Abstract
Introduction
Despite the high prevalence of acne vulgaris and its impact on affected individuals, few studies have provided a detailed characterization of acne phenotypes and their associated risk factors. This study aimed to comprehensively evaluate the prevalence, severity, scarring, and phenotypes of acne, along with their associated risk factors, in a cohort of young Chinese adults, as part of the Singapore and Malaysia Cross-Sectional Genetic Epidemiology Study (SMCGES).
Methods
Participants were randomly and consecutively recruited from universities in Singapore and Malaysia. Data on sociodemographic, familial medical histories of atopic diseases and acne, and lifestyle habits were collected using a validated investigator-administered questionnaire from 6,225 young Chinese adults (mean age = 22.8 ± 5.7 years). A subset of participants underwent clinical assessment for acne severity (n = 2,345), scarring grade (n = 2,345), and phenotypes (n = 1,191) by dermatologically trained personnel.
Results
The prevalence of acne was 56.0%. Among acne cases (n = 3,504), 38.5% had moderate-to-severe acne, 52.8% had scarring, 95.7% presented with blackhead and/or whitehead, and 55.8% had inflammatory phenotypes (e.g., papules, pustules, cysts, and nodules). A parental history of acne emerged as the strongest risk factor associated with all acne phenotypes. Pet ownership (adjusted odds ratio [AOR]: 1.403, 95% confidence interval [CI]: 1.131–1.744, p < 0.05) and occasional alcohol consumption (AOR: 1.328, 95% CI: 1.090–1.617, p < 0.05) were associated with a higher odd for blackhead and/or whitehead. Protective factors included higher parental education levels for acne scarring (AOR: 0.650, 95% CI: 0.459–0.904; p < 0.05), male gender (AOR: 0.365, 95% CI: 0.298–0.446; p < 0.05), and birthplace (AOR: 0.674, 95% CI: 0.555–0.819; p < 0.05) for non-inflammatory phenotypes.
Conclusions
This study, conducted in a well-defined cohort of young Chinese adults from the SMCGES, reinforces familial history as a key risk factor for acne onset, severity, scarring, and phenotype manifestation. The identification of modifiable and environmental factors associated with acne phenotypes offers valuable insights for targeted interventions to improve acne management and control.
Keywords: Acne vulgaris, Ethnic chinese, Epidemiology, Risk factors, Acne phenotypes
Introduction
Acne vulgaris is a highly prevalent inflammatory skin disorder primarily affecting young and older adults, with the 2010 Global Burden of Disease Study approximating a global acne prevalence of 9.38% across all age groups. Moreover, a subsequent follow-up study found a 0.67% annual increase in acne prevalence in Southeast Asia from 1990 to 2019 [1, 2]. Despite its pervasive impact on young and adult populations globally, there is a notable lack of research that comprehensively examines the multifaceted dimensions of acne. The existing epidemiology studies often exhibit limitations such as small sample sizes [3], heterogenous diagnostic criteria [4], reliance on self-reporting [5], and the lack of thorough phenotypic evaluation.
Acne severity is typically assessed by skin lesions and subsequent scarring. Phenotypically, acne includes various manifestations like comedones, papules, pustules, nodules, and cysts [6]. Comedones often termed “non-inflammatory lesions” comprise closed (whiteheads) and open (blackheads) types. Inflammatory features include papules (small, raised red lesions), pustules (like papules), and more severe cases with nodules and cysts, presenting as inflamed swellings over 5 mm in size [7]. Beyond physical discomfort, acne may yield serious psychological consequences, affecting social relationship, self-esteem, and mental health including anxiety and depression [8, 9].
The pathogenesis of acne involves multiple factors contributing to the inflammation in hair follicle units, such as abnormal keratinocytes desquamation [10], excessive sebum production [6], and even Propionibacterium acnes (P. acnes) colonization within the hair follicle. However, the rapid global urbanization and modernization of lifestyle habits have further impacted the development of acne [11]. Therefore, understanding the current epidemiological profile of acne is crucial for informed public health intervention and related preventive research. Here, we conducted a detailed and updated cross-sequential study to understand the prevalence, diverse phenotypes of acne vulgaris, and the interplay of various associated risk factors in a large cohort of young Chinese adults from the subset of Singapore and Malaysia Cross-Sectional Genetic Epidemiology Study (SMCGES) cohort. The findings from this study within the SMCGES cohort offer a timely and regionally specific understanding of acne prevalence and phenotypes which is crucial in advancing our knowledge to address and manage acne.
Methods
Participants and Data Collection
Participants were randomly and unbiasedly sampled from an ongoing, large-scale epidemiology collection in three universities, National University of Singapore (Singapore), Universiti Tunku Abdul Rahman (Malaysia), and Sunway University (Malaysia), between 2008 and 2022. Participants were recruited and signed an informed consent form before participation. A validated investigator-administered questionnaire that followed the standardized protocol of the International Study of Asthma and Childhood in Allergies (ISAAC) was adapted to collect information on demographics, personal lifestyles, personal medical history, and familial medical history. A subset of participants (n = 2,345) was randomly selected and assessed for acne severity, scarring grade, and a smaller group (n = 1,191) of which were assessed for blackhead, whitehead, papule, pustule, nodule, and cyst by dermatologist-trained personnel on site. In this analysis, we have excluded (n = 10,420) individuals with missing data and invalid responses on questions concerning age, sex, race, and acne-related questions. Due to the unequal percentages of other ethnicities (Malay and Indian) in Singapore and Malaysia, individuals were excluded to avoid affecting interpretation and the statistical power of the results, leaving a total of 6,255 participants (Fig. 1). The full SMCGES cohort has been described in detail previously [12–20].
Fig. 1.
Flowchart of subject recruitment and acne presentation, severity, scarring, onset, blackhead and/or whitehead and inflammatory phenotypes among the Singapore/Malaysia Cross-Sectional Genetics Epidemiology Study (SMCGES). Scarring grade and acne severity was assessed by dermatologist-trained personnel.
Acne Definition and Classification
For the interest of this study, we defined an acne case based on two questions. The participants were asked “Have you ever visited a doctor for your acne condition?” or “Do you have scars (keloids) left by acne/boils?”. Participants must answer affirmatively to either one question to be defined as an acne case. Additionally, we defined participants who were dermatologically assessed by trained personnel with a moderate-to-severe acne or grade 3/4 scarring left by acne to be an acne case. On the other hand, participants who answered no to both doctor visit and acne scarring, without a positive acne assessment, were defined as acne controls. Among the several acne grading systems developed, quantitative scales are shown to be more practical and accurate for large-scale epidemiology studies [21]. Based on acne lesions presented by an individual, acne severity were classified as mild, moderate, or severe, depending on the quantities of both non-inflammatory and inflammatory acne lesions. This system was adapted from the Global Evaluation Acne (GEA) scale [22]. The degree of scarring can be classified into four grades based on a system adapted from Qualitative Global Scarring Grading System (QGS) [23] with a few modifications tailored for the study population. Associations between risk factors and different aspects of acne presentation are assessed based on these classification systems. A total of 6,255 participants fulfilled the criteria and were included in the final analysis, with 3,504 (56.0%) acne cases and 2,751 (44.0%) acne controls. In the comparisons of acne severity, those with moderate (n = 353) and severe (n = 6) acne were analysed together (n = 359) due to the small sample size for severe acne. Similarly, for acne scarring comparisons, grade 1 (n = 686) and grade 2 (n = 418) were combined and grade 3 (n = 129) and grade 4 (n = 5) were combined to enlarge sample size. For papule and pustule comparisons, participants were categorized into two groups based on dermatological assessment [1]: participants had papule (n = 1,021) or pustule (n = 468) presentation [2]; participants with no papule (n = 1,027) or pustule (n = 1,526) presentation. Among the 3,504 acne cases, 1,233 (35.2%) had early onset (≤13 years), while 968 (27.6%) had late acne onset (>13 years); 1,303 (37.2%) did not respond due to recall difficulties. A ≤13 years cutoff was chosen as acne before 13 years is often considered prepubertal, while puberty-driven acne typically accelerates between ages 14–18. The classification of acne and its phenotypes is shown in Figure 1.
