The Relationship of Facial Skin Biophysical Properties to Age and the Potential Role of Lifestyles in Chinese Rural and Urban Females

The Relationship of Facial Skin Biophysical Properties to Age and the Potential Role of Lifestyles in Chinese Rural and Urban Females | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Relationship of Facial Skin Biophysical Properties to Age and the Potential Role of Lifestyles in Chinese Rural and Urban Females Xiao-xiao YANG, Xao-dong HUANG, Hao-chen Zhang, Fan YI, Hong MENG, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3874675/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As a developing country, China has a large population base of county women, but previous studies showed a significant lack of attention towards their skin status. This study is supposed to recruit, measure and portray the facial skin biophysical properties of rural females. The individual lifestyles were also investigated and analyzed to assess the risky or protective factors. There were 10 skin parameters measured of 350 rural female aged 18–65 years subjects (skin barrier status, color and elasticity). The trend of subject's skin parameters with age was analyzed through curve fitting, analysis of variance and Krustal-Wallis H test, with 0.05 as the significance threshold. The lifestyles related to "unfavorable" skin parameters were initially evaluated by chi-square test and crude OR, and then re-evaluated by logistic regression model and adjusted OR to control age. Rural females experience significant adverse changes in skin biophysical parameters with age, and they had lower facial skin hydration level and sebum secretion, higher transepidermal water loss and higher melanin content. Urban women had poorer skin gloss, more yellowish skin tone, and poorer elasticity. Self-reported oily or mixed skin was associated with high sebum and hemoglobin content. Skincare product use was a significant protective factor for skin hydration. Sunscreen product use was a protective factor for a number of skin biophysical parameters, but was not statistically significant after controlling for age. In conclusion, distinct skincare measures are recommended for rural and urban women based on their different facial skin biophysical properties and trends with age. Urban and rural comparison Logistic regression model Polynomial fitting Lifestyle habits Odds ratio Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Facial skin status of females is threatened by both endogenous and exogenous aging[ 1 ], characterized by a gradual loss of elasticity and functional viability, as well as dryness, oily deficiencies, and uneven pigmentation[ 2 ]. In recent years, facial skin aging characteristics with a focus on barrier, wrinkles and pigmentation have been objectively and quantitatively evaluated via image techniques and measuring probes[3; 4; 5]. However, the majority of studies were distributed in representative Chinese cities including Beijing, Shanghai, Guangzhou, etc.[6; 7]. As a developing country, China has a large population base of county women, but previous studies showed a significant lack of attention towards them. Females are exposed to diverse environmental stressors on a daily basis, and different environments and lifestyles dramatically affect skin aging. A study based on a systematic review and meta-analysis identified seven significant risk factors for the skin aging phenotype, including age, gender, race, sun exposure, air pollution, nutrition, and smoking[ 8 ]. Ultraviolet radiation is suggested to cause about 90% of skin aging, as characterized by diminished elasticity, increased wrinkles, and gradual loss of tissue composition and function[ 9 ]. It has been reported that air pollution promotes skin aging and inflammation, contributes to skin wrinkles and hyperpigmentation [10; 11; 12]. In addition, diet is closely related to skin aging. High-fat diets cause skin aging primarily by causing oxidative stress in the skin producing inflammatory damage. The diet high in sugar can lead to the accumulation of AGEs and accelerated skin aging. Diets high in salt, spices and extremely vegetarian diets are also considered harmful to skin health[ 13 ]. The use of cosmetic products also affects skin biophysical parameters[ 14 ]. We collected skin biophysical parameters from county females aged 18–65 years old, aiming to portray the age-dependent trends of facial skin biophysical properties of rural Chinese females, in order to supplement the study data of county females. Moreover, the individual lifestyles of the county females were further investigated to assess the influencing factors of skin biophysical properties under different exposures. This study is supposed to provide a scientific basis for the development of personalized skin care strategies of county women in China (Fig. 1). METHODS 1 Subjects A total of 350 healthy female subjects aged 18–65 years from the subordinate counties of Shandong and Heilongjiang provinces were recruited. Considering the effect of temperature on the subjects' skin status, the test period in Si-shui County of Shandong province (35°23' N, 116°33' E) was spring (2017.04 ~ 2017.06), while the test period in Lin-kou County of Heilongjiang province (44°6' N, 129°58' E) was summer (2017.07-2017.08). The study was conducted in accordance with the Declaration of Helsinki and informed consent was obtained from all participants. Inclusion and exclusion criteria for subjects are provided in Supplementary Material A1. 2 Skin parameters evaluation A skin assessment crew of five trained researchers measured 10 skin parameters of subjects by the multi-probe adapted instruments MPA580 and MPA10, including skin hydration (CM), trans epidermal water loss (TEWL), sebum content (SM), pH, melanin content (MI), hemoglobin content (EI), individual type angle (ITA°), skin yellowness (b*), glossiness (GLOSS), and elasticity (R2). The testing instruments and procedures for the 10 skin parameters are provided in Table A1. The skin testing sites of subjects were the points where the nasal extension joints the tangent eye corners of the left and right cheeks. Each subject was measured for 10 skin parameters at the junction point of the left and right cheeks, and each point was measured three times in parallel (except for the CM values, with six parallel measurements at each point). In the subsequent analyses, the mean value of each skin parameter was used. 3 Lifestyle questionnaire Individual lifestyles were investigated through a questionnaire containing 18 questions covering four areas: personal information (self-reported skin type, marital and childbirth status), behavioral habits (diet, smoking, drinking alcohol, sleep, allergens), physical / mental health status (mood, bowel movements, menstruation), and usage habits of cosmetics (sun protection, skincare products). Missing values in the questionnaire were not counted. The questionnaire details are provided in Supplementary Material A2. 4 Statistical analyses All data analyses in this study were done by SPSS 25.0 except for polynomial fitting. 10 skin parameters of the subjects were described by means and standard deviations. (I) The trend of subject's skin parameters with age was fitted through curve fitting tool box of MATLAB R2020a software, with the largest r 2 was selected as the best fitting curve. (II) Subjects were divided into five subgroups according to age: 18–25 (A1), 26–35 (A2), 36–45 (A3), 46–55 (A4), and 56–65 (A5) years old, and the differences in skin parameters among the five age subgroups were analyzed by analysis of variance (ANOVA) and Krustal-Wallis H test, with 0.05 as the significance threshold. (III) We compared the variance of parameters by independent samples t-test with a significance threshold of 0.05 between the 350 rural females versus the 300 urban females. A number of data were corrected according to the results of Levene's test equal variance. (Ⅳ) The risky lifestyles related to skin parameters were initially evaluated by the chi-square test and crude OR. Then we re-evaluated those risky lifestyles by the logistic regression model and adjusted OR, aiming to control the impact of individual age. RESULTS 1 Skin aging trends based on biophysical parameters 1.1 Polynomial Fitting of age-skin parameter We analyzed the correlation between skin parameters and age in rural female subjects by Spearman's rho test. There was no statistically significant correlation between EI values and age ( P > 0.05). A polynomial fit was made between the other nine skin biophysical parameters and the age of the subjects (Table 1 , Fig. 2). In terms of skin barrier function, pH fluctuated less with age, with the lowest pH at 48 years of age; CM, TEWL and SM values decreased monotonically with age. In terms of skin color, the GLOSS and ITA values decrease monotonically with age; the b* value increases from the age of 23 years, and the MI value increases from the age of 33 years. In addition, skin elasticity decreases monotonically with age. 1.2 Skin biophysical properties in different age subgroups The values of CM, TEWL, EI, ITA°, GLOSS, R2 satisfied Levene's chi-square, ANOVA (LSD and S-N-K methods) was used to test for six skin parameters’ variances among subgroups. The other four skin parameters’ variance among subgroups were performed by Krustal-Wallis H-test. Except for the EI values, skin biophysical parameters were significantly different among the five age subgroups (Fig. 3). The results were generally consistent with the trend of the polynomial fitting. The younger people had significantly higher TEWL and SM values (A1 ~ A2). The intermediate people had significantly lower pH values (A4). The intermediate and older people had significantly higher b* and ITA values and significantly lower R2 values (A4 ~ A5). And the older people had significantly higher MI and significantly lower GLOSS and CM values (A5). 