Striae gravidarum in the Han Chinese pregnant population: Identifying genetic markers and risk factors through a prospective cohort study

Striae gravidarum in the Han Chinese pregnant population: Identifying genetic markers and risk factors through a prospective cohort study | 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 Striae gravidarum in the Han Chinese pregnant population: Identifying genetic markers and risk factors through a prospective cohort study Lidan Xiong, Lifeng Yang, Hailun He, Jianguo Chen, Yinshu Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4435203/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Oct, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted 4 You are reading this latest preprint version Abstract Background Striae gravidarum (SG), commonly known as stretch marks, are a frequent connective tissue alteration observed in pregnant women. Postpartum women may feel damaged in their self-image due to SG which can lead to lower self-esteem and emotional problems such as anxiety and depression. The study aimed to evaluate the potential risk factors and genetic associations of SG in a Chinese Han population. Methods A multicenter trial was conducted involving 1017 pregnant women of Chinese Han descent who provided informed consent. Participants completed questionnaires regarding demographics, medical history, and lifestyle factors. Anthropometric measurements and obstetric data were gathered, followed by a genome-wide association study (GWAS). Results The study found that 59% of participants experienced SG. Significant correlations were observed between SG and factors including age, pre-pregnancy weight, maximum pregnancy weight during pregnancy, BMI before and during pregnancy, and maximum abdomen girth. Risk factors for SG included a positive family history, prior experience of striae distensae during adolescence, and specific skin types according to the Fitzpatrick classification. Multivariable logistic regression analysis indicated that age, family history, history of striae distensae, skin types, and pre-pregnancy BMI were notable predictors of SG. The GWAS identified several single nucleotide polymorphisms (SNPs) related to SG presence and severity, implicating genes such as FGF12, RAB38, MUC16, PTPRT, SIPA1L2, PPARGC1A, PTPRD, and ELOVL3. Conclusion The study presents a predictive model for SG risk that includes non-modifiable factors like family history and skin type, and modifiable factors such as pre-pregnancy weight and BMI. The findings provide insights into the genetic basis of SG and may aid in counseling patients on risk reduction strategies. The identified genetic variants offer potential targets for future research into the pathogenesis and prevention of SG. Striae gravidarum Genome-wide association study Risk factors Chinese Han Population Figures Figure 1 Figure 2 Introduction Striae gravidarum (SG), also known as stretch marks or striae distensae, is a common connective tissue change observed in pregnant women[ 1 ]. The prevalence of striae distensae ranges from 11–90% [ 2 – 6 ], while in pregnant women the incident rate ranges from 75–90% during the third trimester [ 7 ]. Initially presenting as flesh-toned atrophic linear plaques with erythema, SG progress to silvery-hypopigmented flesh-toned, atrophic plaques[ 8 ]. SG are mostly localized on the buttocks, lower back, thighs, calves, breasts/chest, abdomen, upper arms, and knees [ 7 ]. The presence of striae gravidarum did not significantly impact the generic quality of life during pregnancy. However, it was associated with reduced overall quality of life and skin quality index among postpartum women [ 9 , 10 ]. Postpartum women experiencing stretch marks may struggle with a negative self-perception, potentially resulting in reduced self-confidence and emotional challenges like anxiety and depression [ 10 ]. Histopathologically, early SG exhibits significant separation of collagen bundles and the appearance of disorganized collagen fibrils that do not form bundles [ 11 ]. Furthermore, there is a substantial disruption in the elastic fiber network, characterized by the emergence of newly synthesized tropoelastin-rich fibrils possibly arising from the irregular synthesis of elastic fiber components [ 12 ]. Given their thin and disorganized nature, it is likely that tropoelastin-rich fibrils do not function in the same way as normal elastic fibers [ 13 ]. These findings lay the groundwork for understanding the pathogenic mechanisms underlying the development of laxity in SG. A cohort study conducted on individuals of European descent revealed a significant association between the SNP rs7787362 (P = 1.8e-23, OR = 0.84) located 40kb upstream of the ELN (elastin) gene and the presence of stretch marks [ 14 ]. Furthermore, genes such as SRPX, HMCN1, TMEM18, PNPLA1, FN1, and NPIPL2 were found to be associated with striae gravidarum in the pregnancy cohort [ 14 ]. The etiology of SG in Han population remains poorly elucidated. While numerous studies have been conducted on the risk factors and treatment of SG, studies focusing on Asian populations are limited and sample sizes are sparse. In this study, we recruited 1017 pregnant women to investigate potential risk factors and performed a genome-wide association analysis of SG in a discovery cohort comprising 1017 cases of Chinese Han descent. Methods Study setting and population The study was conducted as multicenter trial including the obstetrics department of Sichuan University West China Second University Hospital, Chengdu second people’s Hospital, the third people’s hospital of Chengdu, Affiliated Hospital of North Sichuan Medical college and Nanjing Maternity and Child Health Care Hospital, Medical cosmetology department of the first people’s hospital of Changde city, and gynecology department of Songyang group Awake clinic after obtaining institutional review board approval in Sichuan University West China Hospital. The clinical trial is registered under the number CTR2300077737 and was registered on November 17, 2023. Data collection and outcomes All study participants in this research provided informed consent through an online platform, with documentation stored in an electronic database. Data collection spanned a period of 4 months, during which patients from the inpatient department, Clinic, and outpatient department who had recently given birth at term were invited to participate, irrespective of their parity status. To ensure a homogenous ethnic composition, only Chinese Han women were included in the study. A total of 1017 patients from the clinic and outpatient department met the eligibility criteria and agreed to participate in the study. The questionnaires, specifically designed for the study, were completed by the female participants during their medical evaluations. The survey included inquiries about various factors such as level of education background, occupation, income level, family medical history, usage of moisturizing cream, oral or topical corticosteroid use, history of seborrheic dermatitis during adolescence, physical activity routines, smoking habits, alcohol intake, and dietary patterns. Data were collected from the medical charts: age (y), height (cm), pre-pregnancy weight (kg), maximum weight during pregnancy (kg), weight gain during pregnancy (kg), pre-pregnancy BMI (kg/m 2 ), maximum BMI during pregnancy (kg/m 2 ), BMI gain during pregnancy (kg/m 2 ), maximum abdomen girth (cm), maximum uterine height (cm), birth weight (kg), birth length (cm), thyroid disease, gestational diabetes mellitus, polyhydramnios, Number of fetuses, maternal premature infants, delivery gestational age and fetal sex. The data obtained from the physician's assessment included Fitzpatrick skin types I-IV, skin types as determined by the Huaxi self-assessment questionnaires (Huaxi SSQ), and the severity of SG. The Huaxi SSQ, a recently developed tool by West China Hospital of Sichuan University, demonstrates greater reliability and validity among Chinese individuals compared to the Bauman SSQ. Genotype data The samples were sent to WeGene for testing, a platform that has demonstrated efficacy in genome-wide association studies (GWAS) across a range of phenotypes. Saliva samples from participants were obtained and genotyped using WeGene Arrays. Data cleaning for GWAS involved removing individuals with missing key variables, phenotypic outliers exceeding 3 standard deviations from the mean, and abnormal demographic information, such as individuals over the age of 100. The selection process was determined by the GWAS p-value of the training set data within a 500kb region, specifically targeting a P-value < 10e-5 at the leading SNP. Genotypes associated with statistical variances in the European population based on the presence or absence of SG were identified. Then, the top 200 P-values < 10e-5 was selected. Subsequently, the leading SNP at 10e-5 was chosen based on severity. Statistical analyses 1017 questionnaires were collected, and the results were statistically analysed using the IBM SPSS Statistics R26.0.0.0. The comparison of women with SG vs. those without SG was made by performing student's t-test and chi-square test. Multivariable logistic regression analysis was applied to obtain odds ratios for variables with an independent association with striae. Results Descriptive analysis In this study, a total of 1017 pregnant women participated in the questionnaire survey. While some data may be incomplete, a significant portion has been recorded. Among the participants, 602 pregnant women experienced SG, resulting in a morbidity rate of 59% which exceeds half of the pregnant women surveyed. In addition to the abdomen, similar skin lesions were observed in other regions such as the breast, thigh, and buttock (Figure 1). Specifically, 458 women exhibited SG solely on the abdomen, while the remaining participants had SG on the breast (78), thigh (150), and buttock (73). 