Statistical Analysis
Data were cleaned by removing invalid or inconsistent responses before analyzing with R Statistical Software (version 4.3.1 [2023-06-16 ucrt]; R Core Team 2023). Binary logistic regression was used to model the association between disease outcomes (acne presentation and the presentation of papule and pustule) and risk factors analyzed. Ordinal logistic regression was employed to examine the associations between acne severity, scarring, and potential risk factors, given the ordinal nature of the grading system. A key assumption of ordinal logistic regression is the proportional odds assumption, which posits that the relationship between explanatory variables and the outcome remains constant across all threshold levels of the ordinal dependent variable. To assess this assumption, the Brant test was conducted [24]. The Brant test evaluates whether the regression coefficients significantly differ across different cutoff points of the ordinal outcome, which would indicate a violation of the proportional odds assumption. In this study, the Brant test results showed no significant violation of the proportionality assumption (p > 0.05 for all models), supporting appropriateness of using ordinal logistic regression for analysis (online suppl. Table 1; for all online suppl. material, see https://doi.org/10.1159/000547009). Univariable analyses were first conducted, followed by multivariable analyses adjusting for age and sex, except for age and sex, which were adjusted for each other. Results were presented as odds ratios (OR) or adjusted odds ratios (AOR) with 95% confidence intervals (CIs). To account for multiple comparisons, we applied the false discovery rate (FDR) controlling procedure using the Benjamini-Hochberg method [25]. This approach was chosen as it effectively balances type I error control while maintaining statistical power, making it suitable for our analysis, where multiple hypotheses were tested simultaneously. Statistical significance of results was defined as adjusted p < 0.05 after correction of FDR, and the OR/AOR range did not cross 1.000.
Results
General Population Description
This study included 6,255 Chinese participants with an average age of around 22.8 years (SD ± 5.70) from Singapore and Malaysia. Table 1 outlines their demographic characteristics. The overall acne prevalence was high at 56.0%. There were more females than males with a male-to-female ratio of 1.488. Body mass index (BMI) ranged from 13.0 to 44.1 kg/m2 (mean = 21.0, SD ± 5.92), with 53.1% falling within the normal BMI range. No significant differences in the distributions between the acne cases and non-acne control in terms of age (χ2 = 3.843, p = 0.139), sex (χ2 = 0.190, p = 0.663), or BMI (χ2 = 5.703, p = 0.127). Familial acne histories, particularly siblings, were more prevalent in acne cases than non-acne controls.
Table 1.
Demographic characteristics of acne cases and controls among 6,225 young Chinese participants from the Singapore/Malaysia Cross-Sectional Genetics Epidemiology Study (SMCGES) cohort
| Variables | Acne cases1 (n = 3,504) | Non-acne controls1 (n = 2,751) | Overall (n = 6,255) |
|---|---|---|---|
| Age, years | |||
| Mean±SD2 | 22.6±5.0 | 23.0±6.4 | 22.8±5.7 |
| Sex | |||
| Male | 1,400 (40.0%) | 1,115 (40.5%) | 2,515 (40.2%) |
| Female | 2,104 (60.0%) | 1,636 (59.5%) | 3,740 (59.8%) |
| Body mass index, Asian classification (BMI, kg/m2) | |||
| Normal (18.5 <BMI ≤23) | 1,900 (54.2%) | 1,420 (51.6%) | 3,320 (53.1%) |
| Underweight (≤18.5) | 503 (14.4%) | 443 (16.1%) | 946 (15.1%) |
| Overweight (23 <BMI ≤25) | 334 (9.5%) | 271 (9.9%) | 605 (9.7%) |
| Obese (>25) | 308 (8.8%) | 256 (9.3%) | 564 (9%) |
| N/A3 | 334 (13.1%) | 271 (13.1%) | 605 (13.1%) |
| History of familial acne | |||
| Maternal acne | |||
| No | 2,428 (69.3%) | 2,252 (81.9%) | 4,680 (74.8%) |
| Yes | 1,076 (30.7%) | 499 (18.1%) | 1,575 (25.2%) |
| Paternal acne | |||
| No | 2,595 (74.1%) | 2,375 (86.3) | 4,970 (79.5%) |
| Yes | 909 (25.9%) | 376 (13.7%) | 1,285 (20.5%) |
| Parental acne | |||
| None | 2,099 (59.9%) | 2,143 (77.9%) | 4,242 (67.8%) |
| Either one | 825 (23.5%) | 341 (12.4%) | 1,166 (18.6%) |
| Both | 580 (16.6%) | 267 (9.7%) | 847 (13.5%) |
| Sibling acne | |||
| No | 564 (16.1%) | 431 (15.7%) | 995 (15.9%) |
| At least one | 2,940 (83.9%) | 2,320 (84.3%) | 5,260 (84.1%) |
| Familial acne | |||
| No | 439 (12.6) | 401 (14.5%) | 840 (13.4%) |
| At least one | 3,065 (87.5%) | 2,350 (85.4%) | 5,415 (85.6%) |
1An acne case was defined as a subject either who had visited a doctor for acne scarring or being dermatologically assessed to have a moderate-to-severe acne grading and/or scarring. A non-acne control does not fulfill the acne criteria.
2SD refers to standard deviation.
3N/A refers to an invalid or blank response.
Acne Phenotypic Distribution
Figure 1 illustrates the distribution of acne manifestations among 6,255 Chinese participants from SMCGES cohort. Over half the assessed cases (61.5%) were presented with mild acne, while 15.1% had moderate acne, and only 0.3% exhibited severe acne. Scarring was predominantly mild, with half of the subset displaying varying levels, primarily grade 1 scarring (29.3%), and minimal instances of grade 4 scarring (0.2%). A large proportion of participants (95.7%) exhibited blackheads and/or whiteheads. Among those assessed for inflammatory phenotypes (n = 1,191), participants with papules outnumbered those without, with 44.2% having 1–5 papules, 5.90% with 6–10, and 6.6% with 11 or more. Pustules were less common, with 24.9% having 1–5, 2.9% with 6–10, and 0.3% with 11 or more. Nodules (n = 5) and cysts (n = 20) were infrequently observed.
Significant Risk Factors for Acne Severity and Scarring Grade
Unsurprisingly, the risk factors significantly associated with acne aligned with our earlier study [26, 27], highlighting parental acne history, specific lifestyles, and comorbidities (online suppl. Table 2). Table 2 details demographic, lifestyle, and personal comorbidity factors significantly associated with acne severity and scarring grade. In comparison to females, male sex increases the associated odds of higher acne severity (AOR: 1.431, 95% CI: 1.251–1.639, p < 0.001) and acne scarring (AOR: 1.783, 95% CI: 1.563–2.035, p < 0.001). Age was also associated with higher risk of acne severity (age 19–24: AOR: 3.294, 95% CI: 2.754–3.945, p < 0.001; age <19: AOR: 5.004, 95% CI: 3.989–6.287, p < 0.001) and scarring grade (age 19–24: AOR: 1.360, 95% CI: 1.130–1.640, p < 0.01; age <19: AOR: 1.424, 95% CI: 1.136–1.788, p < 0.01). Increased household number was only significantly associated with acne severity (AOR: 1.256, 95% CI: 1.061–1.488, p < 0.05). Parental tertiary education level, either individually or both, are associated with a lower odd for severe acne and higher scarring grade. However, the association became insignificant after correction for multiple comparisons. Physical activity was identified as associated risk factor for acne scarring grades at the frequencies of both once or twice per week (AOR: 1.276, 95% CI: 1.102–1.478, p < 0.01) and most or all days (AOR: 1.406, 95% CI: 1.147–1.721, p < 0.01). In contrast, TV/computer usage exceeding 5 h per day lowered the odds of mild and moderate-to-severe acne (AOR: 0.625, 95% CI: 0.444–0.879, p < 0.05). Other factors including comorbid asthma/atopic dermatitis (AD), as well as lifestyle factors like smoking and sleeping hours, showed no significant association with acne severity and scarring grade (online suppl. Tables 3, 4).
Table 2.