1.3 Comparison of skin biophysical properties between rural and urban females We previously assessed facial skin aging trends in 300 women aged 18–60 years from five cities in China by noninvasive skin assessment[ 6 ]. The sample population of this study was 350 rural women aged 18–65 years, which was similar in sample size and age distribution with above study. Comparing the values of 10 skin biophysical parameters between rural and urban women by independent samples t-test (Table 2 ), we found that rural women had lower facial skin hydration level and sebum secretion, higher transepidermal water loss and higher melanin content. Urban women had poorer skin gloss, more yellowish skin tone, and poorer elasticity. By comparing polynomial fitting curve trends, we found that there were various differences in skin aging trends among urban and rural females. In terms of skin barrier levels, the TEWL values of rural women showed a more stable decreasing trend with age, but fluctuated with age in the urban women samples. Sebum secretion showed a similar behavior in rural and urban women, significantly decreasing with age. In terms of color level, the most obvious difference was that skin gloss decreased with age in rural women but increased in urban women. Skin tone both showed deepening and yellowing with age. In terms of skin elasticity, rural women showed a steady decline in R2 values, but urban women showed a slight rebound after age 47. 2 Risky lifestyles for skin biophysical parameters 2.1 Unfavorable value of skin parameter by box-plot delineation Considering the impact factors on skin biophysical parameters reported in literature, we investigated 18 questions related to four areas, including personal information (self-reported skin type, marital and fertility status), behavioral habits (diet, smoking, alcohol consumption, sleep, allergens), physical/mental health status (mood, bowel movements, menstruation), and cosmetic habits (sunscreen, skincare products), to filter out the risky lifestyles on the skin biophysical parameters. A quadratic approach was used to categorize people with relatively different skin parameters: (I) For a certain skin parameter, values larger than Q2 are labeled as Upper quartiles, and values smaller than Q3 are labeled as Lower quartiles based on box plots; (II) people with relatively "unfavorable" skin parameters are regarded as the positive group, i.e., the Lower quartile for CM, SM, pH, ITA, GLOSS, R2, and the Upper quartile for TEWL, MI, EI, b* are the positive groups (Table 3). CM ≤ 38.80 indicates lower hydration, CM ≥ 62.60 indicates higher hydration. TEWL ≥ 23.20 indicates higher transepidermal water loss, TEWL ≤ 15.70 indicates lower transepidermal water loss. SM ≤ 1.00 indicates less sebum secretion, SM ≥ 9.00 indicates more sebum secretion. pH ≤ 5.66 indicates stronger acidity, pH ≥ 6.08 indicates weaker acidity. MI ≥ 179.50 indicates higher melanin content, MI ≤ 131.17 indicates lower melanin content. EI ≥ 358.17 indicates higher hemoglobin content, EI ≤ 268.83 indicates lower hemoglobin content. b ≥ 12.71 indicates more yellowish skin tone, b ≤ 9.80 indicates less yellowish skin tone. ITA ≤ 42.33 indicates darker skin tone, ITA ≥ 56.00 indicates brighter skin tone. GLOSS ≤ 5.42 indicates worse glow, GLOSS ≥ 7.10 indicates better glow. R2 ≤ 0.66 indicates lower elasticity, R2 ≥ 0.82 indicates higher elasticity. 2.2 Evaluation of risky lifestyles by crude and adjusted OR The chi-square test and crude ORs were used to initially evaluate the risky and protective lifestyles related to "unfavorable" skin parameters (Table A2-A11). Due to the tiny sample sizes (N < 10) of the smoking, drinking alcohol, divorced, widowed, pregnant, hysterectomized exposure groups, the effects of these events on skin biophysical parameters were not discussed in this study. The results of the chi-square test showed that allergic histories, mental stress and irritable mood did not have a statistically significant effect on skin biophysical parameters. Then we re-evaluated the risky and protective lifestyles among the other 13 individual lifestyle items for 10 skin biophysical parameters by the logistic regression model and adjusted OR, aiming to control the impact of individual age. We used transepidermal water loss, pH, hydration, and sebum content as biophysical parameters to assess the skin barrier. After controlling for age, self-reported skin types of oily (OR = 0.099 [95% CI: 0.018, 0.536], P = 0.007) and mixed (OR = 0.253 [95% CI: 0.081, 0.793], P = 0.018) decreased the risk of low sebum secretion to 0.099 times and 0. 253 times, and allergy to food or medicine (OR = 0.259 [95% CI: 0.076, 0.888], P = 0.032) decreased the risk of low sebum secretion to 0.259 times. Self-reporting a skin type of mixed (OR = 5.869 [95% CI: 1.452, 23.729], P = 0.013) increased the risk of high transepidermal water loss to 5.869 times. Use of skincare products (OR = 0.275 [95% CI: 0.018, 0.933], P = 0.038) decreased the risk of low skin hydration content to 0.275 times. Skin yellowness, individual type angle, melanin content, gloss, and hemoglobin content were used as biophysical parameters characterizing skin tone. Use of skincare products and staying up late were protective factors that decreased the risk of dark skin. First menstruation after age 16, insomnia, and a vegetarian diet were risk factors that increased the risk of dark skin. Women who used sunscreen products had better skin gloss, while those who were menopausal and had children showed poorer skin gloss. For skin melanin content, it was likely to be lower in women using sunscreen products and higher in married, childbearing, and menopausal women. However, these factors did not show statistical significance after controlling for age ( P > 0.05). But the risk of higher hemoglobin in women with self-reported oily skin (OR = 7.947 [95% CI: 1.760, 35.887], P = 0.007) was 7.947 times higher than in women with normal skin after controlling for age. We evaluated skin elasticity by R2 value. The results showed that being married, having children, and being post-menopausal increased the risk of poorer skin elasticity and were risk factors. Higher frequency of the sunscreen decreased the risk of skin elasticity deterioration and was a protective factor. And the use of sunscreen all year round consistently decreased the risk of poorer skin elasticity to 0.242 times compared with no sunscreen use. However, these factors did not show statistical significance after controlling for age ( P > 0.05). DISCUSSION As a developing country, China has a large population base of county women. In this study, we measured 10 non-invasive skin parameters of 350 rural female subjects aged 18–65 years from two counties in eastern China. Then we compared the skin biophysical parameters between rural and urban women and found that: (Ⅰ) Rural women had lower facial skin hydration level and sebum secretion, higher transepidermal water loss and higher melanin content . Through a questionnaire survey of rural women, we found that the u s e of skincare products decreased the risk of lower skin hydration content to 0.275 times. To some extent the lower skincare concern of rural women is accompanied by lower hydration and higher transepidermal water loss. Two investigations from the southwestern United States (Utah[ 15 ] and Texas[ 16 ]) showed that rural subjects underutilized sun protection and were more likely