303 pregnant women with SG experienced varying degrees of pruritus during pregnancy, with some cases being severe enough to result in eczematoid changes. Of these women, 599 expressed concerns about the aesthetic impact of SG and expressed a desire to eliminate them. Association between continuous factors and SG Various continuous factors were compared between pregnant women with gestational diabetes and those without gestational diabetes, including age, height, weight before pregnancy, maximum weight during pregnancy, weight gain during pregnancy, BMI before pregnancy, maximum BMI during pregnancy, BMI gain during pregnancy, maximum abdomen girth, maximum uterine height, birth weight, and birth length. Except for maternal height, all other factors exhibited a statistically significant difference with P＜0.05. It can be inferred that factors such as age, pre-pregnancy weight, maximum weight during pregnancy, weight gain during pregnancy, pre-pregnancy BMI, maximum BMI during pregnancy, BMI gain during pregnancy, maximum abdomen girth, maximum uterine height, birth weight, and birth length are significant in the occurrence of SG (Table 1). Table 1. The analysis of prevalence and clinical characteristics of primipara with and without SG (continuous factors) Variates Non-SG SG t P Age (y) 29.34±0.43（418） 27.88±0.29（586） 5.543 ＜0.001 Heigh (cm) 159.99±0.44（418） 160.12±0.66（594） -0.289 0.772 Weight before pregnancy (kg) 51.57±0.68（327） 54.23±0.70（435） -5.360 ＜0.001 Max weight during pregnancy (kg) 65.05±0.83（331） 69.48±0.79（441） -7.496 ＜0.001 Weight gain during pregnancy (kg) BMI before pregnancy (kg/m 2 ) Max BMI during pregnancy (kg/m2) BMI gain during pregnancy (kg/m2) Max abdomen girth (cm) 13.35±0.45（326） 20.11±0.24（327） 25.36±0.29（331） 5.21±1.83（326） 98.47±0.88（301） 15.18±0.47（435） 21.05±0.25（434） 26.97±0.29（440） 5.90±1.87（434） 101.59±0.54（411） -5.208 -5.340 -7.608 -5.003 -6.244 ＜0.001 ＜0.001 ＜0.001 ＜0.001 ＜0.001 Max uterine height (cm) 32.72±0.32（301） 33.94±0.48（410） -6.104 ＜0.001 Birth weight (kg) Birth length (cm) 3.22±0.06（372） 49.10±0.35（354） 3.36±0.05（513） 49.69±0.27（484） -3.382 -2.519 0.001 0.012 The datas were manifested by mean ± standard deviation. SG, striae gravidarum; non-SG, without striae gravidarum; BMI, body mass index. BMI = weight (kg) / height 2 (m 2 ). Statistically significant, P < 0.05. Association between categorical factors and SG Similarly, categorical factors such as level of education, occupation, income level, family medical history, use of moisturizing cream, presence of thyroid disease, use of corticosteroids, history of gestational diabetes mellitus, presence of polyhydramnios, occurrence of striae distensae, level of physical activity, smoking and alcohol consumption habits, dietary patterns, number of previous pregnancies, history of maternal premature birth, gestational age at delivery, fetal sex, Fitzpatrick skin type, and skin type according to the Huaxi SSQ are being examined to discern differences between pregnant women with SG and non-SG. Striae distensae refer to the presence of red striae on the thighs, abdomen, or other areas of the body during the teenage years in women. Huaxi SSQ categorizes individuals based on their skin type, which can be classified as oily, neutral, dry, combination, or sensitive classifications. The results of the study indicate that only family medical (or health) history, the presence of striae distensae, and skin type show a statistically significant difference (P＜0.05) in their contribution to the appearance of SG (Table 2). Table 2. The analysis of prevalence and clinical characteristics of primipara with and without SD (categorical factors) Variates Non-SG SG χ 2 P Degree of education Bachelor degree or less 71 106 0.117 0.732 Bachelor degree or above 347 489 Occupation Physical activity 71 112 0.560 0.454 Mental activity 347 483 Income level ＜5000 129 186 0.018 0.892 ＞5000 289 409 Family history No 313 193 152.12 ＜0.001 Yes 66 280 Moisturizing cream No 224 234 20.157 ＜0.001 Yes 194 361 Thyroid disease No 361 537 3.689 0.055 Yes 57 58 Corticosteroid No 416 581 5.549 0.018 Yes 2 14 Gestational diabetes mellitus No 369 518 0.195 0.659 Yes 49 75 Polyhydramnios No 404 570 0.195 0.659 Yes 14 23 Striae distensae No 360 309 127.985 ＜0.001 Yes 58 286 Amount of exercise ＜1h 154 201 0.934 0.334 ＞1h 264 392 Smoking No 413 585 0.45 0.832 Yes 5 8 Alcohol drinking No 405 572 0.140 0.708 Yes 13 21 Dietary habit omnivore 323 484 2.513 0.113 dietary bias 95 111 The number of fetal Single 402 563 0.856 0.355 Double 16 30 Maternal premature infant No 401 577 0.799 0.371 Yes 17 18 Delivery gestational age ＜37 weeks 51 44 5.941 0.015 ＞37 weeks 361 526 fetal sex Male 190 272 0.329 0.566 Famle 185 245 Fitzpatrick skin type Easy to redden (Ⅰ~Ⅱ) 102 252 35.515 ＜0.001 Easy to tan (Ⅲ~Ⅳ) 316 340 Skin types (Huaxi SSQ) oily / neutral skin 188 208 9.955 0.002 Dry/ combination/ sensitive skin 230 384 SG, striae gravidarum; non-SG, without striae gravidarum. Skin types 1, Fitzpatrick. Skin types 2, common standard according to the degree of oil or dryness. Statistically significant, P < 0.05. Multiple logistic regression analyses The factors which selected by student’s t test and chi-square test are engaged in the logistic regressive. Age, family health history, striae distensae, Fitzpatrick skin type , skin types (Huaxi SSQ), max abdomen girth, BMI before pregnancy and BMI gain during pregnancy are considered to be significant risk factors of SG. In continuous factors, while age is the negative correlation with SG, the others are positive correlation with SG. In categorical factors, positive family history, striae distensae at teen years, skin flushed after sunshine and dry, mixed, sensitive skin are more likely to suffer from SG at pregnancy. The model can correctly identify 76.6% of the objects (Table 3). Table 3. Risk factors for SG of primipara in multiple logistic regression analysis Variates Β SE Wald P OR 95%CI Age -0.68 0.20 11.546 0.001 0.934 0.898~0.972 Family history Striae distensae -1.883 -1.447 0.183 0.184 106.438 62.048 ＜0.001 ＜0.001 0.152 0.235 0.106~0.218 0.164~0.337 Fitzpatrick skin type 0.673 0.169 15.799 ＜0.001 1.961 1.407~2.733 Skin types (Huaxi SSQ) -0.405 0.162 6.278 0.012 0.667 0.486~0.916 Max abdomen girth BMI before pregnancy BMI gain during pregnancy 0.065 0.068 0.118 0.21 0.35 0.44 9.076 3.886 7.096 0.003 0.049 0.008 1.067 1.071 1.126 1.023~1.113 1.000~1.146 1.032~1.228 SE, standard error; OR, odds ratio; CI, confidence interval; BMI, body mass index. Skin types 1, Fitzpatrick. Skin types 2, common standard according to the degree of oil or dryness. Statistically significant, P < 0.05. Due to challenges in accurately measuring maximum abdomen girth and BMI gain during pregnancy prior to conception, these factors were excluded from the predictive model for gestational diabetes. Following adjustments, variables such as age, family history, presence of striae distensae, Fitzpatrick skin type and Skin types (Huaxi SSQ), and pre-pregnancy BMI were retained. Statistical analysis revealed a significant difference in the model (χ2 = 278.983, P＜0.001), with the new model correctly identifying 74.5% of cases (Table 4). In addition, the comparison between primipara and multipara was shown in the supplementary material. Table 4. Risk factors for SG of primipara in multiple logistic regression analysis ( After adjustment) Variates Β SE Wald P OR 95%CI Age -0.059 0.021 7.485 0.006 0.943 0.904~0.983 Family history Striae distensae -1.828 -1.573 0.180 0.199 102.863 62.465 ＜0.001 ＜0.001 0.161 0.207 0.113~0.229 0.140~0.306 Fitzpatrick skin type 0.740 0.181 16.794 ＜0.001 2.097 1.472~2.988 Skin types (Huaxi SSQ) -0.366 0.173 4.455 0.035 0.694 0.494~0.974 BMI before pregnancy 0.086 0.040 4.600 0.032 1.090 1.007~1.180 SE, standard error; OR, odds ratio; CI, confidence interval; BMI, body mass index. Skin types 1, Fitzpatrick. Skin types 2, common standard according to the degree of oil or dryness. Statistically significant, P < 0.05. Association between SNP and SG Genotypes associated with statistical variances in the European population based on the presence or absence of SG were identified in supplementary Table S3[14]. In our research, we conducted a genome-wide association analysis of SG in a discovery cohort of our 1017 cases of Chinese Han descent. We attempted to identify genome-wide significant association; however, no region exhibited a strong correlation with SG (Figure 2). According to the presence or absence of SG, there are five regions related to four genes significantly associated with striae gravidarum (Table 5). The first region, rs2366666 (P＜5.38E-05), lies in the FGF12 (fibroblast growth factor 12) gene. The second association, rs7117666 (P＜1.32E-05), lies 25kb upstream of the RAB38 (member RAS oncogene family) gene. The third association, rs12052152 (P＜5.33E-05), lies in the MUC16 (mucin 16, cell surface associated) gene. The fourth association, rs77013023 (P＜5.08E-05), lies in the PTPRT (protein tyrosine phosphatase receptor type T) gene. Table 5. Index SNPs related to dermatology in top 200 classified according to the presence or absence of SG SNPs CHR BP Gene A1 BETA SE L95 U95 P rs2366666 3 192115018 FGF12 A 1.562 0.1104 1.258 1.939 5.38E-05 rs7117666 11 87821219 RAB38(u) C 1.552 0.1009 1.274 1.892 1.32E-05 - 11 87823940 C 1.545 0.1011 1.268 1.884 1.66E-05 rs12052152 19 9114906 MUC16 G 0.6325 0.1134 0.5065 0.7899 5.33E-05 rs77013023 20 41889504 PTPRT C 0.4721 0.1852 0.3284 0.6788 5.08E-05 CHR, Chromosome; BP, position; Gene is gene that is the most likely candidate for the association or the association or the closest gene. Whether the single-nucleotide polymorphism (SNP) is upstream (u), downstream (d), or within (i) the gene is indicated in parentheses; Statistically significant, P < E-05 According to the severity of SG, there are twelve regions related to four genes significantly associated with striae gravidarum (Table 6). The first region, rs75963530 (P＜1.52E-05), lies in the SIPA1L2 (signal induced proliferation associated 1 like 2) gene. The second association, rs184656123 (P＜6.42E-06), lies in the PPARGC1A (PPARG coactivator 1 alpha) gene and rs61261762 (P＜7.56E-06), lies in the PPARGC1A (PPARG coactivator 1 alpha) gene. The third association, rs28705153 (P＜5.46E-08), rs10959038 (P＜1.67E-07), rs592203 (P＜1.67E-07), rs10959044 (P＜6.48E-07), rs10959021 (P＜1.71E-05) and rs57211494 (P＜1.83E-05) all lies in the PTPRD (protein tyrosine phosphatase receptor type D) gene. The fourth association, rs57630487 (P＜1.26E-05), rs4523633 (P＜1.80E-05), and rs140247130 (P＜1.80E-05) lies 13kb, 6kb and 1kb upstream of the ELOVL3 (ELOVL fatty acid elongase 3) gene, respectively. Table 6. Index SNPs related to dermatology in top 200 classified according to the severity of SG SNPs CHR BP Gene A1 BETA SE L95 U95 P rs75963530 1 232605835 SIPA1L2 T 0.4333 0.09963 0.2381 0.6286 1.52E-05 rs184656123 4 23846465 PPARGC1A C 0.766 0.1688 0.4352 1.097 6.42E-06 rs61261762 4 23845240 A 0.747 0.1659 0.4218 1.072 7.56E-06 rs28705153 9 10328041 PTPRD T 0.4652 0.08487 0.2989 0.6316 5.46E-08 rs10959038 9 10308785 A 0.4552 0.08631 0.286 0.6243 1.67E-07 rs592203 9 10310358 A 0.4552 0.08631 0.286 0.6243 1.67E-07 rs10959044 9 10324516 A 0.4105 0.08191 0.2499 0.571 6.48E-07 rs10959021 9 10285493 C 0.3179 0.07354 0.1737 0.462 1.71E-05 rs57211494 9 8943040 G -0.1843 0.04278 -0.2681 -0.1004 1.83E-05 rs57630487 10 103972747 ELOVL3 C 0.2295 0.05227 0.1271 0.332 1.26E-05 rs4523633 10 103979285 A 0.2177 0.05049 0.1187 0.3167 1.80E-05 rs140247130 10 103984610 T 0.2177 0.05049 0.1187 0.3167 1.80E-05 CHR, Chromosome; BP, position; Gene is gene that is the most likely candidate for the association or the association or the closest gene. Whether the single-nucleotide polymorphism (SNP) is upstream (u), downstream (d), or within (i) the gene is indicated in parentheses; Statistically significant, P < E-05 Discussion Our study focused on Chinese Han women, showing that SG is common with a prevalence of over 59% primarily on the abdomen. This aligns with findings from prior studies[ 15 ]. Also, it was