Association between selected factors (demographic and lifestyle), acne severity, and acne scarring
| Variablesa | Mild acne (n = 2,432) | Moderate-to-severe acne (n = 359) | Acne severity (mild acne vs. moderate-to-severe acne) | Grade 1/2 scarring (n = 1,471) | Grade 3/4 scarring (n = 135) | Acne scarring (grade 1/2 scarring vs. grade 3/4 scarring) |
|---|---|---|---|---|---|---|
| Sex | ||||||
| Female | 1,507 (62.0%) | 208 (57.9%) | Ref | 848 (57.6%) | 53 (39.3%) | Ref |
| Male | 925 (38.0%) | 151 (42.1%) | 1.431 (1.251–1.639), <0.001 | 623 (42.4%) | 82 (60.7%) | 1.783 (1.563–2.035), <0.001 |
| Age, years | ||||||
| >24 | 259 (10.6%) | 13 (3.6%) | Ref | 192 (13.1%) | 12 (8.9%) | Ref |
| 19–24 | 1,708 (70.2%) | 250 (69.6%) | 3.294 (2.754–3.945), <0.001 | 1,015 (69.0%) | 100 (74.1%) | 1.360 (1.130–1.640), 0.005 |
| <19 | 465 (19.1%) | 96 (26.7%) | 5.004 (3.989–6.287), <0.001 | 264 (17.9%) | 23 (17.0%) | 1.424 (1.136–1.788), 0.009 |
| Household number | ||||||
| ≤3 | 492 (20.2%) | 68 (18.9%) | Ref | 296 (20.1%) | 29 (21.5%) | Ref |
| 4 | 803 (33.0%) | 119 (33.1%) | 1.093 (0.916–1.304), 0.539 | 483 (32.8%) | 50 (37.0%) | 1.124 (0.941–1.344), 0.485 |
| ≥5 | 1,103 (45.4%) | 165 (46.0%) | 1.256 (1.061–1.488), 0.032 | 672 (45.7%) | 55 (40.7%) | 1.217 (1.027–1.443), 0.106 |
| Paternal education | ||||||
| Primary | 227 (9.3%) | 38 (10.6%) | Ref | 158 (10.7%) | 10 (7.4%) | Ref |
| Secondary | 829 (34.1%) | 125 (34.8%) | 0.953 (0.752–1.207), 0.829 | 516 (35.1%) | 38 (28.1%) | 0.948 (0.754–1.194), 0.841 |
| Tertiary | 1,061 (43.6%) | 142 (39.6%) | 0.697 (0.553–0.877), 0.012 | 588 (40.0%) | 55 (40.7%) | 0.713 (0.570–0.894), 0.012 |
| Maternal education | ||||||
| Primary | 235 (9.7%) | 32 (8.9%) | Ref | 161 (10.9%) | 8 (5.9%) | Ref |
| Secondary | 984 (40.5%) | 152 (42.3%) | 0.942 (0.748–1.185), 0.765 | 605 (41.1%) | 46 (34.1%) | 0.896 (0.716–1.124), 0.629 |
| Tertiary | 902 (37.1%) | 120 (33.4%) | 0.668 (0.530–0.841), 0.007 | 498 (33.9%) | 49 (36.3%) | 0.713 (0.550–0.867), 0.005 |
| Parental education | ||||||
| Primary | 98 (4.0%) | 11 (3.1%) | Ref | 72 (4.9%) | 2 (1.5%) | Ref |
| Secondary | 1,299 (53.4%) | 199 (55.4%) | 1.027 (0.74–1.423), 0.929 | 798 (54.2%) | 63 (46.7%) | 0.893 (0.652–1.229), 0.784 |
| Tertiary | 718 (29.5%) | 93 (25.9%) | 0.704 (0.503–0.983), 0.156 | 390 (26.5%) | 37 (27.4%) | 0.650 (0.469–0.904), 0.038 |
| Physical activity | ||||||
| Never or only occasionally | 727 (29.9%) | 108 (30.1%) | Ref | 398 (27.1%) | 31 (23.0%) | Ref |
| Once or twice per week | 1,330 (54.7%) | 182 (50.7%) | 1.050 (0.907–1.215), 0.693 | 809 (55.0%) | 74 (54.8%) | 1.276 (1.102–1.478), 0.005 |
| Most or all days | 351 (14.4%) | 65 (18.1%) | 1.013 (0.823–1.248), 0.937 | 247 (16.8%) | 27 (20.0%) | 1.406 (1.147–1.721), 0.005 |
| TV/computer usage (h/day) | ||||||
| <1 | 102 (4.2%) | 15 (4.2%) | Ref | 64 (4.4%) | 3 (2.2%) | Ref |
| 1–3 | 599 (24.6%) | 100 (27.9%) | 1.142 (0.801–1.625), 0.663 | 348 (23.7%) | 41 (30.4%) | 1.091 (0.778–1.536), 0.838 |
| 3–5 | 835 (34.3%) | 120 (33.4%) | 1.004 (0.710–1.417), 0.997 | 487 (33.1%) | 47 (34.8%) | 1.020 (0.733–1.426), 0.939 |
| >5 | 886 (36.4%) | 121 (33.7%) | 0.625 (0.444–0.879), 0.032 | 567 (38.5%) | 44 (32.6%) | 0.848 (0.612–1.181), 0.625 |
Results are presented as AOR (95% CI) and p values. Multivariable logistic regression analysis was adjusted for age, sex, and parental acne for all indicated variables, except age and sex. p values were adjusted for multiple comparison using false discovery rate. p values <0.05 were statistically significant and written in bold.
AOR, adjusted odds ratio; CI, confidence interval.
aVariables may contain missing or invalid responses that were excluded from the logistic regression analysis; therefore, NA proportions are not displayed in this table.
Factors Associated with Non-Inflammatory Phenotypes and Inflammatory Phenotypes
Several factors showed significant associations with non-inflammatory and inflammatory phenotypes (Table 3). Compared to females, males have a higher associated odds of inflammatory acne phenotypes (AOR: 1.355, 95% CI: 1.124–1.636, p < 0.01) but lowered odds of non-inflammatory acne phenotypes (AOR: 0.365, 95% CI: 298–0.446, p < 0.001). Similarly, migrants have lower associated odds of non-inflammatory phenotypes (AOR: 0.674, 95% CI: 0.555–0.819, p < 0.001) and higher associated odds of inflammatory phenotypes (AOR: 1.442, 95% CI: 1.187–1.755, p < 0.001).
Table 3.
Association between selected factors (demographic, lifestyle, and comorbidities) and acne phenotypes (non-inflammatory blackhead and/or whitehead, and inflammatory acne)
| Variablesa | No blackhead or whitehead acne controls (n = 327) | Blackhead and/or whitehead, cases (n = 5,759) | Presentation of blackhead and/or whitehead (no blackhead or whitehead vs. having blackhead and/or whitehead) | No inflammatory phenotype (n = 914) | Inflammatory phenotype (n = 1,153) | Presentation of inflammatory acne (no inflammatory phenotype vs. inflammatory phenotype) |
|---|---|---|---|---|---|---|
| Sex | ||||||
| Female | 174 (53.2%) | 3,552 (61.7%) | Ref | 630 (68.9%) | 716 (62.1%) | Ref |
| Male | 290 (88.7%) | 2,207 (38.3%) | 0.365 (0.298–0.446), <0.001 | 284 (31.1%) | 437 (37.9%) | 1.355 (1.124–1.636), 0.009 |
| Age, years | ||||||
| >24 | 83 (25.4%) | 846 (14.7%) | Ref | 277 (30.3%) | 192 (16.7%) | Ref |
| 19–24 | 317 (96.9%) | 3,909 (67.9%) | 1.315 (1.013–1.691), 0.149 | 547 (59.8%) | 818 (70.9%) | 2.131 (1.721–2.642), <0.001 |
| <19 | 64 (19.6%) | 1,004 (17.4%) | 1.183 (0.837–1.676), 0.540 | 90 (9.8%) | 143 (12.4%) | 2.397 (1.738–3.321), <0.001 |
| Birthplace | ||||||
| Local | 262 (80.1%) | 3,681 (63.9%) | Ref | 651 (71.2%) | 763 (66.2%) | Ref |
| Migrant | 201 (61.5%) | 2,056 (35.7%) | 0.674 (0.555–0.819), <0.001 | 258 (28.2%) | 388 (33.7%) | 1.442 (1.187–1.755), <0.001 |
| Pets ownership | ||||||
| No | 227 (69.4%) | 2,288 (39.7%) | Ref | 447 (48.9%) | 521 (45.2%) | Ref |
| Yes | 152 (46.5%) | 2,362 (41.0%) | 1.403 (1.131–1.744), 0.021 | 465 (50.9%) | 527 (45.7%) | 0.936 (0.780–1.122), 0.900 |
| Alcohol intake | ||||||
| Nondrinker | 213 (65.1%) | 2,464 (42.8%) | Ref | 415 (45.4%) | 484 (42.0%) | Ref |
| Occasional | 236 (72.2%) | 3,136 (54.5%) | 1.328 (1.090–1.617), 0.035 | 475 (52.0%) | 630 (54.6%) | 1.047 (0.872–1.257), 0.929 |
| Frequent | 14 (4.3%) | 141 (2.4%) | 1.143 (0.666–2.118), 0.750 | 23 (2.5%) | 36 (3.1%) | 1.306 (0.749–2.325), 0.785 |
| Allergic rhinitis presentation | ||||||
| Control | 295 (90.2%) | 3,093 (53.7%) | Ref | 464 (50.8%) | 566 (49.1%) | Ref |
| Case | 169 (51.7%) | 2,666 (46.3%) | 1.483 (1.218–1.810), <0.001 | 450 (49.2%) | 587 (50.9%) | 1.010 (0.845–1.207), 0.986 |
Results are presented as AOR (95% CI) and p values. Multivariable logistic regression analysis was adjusted for age, sex, and parental acne for all indicated variables, except age and sex. p values were adjusted for multiple comparison using false discovery rate. p values <0.05 were statistically significant and written in bold.