to sunburns than urban subjects, which may explain the higher melanin content of rural women's skin in this study. In addition, we suggest that the significantly lower sebum production in rural women may be due to the non-invasive skin testing period being fall and winter, whereas the higher sebum production in urban women may be due to the testing period being summer. It has been proven that the seasonal changes in humidity and temperature are an important factor influencing the skin sebum content[ 17 ]. In general, these are advised for rural women to increase the frequency of skincare products and sunscreens to combat the contribution of ultraviolet rays to melanin accumulation and skin dryness. (Ⅱ) Urban women had poorer skin gloss, more yellowish skin tone, and poorer elasticity. There is a consensus that megacities residents usually suffer from huge heat islands and air pollution[ 18 ]. And the megacities residents also typically face more psychological stress than rural residents due to social inequalities and strained urban resources[ 19 ]. Air pollution contributes to extrinsic skin aging, and mostly derives from the burning of fossil fuels and industrial activity in urban centers. Park SY et al. analyzed the effect of particulate matter exposure on human dermal fibroblasts. It was proved that particulate pollution can induce inflammatory phenotypes in fibroblasts, with increased metalloprotein synthesis, reduced collagen and elastin synthesis in the dermis[ 20 ]. This is in agreement with our results of lower skin elasticity in urban women. Several studies have emphasized the aggravation of skin pigmentation and lentigines by air pollution, like particulate matter and nitrogen dioxide[21; 22; 23]. The results of quantitative surveys of Asian women showed a strong association between psychological stress and dull skin[ 24 ]. We suggested that urban women faced relatively higher levels of psychological stress, which contributed to poorer gloss and exhibited dull skin. (Ⅲ) Woman with self-reported oily or mixed skin types had higher sebum secretion, hemoglobin content and transepidermal water loss. After controlling for age, the risk of lower sebum secretion in women with self-reported skin types of oily and mixed decreased to 0.099 times and 0. 253 times than in women with normal skin. The risk of higher hemoglobin content in women with self-reported oily skin was 7.947 times higher than in women with normal skin. Self-reported skin type of mixed increased the risk of higher transepidermal water loss to 5.869 times than with normal skin. A noninvasive skin measurement and clinical scoring study performed by dermatologists found that oily and mixed skin types had higher objective scores for both sebum content and transepidermal water loss compared to normal skin[ 25 ], which is consistent with our results. However, it has also been shown that there is no difference in TEWL between oily and normal skin[ 26 ]. But considering the wider age range and the use of age control models in our study, we agree more with the relationship between TEWL and oily and mixed skin types. CONCLUSION This study analyzed the facial skin aging trends and influence of lifestyles among the rural and urban females. Longitudinally, urban and rural females all experience significant adverse changes in skin biophysical parameters with age. Horizontally, rural women had lower facial skin hydration level and sebum secretion, higher transepidermal water loss and higher melanin content. Urban women had poorer skin gloss, more yellowish skin tone, and poorer elasticity. Moreover, self-reported oily or mixed skin associated with high sebum, hemoglobin content and transepidermal water loss. Skincare product use was a protective factor for skin hydration. Sunscreen product use was a protective factor for some parameters, but was not statistically significant after controlling for age. Rural women are advised to increase the frequency of skincare products and sunscreens to combat the melanin accumulation and skin dryness. And urban women are advised to emphasize the adverse impact of environmental pollution, stay-up late and stress on skin elasticity and tone. Declarations Funding sources There is no funding source. Data availability statement Data are available upon request. Conflict of interest All the authors declare that they have no conflict of interest relevant to this study. Ethical approval The study was conducted in accordance with the Declaration of Helsinki and informed consent was obtained from all participants, and ethical approval was obtained from the Ethics Committee of Beijing University of Chinese Medicine (reference 2017BZHYLL0501, 1 st May, 2017). Consent for publication Not applicable—our manuscript does not contain data from any individual person. Author contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiao-xiao YANG, Hao-chen Zhang and Xao-dong HUANG. The first draft of the manuscript was written by Xiao-xiao YANG. In addition, Yue WU, Fan YI, Hong MENG and Yin-mao Dong commented on previous versions of the manuscript. All authors read and approved the final manuscript. References S.H. Shin, Y.H. Lee, N.-K. Rho, and K.Y. Park, Skin aging from mechanisms to interventions: focusing on dermal aging. Front Physiol 14 (2023) 1195272. https://doi.org/10.3389/fphys.2023.1195272 . F. Papaccio, A. D Arino, S. Caputo, and B. Bellei, Focus on the Contribution of Oxidative Stress in Skin Aging. Antioxidants (Basel) 11 (2022). https://doi.org/10.3390/antiox11061121 . S. Amano, T. Yoshikawa, C. Ito, I. Mabuchi, K. Kikuchi, M. Ooguri, and C. Yasuda, Prediction and association analyses of skin phenotypes in Japanese females using genetic, environmental, and physical features. 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Ma, Perception, understanding, and association between psychological stress and skin aging: Quantitative surveys of Asian women aged 18–34 years, dermatologists, and psychologists in China and Japan. J Cosmet Dermatol 22 (2023) 2297–2307. https://doi.org/10.1111/jocd.15732 . D.G. Mercurio, J.H. Segura, M.B. Demets, and P.M. Maia Campos, Clinical scoring and instrumental analysis to evaluate skin types. Clin Exp Dermatol 38 (2013) 302-8; quiz 308-9. https://doi.org/10.1111/ced.12105 . M.O. de Melo, and P. Maia Campos, Characterization of oily mature skin by biophysical and skin imaging techniques. Skin Res Technol 24 (2018) 386–395. https://doi.org/10.1111/srt.12441 . Tables Table 1. Correlation and fitting equations between skin biophysical parameters and age Skin parameter Mean ± SD Correlation coefficient Polynomial Fitting Function r 2 CM 55.13±18.22 －0.191 *** f (x) = 0.003749x 2 －0.6411x＋74.47 0.259 TEWL 20.61±7.32 －0.352 *** f (x) = 0.003889x 2 －0.5202x＋34.24 0.601 SM 8.11±11.45 －0.350 *** f (x) = 0.002773x 2 －0.5699x＋25.9 0.465 pH 6.00±0.43 －0.261 *** f (x) = 0.000646x 2 －0.06247x＋7.365 0.404 MI 160.44±40.07 0.210 *** f (x) = 0.05211x 2 －3.403x＋209.4 0.305 EI 315.51±63.85 0.046 / / b* 11.93±2.26 0.439 *** f (x) = 0.002433x 2 －0.1096x＋12.24 0.660 ITA° 47.30±10.37 －0.527 *** f (x) = －0.005191x 2 －0.02877x＋56.52 0.642 GLOSS 6.49±1.40 －0.171 ** f (x) = 0.0006803x 2 －0.09475x＋9.074 0.246 R2 0.72±0.12 －0.305 *** f (x) = －0.0000459x 2 －0.0003175x＋0.7884 0.534 “***” indicates P < 0.001, “**” indicates P < 0.010. Table 2 The comparison of skin biophysical properties between rural and urban females Parameters Mean ± SD RF vs. UR P value Urban females (UF) Rural females (RF) Age 38.93 ± 12.39 39.03 ± 11.37 0.912 pH 6.01 ± 0.44 6.00 ± 0.43 0.795 CM 60.36 ± 11.49 55.13 ± 18.22 ↓ < 0.001 TEWL 15.79 ± 4.16 20.61 ± 7.32 ↑ < 0.001 SM 42.29 ± 31.68 8.11 ± 11.45 ↓ < 0.001 EI 325.15 ± 58.35 315.51 ± 63.85 0.046 MI 142.63 ± 31.83 160.44 ± 40.07 ↑ < 0.001 b* 12.95 ± 2.62 11.93 ± 2.26 ↓ < 0.001 ITA° 40.62 ± 8.90 47.30 ± 10.37 ↑ < 0.001 GLOSS 4.20 ± 1.24 6.49 ± 1.40 ↑ < 0.001 R2 0.61 ± 0.10 0.72 ± 0.12 ↑ < 0.001 Table 3 The lower and upper quartile values of the biophysical parameters Variable Lower quartile (≤25%) Upper quartile (≥75%) N Min (Q4) Max (Q3) N Min (Q2) Max (Q1) CM 71 8.80 38.80 70 62.60 88.00 TEWL 71 7.80 15.70 63 23.20 34.30 SM 88 0.00 1.00 45 9.00 21.00 pH 72 5.02 5.66 69 6.08 6.60 MI 69 71.30 131.33 57 178.33 238.67 EI 69 158.33 269.00 66 358.00 489.00 b* 69 6.89 9.80 68 12.70 16.42 ITA° 61 23.33 42.33 72 56.00 68.33 GLOSS 67 3.48 5.42 69 7.10 9.53 R2 72 0.42 0.66 68 0.82 0.99 Additional Declarations No competing interests reported. 