Experiential Value, and Repurchase Intention for Medical Cosmetology Clinic
• Balanced Scorecard-Based Analysis of Customer Expectations for Cosmetology Services
• Mycology-Nanotechnology Interface: Applications in Medicine and Cosmetology
• Development of Shrewd Cosmetology Model Through Fuzzy Logic
• Skin Bioengineering: Techniques and Applications in Dermatology and Cosmetology
• Prospects of Marine Sponge Collagen and Its Applications in Cosmetology
• Cosmetology Teachers’ Perception of Untact Online Cosmetology Courses and Their Barriers and Effectiveness
• Polymeric Injectable Fillers for Cosmetology: Current Status, Future Trends, and Regulatory Perspectives
• Knowledge About Ultraviolet Radiation Hazards and Tanning Behavior of Cosmetology and Medical Students
• Use of Medicinal Plants in Dermato-Cosmetology
• Role of Metal Ions Complexes and Their Ligands in Medicine, Pharmacy, and Cosmetology
• Herbal Cosmeceuticals: New Opportunities in Cosmetology
• Hyaluronic Acid Based Microneedle Array: Recent Applications in Drug Delivery and Cosmetology
• Signaling Molecules of Human Skin Cells as the Targets for Injection Cosmetology
• Younger Cosmetology Workers and Environmental and Occupational Asthma Triggers at Training Sites and in Salons
• The Assessment of Toxic Metals in Plants Used in Cosmetics and Cosmetology
• Risk Factors for Asthma Among Cosmetology Professionals in Colorado
• Role of Nanotechnology in the World of Cosmetology: A Review
• PEG Grafted Polymethacrylates Bearing Antioxidants in Cosmetology
• The Main Reason for Demand and Growth in Cosmetology
• The Importance and Perspective of Plant-Based Squalene in Cosmetology
• Effect of Physical Attractiveness and Customer Perceived Service Quality in the Cosmetology Industry
• The Dedicated Multispectral Imaging System Applied to Dermatology and Cosmetology
• Pediatric Endo-Cosmetology and the Evolution of Growth Diagnosis and Treatment
• Microneedles as Enhancer of Drug Absorption Through the Skin and Applications in Medicine and Cosmetology
• What Is the Future of Cosmetology?
• How Is Electricity Used in Cosmetology?
• Are Natural Skin Care Products the Answer to All Problems?
• Why Is Cosmetology Important to Our Society?
• Is Cosmetology a Good Career?
• What Is the Difference Between Organic and Natural Skin Care?
• Which Country Started Cosmetology First?
• How Does the Growing Population of Millennials Impact Spa and the Skin Care Industry?
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• Can Antioxidants Improve Skin Care?
• Why Is Electricity Important in Cosmetology?
• Does Cosmetology Have a Positive Effect on Health?
• Which Country Is Best for Cosmetology?
• What Skills Does Cosmetology Require?
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• What Are the Principles of Cosmetology?
• Does Organic Skin Care Work?
• Why Is Safety Important in Cosmetology?
• What Is the Most Common Ingredient in Skincare Products?
• Does Cosmetology Include Fashion?
• Who Has the Biggest Influence on Cosmetology?
• What Is the Difference Between Cosmetology and Esthetics?
• Are Skin Care Products the Right Choice for Great Skin?
• Why Is the Cosmetology Industry Growing?
• What Is the Best Skin Care Routine for Winter?
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