AOR, adjusted odds ratio; CI, confidence interval.
aVariables may contain missing or invalid responses that were excluded from the logistic regression analysis; therefore, NA proportions are not displayed in this table.
A younger age below 19 is a stronger associated risk factor for inflammatory phenotypes (AOR: 2.397, 95% CI: 1.738–3.321, p < 0.001) as compared to the age group between 19 and 24 (AOR: 2.131, 95% CI: 1.721–2.262, p < 0.001). Interestingly, age was not associated with the non-inflammatory phenotypes. Pet ownerships (AOR: 1.403, 95% CI: 1.131–1.744, p < 0.05), occasional alcohol drinking (AOR: 1.328, 95% CI: 1.090–1.617, p < 0.05), and symptomatic histories of AR (AOR: 1.483, 95% CI: 1.218–1.810, p < 0.001) were associated with a higher odd of non-inflammatory phenotypes only. Several demographic and lifestyle factors, including parental education levels, TV/Computer usage time, physical activity, smoking, and sleeping hours, did not exhibit statistically significant associations with either acne phenotypes (online suppl. Tables 5, 6).
Significant Risk Factors for Late-Onset Acne
Our findings showed that late acne onset was associated with male sex (AOR: 1.269, 95% CI: 1.063–1.514, p < 0.01), underweight BMI (AOR: 1.514, 95% CI: 1.158–1.982, p < 0.01), and migrant status (AOR: 1.348, 95% CI: 1.132–1.606, p < 0.01). Furthermore, paternal smoking (AOR: 1.446, 95% CI: 1.062–1.970, p < 0.05) or at least one parent smoking (AOR: 1.460, 95% CI: 1.070–1.992, p < 0.05) were associated with a higher odds of late acne onset, suggesting a potential role of smoking in triggering its development. In contrast, higher maternal (AOR: 0.674, 95% CI: 0.489–0.928, p < 0.05) and paternal (AOR: 0.667, 95% CI: 0.489–0.909, p < 0.05) education levels were associated with a lower odds of late acne onset (Table 4). These associated risk factors largely reflect those identified for acne presentation. Other factors analyzed did not show any statistically significant association (online suppl. Table 7).
Table 4.
Association between selected factors (demographic and lifestyle) and acne onset
| Variablesa | Early acne onset (≤13 years) (n = 1,233) | Late acne onset (>13 years) (n = 968) | Acne onset (early onset vs. late onset) |
|---|---|---|---|
| Sex | |||
| Female | 830 (67.3%) | 598 (61.8%) | Ref |
| Male | 403 (32.7%) | 370 (38.2%) | 1.269 (1.063–1.514), 0.008 |
| Body mass index (Asian classification) | |||
| Normal | 574 (46.6%) | 455 (47.0%) | Ref |
| Underweight | 132 (10.7%) | 149 (15.4%) | 1.514(1.158–1.982), 0.002 |
| Overweight | 129 (10.5%) | 111 (11.5%) | 1.049 (0.787–1.396), 0.743 |
| Obese | 166 (13.5%) | 99 (10.2%) | 0.737 (0.555–0.973), 0.032 |
| Maternal education | |||
| Primary | 94 (7.6%) | 101 (10.4%) | Ref |
| Secondary | 457 (37.1%) | 350 (36.2%) | 0.769 (0.558–1.059), 0.107 |
| Tertiary | 520 (42.2%) | 339 (35.0%) | 0.674 (0.489–0.928), 0.016 |
| Paternal education | |||
| Primary | 99 (8.0%) | 103 (10.6%) | Ref |
| Secondary | 406 (32.9%) | 320 (33.1%) | 0.799 (0.582–1.095), 0.162 |
| Tertiary | 562 (45.6%) | 366 (37.8%) | 0.667 (0.489–0.909), 0.010 |
| Parental education | |||
| Primary | 39 (3.2%) | 45 (4.6%) | Ref |
| Secondary | 612 (49.6%) | 487 (50.3%) | 0.755 (0.479–1.184), 0.222 |
| Tertiary | 415 (33.7%) | 255 (26.3%) | 0.601 (0.377–0.956), 0.032 |
| Birthplace | |||
| Local | 660 (53.5%) | 462 (47.7%) | Ref |
| Migrate | 566 (45.9%) | 496 (51.2%) | 1.348 (1.132–1.606), 0.001 |
| Paternal smoking status | |||
| No | 1,126 (91.3%) | 866 (89.5%) | Ref |
| Yes | 107 (8.7%) | 102 (10.5%) | 1.446 (1.062–1.970), 0.019 |
| Parental smoking status | |||
| None | 1,122 (91.0%) | 862 (89.0%) | Ref |
| One parent | 106 (8.6%) | 101 (10.4%) | 1.460 (1.070–1.992), 0.017 |
| Both parents | 5 (0.4%) | 5 (0.5%) | 1.342 (0.365–4.932), 0.649 |
Results are presented as AOR (95% CI) and p values. Multivariable logistic regression analysis was adjusted for age, sex, and parental acne for all indicated variables, except for sex itself. p values were adjusted for multiple comparison using false discovery rate. p values <0.05 were statistically significant and written in bold.
AOR, adjusted odds ratio; CI, confidence interval.
aVariables may contain missing or invalid responses that were excluded from the logistic regression analysis; therefore, NA proportions are not displayed in this table.
Positive Familial History Is the Strongest Risk Factor
Finally, a positive parental and siblings’ history of allergic diseases (acne, AD, AR, and asthma) emerged as strong associated risk factors for severe acne and higher acne scarring grades (Table 5). Compared to maternal or paternal history of acne alone, the combined parental acne category showed a higher associated odds for acne severity, non-inflammatory phenotypes, and inflammatory phenotypes, suggesting potential additive effects. Parental acne was particularly associated with non-inflammatory phenotypes (either parent: AOR: 1.622, 95% CI: 1.240–2.156, p < 0.05; both parents: AOR: 3.610, 95% CI: 2.420–5.650, p < 0.001). Interestingly, a positive history of parental acne was associated with a lower odds of late acne onset (AOR: 0.725, 95% CI: 0.554–0.945, p < 0.05), whereas having siblings increased the odds of late acne onset (AOR: 1.264, 95% CI: 1.025–1.561, p < 0.05). Subgroup analyses by country revealed that a parental history of acne remained a significant risk factor for acne presentation in both Singapore and Malaysia, reinforcing the role of genetic predisposition. Given the smaller sample size in the Malaysia subgroup (n = 1,403 vs. n = 4,852 in Singapore), some associations may be limited by statistical power (online suppl. Table 8).
Table 5.