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0.001, “**” indicates \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, “*” indicates \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3874675/v1/7d73425e39d814bcf67c9363.png"},{"id":50761813,"identity":"c6b872d3-a1e9-47ae-8c3d-a05d6b479844","added_by":"auto","created_at":"2024-02-06 23:11:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1217026,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3874675/v1/a8d793f4-f51e-4df7-971b-21052d1261be.pdf"},{"id":49992279,"identity":"d42fec02-416a-4685-9665-fbd5201c198c","added_by":"auto","created_at":"2024-01-22 18:54:51","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":56213,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3874675/v1/978c153bd2de0cbd86f5a1e5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Relationship of Facial Skin Biophysical Properties to Age and the Potential Role of Lifestyles in Chinese Rural and Urban Females","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eFacial skin status of females is threatened by both endogenous and exogenous aging[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], characterized by a gradual loss of elasticity and functional viability, as well as dryness, oily deficiencies, and uneven pigmentation[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In recent years, facial skin aging characteristics with a focus on barrier, wrinkles and pigmentation have been objectively and quantitatively evaluated via image techniques and measuring probes[3; 4; 5]. However, the majority of studies were distributed in representative Chinese cities including Beijing, Shanghai, Guangzhou, etc.[6; 7]. As a developing country, China has a large population base of county women, but previous studies showed a significant lack of attention towards them.\u003c/p\u003e \u003cp\u003eFemales are exposed to diverse environmental stressors on a daily basis, and different environments and lifestyles dramatically affect skin aging. A study based on a systematic review and meta-analysis identified seven significant risk factors for the skin aging phenotype, including age, gender, race, sun exposure, air pollution, nutrition, and smoking[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ultraviolet radiation is suggested to cause about 90% of skin aging, as characterized by diminished elasticity, increased wrinkles, and gradual loss of tissue composition and function[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It has been reported that air pollution promotes skin aging and inflammation, contributes to skin wrinkles and hyperpigmentation [10; 11; 12]. In addition, diet is closely related to skin aging. High-fat diets cause skin aging primarily by causing oxidative stress in the skin producing inflammatory damage. The diet high in sugar can lead to the accumulation of AGEs and accelerated skin aging. Diets high in salt, spices and extremely vegetarian diets are also considered harmful to skin health[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The use of cosmetic products also affects skin biophysical parameters[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe collected skin biophysical parameters from county females aged 18\u0026ndash;65 years old, aiming to portray the age-dependent trends of facial skin biophysical properties of rural Chinese females, in order to supplement the study data of county females. Moreover, the individual lifestyles of the county females were further investigated to assess the influencing factors of skin biophysical properties under different exposures. This study is supposed to provide a scientific basis for the development of personalized skin care strategies of county women in China (Fig.\u0026nbsp;1).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1 Subjects\u003c/h2\u003e \u003cp\u003eA total of 350 healthy female subjects aged 18\u0026ndash;65 years from the subordinate counties of Shandong and Heilongjiang provinces were recruited. Considering the effect of temperature on the subjects' skin status, the test period in Si-shui County of Shandong province (35\u0026deg;23' N, 116\u0026deg;33' E) was spring (2017.04\u0026thinsp;~\u0026thinsp;2017.06), while the test period in Lin-kou County of Heilongjiang province (44\u0026deg;6' N, 129\u0026deg;58' E) was summer (2017.07-2017.08). The study was conducted in accordance with the Declaration of Helsinki and informed consent was obtained from all participants. Inclusion and exclusion criteria for subjects are provided in Supplementary Material A1.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2 Skin parameters evaluation\u003c/h2\u003e \u003cp\u003eA skin assessment crew of five trained researchers measured 10 skin parameters of subjects by the multi-probe adapted instruments MPA580 and MPA10, including skin hydration (CM), trans epidermal water loss (TEWL), sebum content (SM), pH, melanin content (MI), hemoglobin content (EI), individual type angle (ITA\u0026deg;), skin yellowness (b*), glossiness (GLOSS), and elasticity (R2). The testing instruments and procedures for the 10 skin parameters are provided in Table A1. The skin testing sites of subjects were the points where the nasal extension joints the tangent eye corners of the left and right cheeks. Each subject was measured for 10 skin parameters at the junction point of the left and right cheeks, and each point was measured three times in parallel (except for the CM values, with six parallel measurements at each point). In the subsequent analyses, the mean value of each skin parameter was used.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3 Lifestyle questionnaire\u003c/h2\u003e \u003cp\u003eIndividual lifestyles were investigated through a questionnaire containing 18 questions covering four areas: personal information (self-reported skin type, marital and childbirth status), behavioral habits (diet, smoking, drinking alcohol, sleep, allergens), physical / mental health status (mood, bowel movements, menstruation), and usage habits of cosmetics (sun protection, skincare products). Missing values in the questionnaire were not counted. The questionnaire details are provided in Supplementary Material A2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4 Statistical analyses\u003c/h2\u003e \u003cp\u003eAll data analyses in this study were done by SPSS 25.0 except for polynomial fitting. 10 skin parameters of the subjects were described by means and standard deviations. (I) The trend of subject's skin parameters with age was fitted through curve fitting tool box of MATLAB R2020a software, with the largest r\u003csup\u003e2\u003c/sup\u003e was selected as the best fitting curve. (II) Subjects were divided into five subgroups according to age: 18\u0026ndash;25 (A1), 26\u0026ndash;35 (A2), 36\u0026ndash;45 (A3), 46\u0026ndash;55 (A4), and 56\u0026ndash;65 (A5) years old, and the differences in skin parameters among the five age subgroups were analyzed by analysis of variance (ANOVA) and Krustal-Wallis H test, with 0.05 as the significance threshold. (III) We compared the variance of parameters by independent samples t-test with a significance threshold of 0.05 between the 350 rural females versus the 300 urban females. A number of data were corrected according to the results of Levene's test equal variance. (Ⅳ) The risky lifestyles related to skin parameters were initially evaluated by the chi-square test and crude OR. Then we re-evaluated those risky lifestyles by the logistic regression model and adjusted OR, aiming to control the impact of individual age.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1 Skin aging trends based on biophysical parameters\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e1.1 Polynomial Fitting of age-skin parameter\u003c/h2\u003e \u003cp\u003eWe analyzed the correlation between skin parameters and age in rural female subjects by Spearman's rho test. There was no statistically significant correlation between EI values and age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). A polynomial fit was made between the other nine skin biophysical parameters and the age of the subjects (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;2). In terms of skin barrier function, pH fluctuated less with age, with the lowest pH at 48 years of age; CM, TEWL and SM values decreased monotonically with age. In terms of skin color, the GLOSS and ITA values decrease monotonically with age; the b* value increases from the age of 23 years, and the MI value increases from the age of 33 years. In addition, skin elasticity decreases monotonically with age.