disclosed that SG has a predilection to the abdomen and the thighs and/or breasts [ 15 ]. The average age of pregnant woman with SG is 27.88 ± 0.29, whereas the average age of pregnant woman with non-SG is 29.34 ± 0.43, which is consistent with the findings of the studies reporting that younger women were more likely to develop SG [ 16 – 19 ].Researchers have found that weight and BMI before and during pregnancy, as well as factors like max abdomen girth and uterine height, were significantly higher in patients who developed stretch marks during pregnancy[ 15 , 18 , 20 – 22 ]. These factors are all interrelated and affect the degree of skin stretching. Additional factors that were found to be significant in the development of striae gravidarum included family history, pre-existing striae distensae, Fitzpatrick skin types, and skin types (Huaxi SSQ). Family history was shown to have a substantial impact on the occurrence of striae gravidarum, increasing the risk by over fourfold, aligning with previous research[ 15 , 17 , 22 ]. In our research, we refined the relationship between the two skin types and striae gravidarum. We found that individuals with skin easy to redden (Ⅰ~Ⅱ) were more likely to appear SG comparing with those with skin easy to tan (Ⅲ~Ⅳ). In addition, dry/combination/ sensitive skin types have more tendency to occur SG compared with oily/neutral skin. Furthermore, other potential factors such as education, occupation, medical history, lifestyle habits, and pregnancy details were also examined, but none were found to be linked to SG. Moisturizing cream application on the abdomen did not prevent stretch marks in our study, consistent with previous research[ 23 – 25 ].The study did not investigate the link between hormone levels and stretch mark occurrence. Striae gravidarum is a complex polygenetic associated manifestation. The results of GWAS would further reveal the possible genes that regulate and control the occurrence of striae gravidarum. GWAS can identify genes that influence its occurrence, such as ELN, SRPX, HMCN1, and TMEM18[ 14 ]. Only rs7787362 (ELN) and rs10798036 (HMCN1) have been verified through our collected cases. Elastic fibers provide recoil to tissues that undergo repeated deformation, such as blood vessels, lungs, and skin. Composed of elastin and its accessory proteins, the fibers are produced within a restricted developmental window and are stable for decades. Their eventual breakdown is associated with a loss of tissue resiliency and aging [ 26 ]. Rs10798036 in the HMCN1(hemicentin-1) gene is linked to age-related macular degeneration, but it remains unclear how this gene might be connected to the risk of developing stretch marks.[ 14 ]. We identified potential skin-related genes in the top 200 loci from our GWAS results and analyzed them based on the presence or absence of SG and the severity of SG. Concerning the presence or absence of SG, FGF12, RAB38(u), MUC16 (mucin 16), and PTPRT(protein tyrosine phosphatase receptor type T) are obtained from the meaningful SNPs. This gene encodes a protein in the fibroblast growth factor (FGF) family, known for its role in cell growth, broad mitogenic and cell survival activities [ 27 – 30 ]. RAB38 is mainly expressed in the skin and has been linked to diseases like pancreatic cancer and glioma but has not been well studied in dermatology[ 31 – 32 ]. MUC16 encodes a membrane-tethered mucin protein with extracellular, tandem repeat, and transmembrane domains. It is part of the mucin family, which helps form a protective mucous barrier on epithelial surfaces[ 33 – 36 ]. PTPRT is a signaling molecule that regulates cellular processes. It has an extracellular region, a transmembrane region, and two catalytic domains, making it a receptor-type PTP. The extracellular region contains MAM, Ig-like, and fibronectin repeats[ 37 , 38 ]. According to the severity of SG, SIPA1L2 (Homo sapiens signal induced proliferation associated 1 like 2), PPARGC1A (homo sapiens PPARG coactivator 1 alpha), PTPRD (protein tyrosine phosphatase receptor type D) and ELOVL3(ELOVL fatty acid elongase 3) have been picked out. SIPA1L2 has been linked to various nervous system diseases and may have a role in skin health that has not yet been investigated[ 39 , 40 ].PPARGC1A is a transcriptional coactivator that regulates genes related to energy metabolism. It interacts with PPAR gamma and other transcription factors, such as CREB and NRFs, to control mitochondrial biogenesis and muscle fiber type[ 41 – 43 ]. PTPRD is a member of the PTP family and promotes neurite growth and regulates axon guidance in neurons[ 44 – 46 ]. ELOVL3 encodes a protein that belongs to the GNS1/SUR4 family. Members of this family play a role in elongation of long chain fatty acids to provide precursors for synthesis of sphingolipids and ceramides [ 47 – 49 ]. Conclusion In conclusion, certain factors linked to stretch marks (SG) are unchangeable like family history and skin type, while others such as weight and gestational age can be controlled. Our model predicts the risk of developing stretch marks based on factors such as age, family history, pre-pregnancy stretch marks, skin type, pre-pregnancy BMI, and associated genes. This predictive analysis can significantly assist in expectant mothers’ counseling and personalized care recommendations. Declarations Author Contribution Li Li and Yuanyuan Han were responsible for the conception and design of the study. Lidan Xiong and Lifeng Yang collected and analyzed the data, while Jianguo Chen and Yinshu Wang contributed to the acquisition of the anthropometric measurements and obstetric data. Xiuju Dong and Hailun He performed the statistical analysis. The initial draft of the manuscript was written by Hailun He, Lifeng Yang, and Lidan Xiong, with significant input from Li Li and Yuanyuan Han regarding the interpretation of the GWAS results. The final review and editing of the manuscript was conducted by all authors. Data Availability The datasets are available from the corresponding author upon reasonable request. Funding declaration: There was no Funding. References Brennan M, Young G, Devane D. Topical preparations for preventing stretch marks in pregnancy. Cochrane Database Syst Rev. 2012;11:Cd000066. Al-Himdani S, et al. 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Clinical associations of striae gravidarum. J Obstet gynaecology: J Inst Obstet Gynecol. 2004;24(3):270–1. Osman H, et al. Risk factors for the development of striae gravidarum. Am J Obstet Gynecol. 2007;196(1):e621–5. Liu L, et al. Risk factors of striae gravidarum in Chinese primiparous women. PLoS ONE. 2018;13(6):e0198720. Lee WL, Yeh CC, Wang PH. Younger pregnant women have a higher risk of striae gravidarum, the study said. J Chin Med Assoc. 2016;79(5):235–6. Farahnik B, et al. Striae gravidarum: Risk factors, prevention, and management. Int J Womens Dermatol. 2017;3(2):77–85. Tang-Lin L, et al. Prevalence of striae gravidarum in a multi-ethnic Asian population and the associated risk factors. Australas J Dermatol. 2017;58(3):e154–5. Picard D, et al. Incidence and risk factors for striae gravidarum. J Am Acad Dermatol. 2015;73(4):699–700. Buchanan K, Fletcher HM, Reid M. Prevention of striae gravidarum with cocoa butter cream. Int J Gynaecol Obstet. 2010;108(1):65–8. Taavoni S, et al. Effects of olive oil on striae gravidarum in the second trimester of pregnancy. Complement Ther Clin Pract. 2011;17(3):167–9. Soltanipoor F, et al. The effect of olive oil on prevention of striae gravidarum: a randomized controlled clinical trial. Complement Ther Med. 2012;20(5):263–6. Duque Lasio ML, Kozel BA. Elastin-driven genetic diseases. Matrix Biol, 2018. 71–2: pp. 144–160. Peng Y, et al. Increased levels of circulating fibroblast growth factor 21 in children with Kawasaki disease. Clin experimental Med. 2019;19(4):457–62. Oda Y, et al. Entire FGF12 duplication by complex chromosomal rearrangements associated with West syndrome. J Hum Genet. 2019;64(10):1005–14. Bhushan A, et al. Identification and Validation of Fibroblast Growth Factor 12 Gene as a Novel Potential Biomarker in Esophageal Cancer Using Cancer Genomic Datasets. OMICS. 2017;21(10):616–31. Li Q et al. FGF12De Novo (Fibroblast Growth Factor 12) Functional Variation Is Potentially Associated With Idiopathic Ventricular Tachycardia. J Am Heart Association, 2017. 6(8). Li B, et al. High expression of RAB38 promotes malignant progression of pancreatic cancer. Mol Med Rep. 2019;19(2):909–18. Wang H, Jiang C. RAB38 confers a poor prognosis, associated with malignant progression and subtype preference in glioma. Oncol Rep. 2013;30(5):2350–6. Huang L, et al. Serum levels of cancer antigen 125 before hormone replacement therapy are not associated with clinical outcome of frozen embryo transfer in women with adenomyosis. J Int Med Res. 2021;49(4):3000605211005878. Giamougiannis P, Martin-Hirsch P, Martin F. The evolving role of MUC16 (CA125) in the transformation of ovarian cells and the progression of neoplasia. Carcinogenesis. 2021;42(3):327–43. Sahin A, Kaya H, Avci O. Cancer antigen-125 is a predictor of mortality in patients with pulmonary arterial hypertension. Clin Biochem. 2021;89:58–62. Sayyadi B, et al. CA125 levels in pregnancy: A case-control study amongst pregnant women in Aminu Kano teaching hospital, North-West Nigeria. Niger Postgrd Med J. 2020;27(4):325–30. Pasquo A, et al. Structural stability of human protein tyrosine phosphatase ρ catalytic domain: effect of point mutations. PLoS ONE. 2012;7(2):e32555. Niemöller C, et al. Single cell genotyping of exome sequencing-identified mutations to characterize the clonal composition and evolution of inv(16) AML in a CBL mutated clonal hematopoiesis. Leuk Res. 2016;47:41–6. Tao F, et al. Variation in SIPA1L2 is correlated with phenotype modification in Charcot- Marie- Tooth disease type 1A. Ann Neurol. 2019;85(3):316–30. Yang X, et al. Polymorphism in MIR4697 but not VPS13C, GCH1, or SIPA1L2 is associated with risk of Parkinson's disease in a Han Chinese population. Neurosci Lett. 2017;650:8–11. Esterbauer H, et al. Human peroxisome proliferator activated receptor gamma coactivator 1 (PPARGC1) gene: cDNA sequence, genomic organization, chromosomal localization, and tissue expression. Genomics. 1999;62(1):98–102. Michi A, et al. PGC-1α mediates a metabolic host defense response in human airway epithelium during rhinovirus infections. Nat Commun. 2021;12(1):3669. Kolić I, et al. Association study of rs7799039, rs1137101 and rs8192678 gene variants with disease susceptibility/severity and corresponding LEP, LEPR and PGC1A gene expression in multiple sclerosis. Gene. 2021;774:145422. Wu L, et al. Loss of Tyrosine Phosphatase Delta Promotes Gastric Cancer Progression via Signal Transducer and Activator of Transcription 3 Pathways. Dig Dis Sci. 2019;64(11):3164–72. Painter J, et al. Genetic overlap between endometriosis and endometrial cancer: evidence from cross-disease genetic correlation and GWAS meta-analyses. Cancer Med. 2018;7(5):1978–87. Pervjakova N, et al. Genome-wide analysis of nuclear magnetic resonance metabolites revealed parent-of-origin effect on triglycerides in medium very low-density lipoprotein in PTPRD gene. Biomark Med. 2018;12(5):439–46. Monné M, et al. N-tail translocation in a eukaryotic polytopic membrane protein: synergy between neighboring transmembrane segments. Eur J Biochem. 