Association between selected factors (demographic and lifestyle), acne severity, acne scarring, blackhead and/or whitehead, inflammatory phenotypes, and acne onset
| Variablesa | Mild acne (n = 2,432) | Moderate-to-severe acne (n = 359) | Acne severity | Grade 1/2 scarring (n = 1,471) | Grade 3/4 scarring (n = 135) | Acne scarring | No blackhead or whitehead acne controls (n = 327) | Blackhead and/or whitehead, cases (n = 5,759) | Presentation of blackhead and/or whitehead | No inflammatory phenotype (n = 914) | Inflammatory phenotype (n = 1,153) | Presentation of inflammatory acne | Early acne onset (≤13 years) (n = 1,233) | Late acne onset (>13 years) (n = 968) | Acne onset |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Maternal acne | |||||||||||||||
| Control | 1,907 (78.4%) | 248 (69.1%) | Ref | 1,100 (74.8%) | 88 (65.2%) | Ref | 400 (86.2%) | 4,255 (73.9%) | Ref | 805 (88.1%) | 940 (81.5%) | Ref | 915 (41.6%) | 770 (35.0%) | Ref |
| Case | 525 (21.6%) | 111 (30.9%) | 1.646 (1.398–1.939), <0.001 | 371 (25.2%) | 47 (34.8%) | 1.802 (1.543–2.105), <0.001 | 64 (13.8%) | 1,504 (26.1%) | 2.272 (1.742–3.009), <0.001 | 109 (11.9%) | 213 (18.5%) | 1.611 (1.254–2.079), 0.009 | 318 (14.4%) | 198 (9.0%) | 0.747 (0.608–0.916), 0.005 |
| Paternal acne | |||||||||||||||
| Control | 2,008 (82.6%) | 269 (74.9%) | Ref | 1,168 (79.4%) | 95 (70.4%) | Ref | 416 (89.7%) | 4,526 (78.6%) | Ref | 834 (91.2%) | 987 (85.6%) | Ref | 966 (43.9%) | 803 (36.5%) | Ref |
| Case | 424 (17.4%) | 90 (25.1%) | 1.617 (1.355–1.933), <0.001 | 303 (20.6%) | 40 (29.6%) | 1.814 (1.533–2.147), <0.001 | 48 (10.3%) | 1,233 (21.4%) | 2.467 (1.832–3.395), <0.001 | 80 (8.8%) | 166 (14.4%) | 1.702 (1.283–2.276), 0.009 | 267 (12.1%) | 165 (7.5%) | 0.747 (0.600–0.929), 0.009 |
| Parental acne | |||||||||||||||
| None | 1,768 (72.7%) | 214 (59.6%) | Ref | 993 (67.5%) | 70 (51.9%) | Ref | 376 (81.0%) | 3,842 (66.7%) | Ref | 757 (82.8%) | 862 (74.8%) | Ref | 820 (37.3%) | 713 (32.4%) | Ref |
| One | 379 (15.6%) | 89 (24.8%) | 1.610 (1.341–1.934), <0.001 | 282 (19.2%) | 43 (31.9%) | 2.072 (1.74–2.466), <0.001 | 64 (13.8%) | 1,097 (19.0%) | 1.622 (1.240–2.156), 0.012 | 125 (13.7%) | 203 (17.6%) | 1.372 (1.074–1.759), 0.098 | 241 (10.9%) | 147 (6.7%) | 0.712 (0.565–0.895), 0.004 |
| Both | 285 (11.7%) | 56 (15.6%) | 1.782 (1.436–2.215), <0.001 | 196 (13.3%) | 22 (16.3%) | 1.858 (1.513–2.282), <0.001 | 24 (5.2%) | 820 (14.2%) | 3.610 (2.420–5.650), <0.001 | 32 (3.5%) | 88 (7.6%) | 2.332 (1.546–3.603), <0.001 | 172 (7.8%) | 108 (4.9%) | 0.725 (0.554–0.945), 0.018 |
| Siblings AD | |||||||||||||||
| Control | 1,657 (68.1%) | 206 (57.4%) | Ref | 941 (64.0%) | 85 (63.0%) | Ref | 368 (79.3%) | 4,324 (75.1%) | Ref | 729 (79.8%) | 866 (75.1%) | Ref | 638 (32.6%) | 481 (24.6%) | Ref |
| Case | 775 (31.9%) | 153 (42.6%) | 1.231 (1.066–1.421), 0.012 | 530 (36.0%) | 50 (37.0%) | 1.420 (1.234–1.635), <0.001 | 96 (20.7%) | 1,435 (24.9%) | 1.127 (0.889–1.442), 0.600 | 185 (20.2%) | 287 (24.9%) | 1.341 (1.079–1.669), 0.062 | 439 (22.4%) | 398 (20.3%) | 1.28 (1.064–1.540), 0.009 |
| Siblings AR | |||||||||||||||
| Control | 1,909 (78.5%) | 256 (71.3%) | Ref | 1,108 (75.3%) | 98 (72.6%) | Ref | 397 (85.6%) | 4,885 (84.8%) | Ref | 844 (92.3%) | 1,014 (87.9%) | Ref | 764 (39.4%) | 589 (30.4%) | Ref |
| Case | 523 (21.5%) | 103 (28.7%) | 1.248 (1.060–1.471), 0.021 | 363 (24.7%) | 37 (27.4%) | 1.491 (1.273–1.745), <0.001 | 67 (14.4%) | 874 (15.2%) | 0.965 (0.736–1.283), 0.750 | 70 (7.7%) | 139 (12.1%) | 1.656 (1.221–2.265), 0.009 | 305 (15.7%) | 282 (14.5%) | 1.285 (1.052–1.570), 0.014 |
| Siblings asthma | |||||||||||||||
| Control | 1,693 (69.6%) | 233 (64.9%) | Ref | 988 (67.2%) | 92 (68.1%) | Ref | 368 (79.3%) | 4,406 (76.5%) | Ref | 788 (86.2%) | 912 (79.1%) | Ref | 685 (35.0%) | 540 (27.6%) | Ref |
| Case | 739 (30.4%) | 126 (35.1%) | 1.301 (1.122–1.509), <0.001 | 483 (32.8%) | 43 (31.9%) | 1.375 (1.190–1.588), <0.001 | 96 (20.7%) | 1,353 (23.5%) | 1.060 (0.834–1.357), 0.932 | 126 (13.8%) | 241 (20.9%) | 1.635 (1.285–2.088), <0.001 | 395 (20.2%) | 337 (17.2%) | 1.148 (0.949–1.389), 0.156 |
Results are presented as AOR (95% CI) and p values. Multivariable logistic regression analysis was adjusted for age, sex, and parental acne for all indicated variables. p values were adjusted for multiple comparison using false discovery rate. p values <0.05 were statistically significant and written in bold.
AOR, adjusted odds ratio; CI, confidence interval.
aVariables may contain missing or invalid responses that were excluded from the logistic regression analysis; therefore, NA proportions are not displayed in this table.
Individuals with siblings affected by AD, AR, and asthma had higher odds of severe acne (AD: AOR: 1.231, 95% CI: 1.066–1.421, p < 0.05; AR: AOR: 1.248, 95% CI: 1.060–1.471, p < 0.05; asthma: AOR: 1.301, 95% CI: 1.122–1.509, p < 0.001) and higher scarring grade (AD: AOR: 1.420, 95% CI: 1.234–1.635, p < 0.001; AR: AOR: 1.491, 95% CI: 1.273–1.745, p < 0.001; asthma: AOR: 1.375, 95% CI: 1.190–1.588, p < 0.001). Siblings’ history of AR and asthma was also significantly associated with inflammatory acne phenotypes (AR: AOR: 1.656, 95% CI: 1.221–2.265, p < 0.01; asthma: AOR: 1.635, 95% CI: 1.285–2.088, p < 0.001) and late onset of acne (AR: AOR: 1.285; 95% CI: 1.052–1.570; p < 0.05). However, no significant association was found between siblings’ history of allergic diseases and non-inflammatory phenotypes.
Discussion
In our large epidemiological cohort comprising young Chinese adults from Singapore and Malaysia, demographic, lifestyles, and familial factors were associated with a higher odds of acne severity and scarring grade in addition to mere acne presentation. These factors included male gender, younger age group, higher household number, lower parental education, and frequent engagement in physical activities. Notably, positive family histories of acne and atopic diseases were statistically associated with acne outcomes, suggesting the influence of genetic and environmental factors on acne pathogenesis. It is important to emphasize, however, that while these factors are statistically related to acne severity and scarring, the study’s cross-sectional design cannot establish causality, and further longitudinal studies are needed to better understand these relationships.
In this study, a parental history strongly predisposes individuals to acne manifestation, with paternal acne demonstrating a slightly stronger effect, aligning with findings from our previous meta-analysis [28]. Similarly, a parental history of acne emerged as a risk factor for all other investigated aspects of acne, albeit with varying influences parental history exhibited additive effects on acne severity, consistent with previous results [29], as well as non-inflammatory phenotypes and inflammatory phenotypes, but not on acne scarring. This discrepancy may stem from acne scarring being influenced by different genetic factors that contributed to the condition and being more amenable to be intervention through skincare practices [30]. The complex interactions between inherited genes from parents may underlie these additive effects. Wang et al. [31] reported the involvement of SELL, TP63, and DDB2 in the pathogenesis of severe acne and genes that have been individually implicated in forming acne lesions or influencing skin physiology [32, 33]. Although no significant association was observed for siblings’ acne history, the potentiating effects of siblings’ history of AD, AR, or asthma were similar across other investigated aspects of acne. Genes that related to immune diseases may explain these associations; for instance, IL-1 family members were found to be elevated in AR, AD, and asthma [34, 35], including IL-36 found increased in acne lesions [36]. Specific immune response cells, particularly Th1 and Th17 cells, play a role in acne development [37, 38], also implicated in the disease pathogenesis of these allergic disorders [39, 40]. The associations with familial history align with reported results on severe acne [29, 41], while some found no significant association [42, 43]. In the future research, genome-wide association studies (GWAS) or gene expression profiling can be performed to identify risk loci or transcriptional changes that differ based on genetic predisposition to acne, which would help elucidate the role of familial medical history on acne.