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Skin biophysical properties in different age subgroups\u003c/h2\u003e \u003cp\u003eThe values of CM, TEWL, EI, ITA\u0026deg;, GLOSS, R2 satisfied Levene's chi-square, ANOVA (LSD and S-N-K methods) was used to test for six skin parameters\u0026rsquo; variances among subgroups. The other four skin parameters\u0026rsquo; variance among subgroups were performed by Krustal-Wallis H-test. Except for the EI values, skin biophysical parameters were significantly different among the five age subgroups (Fig.\u0026nbsp;3). The results were generally consistent with the trend of the polynomial fitting. The younger people had significantly higher TEWL and SM values (A1\u0026thinsp;~\u0026thinsp;A2). The intermediate people had significantly lower pH values (A4). The intermediate and older people had significantly higher b* and ITA values and significantly lower R2 values (A4\u0026thinsp;~\u0026thinsp;A5). And the older people had significantly higher MI and significantly lower GLOSS and CM values (A5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Comparison of skin biophysical properties between rural and urban females\u003c/h2\u003e \u003cp\u003eWe previously assessed facial skin aging trends in 300 women aged 18\u0026ndash;60 years from five cities in China by noninvasive skin assessment[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The sample population of this study was 350 rural women aged 18\u0026ndash;65 years, which was similar in sample size and age distribution with above study. Comparing the values of 10 skin biophysical parameters between rural and urban women by independent samples t-test (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), we found that rural women had lower facial skin hydration level and sebum secretion, higher transepidermal water loss and higher melanin content. Urban women had poorer skin gloss, more yellowish skin tone, and poorer elasticity.\u003c/p\u003e \u003cp\u003eBy comparing polynomial fitting curve trends, we found that there were various differences in skin aging trends among urban and rural females. In terms of skin barrier levels, the TEWL values of rural women showed a more stable decreasing trend with age, but fluctuated with age in the urban women samples. Sebum secretion showed a similar behavior in rural and urban women, significantly decreasing with age. In terms of color level, the most obvious difference was that skin gloss decreased with age in rural women but increased in urban women. Skin tone both showed deepening and yellowing with age. In terms of skin elasticity, rural women showed a steady decline in R2 values, but urban women showed a slight rebound after age 47.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2 Risky lifestyles for skin biophysical parameters\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.1 Unfavorable value of skin parameter by box-plot delineation\u003c/h2\u003e \u003cp\u003eConsidering the impact factors on skin biophysical parameters reported in literature, we investigated 18 questions related to four areas, including personal information (self-reported skin type, marital and fertility status), behavioral habits (diet, smoking, alcohol consumption, sleep, allergens), physical/mental health status (mood, bowel movements, menstruation), and cosmetic habits (sunscreen, skincare products), to filter out the risky lifestyles on the skin biophysical parameters.\u003c/p\u003e \u003cp\u003e A quadratic approach was used to categorize people with relatively different skin parameters: (I) For a certain skin parameter, values larger than Q2 are labeled as Upper quartiles, and values smaller than Q3 are labeled as Lower quartiles based on box plots; (II) people with relatively \"unfavorable\" skin parameters are regarded as the positive group, i.e., the Lower quartile for CM, SM, pH, ITA, GLOSS, R2, and the Upper quartile for TEWL, MI, EI, b* are the positive groups (Table\u0026nbsp;3). CM\u0026thinsp;\u0026le;\u0026thinsp;38.80 indicates lower hydration, CM\u0026thinsp;\u0026ge;\u0026thinsp;62.60 indicates higher hydration. TEWL\u0026thinsp;\u0026ge;\u0026thinsp;23.20 indicates higher transepidermal water loss, TEWL\u0026thinsp;\u0026le;\u0026thinsp;15.70 indicates lower transepidermal water loss. SM\u0026thinsp;\u0026le;\u0026thinsp;1.00 indicates less sebum secretion, SM\u0026thinsp;\u0026ge;\u0026thinsp;9.00 indicates more sebum secretion. pH\u0026thinsp;\u0026le;\u0026thinsp;5.66 indicates stronger acidity, pH\u0026thinsp;\u0026ge;\u0026thinsp;6.08 indicates weaker acidity. MI\u0026thinsp;\u0026ge;\u0026thinsp;179.50 indicates higher melanin content, MI\u0026thinsp;\u0026le;\u0026thinsp;131.17 indicates lower melanin content. EI\u0026thinsp;\u0026ge;\u0026thinsp;358.17 indicates higher hemoglobin content, EI\u0026thinsp;\u0026le;\u0026thinsp;268.83 indicates lower hemoglobin content. b\u0026thinsp;\u0026ge;\u0026thinsp;12.71 indicates more yellowish skin tone, b\u0026thinsp;\u0026le;\u0026thinsp;9.80 indicates less yellowish skin tone. ITA\u0026thinsp;\u0026le;\u0026thinsp;42.33 indicates darker skin tone, ITA\u0026thinsp;\u0026ge;\u0026thinsp;56.00 indicates brighter skin tone. GLOSS\u0026thinsp;\u0026le;\u0026thinsp;5.42 indicates worse glow, GLOSS\u0026thinsp;\u0026ge;\u0026thinsp;7.10 indicates better glow. R2\u0026thinsp;\u0026le;\u0026thinsp;0.66 indicates lower elasticity, R2\u0026thinsp;\u0026ge;\u0026thinsp;0.82 indicates higher elasticity.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Evaluation of risky lifestyles by crude and adjusted OR\u003c/h2\u003e \u003cp\u003eThe chi-square test and crude ORs were used to initially evaluate the risky and protective lifestyles related to \"unfavorable\" skin parameters (Table A2-A11). Due to the tiny sample sizes (N\u0026thinsp;\u0026lt;\u0026thinsp;10) of the smoking, drinking alcohol, divorced, widowed, pregnant, hysterectomized exposure groups, the effects of these events on skin biophysical parameters were not discussed in this study. The results of the chi-square test showed that allergic histories, mental stress and irritable mood did not have a statistically significant effect on skin biophysical parameters. Then we re-evaluated the risky and protective lifestyles among the other 13 individual lifestyle items for 10 skin biophysical parameters by the logistic regression model and adjusted OR, aiming to control the impact of individual age.\u003c/p\u003e \u003cp\u003eWe used transepidermal water loss, pH, hydration, and sebum content as biophysical parameters to assess the skin barrier. After controlling for age, self-reported skin types of oily (OR\u0026thinsp;=\u0026thinsp;0.099 [95% CI: 0.018, 0.536], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and mixed (OR\u0026thinsp;=\u0026thinsp;0.253 [95% CI: 0.081, 0.793], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) decreased the risk of low sebum secretion to 0.099 times and 0. 253 times, and allergy to food or medicine (OR\u0026thinsp;=\u0026thinsp;0.259 [95% CI: 0.076, 0.888], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) decreased the risk of low sebum secretion to 0.259 times. Self-reporting a skin type of mixed (OR\u0026thinsp;=\u0026thinsp;5.869 [95% CI: 1.452, 23.729], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) increased the risk of high transepidermal water loss to 5.869 times. Use of skincare products (OR\u0026thinsp;=\u0026thinsp;0.275 [95% CI: 0.018, 0.933], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) decreased the risk of low skin hydration content to 0.275 times.\u003c/p\u003e \u003cp\u003eSkin yellowness, individual type angle, melanin content, gloss, and hemoglobin content were used as biophysical parameters characterizing skin tone. Use of skincare products and staying up late were protective factors that decreased the risk of dark skin. First menstruation after age 16, insomnia, and a vegetarian diet were risk factors that increased the risk of dark skin. Women who used sunscreen products had better skin gloss, while those who were menopausal and had children showed poorer skin gloss. For skin melanin content, it was likely to be lower in women using sunscreen products and higher in married, childbearing, and menopausal women. However, these factors did not show statistical significance after controlling for age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). But the risk of higher hemoglobin in women with self-reported oily skin (OR\u0026thinsp;=\u0026thinsp;7.947 [95% CI: 1.760, 35.887], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) was 7.947 times higher than in women with normal skin after controlling for age.