1999;263(1):264–9. Kobayashi T, Zadravec D, Jacobsson A. ELOVL2 overexpression enhances triacylglycerol synthesis in 3T3-L1 and F442A cells. FEBS Lett. 2007;581(17):3157–63. Westerberg R, et al. Role for ELOVL3 and fatty acid chain length in development of hair and skin function. J Biol Chem. 2004;279(7):5621–9. Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 13 Oct, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 28 May, 2024 Editor assigned by journal 28 May, 2024 Submission checks completed at journal 22 May, 2024 First submitted to journal 17 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4435203","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307648231,"identity":"f47bf926-9e5a-438e-b7b2-6cf490baca64","order_by":0,"name":"Lidan Xiong","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lidan","middleName":"","lastName":"Xiong","suffix":""},{"id":307648233,"identity":"ba261d1e-444d-49f4-acd3-56aa3ce05abe","order_by":1,"name":"Lifeng Yang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lifeng","middleName":"","lastName":"Yang","suffix":""},{"id":307648234,"identity":"d927c126-19b1-4a43-846d-c65d257e4bb7","order_by":2,"name":"Hailun He","email":"","orcid":"","institution":"The Third People’s Hospital of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Hailun","middleName":"","lastName":"He","suffix":""},{"id":307648236,"identity":"ba51e7dd-f693-4426-bdba-ed2bd3590629","order_by":3,"name":"Jianguo Chen","email":"","orcid":"","institution":"Hangzhou Songyang Biotechnology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"Chen","suffix":""},{"id":307648237,"identity":"deba74aa-35d0-43d7-aabf-8e376caa45b3","order_by":4,"name":"Yinshu Wang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yinshu","middleName":"","lastName":"Wang","suffix":""},{"id":307648238,"identity":"7bc0f392-b24e-4c89-a5e6-9dd7707b0197","order_by":5,"name":"Xiuju Dong","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xiuju","middleName":"","lastName":"Dong","suffix":""},{"id":307648240,"identity":"dfcc3c87-cd89-4160-b1d4-58e22bf64664","order_by":6,"name":"Li Li","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Li","suffix":""},{"id":307648241,"identity":"50c64719-aafb-41de-9948-55731c1433e0","order_by":7,"name":"Yuanyuan Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACAyQ24wMGhgOkaWE2IFkLmwRRWszZzx6T5qm5k7jh+Nlj1UCGPf+MBMbPBXi0WPbkpUnzHHuWuOFMXtptEGPGjQRm6Rn4HHYgx0yah+1w4gYg43Zuw+EEA4kENmYefFrOvwFq+QfUAmQUA7XYE9ZyA2gLbxtQC5DBDNTCuIGwljfGlnP7DhvPBDKk/xw7nDjjzMNmafwOyzG88ebbYdk+IOPjjJrD9vztyQc/49MCBCzA6GBwbEAIMDbgUAkHzB+AhD0hVaNgFIyCUTCCAQA8+1K9Xo+sSQAAAABJRU5ErkJggg==","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-05-17 08:11:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4435203/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4435203/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12884-025-08144-4","type":"published","date":"2025-10-13T15:58:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57952361,"identity":"11401145-3078-4a28-8d38-d6f79213ccb6","added_by":"auto","created_at":"2024-06-07 22:56:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79085,"visible":true,"origin":"","legend":"\u003cp\u003eThe predilection site of striae gravidarum on the abdomen, breast, thigh and buttock\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4435203/v1/649c12ab2980946b487cae57.png"},{"id":57952363,"identity":"ff88f4f2-dbbe-4083-8125-55b99d85f081","added_by":"auto","created_at":"2024-06-07 22:56:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2918424,"visible":true,"origin":"","legend":"\u003cp\u003eAggregated Manhattan plot of association P-values for the SG examined. Each dot represents a SNP. For each genome-wide significant hit (-log\u003csub\u003e10\u003c/sub\u003e P-value \u0026gt;5; blue line), we indicate the trait showing the strongest association.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4435203/v1/15d4528cb71d812c9c26a6c5.png"},{"id":93956763,"identity":"e5e3d520-2526-49b9-a35b-e6af09ffda03","added_by":"auto","created_at":"2025-10-20 16:12:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5299953,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4435203/v1/efa5a40d-b513-4873-9317-f4728ef7c608.pdf"},{"id":57952362,"identity":"dcc21e9c-bcc0-4e3e-a0a1-d6db2769f1ff","added_by":"auto","created_at":"2024-06-07 22:56:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18458,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4435203/v1/be2ed1275e3294debff5c728.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Striae gravidarum in the Han Chinese pregnant population: Identifying genetic markers and risk factors through a prospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStriae gravidarum (SG), also known as stretch marks or striae distensae, is a common connective tissue change observed in pregnant women[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevalence of striae distensae ranges from 11\u0026ndash;90% [\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], while in pregnant women the incident rate ranges from 75\u0026ndash;90% during the third trimester [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Initially presenting as flesh-toned atrophic linear plaques with erythema, SG progress to silvery-hypopigmented flesh-toned, atrophic plaques[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. SG are mostly localized on the buttocks, lower back, thighs, calves, breasts/chest, abdomen, upper arms, and knees [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The presence of striae gravidarum did not significantly impact the generic quality of life during pregnancy. However, it was associated with reduced overall quality of life and skin quality index among postpartum women [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Postpartum women experiencing stretch marks may struggle with a negative self-perception, potentially resulting in reduced self-confidence and emotional challenges like anxiety and depression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHistopathologically, early SG exhibits significant separation of collagen bundles and the appearance of disorganized collagen fibrils that do not form bundles [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, there is a substantial disruption in the elastic fiber network, characterized by the emergence of newly synthesized tropoelastin-rich fibrils possibly arising from the irregular synthesis of elastic fiber components [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given their thin and disorganized nature, it is likely that tropoelastin-rich fibrils do not function in the same way as normal elastic fibers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings lay the groundwork for understanding the pathogenic mechanisms underlying the development of laxity in SG. A cohort study conducted on individuals of European descent revealed a significant association between the SNP rs7787362 (P\u0026thinsp;=\u0026thinsp;1.8e-23, OR\u0026thinsp;=\u0026thinsp;0.84) located 40kb upstream of the ELN (elastin) gene and the presence of stretch marks [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, genes such as SRPX, HMCN1, TMEM18, PNPLA1, FN1, and NPIPL2 were found to be associated with striae gravidarum in the pregnancy cohort [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The etiology of SG in Han population remains poorly elucidated.\u003c/p\u003e \u003cp\u003eWhile numerous studies have been conducted on the risk factors and treatment of SG, studies focusing on Asian populations are limited and sample sizes are sparse. In this study, we recruited 1017 pregnant women to investigate potential risk factors and performed a genome-wide association analysis of SG in a discovery cohort comprising 1017 cases of Chinese Han descent.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting and population\u003c/h2\u003e \u003cp\u003e The study was conducted as multicenter trial including the obstetrics department of Sichuan University West China Second University Hospital, Chengdu second people\u0026rsquo;s Hospital, the third people\u0026rsquo;s hospital of Chengdu, Affiliated Hospital of North Sichuan Medical college and Nanjing Maternity and Child Health Care Hospital, Medical cosmetology department of the first people\u0026rsquo;s hospital of Changde city, and gynecology department of Songyang group Awake clinic after obtaining institutional review board approval in Sichuan University West China Hospital. The clinical trial is registered under the number CTR2300077737 and was registered on November 17, 2023.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection and outcomes\u003c/h2\u003e \u003cp\u003e All study participants in this research provided informed consent through an online platform, with documentation stored in an electronic database. Data collection spanned a period of 4 months, during which patients from the inpatient department, Clinic, and outpatient department who had recently given birth at term were invited to participate, irrespective of their parity status. To ensure a homogenous ethnic composition, only Chinese Han women were included in the study. A total of 1017 patients from the clinic and outpatient department met the eligibility criteria and agreed to participate in the study.\u003c/p\u003e \u003cp\u003eThe questionnaires, specifically designed for the study, were completed by the female participants during their medical evaluations. The survey included inquiries about various factors such as level of education background, occupation, income level, family medical history, usage of moisturizing cream, oral or topical corticosteroid use, history of seborrheic dermatitis during adolescence, physical activity routines, smoking habits, alcohol intake, and dietary patterns.\u003c/p\u003e \u003cp\u003eData were collected from the medical charts: age (y), height (cm), pre-pregnancy weight (kg), maximum weight during pregnancy (kg), weight gain during pregnancy (kg), pre-pregnancy BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), maximum BMI during pregnancy (kg/m\u003csup\u003e2\u003c/sup\u003e), BMI gain during pregnancy (kg/m\u003csup\u003e2\u003c/sup\u003e), maximum abdomen girth (cm), maximum uterine height (cm), birth weight (kg), birth length (cm), thyroid disease, gestational diabetes mellitus, polyhydramnios, Number of fetuses, maternal premature infants, delivery gestational age and fetal sex.