The estimated prevalence of acne in the Singapore and Malaysia Chinese population (56.0%) exhibits variations when compared to those previously reported in China (46.8%) [44] and even in Singapore (88.0%) [45]. These differences may stem from variations in the age groups or geographical locations of the studies' populations, although our estimate aligns closely with the comparable age group in China and Europe [46, 47]. Interestingly, the prevalence of moderate and severe acne at 15.1% and 0.3%, respectively, were notably lower than those observed in a previous Singaporean study focused on participants aged 13–19 [48]. This discrepancy might be attributed to the involvement of different age groups, as age has been established as a risk factor related to acne severity [28]. However, it is crucial to acknowledge the challenges in comparing results across studies that employ diverse severity grading systems for acne, given the absence of consensus on criteria for grading [6]. Due to the limited availability of targeted research on the prevalence of specific acne phenotypes, we were unable to conduct further meaningful comparisons between our findings and other existing studies at this point.
Some studies investigating demographic factors influencing acne have reported a higher prevalence in female than male [49, 50]. While our study did not identify sex and age as strong risk factors associated with acne presentation, we observed associations with acne severity, scarring grade, and inflammatory phenotypes. This is especially so for male individuals from younger age groups, aligning with previous reports [4, 28, 42, 51, 52]. This association may be potentially explained by inflammatory responses influenced by hormonal factors that impacted acne scar development [52], and contributing to increased acne severity. For instance, androstenedione and dehydroepiandrosterone correlated with acne scarring in males, while dihydrotestosterone and insulin-like growth factor 1 were associated with acne scarring in females [53]. Furthermore, serum levels of insulin-like growth factor 1 were significantly higher in individuals under 21 presenting with acne [54]. However, regional variations were noted, as observed in North India where adult acne tended to be more inflammatory compared to adolescent acne [55]. This suggests that trends in acne phenotypes may not be universally applicable across ethnicities or countries. The contrasting effects of migration on non-inflammatory and inflammatory phenotypes may be attributed to differences in environmental exposures and lifestyle factors like diet and climate, which vary across cultures and countries. Studies have indicated that a high-fat diet in modernized societies was associated with acne inflammation by potentially upregulating pro-inflammatory cytokines such as interleukin-8 [32, 56]. Additionally, higher temperature and higher humidity were shown to contribute to acne flare initiation [57], emphasizing the importance of external factors that differ in various regions.
We acknowledge several limitations in our study. First, the participants were exclusively Chinese individuals recruited from universities, which limits the generalizability of our findings to other ethnic groups. The cross-sectional nature of the study prevents us from making definitive conclusions about the causal relationships between the identified risk factors and acne outcomes. Additionally, due to the presence of invalid responses, some data had to be excluded, and not all participants underwent full assessment for their acne phenotypes, resulting in incomplete profiles for some participants. While a subgroup analysis comparing Singapore and Malaysia was conducted, the uneven sample sizes limited statistical power, leading to weaker or inconsistent associations for some risk factors. Given these limitations, findings should be interpreted with caution, and future studies with larger, more balanced samples are needed to better assess regional differences. Despite these limitations, the robust sample size (n = 6,255) allows for reliable epidemiological analysis. These findings offer important insights into the statistical relationships between risk factors and acne phenotypes, while suggesting potential avenues for future research to explore causal mechanisms. Future studies should consider factors such as dietary habits, stress, and weather conditions and could benefit from longitudinal designs to better capture cause-and-effect relationships. Genetic studies, including omics approaches and functional mapping, may help uncover the genetic basis of acne, especially in relation to family histories of acne and atopic diseases [58, 59].
In summary, positive familial history emerged as a strong predisposing factor associated with acne presentation, severity, scarring, and the presence of both non-inflammatory and inflammatory phenotypes among young Chinese participants from the SMCGES cohort. Additionally, demographic factors (age, sex, birthplace, parental education level), certain lifestyle factors (e.g., TV/computer usage, alcohol consumption), and the comorbidity of AR were statistically associated with various aspects of acne presentation. Given the high and increasing prevalence of acne, further research is needed to explore its pathogenesis. While our study has identified key factors related to acne outcomes, it is essential to note that these relationships are statistical and not causal. More detailed genetic analyses could provide insights into the genetic underpinning of acne, particularly in relation to family history and comorbid allergic conditions.
Acknowledgments
We extend our sincere appreciation to all participants and their families for their willingness to take part in the SMCGES study. We also would like to thank the past and current lab members who participate in the epidemiology collection.
Statement of Ethics
This study was conducted in accordance with the principles of the Declaration of Helsinki and Good Clinical Practices and in compliance with local regulatory requirements. Cross-sectional studies in Singapore were conducted on the National University of Singapore (NUS) campus annually between 2005 and 2022 under the approval of the Institutional Review Board (NUS-IRB reference code: NUS-07-023, NUS-09-256, NUS-10-445, NUS-13-075, NUS-14-150, and NUS-18-036) and by the Helsinki declaration. Cross-sectional studies in Malaysia were held in the Universiti Tunku Abdul Rahman (UTAR) and Sunway University. Ethical approval was granted, respectively, from the Scientific and Ethical Review Committee (SERC) of UTAR (ref. code: U/SERC/03/2016) and Sunway University Research Ethics Committee (ref. code: SUREC 2019/029). Before the data collection, all participants involved signed an informed consent form.
Conflict of Interest Statement
F.T.C. reports grants from the National University of Singapore, Singapore Ministry of Education Academic Research Fund, Singapore Immunology Network, National Medical Research Council (NMRC) (Singapore), Biomedical Research Council (BMRC) (Singapore), National Research Foundation (NRF) (Singapore), Singapore Food Agency (SFA), Singapore’s Economic Development Board (EDB), and the Agency for Science Technology and Research (A*STAR) (Singapore), during the conduct of the study; and consulting fees from Sime Darby Technology Centre; First Resources Ltd.; Genting Plantation, Olam International, Musim Mas, and Syngenta Crop Protection, outside the submitted work. The other authors declare no other competing interests. This research is supported by the National Research Foundation Singapore under its Open Fund-Large Collaborative Grant (MOH-001636) (A-8002641-00-00) and administered by the Singapore Ministry of Health’s National Medical Research Council.
Funding Sources
F.T.C. received grants from the National University of Singapore (N-154-000-038-001 [E-154-00-0017-01], C141-000-077-001 [E-141-00-0096-01]), the Singapore Ministry of Education Academic Research Fund (R-154-000-191-112, R-154-000-404-112, R-154-000-553-112, R-154-000-565-112, R-154-000-630-112, R-154-000-A08-592, R-154-000-A27-597, R-154-000-A91-592, R-154-000-A95-592, and R-154-000-B99-114), the Biomedical Research Council (BMRC) (Singapore) (BMRC/01/1/21/18/077, BMRC/04/1/21/19/315, and BMRC/APG2013/108), the Singapore Immunology Network (SIgN-06-006; SIgN-08-020), the National Medical Research Council (NMRC) (Singapore) (NMRC/1150/2008, OFIRG20nov-0033, and MOH-001636 [OFLCG23may-0038, A-8002641-00-00]), the National Research Foundation (NRF) (Singapore) (NRF-MP-2020-0004), the Singapore Food Agency (SFA) (SFS_RND_SUFP_001_04 and W22W3D0006), Singapore’s Economic Development Board (EDB) (A-8002576-00-00), and the Agency for Science Technology and Research (A*STAR) (Singapore) (H17/01/a0/008 and APG2013/108). The funding agencies had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Author Contributions
F.T.C. conceived and supervised the current research study. Z.H. and J.J.L. conducted the literature review, performed data and statistical analyses, interpreted the findings, and co-wrote the manuscript. J.J.L. also critically revised the manuscript. Z.H., J.J.L., K.R., and Y.-H.S. assisted in the epidemiology collection and collated the data. All authors have read and approved the final manuscript for submission.