\u003c/p\u003e \u003cp\u003eWe evaluated skin elasticity by R2 value. The results showed that being married, having children, and being post-menopausal increased the risk of poorer skin elasticity and were risk factors. Higher frequency of the sunscreen decreased the risk of skin elasticity deterioration and was a protective factor. And the use of sunscreen all year round consistently decreased the risk of poorer skin elasticity to 0.242 times compared with no sunscreen use. However, these factors did not show statistical significance after controlling for age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAs a developing country, China has a large population base of county women. In this study, we measured 10 non-invasive skin parameters of 350 rural female subjects aged 18\u0026ndash;65 years from two counties in eastern China. Then we compared the skin biophysical parameters between rural and urban women and found that:\u003c/p\u003e \u003cp\u003e \u003cb\u003e(Ⅰ) Rural women had lower facial skin hydration level and sebum secretion, higher transepidermal water loss and higher melanin content\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThrough a questionnaire survey of rural women, we found that the u\u003cb\u003es\u003c/b\u003ee of skincare products decreased the risk of lower skin hydration content to 0.275 times. To some extent the lower skincare concern of rural women is accompanied by lower hydration and higher transepidermal water loss. Two investigations from the southwestern United States (Utah[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and Texas[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]) showed that rural subjects underutilized sun protection and were more likely to sunburns than urban subjects, which may explain the higher melanin content of rural women's skin in this study. In addition, we suggest that the significantly lower sebum production in rural women may be due to the non-invasive skin testing period being fall and winter, whereas the higher sebum production in urban women may be due to the testing period being summer. It has been proven that the seasonal changes in humidity and temperature are an important factor influencing the skin sebum content[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In general, these are advised for rural women to increase the frequency of skincare products and sunscreens to combat the contribution of ultraviolet rays to melanin accumulation and skin dryness.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(Ⅱ) Urban women had poorer skin gloss, more yellowish skin tone, and poorer elasticity.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere is a consensus that megacities residents usually suffer from huge heat islands and air pollution[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. And the megacities residents also typically face more psychological stress than rural residents due to social inequalities and strained urban resources[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Air pollution contributes to extrinsic skin aging, and mostly derives from the burning of fossil fuels and industrial activity in urban centers. Park SY et al. analyzed the effect of particulate matter exposure on human dermal fibroblasts. It was proved that particulate pollution can induce inflammatory phenotypes in fibroblasts, with increased metalloprotein synthesis, reduced collagen and elastin synthesis in the dermis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This is in agreement with our results of lower skin elasticity in urban women. Several studies have emphasized the aggravation of skin pigmentation and lentigines by air pollution, like particulate matter and nitrogen dioxide[21; 22; 23]. The results of quantitative surveys of Asian women showed a strong association between psychological stress and dull skin[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We suggested that urban women faced relatively higher levels of psychological stress, which contributed to poorer gloss and exhibited dull skin.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(Ⅲ) Woman with self-reported oily or mixed skin types had higher sebum secretion, hemoglobin content and transepidermal water loss.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter controlling for age, the risk of lower sebum secretion in women with self-reported skin types of oily and mixed decreased to 0.099 times and 0. 253 times than in women with normal skin. The risk of higher hemoglobin content in women with self-reported oily skin was 7.947 times higher than in women with normal skin. Self-reported skin type of mixed increased the risk of higher transepidermal water loss to 5.869 times than with normal skin.\u003c/p\u003e \u003cp\u003eA noninvasive skin measurement and clinical scoring study performed by dermatologists found that oily and mixed skin types had higher objective scores for both sebum content and transepidermal water loss compared to normal skin[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which is consistent with our results. However, it has also been shown that there is no difference in TEWL between oily and normal skin[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. But considering the wider age range and the use of age control models in our study, we agree more with the relationship between TEWL and oily and mixed skin types.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study analyzed the facial skin aging trends and influence of lifestyles among the rural and urban females. Longitudinally, urban and rural females all experience significant adverse changes in skin biophysical parameters with age. Horizontally, rural women had lower facial skin hydration level and sebum secretion, higher transepidermal water loss and higher melanin content. Urban women had poorer skin gloss, more yellowish skin tone, and poorer elasticity.\u003c/p\u003e \u003cp\u003eMoreover, self-reported oily or mixed skin associated with high sebum, hemoglobin content and transepidermal water loss. Skincare product use was a protective factor for skin hydration. Sunscreen product use was a protective factor for some parameters, but was not statistically significant after controlling for age. Rural women are advised to increase the frequency of skincare products and sunscreens to combat the melanin accumulation and skin dryness. And urban women are advised to emphasize the adverse impact of environmental pollution, stay-up late and stress on skin elasticity and tone.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding source.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare that they have no conflict of interest relevant to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and informed consent was obtained from all participants, and ethical approval was obtained from the Ethics Committee of Beijing University of Chinese Medicine (reference 2017BZHYLL0501, 1\u003csup\u003est\u003c/sup\u003e May, 2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026mdash;our manuscript does not contain data from any individual person.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiao-xiao YANG, Hao-chen Zhang and Xao-dong HUANG. The first draft of the manuscript was written by Xiao-xiao YANG. In addition, Yue WU, Fan YI, Hong MENG and Yin-mao Dong commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eS.H. Shin, Y.H. Lee, N.-K. Rho, and K.Y. Park, Skin aging from mechanisms to interventions: focusing on dermal aging. Front Physiol 14 (2023) 1195272.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphys.2023.1195272\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2023.1195272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF. Papaccio, A. D Arino, S. Caputo, and B. 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J Invest Dermatol 136 (2016) 1053\u0026ndash;1056.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jid.2015.12.045\u003c/span\u003e\u003cspan address=\"10.1016/j.jid.2015.12.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS.S. Anwar, M.A. Apolinar, and L. Ma, Perception, understanding, and association between psychological stress and skin aging: Quantitative surveys of Asian women aged 18\u0026ndash;34 years, dermatologists, and psychologists in China and Japan. J Cosmet Dermatol 22 (2023) 2297\u0026ndash;2307.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jocd.15732\u003c/span\u003e\u003cspan address=\"10.1111/jocd.15732\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD.G. Mercurio, J.H. Segura, M.B. Demets, and P.M. Maia Campos, Clinical scoring and instrumental analysis to evaluate skin types. Clin Exp Dermatol 38 (2013) 302-8; quiz 308-9.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ced.12105\u003c/span\u003e\u003cspan address=\"10.1111/ced.12105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM.O. de Melo, and P. Maia Campos, Characterization of oily mature skin by biophysical and skin imaging techniques. Skin Res Technol 24 (2018) 386\u0026ndash;395.