\u003c/p\u003e \u003cp\u003eThe data obtained from the physician's assessment included Fitzpatrick skin types I-IV, skin types as determined by the Huaxi self-assessment questionnaires (Huaxi SSQ), and the severity of SG. The Huaxi SSQ, a recently developed tool by West China Hospital of Sichuan University, demonstrates greater reliability and validity among Chinese individuals compared to the Bauman SSQ.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenotype data\u003c/h2\u003e \u003cp\u003eThe samples were sent to WeGene for testing, a platform that has demonstrated efficacy in genome-wide association studies (GWAS) across a range of phenotypes. Saliva samples from participants were obtained and genotyped using WeGene Arrays. Data cleaning for GWAS involved removing individuals with missing key variables, phenotypic outliers exceeding 3 standard deviations from the mean, and abnormal demographic information, such as individuals over the age of 100. The selection process was determined by the GWAS p-value of the training set data within a 500kb region, specifically targeting a P-value\u0026thinsp;\u0026lt;\u0026thinsp;10e-5 at the leading SNP. Genotypes associated with statistical variances in the European population based on the presence or absence of SG were identified. Then, the top 200 P-values\u0026thinsp;\u0026lt;\u0026thinsp;10e-5 was selected. Subsequently, the leading SNP at 10e-5 was chosen based on severity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003e1017 questionnaires were collected, and the results were statistically analysed using the IBM SPSS Statistics R26.0.0.0. The comparison of women with SG vs. those without SG was made by performing student's t-test and chi-square test. Multivariable logistic regression analysis was applied to obtain odds ratios for variables with an independent association with striae.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a total of 1017 pregnant women participated in the questionnaire survey. While some data may be incomplete, a significant portion has been recorded. Among the participants, 602 pregnant women experienced SG, resulting in a morbidity rate of 59% which exceeds half of the pregnant women surveyed. In addition to the abdomen, similar skin lesions were observed in other regions such as the breast, thigh, and buttock (Figure 1). Specifically, 458 women exhibited SG solely on the abdomen, while the remaining participants had SG on the breast (78), thigh (150), and buttock (73). 303 pregnant women with SG experienced varying degrees of pruritus during pregnancy, with some cases being severe enough to result in eczematoid changes. Of these women, 599 expressed concerns about the aesthetic impact of SG and expressed a desire to eliminate them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between continuous factors and SG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVarious continuous factors were compared between pregnant women with gestational diabetes and those without gestational diabetes, including age, height, weight before pregnancy, maximum weight during pregnancy, weight gain during pregnancy, BMI before pregnancy, maximum BMI during pregnancy, BMI gain during pregnancy, maximum abdomen girth, maximum uterine height, birth weight, and birth length. Except for maternal height, all other factors exhibited a statistically significant difference with P＜0.05. It can be inferred that factors such as age, pre-pregnancy weight, maximum weight during pregnancy, weight gain during pregnancy, pre-pregnancy BMI, maximum BMI during pregnancy, BMI gain during pregnancy, maximum abdomen girth, maximum uterine height, birth weight, and birth length are significant in the occurrence of SG (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. The analysis of prevalence and clinical characteristics of primipara with and without SG (continuous factors)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08247422680412%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-SG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08247422680412%\" valign=\"top\"\u003e\n \u003cp\u003eAge (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e29.34\u0026plusmn;0.43（418）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e27.88\u0026plusmn;0.29（586）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e5.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08247422680412%\" valign=\"top\"\u003e\n \u003cp\u003eHeigh (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e159.99\u0026plusmn;0.44（418）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e160.12\u0026plusmn;0.66（594）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e-0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08247422680412%\" valign=\"top\"\u003e\n \u003cp\u003eWeight before pregnancy (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e51.57\u0026plusmn;0.68（327）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e54.23\u0026plusmn;0.70（435）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e-5.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08247422680412%\" valign=\"top\"\u003e\n \u003cp\u003eMax weight during pregnancy (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e65.05\u0026plusmn;0.83（331）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e69.48\u0026plusmn;0.79（441）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e-7.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08247422680412%\" valign=\"top\"\u003e\n \u003cp\u003eWeight gain during pregnancy (kg)\u003c/p\u003e\n \u003cp\u003eBMI before pregnancy (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eMax BMI during pregnancy (kg/m2)\u003c/p\u003e\n \u003cp\u003eBMI gain during pregnancy (kg/m2)\u003c/p\u003e\n \u003cp\u003eMax abdomen girth (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e13.35\u0026plusmn;0.45（326）\u003c/p\u003e\n \u003cp\u003e20.11\u0026plusmn;0.24（327）\u003c/p\u003e\n \u003cp\u003e25.36\u0026plusmn;0.29（331）\u003c/p\u003e\n \u003cp\u003e5.21\u0026plusmn;1.83（326）\u003c/p\u003e\n \u003cp\u003e98.47\u0026plusmn;0.88（301）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e15.18\u0026plusmn;0.47（435）\u003c/p\u003e\n \u003cp\u003e21.05\u0026plusmn;0.25（434）\u003c/p\u003e\n \u003cp\u003e26.97\u0026plusmn;0.29（440）\u003c/p\u003e\n \u003cp\u003e5.90\u0026plusmn;1.87（434）\u003c/p\u003e\n \u003cp\u003e101.59\u0026plusmn;0.54（411）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e-5.208\u003c/p\u003e\n \u003cp\u003e-5.340\u003c/p\u003e\n \u003cp\u003e-7.608\u003c/p\u003e\n \u003cp\u003e-5.003\u003c/p\u003e\n \u003cp\u003e-6.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08247422680412%\" valign=\"top\"\u003e\n \u003cp\u003eMax uterine height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e32.72\u0026plusmn;0.32（301）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e33.94\u0026plusmn;0.48（410）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e-6.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08247422680412%\" valign=\"top\"\u003e\n \u003cp\u003eBirth weight (kg)\u003c/p\u003e\n \u003cp\u003eBirth length (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e3.22\u0026plusmn;0.06（372）\u003c/p\u003e\n \u003cp\u003e49.10\u0026plusmn;0.35（354）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003e3.36\u0026plusmn;0.05（513）\u003c/p\u003e\n \u003cp\u003e49.69\u0026plusmn;0.27（484）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e-3.382\u003c/p\u003e\n \u003cp\u003e-2.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe datas were manifested by mean \u0026plusmn; standard deviation. SG, striae gravidarum; non-SG, without striae gravidarum; BMI, body mass index.\u003c/p\u003e\n\u003cp\u003eBMI = weight (kg) / height\u003csup\u003e2\u003c/sup\u003e (m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eStatistically significant, P \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between categorical factors and SG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilarly, categorical factors such as level of education, occupation, income level, family medical history, use of moisturizing cream, presence of thyroid disease, use of corticosteroids, history of gestational diabetes mellitus, presence of polyhydramnios, occurrence of striae distensae, level of physical activity, smoking and alcohol consumption habits, dietary patterns, number of previous pregnancies, history of maternal premature birth, gestational age at delivery, fetal sex, Fitzpatrick skin type, and skin type according to the Huaxi SSQ are being examined to discern differences between pregnant women with SG and non-SG.\u003c/p\u003e\n\u003cp\u003eStriae distensae refer to the presence of red striae on the thighs, abdomen, or other areas of the body during the teenage years in women. Huaxi SSQ categorizes individuals based on their skin type, which can be classified as oily, neutral, dry, combination, or sensitive classifications. The results of the study indicate that only family medical (or health) history, the presence of striae distensae, and skin type show a statistically significant difference (P＜0.05) in their contribution to the appearance of SG (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. The analysis of prevalence and clinical characteristics of primipara with and without SD (categorical factors)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.94845360824742%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-SG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDegree of education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eBachelor degree or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.25373134328358%\" valign=\"top\"\u003e\n \u003cp\u003eBachelor degree or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.432835820895523%\" valign=\"top\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.462686567164179%\" valign=\"top\"\u003e\n \u003cp\u003e489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.25373134328358%\" valign=\"top\"\u003e\n \u003cp\u003eMental activity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.432835820895523%\" valign=\"top\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.462686567164179%\" valign=\"top\"\u003e\n \u003cp\u003e483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIncome level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e＜5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.25373134328358%\" valign=\"top\"\u003e\n \u003cp\u003e＞5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.432835820895523%\" valign=\"top\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.462686567164179%\" valign=\"top\"\u003e\n \u003cp\u003e409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eFamily history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e152.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eMoisturizing cream\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e20.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eThyroid disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e3.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCorticosteroid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e5.