Funding Statement
F.T.C. received grants from the National University of Singapore (N-154-000-038-001 [E-154-00-0017-01], C141-000-077-001 [E-141-00-0096-01]), the Singapore Ministry of Education Academic Research Fund (R-154-000-191-112, R-154-000-404-112, R-154-000-553-112, R-154-000-565-112, R-154-000-630-112, R-154-000-A08-592, R-154-000-A27-597, R-154-000-A91-592, R-154-000-A95-592, and R-154-000-B99-114), the Biomedical Research Council (BMRC) (Singapore) (BMRC/01/1/21/18/077, BMRC/04/1/21/19/315, and BMRC/APG2013/108), the Singapore Immunology Network (SIgN-06-006; SIgN-08-020), the National Medical Research Council (NMRC) (Singapore) (NMRC/1150/2008, OFIRG20nov-0033, and MOH-001636 [OFLCG23may-0038, A-8002641-00-00]), the National Research Foundation (NRF) (Singapore) (NRF-MP-2020-0004), the Singapore Food Agency (SFA) (SFS_RND_SUFP_001_04 and W22W3D0006), Singapore’s Economic Development Board (EDB) (A-8002576-00-00), and the Agency for Science Technology and Research (A*STAR) (Singapore) (H17/01/a0/008 and APG2013/108). The funding agencies had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data Availability Statement
The data that support the findings of this study are not publicly available due to the privacy of participants but are available from the corresponding author (F.T.C.) upon reasonable request.
Supplementary Material.
Supplementary Material.
Supplementary Material.
Supplementary Material.
Supplementary Material.
Supplementary Material.
Supplementary Material.
Supplementary Material.
References
- 1. Chen H, Zhang T, Yin X, Man J, Yang X, Lu M. Magnitude and temporal trend of acne vulgaris burden in 204 countries and territories from 1990 to 2019: an analysis from the Global Burden of disease study 2019. Br J Dermatol. 2022;186(4):673–83. [DOI] [PubMed] [Google Scholar]
- 2. Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C, Ezzati M, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012;380(9859):2163–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Ismail NH, Manaf ZA, Azizan NZ. High glycemic load diet, milk and ice cream consumption are related to acne vulgaris in Malaysian young adults: a case control study. BMC Dermatol. 2012;12(1):13–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. White GM. Recent findings in the epidemiologic evidence, classification, and subtypes of acne vulgaris. J Am Acad Dermatol. 1998;39(2 Pt 3):S34–7. [DOI] [PubMed] [Google Scholar]
- 5. Ali F. Determination of various risk factors associated with acne vulgaris infection in quetta, Pakistan. Pure Appl Biol. 2019;8(3). [Google Scholar]
- 6. Williams HC, Dellavalle RP, Garner S. Acne vulgaris. Lancet. 2012;379(9813):361–72. [DOI] [PubMed] [Google Scholar]
- 7. Mahto A. Acne vulgaris. Medicine. 2017;45(6):386–9. [Google Scholar]
- 8. Nguyen CM, Beroukhim K, Danesh MJ, Babikian A, Koo J, Leon A. The psychosocial impact of acne, vitiligo, and psoriasis: a review. Clin Cosmet Investig Dermatol. 2016;9:383–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Salman A, Kurt E, Topcuoglu V, Demircay Z. Social anxiety and quality of life in vitiligo and acne patients with facial involvement: a cross-sectional controlled study. Am J Clin Dermatol. 2016;17(3):305–11. [DOI] [PubMed] [Google Scholar]
- 10. Clayton RW, Göbel K, Niessen CM, Paus R, van Steensel MAM, Lim X. Homeostasis of the sebaceous gland and mechanisms of acne pathogenesis. Br J Dermatol. 2019;181(4):677–90. [DOI] [PubMed] [Google Scholar]
- 11. Cordain L, Lindeberg S, Hurtado M, Hill K, Eaton SB, Brand-Miller J. Acne vulgaris: a disease of Western civilization. Arch Dermatol. 2002;138(12):1584–90. [DOI] [PubMed] [Google Scholar]
- 12. Lim JJ, Lim YYE, Ng JY, Malipeddi P, Ng YT, Teo WY, et al. An update on the prevalence, chronicity, and severity of atopic dermatitis and the associated epidemiological risk factors in the singapore/malaysia Chinese young adult population: a detailed description of the singapore/malaysia cross-sectional genetics epidemiology study (SMCGES) cohort. World Allergy Organ J. 2022;15(12):100722. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Wong QYA, Lim JJ, Ng JY, Malipeddi P, Teo WY, Lim YYE, et al. Allergic rhinitis in Chinese young adults from the singapore/malaysia cross-sectional genetics epidemiology study (SMCGES) cohort: prevalence, patterns, and epidemiology of allergic rhinitis. World Allergy Organ J. 2022;15(10):100704. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Wong QYA, Lim JJ, Ng JY, Malipeddi P, Lim YYE, Sio YY, et al. An updated prevalence of asthma, its phenotypes, and the identification of the potential asthma risk factors among young Chinese adults recruited in Singapore. World Allergy Organ J. 2023;16(3):100757. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Lim JJ, Lim Y, Ng J, Malipeddi P, Ng YT, Teo W, et al. Burgers, fast foods, and increased associated risk for atopic dermatitis: a cross-sectional study of dietary habits among young Chinese adults in singapore/malaysia. Dermatology. 2024;240(1):77–84. [DOI] [PubMed] [Google Scholar]
- 16. Lim JJ, Reginald K, Say YH, Liu MH, Chew FT. Dietary protein intake and associated risks for atopic dermatitis, intrinsic eczema, and allergic sensitization among young Chinese adults in Singapore/Malaysia: key findings from a cross-sectional Study. JID Innov. 2023;3(6):100224. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Lim JJ, Reginald K, Say YH, Liu MH, Chew FT. A dietary pattern for high estimated total fat amount is associated with enhanced allergy sensitization and atopic diseases among Singapore/Malaysia young Chinese adults. Int Arch Allergy Immunol. 2023;184(10):975–84. [DOI] [PubMed] [Google Scholar]
- 18. Lim JJ, Lim SW, Reginald K, Say YH, Liu MH, Chew FT. Association of frequent intake of trans Fatty acids and saturated Fatty acids in diets with increased susceptibility of atopic dermatitis exacerbation in young Chinese adults: a cross-sectional Study in Singapore/Malaysia. Skin Health Dis. 2024;4(4):330. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Lim JJ, Reginald K, Say YH, Liu MH, Chew FT. Frequent intake of high fiber and probiotic diets lowers risks associated with atopic dermatitis and house dust mite allergy: a cross-sequential study of young Chinese adults from Singapore and Malaysia. Eur J Nutr. 2024;64(1):38. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Lim JJ, Reginald K, Say YH, Liu M, Chew F. Associations between self-reported dietary intake and atopic dermatitis risk in young adults from Singapore and Malaysia. Clin Exp Allergy. 2025;55(6):493–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Bernardis E, Shou H, Barbieri JS, McMahon PJ, Perman MJ, Rola LA, et al. Development and initial validation of a multidimensional acne global grading System integrating primary lesions and secondary changes. JAMA Dermatol. 2020;156(3):296–302. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Dreno B, Poli F. Epidemiology of acne. Dermatology. 2003;206(1):7–10. [DOI] [PubMed] [Google Scholar]
- 23. Goodman GJ, Baron JA. Postacne scarring: a qualitative global scarring grading System. Dermatol Surg. 2006;32(12):1458–66. [DOI] [PubMed] [Google Scholar]
- 24. Brant R. Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics. 1990;46(4):1171–8. [PubMed] [Google Scholar]
- 25. Glickman ME, Rao SR, Schultz MR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol. 2014;67(8):850–7. [DOI] [PubMed] [Google Scholar]
- 26. Say YH, Heng AHS, Reginald K, Wong YR, Teh KF, Rawanan Shah SM, et al. Modifiable and non-modifiable epidemiological risk factors for acne, acne severity and acne scarring among Malaysian Chinese: a cross-sectional study. BMC Public Health. 2021;21(1):601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Heng AHS, Say YH, Sio YY, Ng YT, Chew FT. Epidemiological risk factors associated with Acne Vulgaris presentation, severity, and scarring in a Singapore Chinese population: a cross-sectional Study. Dermatology. 2022;238(2):226–35. [DOI] [PubMed] [Google Scholar]