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/srt.12441\u003c/span\u003e\u003cspan address=\"10.1111/srt.12441\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Correlation and fitting equations between skin biophysical parameters and age\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"556\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkin parameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.855855855855856%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.594594594594595%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.288288288288285%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolynomial Fitting Function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.927927927927928%\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003eCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.855855855855856%\"\u003e\n \u003cp\u003e55.13\u0026plusmn;18.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.594594594594595%\"\u003e\n \u003cp\u003e－0.191\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.288288288288285%\"\u003e\n \u003cp\u003ef (x) = 0.003749x\u003csup\u003e2\u003c/sup\u003e－0.6411x＋74.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.927927927927928%\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003eTEWL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.855855855855856%\"\u003e\n \u003cp\u003e20.61\u0026plusmn;7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.594594594594595%\"\u003e\n \u003cp\u003e－0.352\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.288288288288285%\"\u003e\n \u003cp\u003ef (x) = 0.003889x\u003csup\u003e2\u003c/sup\u003e－0.5202x＋34.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.927927927927928%\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.855855855855856%\"\u003e\n \u003cp\u003e8.11\u0026plusmn;11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.594594594594595%\"\u003e\n \u003cp\u003e－0.350\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.288288288288285%\"\u003e\n \u003cp\u003ef (x) = 0.002773x\u003csup\u003e2\u003c/sup\u003e－0.5699x＋25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.927927927927928%\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.855855855855856%\"\u003e\n \u003cp\u003e6.00\u0026plusmn;0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.594594594594595%\"\u003e\n \u003cp\u003e－0.261\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.288288288288285%\"\u003e\n \u003cp\u003ef (x) = 0.000646x\u003csup\u003e2\u003c/sup\u003e－0.06247x＋7.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.927927927927928%\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003eMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.855855855855856%\"\u003e\n \u003cp\u003e160.44\u0026plusmn;40.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.594594594594595%\"\u003e\n \u003cp\u003e0.210\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.288288288288285%\"\u003e\n \u003cp\u003ef (x) = 0.05211x\u003csup\u003e2\u003c/sup\u003e－3.403x＋209.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.927927927927928%\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003eEI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.855855855855856%\"\u003e\n \u003cp\u003e315.51\u0026plusmn;63.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.594594594594595%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.288288288288285%\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.927927927927928%\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003eb*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.855855855855856%\"\u003e\n \u003cp\u003e11.93\u0026plusmn;2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.594594594594595%\"\u003e\n \u003cp\u003e0.439\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.288288288288285%\"\u003e\n \u003cp\u003ef (x) = 0.002433x\u003csup\u003e2\u003c/sup\u003e－0.1096x＋12.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.927927927927928%\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003eITA\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.855855855855856%\"\u003e\n \u003cp\u003e47.30\u0026plusmn;10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.594594594594595%\"\u003e\n \u003cp\u003e－0.527\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.288288288288285%\"\u003e\n \u003cp\u003ef (x) =\u0026nbsp;－0.005191x\u003csup\u003e2\u003c/sup\u003e－0.02877x＋56.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.927927927927928%\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003eGLOSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.855855855855856%\"\u003e\n \u003cp\u003e6.49\u0026plusmn;1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.594594594594595%\"\u003e\n \u003cp\u003e－0.171\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.288288288288285%\"\u003e\n \u003cp\u003ef (x) = 0.0006803x\u003csup\u003e2\u003c/sup\u003e－0.09475x＋9.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.927927927927928%\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.72\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.594594594594595%\"\u003e\n \u003cp\u003e－0.305\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.288288288288285%\"\u003e\n \u003cp\u003ef (x) =\u0026nbsp;－0.0000459x\u003csup\u003e2\u003c/sup\u003e－0.0003175x＋0.7884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.927927927927928%\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026ldquo;***\u0026rdquo; indicates \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, \u0026ldquo;**\u0026rdquo; indicates \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.010.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 The comparison of skin biophysical properties between rural and urban females\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"504\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.452380952380953%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.388888888888886%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRF vs. UR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.880952380952381%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.775193798449614%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban females (UF)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.224806201550386%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural females (RF)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48906560636183%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.043737574552683%\"\u003e\n \u003cp\u003e38.93 \u0026plusmn; 12.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24850894632207%\"\u003e\n \u003cp\u003e39.03 \u0026plusmn; 11.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.910536779324056%\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48906560636183%\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.043737574552683%\"\u003e\n \u003cp\u003e6.01 \u0026plusmn; 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24850894632207%\"\u003e\n \u003cp\u003e6.00 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.910536779324056%\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48906560636183%\"\u003e\n \u003cp\u003eCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.043737574552683%\"\u003e\n \u003cp\u003e60.36 \u0026plusmn; 11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24850894632207%\"\u003e\n \u003cp\u003e55.13 \u0026plusmn; 18.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.910536779324056%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48906560636183%\"\u003e\n \u003cp\u003eTEWL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.043737574552683%\"\u003e\n \u003cp\u003e15.79 \u0026plusmn; 4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24850894632207%\"\u003e\n \u003cp\u003e20.61 \u0026plusmn; 7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.910536779324056%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48906560636183%\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.043737574552683%\"\u003e\n \u003cp\u003e42.29 \u0026plusmn; 31.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24850894632207%\"\u003e\n \u003cp\u003e8.11 \u0026plusmn; 11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.910536779324056%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48906560636183%\"\u003e\n \u003cp\u003eEI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.043737574552683%\"\u003e\n \u003cp\u003e325.15 \u0026plusmn; 58.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24850894632207%\"\u003e\n \u003cp\u003e315.51 \u0026plusmn; 63.