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.25373134328358%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.432835820895523%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.462686567164179%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGestational diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.25373134328358%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.432835820895523%\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.462686567164179%\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003ePolyhydramnios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eStriae distensae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e127.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eAmount of exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e＜1h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e＞1h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eDietary habit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eomnivore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e2.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003edietary bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eThe number of fetal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eDouble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eMaternal premature infant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eDelivery gestational age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e＜37 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e5.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e＞37 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003efetal sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eFamle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eFitzpatrick skin type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eEasy to redden (Ⅰ~Ⅱ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e35.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eEasy to tan (Ⅲ~Ⅳ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eSkin types (Huaxi SSQ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eoily / neutral skin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e9.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eDry/ combination/ sensitive skin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSG, striae gravidarum; non-SG, without striae gravidarum. Skin types 1, Fitzpatrick. Skin types 2, common standard according to the degree of oil or dryness.\u003c/p\u003e\n\u003cp\u003eStatistically significant, P \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple logistic regression analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe factors which selected by student\u0026rsquo;s t test and chi-square test are engaged in the logistic regressive. Age, family health history, striae distensae, Fitzpatrick skin type , skin types (Huaxi SSQ), max abdomen girth, BMI before pregnancy and BMI gain during pregnancy are considered to be significant risk factors of SG. In continuous factors, while age is the negative correlation with SG, the others are positive correlation with SG. In categorical factors, positive family history, striae distensae at teen years, skin flushed after sunshine and dry, mixed, sensitive skin are more likely to suffer from SG at pregnancy. The model can correctly identify 76.6% of the objects (Table 3). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Risk factors for SG of primipara in multiple logistic regression analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWald\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e11.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003e0.898~0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003eFamily history\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eStriae distensae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e-1.883\u003c/p\u003e\n \u003cp\u003e-1.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e106.438\u003c/p\u003e\n \u003cp\u003e62.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003e0.106~0.218\u003c/p\u003e\n \u003cp\u003e0.164~0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003eFitzpatrick skin type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e15.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e1.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003e1.407~2.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003eSkin types (Huaxi SSQ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e-0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e6.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003e0.486~0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003eMax abdomen girth\u003c/p\u003e\n \u003cp\u003eBMI before pregnancy\u003c/p\u003e\n \u003cp\u003eBMI gain during pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e9.076\u003c/p\u003e\n \u003cp\u003e3.886\u003c/p\u003e\n \u003cp\u003e7.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e1.067\u003c/p\u003e\n \u003cp\u003e1.071\u003c/p\u003e\n \u003cp\u003e1.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003e1.023~1.113\u003c/p\u003e\n \u003cp\u003e1.000~1.146\u003c/p\u003e\n \u003cp\u003e1.032~1.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSE, standard error; OR, odds ratio; CI, confidence interval; BMI, body mass index. Skin types 1, Fitzpatrick. Skin types 2, common standard according to the degree of oil or dryness.\u003c/p\u003e\n\u003cp\u003eStatistically significant, P \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDue to challenges in accurately measuring maximum abdomen girth and BMI gain during pregnancy prior to conception, these factors were excluded from the predictive model for gestational diabetes. Following adjustments, variables such as age, family history, presence of striae distensae, Fitzpatrick skin type and Skin types (Huaxi SSQ), and pre-pregnancy BMI were retained. Statistical analysis revealed a significant difference in the model (\u0026chi;2 = 278.983, P＜0.001), with the new model correctly identifying 74.5% of cases (Table 4). In addition, the comparison between primipara and multipara was shown in the supplementary material.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Risk factors for SG of primipara in multiple logistic regression analysis ( After adjustment)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.8659793814433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWald\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.8659793814433%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e7.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e0.904~0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.8659793814433%\" valign=\"top\"\u003e\n \u003cp\u003eFamily history\u003c/p\u003e\n \u003cp\u003eStriae distensae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e-1.828\u003c/p\u003e\n \u003cp\u003e-1.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e102.863\u003c/p\u003e\n \u003cp\u003e62.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e0.113~0.229\u003c/p\u003e\n \u003cp\u003e0.140~0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.8659793814433%\" valign=\"top\"\u003e\n \u003cp\u003eFitzpatrick skin type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e16.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e1.472~2.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.8659793814433%\" valign=\"top\"\u003e\n \u003cp\u003eSkin types (Huaxi SSQ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e-0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e4.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e0.494~0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.8659793814433%\" valign=\"top\"\u003e\n \u003cp\u003eBMI before pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e4.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e1.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e1.007~1.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSE, standard error; OR, odds ratio; CI, confidence interval; BMI, body mass index. Skin types 1, Fitzpatrick. Skin types 2, common standard according to the degree of oil or dryness.\u003c/p\u003e\n\u003cp\u003eStatistically significant, P \u0026lt; 0.05.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between SNP and SG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenotypes associated with statistical variances in the European population based on the presence or absence of SG were identified in supplementary Table S3[14].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIn our research, we conducted a genome-wide association analysis of SG in a discovery cohort of our 1017 cases of Chinese Han descent. We attempted to identify genome-wide significant association; however, no region exhibited a strong correlation with SG (Figure 2). According to the presence or absence of SG, there are five regions related to four genes significantly associated with striae gravidarum (Table 5). The first region, rs2366666 (P＜5.38E-05), lies in the FGF12 (fibroblast growth factor 12) gene. The second association, rs7117666 (P＜1.32E-05), lies 25kb upstream of the RAB38 (member RAS oncogene family) gene. The third association, rs12052152 (P＜5.33E-05), lies in the MUC16 (mucin 16, cell surface associated) gene. The fourth association, rs77013023 (P＜5.08E-05), lies in the PTPRT (protein tyrosine phosphatase receptor type T) gene.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. Index SNPs related to dermatology in top 200 classified according to the presence or absence of SG\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBETA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eL95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eU95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003ers2366666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e192115018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003eFGF12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e1.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e0.1104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e1.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e1.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e5.38E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003ers7117666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e87821219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" rowspan=\"2\"\u003e\n \u003cp\u003eRAB38(u)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e1.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e0.1009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e1.