- 28. Heng AHS, Chew FT. Systematic review of the epidemiology of acne vulgaris. Sci Rep. 2020;10(1):5754. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Karciauskiene J, Valiukeviciene S, Gollnick H, Stang A. The prevalence and risk factors of adolescent acne among schoolchildren in Lithuania: a cross-sectional study. J Eur Acad Dermatol Venereol. 2014;28(6):733–40. [DOI] [PubMed] [Google Scholar]
- 30. Fabbrocini G, Annunziata MC, D’Arco V, De Vita V, Lodi G, Mauriello MC, et al. Acne scars: pathogenesis, classification and treatment. Dermatol Res Pract. 2010;2010:e893080. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Wang H, Guo M, Shen S, He L, Zhang X, Zuo X, et al. Variants in SELL, MRPS 36P2, TP 63, DDB 2, CACNA 1H, ADAM 19, GNAI 1, CDH 13 and GABRG 2 interact to confer risk of acne in Chinese population. J Dermatol. 2015;42(4):378–81. [DOI] [PubMed] [Google Scholar]
- 32. Trivedi NR, Gilliland KL, Zhao W, Liu W, Thiboutot DM. Gene array expression profiling in acne lesions reveals marked upregulation of genes involved in inflammation and matrix remodeling. J Invest Dermatol. 2006;126(5):1071–9. [DOI] [PubMed] [Google Scholar]
- 33. Yang A, Schweitzer R, Sun D, Kaghad M, Walker N, Bronson RT, et al. p63 is essential for regenerative proliferation in limb, craniofacial and epithelial development. Nature. 1999;398(6729):714–8. [DOI] [PubMed] [Google Scholar]
- 34. Sobkowiak P, Langwiński W, Nowakowska J, Wojsyk-Banaszak I, Szczepankiewicz D, Jenerowicz D, et al. Neuroinflammatory gene expression pattern is similar between allergic rhinitis and atopic dermatitis but distinct from atopic asthma. BioMed Res Int. 2020;2020:e7196981. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Tsang MSM, Sun X, Wong CK. The role of new IL-1 family members (IL-36 and IL-38) in atopic Dermatitis, allergic asthma, and allergic rhinitis. Curr Allergy Asthma Rep. 2020;20(8):40. [DOI] [PubMed] [Google Scholar]
- 36. Di Caprio R, Balato A, Caiazzo G, Lembo S, Raimondo A, Fabbrocini G, et al. IL-36 cytokines are increased in acne and hidradenitis suppurativa. Arch Dermatol Res. 2017;309(8):673–8. [DOI] [PubMed] [Google Scholar]
- 37. Kistowska M, Meier B, Proust T, Feldmeyer L, Cozzio A, Kuendig T, et al. Propionibacterium acnes promotes Th17 and Th17/Th1 responses in acne patients. J Invest Dermatol. 2015;135(1):110–8. [DOI] [PubMed] [Google Scholar]
- 38. Agak GW, Kao S, Ouyang K, Qin M, Moon D, Butt A, et al. Phenotype and antimicrobial activity of Th17 cells induced by Propionibacterium acnes strains associated with healthy and acne skin. J Invest Dermatol. 2018;138(2):316–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Albano GD, Di Sano C, Bonanno A, Riccobono L, Gagliardo R, Chanez P, et al. Th17 immunity in children with allergic asthma and rhinitis: a pharmacological approach. PLoS One. 2013;8(4):e58892. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Esaki H, Brunner PM, Renert-Yuval Y, Czarnowicki T, Huynh T, Tran G, et al. Early-onset pediatric atopic dermatitis is TH2 but also TH17 polarized in skin. J Allergy Clin Immunol. 2016;138(6):1639–51. [DOI] [PubMed] [Google Scholar]
- 41. Di Landro A, Cazzaniga S, Parazzini F, Ingordo V, Cusano F, Atzori L, et al. Family history, body mass index, selected dietary factors, menstrual history, and risk of moderate to severe acne in adolescents and young adults. J Am Acad Dermatol. 2012;67(6):1129–35. [DOI] [PubMed] [Google Scholar]
- 42. Kaminsky A, Florez-White M, Bagatin E, Arias MI, Iberian Latin American Acne Studies Group GILEA - Grupo Ibero-Latinoamericano de Estudio del Acne . Large prospective study on adult acne in Latin America and the Iberian Peninsula: risk factors, demographics, and clinical characteristics. Int J Dermatol. 2019;58(11):1277–82. [DOI] [PubMed] [Google Scholar]
- 43. Hussein SMA, Hussein HA, Vari CE, Todoran N, Hussein HA, Ciurba A, et al. Diet, smoking and family history as potential risk factors in Acne Vulgaris: a community-based Study. Acta Marisiensis Seria Med. 2016;62(2):173–81. [Google Scholar]
- 44. Shen Y, Wang T, Zhou C, Wang X, Ding X, Tian S, et al. Prevalence of acne vulgaris in Chinese adolescents and adults: a community-based study of 17,345 subjects in six cities. Acta Derm Venereol. 2012;92(1):40–4. [DOI] [PubMed] [Google Scholar]
- 45. Oon HH, Wong SN, Aw DCW, Cheong WK, Goh CL, Tan HH. Acne Management Guidelines by the dermatological Society of Singapore. J Clin Aesthet Dermatol. 2019;12(7):34–50. [PMC free article] [PubMed] [Google Scholar]
- 46. Wei B, Pang Y, Zhu H, Qu L, Xiao T, Wei HC, et al. The epidemiology of adolescent acne in North East China. J Eur Acad Dermatol Venereol. 2010;24(8):953–7. [DOI] [PubMed] [Google Scholar]
- 47. Wolkenstein P, Machovcová A, Szepietowski J, Tennstedt D, Veraldi S, Delarue A. Acne prevalence and associations with lifestyle: a cross-sectional online survey of adolescents/young adults in 7 European countries. J Eur Acad Dermatol Venereol. 2018;32(2):298–306. [DOI] [PubMed] [Google Scholar]
- 48. Tan H, Tan AWH, Barkham T, Yan X, Zhu M. Community‐based study of acne vulgaris in adolescents in Singapore. Br J Dermatol. 2007;157(3):547–51. [DOI] [PubMed] [Google Scholar]
- 49. Raman S. A comprehensive review on acne, its pathogenesis, treatment, in-vitro and in-vivo models for induction and evaluation methods. 2019. [cited 2023 Apr 7]. Available from: https://www.semanticscholar.org/paper/A-COMPREHENSIVE-REVIEW-ON-ACNE%2C-ITS-PATHOGENESIS%2C-Raman/86a7c32f34647445b3b03fe7ac711c3f289c4c2b
- 50. Tan JKL, Bhate K. A global perspective on the epidemiology of acne. Br J Dermatol. 2015;172(Suppl 1):3–12. [DOI] [PubMed] [Google Scholar]
- 51. Lynn DD, Umari T, Dunnick CA, Dellavalle RP. The epidemiology of acne vulgaris in late adolescence. AHMT. 2016;7:13–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52. Layton AM, Henderson CA, Cunliffe WJ. A clinical evaluation of acne scarring and its incidence. Clin Exp Dermatol. 1994;19(4):303–8. [DOI] [PubMed] [Google Scholar]
- 53. Cappel M, Mauger D, Thiboutot D. Correlation between serum levels of insulin-like growth factor 1, dehydroepiandrosterone sulfate, and dihydrotestosterone and acne lesion counts in adult women. Arch Dermatol. 2005;141(3):333–8. [DOI] [PubMed] [Google Scholar]
- 54. Akpinar Kara Y. Evaluation of serum insulin‐like growth factor‐1, insulin, glucose levels in patients with adolescent and post‐adolescent acne. J Cosmet Dermatol. 2022;21(3):1292–6. [DOI] [PubMed] [Google Scholar]
- 55. Khunger N, Kumar C. A clinico-epidemiological study of adult acne: is it different from adolescent acne? Indian J Dermatol Venereol Leprol. 2012;78(3):335–41. [DOI] [PubMed] [Google Scholar]
- 56. Phillips FC, Gurung P, Kanneganti TD. Microbiota and caspase-1/caspase-8 regulate IL-1β-mediated bone disease. Gut Microbes. 2016;7(4):334–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57. Narang I, Sardana K, Bajpai R, Garg VK. Seasonal aggravation of acne in summers and the effect of temperature and humidity in a study in a tropical setting. J Cosmet Dermatol. 2019;18(4):1098–104. [DOI] [PubMed] [Google Scholar]
- 58. Mitchell BL, Saklatvala JR, Dand N, Hagenbeek FA, Li X, Min JL, et al. Genome-wide association meta-analysis identifies 29 new acne susceptibility loci. Nat Commun. 2022;13(1):702. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59. Mina-Vargas A, Colodro-Conde L, Grasby K, Zhu G, Gordon S, Medland SE, et al. Heritability and GWAS analyses of acne in Australian adolescent twins. Twin Res Hum Genet. 2017;20(6):541–9. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are not publicly available due to the privacy of participants but are available from the corresponding author (F.T.C.) upon reasonable request.