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.910536779324056%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48906560636183%\"\u003e\n \u003cp\u003eMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.043737574552683%\"\u003e\n \u003cp\u003e142.63 \u0026plusmn; 31.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24850894632207%\"\u003e\n \u003cp\u003e160.44 \u0026plusmn; 40.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.910536779324056%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48906560636183%\"\u003e\n \u003cp\u003eb*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.043737574552683%\"\u003e\n \u003cp\u003e12.95 \u0026plusmn; 2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24850894632207%\"\u003e\n \u003cp\u003e11.93 \u0026plusmn; 2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.910536779324056%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48906560636183%\"\u003e\n \u003cp\u003eITA\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.043737574552683%\"\u003e\n \u003cp\u003e40.62 \u0026plusmn; 8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24850894632207%\"\u003e\n \u003cp\u003e47.30 \u0026plusmn; 10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.910536779324056%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48906560636183%\"\u003e\n \u003cp\u003eGLOSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.043737574552683%\"\u003e\n \u003cp\u003e4.20 \u0026plusmn; 1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24850894632207%\"\u003e\n \u003cp\u003e6.49 \u0026plusmn; 1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.910536779324056%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48906560636183%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.043737574552683%\"\u003e\n \u003cp\u003e0.61 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24850894632207%\"\u003e\n \u003cp\u003e0.72 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.910536779324056%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 The lower and upper quartile values of the biophysical parameters\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"457\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.973799126637555%\" rowspan=\"2\" style=\"width: 10.8683%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.01310043668122%\" colspan=\"3\" style=\"width: 40.7131%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower quartile (\u0026le;25%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.01310043668122%\" colspan=\"4\" style=\"width: 22.3542%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper quartile (\u0026ge;75%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.508951406649617%\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.18158567774936%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin (Q4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.18158567774936%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax (Q3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.508951406649617%\" colspan=\"2\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.18158567774936%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin (Q2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.18158567774936%\" style=\"width: 7.7631%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax (Q1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\" style=\"width: 10.8683%;\"\u003e\n \u003cp\u003eCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e8.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e38.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" colspan=\"2\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e62.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 7.7631%;\"\u003e\n \u003cp\u003e88.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\" style=\"width: 10.8683%;\"\u003e\n \u003cp\u003eTEWL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e15.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" colspan=\"2\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e23.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 7.7631%;\"\u003e\n \u003cp\u003e34.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\" style=\"width: 10.8683%;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" colspan=\"2\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 7.7631%;\"\u003e\n \u003cp\u003e21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\" style=\"width: 10.8683%;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" colspan=\"2\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 7.7631%;\"\u003e\n \u003cp\u003e6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\" style=\"width: 10.8683%;\"\u003e\n \u003cp\u003eMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e71.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e131.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" colspan=\"2\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e178.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 7.7631%;\"\u003e\n \u003cp\u003e238.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\" style=\"width: 10.8683%;\"\u003e\n \u003cp\u003eEI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e158.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e269.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" colspan=\"2\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e358.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 7.7631%;\"\u003e\n 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style=\"width: 10.8683%;\"\u003e\n \u003cp\u003eGLOSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e5.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\" colspan=\"2\" style=\"width: 8.2806%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 14.1461%;\"\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\" style=\"width: 7.7631%;\"\u003e\n \u003cp\u003e9.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\" style=\"width: 10.8683%;\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n 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\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urban and rural comparison, Logistic regression model, Polynomial fitting, Lifestyle habits, Odds ratio","lastPublishedDoi":"10.21203/rs.3.rs-3874675/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3874675/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs a developing country, China has a large population base of county women, but previous studies showed a significant lack of attention towards their skin status. This study is supposed to recruit, measure and portray the facial skin biophysical properties of rural females. The individual lifestyles were also investigated and analyzed to assess the risky or protective factors. There were 10 skin parameters measured of 350 rural female aged 18\u0026ndash;65 years subjects (skin barrier status, color and elasticity). The trend of subject's skin parameters with age was analyzed through curve fitting, analysis of variance and Krustal-Wallis H test, with 0.05 as the significance threshold. The lifestyles related to \"unfavorable\" skin parameters were initially evaluated by chi-square test and crude OR, and then re-evaluated by logistic regression model and adjusted OR to control age. Rural females experience significant adverse changes in skin biophysical parameters with age, and they had lower facial skin hydration level and sebum secretion, higher transepidermal water loss and higher melanin content. Urban women had poorer skin gloss, more yellowish skin tone, and poorer elasticity. Self-reported oily or mixed skin was associated with high sebum and hemoglobin content. Skincare product use was a significant protective factor for skin hydration. Sunscreen product use was a protective factor for a number of skin biophysical parameters, but was not statistically significant after controlling for age. In conclusion, distinct skincare measures are recommended for rural and urban women based on their different facial skin biophysical properties and trends with age.\u003c/p\u003e","manuscriptTitle":"The Relationship of Facial Skin Biophysical Properties to Age and the Potential Role of Lifestyles in Chinese Rural and Urban Females","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-22 18:46:46","doi":"10.21203/rs.3.rs-3874675/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1fd7c9e1-3ae7-4693-a5b4-3679a02ccb7a","owner":[],"postedDate":"January 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-06T23:03:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-22 18:46:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3874675","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3874675","identity":"rs-3874675","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}