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e1.32E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e87823940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e0.1011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1.66E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003ers12052152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e9114906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003eMUC16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e0.6325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e0.1134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e0.5065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e0.7899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e5.33E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003ers77013023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e41889504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\"\u003e\n \u003cp\u003ePTPRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e0.4721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e0.1852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e0.3284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e0.6788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e5.08E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCHR, Chromosome; BP, position; Gene is gene that is the most likely candidate for the association or the association or the closest gene. Whether the single-nucleotide polymorphism (SNP) is upstream (u), downstream (d), or within (i) the gene is indicated in parentheses; Statistically significant, P \u0026lt; E-05\u003c/p\u003e\n\u003cp\u003eAccording to the severity of SG, there are twelve regions related to four genes significantly associated with striae gravidarum (Table 6). The first region, rs75963530 (P＜1.52E-05), lies in the SIPA1L2 (signal induced proliferation associated 1 like 2) gene. The second association, rs184656123 (P＜6.42E-06), lies in the PPARGC1A (PPARG coactivator 1 alpha) gene and rs61261762 (P＜7.56E-06), lies in the PPARGC1A (PPARG coactivator 1 alpha) gene. The third association, rs28705153 (P＜5.46E-08), rs10959038 (P＜1.67E-07), rs592203 (P＜1.67E-07), rs10959044 (P＜6.48E-07), rs10959021 (P＜1.71E-05) and rs57211494 (P＜1.83E-05) all lies in the PTPRD (protein tyrosine phosphatase receptor type D) gene. The fourth association, rs57630487 (P＜1.26E-05), rs4523633 (P＜1.80E-05), and rs140247130 (P＜1.80E-05) lies 13kb, 6kb and 1kb \u0026nbsp;upstream of the ELOVL3 (ELOVL fatty acid elongase 3) gene, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6. Index SNPs related to dermatology in top 200 classified according to the severity of SG\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBETA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eL95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eU95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003ers75963530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e232605835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eSIPA1L2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\" valign=\"top\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.4333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.09963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.2381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.6286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e1.52E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003ers184656123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e23846465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" rowspan=\"2\"\u003e\n \u003cp\u003ePPARGC1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.1688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.4352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e1.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e6.42E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.814814814814815%\"\u003e\n \u003cp\u003ers61261762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e23845240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.938271604938271%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.1659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.4218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" valign=\"top\"\u003e\n \u003cp\u003e7.56E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003ers28705153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e10328041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" rowspan=\"6\"\u003e\n \u003cp\u003ePTPRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\" valign=\"top\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.4652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.08487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.2989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.6316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e5.46E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.814814814814815%\"\u003e\n \u003cp\u003ers10959038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e10308785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.938271604938271%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.4552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.08631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.6243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" valign=\"top\"\u003e\n \u003cp\u003e1.67E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.814814814814815%\"\u003e\n \u003cp\u003ers592203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e10310358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.938271604938271%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.4552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.08631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.6243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" valign=\"top\"\u003e\n \u003cp\u003e1.67E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.814814814814815%\"\u003e\n \u003cp\u003ers10959044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e10324516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.938271604938271%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.4105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.08191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.2499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" valign=\"top\"\u003e\n \u003cp\u003e6.48E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.814814814814815%\"\u003e\n \u003cp\u003ers10959021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e10285493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.938271604938271%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.3179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.07354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.1737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" valign=\"top\"\u003e\n \u003cp\u003e1.71E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.814814814814815%\"\u003e\n \u003cp\u003ers57211494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e8943040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.938271604938271%\" valign=\"top\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e-0.1843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.04278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e-0.2681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e-0.1004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" valign=\"top\"\u003e\n \u003cp\u003e1.83E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003ers57630487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e103972747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" rowspan=\"3\"\u003e\n \u003cp\u003eELOVL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.2295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.05227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.1271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e1.26E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.814814814814815%\"\u003e\n \u003cp\u003ers4523633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e103979285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.938271604938271%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.2177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.05049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.1187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.3167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" valign=\"top\"\u003e\n \u003cp\u003e1.80E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.814814814814815%\"\u003e\n \u003cp\u003ers140247130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\"\u003e\n \u003cp\u003e103984610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.938271604938271%\" valign=\"top\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.2177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.05049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.1187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e0.3167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.345679012345679%\" valign=\"top\"\u003e\n \u003cp\u003e1.80E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCHR, Chromosome; BP, position; Gene is gene that is the most likely candidate for the association or the association or the closest gene. Whether the single-nucleotide polymorphism (SNP) is upstream (u), downstream (d), or within (i) the gene is indicated in parentheses; Statistically significant, P \u0026lt; E-05\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study focused on Chinese Han women, showing that SG is common with a prevalence of over 59% primarily on the abdomen. This aligns with findings from prior studies[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Also, it was disclosed that SG has a predilection to the abdomen and the thighs and/or breasts [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe average age of pregnant woman with SG is 27.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29, whereas the average age of pregnant woman with non-SG is 29.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43, which is consistent with the findings of the studies reporting that younger women were more likely to develop SG [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].Researchers have found that weight and BMI before and during pregnancy, as well as factors like max abdomen girth and uterine height, were significantly higher in patients who developed stretch marks during pregnancy[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These factors are all interrelated and affect the degree of skin stretching.\u003c/p\u003e \u003cp\u003eAdditional factors that were found to be significant in the development of striae gravidarum included family history, pre-existing striae distensae, Fitzpatrick skin types, and skin types (Huaxi SSQ). Family history was shown to have a substantial impact on the occurrence of striae gravidarum, increasing the risk by over fourfold, aligning with previous research[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In our research, we refined the relationship between the two skin types and striae gravidarum. We found that individuals with skin easy to redden (Ⅰ~Ⅱ) were more likely to appear SG comparing with those with skin easy to tan (Ⅲ~Ⅳ). In addition, dry/combination/ sensitive skin types have more tendency to occur SG compared with oily/neutral skin.\u003c/p\u003e \u003cp\u003eFurthermore, other potential factors such as education, occupation, medical history, lifestyle habits, and pregnancy details were also examined, but none were found to be linked to SG. Moisturizing cream application on the abdomen did not prevent stretch marks in our study, consistent with previous research[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].The study did not investigate the link between hormone levels and stretch mark occurrence.\u003c/p\u003e \u003cp\u003eStriae gravidarum is a complex polygenetic associated manifestation. The results of GWAS would further reveal the possible genes that regulate and control the occurrence of striae gravidarum. GWAS can identify genes that influence its occurrence, such as ELN, SRPX, HMCN1, and TMEM18[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Only rs7787362 (ELN) and rs10798036 (HMCN1) have been verified through our collected cases. Elastic fibers provide recoil to tissues that undergo repeated deformation, such as blood vessels, lungs, and skin. Composed of elastin and its accessory proteins, the fibers are produced within a restricted developmental window and are stable for decades. Their eventual breakdown is associated with a loss of tissue resiliency and aging [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Rs10798036 in the HMCN1(hemicentin-1) gene is linked to age-related macular degeneration, but it remains unclear how this gene might be connected to the risk of developing stretch marks.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe identified potential skin-related genes in the top 200 loci from our GWAS results and analyzed them based on the presence or absence of SG and the severity of SG. Concerning the presence or absence of SG, FGF12, RAB38(u), MUC16 (mucin 16), and PTPRT(protein tyrosine phosphatase receptor type T) are obtained from the meaningful SNPs. This gene encodes a protein in the fibroblast growth factor (FGF) family, known for its role in cell growth, broad mitogenic and cell survival activities [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. RAB38 is mainly expressed in the skin and has been linked to diseases like pancreatic cancer and glioma but has not been well studied in dermatology[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. MUC16 encodes a membrane-tethered mucin protein with extracellular, tandem repeat, and transmembrane domains. It is part of the mucin family, which helps form a protective mucous barrier on epithelial surfaces[\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. PTPRT is a signaling molecule that regulates cellular processes. It has an extracellular region, a transmembrane region, and two catalytic domains, making it a receptor-type PTP. The extracellular region contains MAM, Ig-like, and fibronectin repeats[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the severity of SG, SIPA1L2 (Homo sapiens signal induced proliferation associated 1 like 2), PPARGC1A (homo sapiens PPARG coactivator 1 alpha), PTPRD (protein tyrosine phosphatase receptor type D) and ELOVL3(ELOVL fatty acid elongase 3) have been picked out. SIPA1L2 has been linked to various nervous system diseases and may have a role in skin health that has not yet been investigated[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].PPARGC1A is a transcriptional coactivator that regulates genes related to energy metabolism. It interacts with PPAR gamma and other transcription factors, such as CREB and NRFs, to control mitochondrial biogenesis and muscle fiber type[\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. PTPRD is a member of the PTP family and promotes neurite growth and regulates axon guidance in neurons[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. ELOVL3 encodes a protein that belongs to the GNS1/SUR4 family. Members of this family play a role in elongation of long chain fatty acids to provide precursors for synthesis of sphingolipids and ceramides [\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, certain factors linked to stretch marks (SG) are unchangeable like family history and skin type, while others such as weight and gestational age can be controlled. Our model predicts the risk of developing stretch marks based on factors such as age, family history, pre-pregnancy stretch marks, skin type, pre-pregnancy BMI, and associated genes. This predictive analysis can significantly assist in expectant mothers\u0026rsquo; counseling and personalized care recommendations.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLi Li and Yuanyuan Han were responsible for the conception and design of the study. Lidan Xiong and Lifeng Yang collected and analyzed the data, while Jianguo Chen and Yinshu Wang contributed to the acquisition of the anthropometric measurements and obstetric data. Xiuju Dong and Hailun He performed the statistical analysis. The initial draft of the manuscript was written by Hailun He, Lifeng Yang, and Lidan Xiong, with significant input from Li Li and Yuanyuan Han regarding the interpretation of the GWAS results. The final review and editing of the manuscript was conducted by all authors.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets are available from the corresponding author upon reasonable request.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding declaration:\u0026nbsp;\u003c/strong\u003eThere was no Funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrennan M, Young G, Devane D. Topical preparations for preventing stretch marks in pregnancy. Cochrane Database Syst Rev. 2012;11:Cd000066.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Himdani S, et al. Striae distensae: a comprehensive review and evidence-based evaluation of prophylaxis and treatment. Br J Dermatol. 2014;170(3):527\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu L, Ma H, Li Y. Interventions for the treatment of stretch marks: a systematic review. 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J Am Acad Dermatol. 2015;73(4):699\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuchanan K, Fletcher HM, Reid M. Prevention of striae gravidarum with cocoa butter cream. Int J Gynaecol Obstet. 2010;108(1):65\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaavoni S, et al. Effects of olive oil on striae gravidarum in the second trimester of pregnancy. Complement Ther Clin Pract. 2011;17(3):167\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoltanipoor F, et al. The effect of olive oil on prevention of striae gravidarum: a randomized controlled clinical trial. Complement Ther Med. 2012;20(5):263\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuque Lasio ML, Kozel BA. Elastin-driven genetic diseases. Matrix Biol, 2018. 71\u0026ndash;2: pp. 144\u0026ndash;160.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng Y, et al. 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Biomark Med. 2018;12(5):439\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonn\u0026eacute; M, et al. N-tail translocation in a eukaryotic polytopic membrane protein: synergy between neighboring transmembrane segments. Eur J Biochem. 1999;263(1):264\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi T, Zadravec D, Jacobsson A. ELOVL2 overexpression enhances triacylglycerol synthesis in 3T3-L1 and F442A cells. FEBS Lett. 2007;581(17):3157\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWesterberg R, et al. Role for ELOVL3 and fatty acid chain length in development of hair and skin function. J Biol Chem. 2004;279(7):5621\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Striae gravidarum, Genome-wide association study, Risk factors, Chinese Han Population","lastPublishedDoi":"10.21203/rs.3.rs-4435203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4435203/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eStriae gravidarum (SG), commonly known as stretch marks, are a frequent connective tissue alteration observed in pregnant women. Postpartum women may feel damaged in their self-image due to SG which can lead to lower self-esteem and emotional problems such as anxiety and depression. The study aimed to evaluate the potential risk factors and genetic associations of SG in a Chinese Han population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA multicenter trial was conducted involving 1017 pregnant women of Chinese Han descent who provided informed consent. Participants completed questionnaires regarding demographics, medical history, and lifestyle factors. Anthropometric measurements and obstetric data were gathered, followed by a genome-wide association study (GWAS).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study found that 59% of participants experienced SG. Significant correlations were observed between SG and factors including age, pre-pregnancy weight, maximum pregnancy weight during pregnancy, BMI before and during pregnancy, and maximum abdomen girth. Risk factors for SG included a positive family history, prior experience of striae distensae during adolescence, and specific skin types according to the Fitzpatrick classification. Multivariable logistic regression analysis indicated that age, family history, history of striae distensae, skin types, and pre-pregnancy BMI were notable predictors of SG. The GWAS identified several single nucleotide polymorphisms (SNPs) related to SG presence and severity, implicating genes such as FGF12, RAB38, MUC16, PTPRT, SIPA1L2, PPARGC1A, PTPRD, and ELOVL3.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study presents a predictive model for SG risk that includes non-modifiable factors like family history and skin type, and modifiable factors such as pre-pregnancy weight and BMI. The findings provide insights into the genetic basis of SG and may aid in counseling patients on risk reduction strategies. The identified genetic variants offer potential targets for future research into the pathogenesis and prevention of SG.\u003c/p\u003e","manuscriptTitle":"Striae gravidarum in the Han Chinese pregnant population: Identifying genetic markers and risk factors through a prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 22:56:10","doi":"10.21203/rs.3.rs-4435203/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-28T09:43:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-28T04:36:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-23T02:53:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2024-05-17T08:10:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f3ca2f66-8056-4154-8ab4-a42f5b2944ef","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:09:38+00:00","versionOfRecord":{"articleIdentity":"rs-4435203","link":"https://doi.org/10.1186/s12884-025-08144-4","journal":{"identity":"bmc-pregnancy-and-childbirth","isVorOnly":false,"title":"BMC Pregnancy and Childbirth"},"publishedOn":"2025-10-13 15:58:16","publishedOnDateReadable":"October 13th, 2025"},"versionCreatedAt":"2024-06-07 22:56:10","video":"","vorDoi":"10.1186/s12884-025-08144-4","vorDoiUrl":"https://doi.org/10.1186/s12884-025-08144-4","workflowStages":[]},"version":"v1","identity":"rs-4435203","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4435203","identity":"rs-4435203","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}