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. 2024 Jul 19;19(7):e0307140. doi: 10.1371/journal.pone.0307140

Association between body mass index and atopic dermatitis among adolescents: Findings from a national cross-sectional study in Korea

Jae Hyeok Lim 1,2, Yun Seo Jang 1,2, Dan Bi Kim 1,2, Suk-Yong Jang 2,3, Eun-Cheol Park 2,4,*
Editor: Dong Keon Yon5
PMCID: PMC11259265  PMID: 39028723

Abstract

Background

The association between atopic dermatitis and childhood overweight and obesity has been studied extensively, but the results are inconclusive; most studies have focused on body mass index as a measure of obesity, with few investigating the relationship with underweight. Therefore, this study aimed to investigate the association between body mass index levels and atopic dermatitis in Korean adolescents.

Methods

3-year (2019–2021) of Korea Youth Risk Behavior Web-based Survey were used. Body mass index was used to measure obesity and a recent diagnosis within the past year was used as the criterion for atopic dermatitis. Multiple logistic regression analyses were performed to explore the associations. The odds ratios (ORs) and 95% confidence intervals (CIs) were calculated.

Results

A total of 144,183 adolescents aged 12–18 years were included in this study (74,704 males and 69,479 females). Over the past year, 5.4% of males and 7.3% of females were diagnosed with atopic dermatitis in the study population. Adolescents with normal weight (males [OR: 1.19, CI: 1.02–1.38]; females [OR: 1.26, CI: 1.10–1.43]) and overweight (males [OR: 1.37, CI: 1.16–1.61]; females [OR: 1.37, CI: 1.19–1.58]) were more likely to develop atopic dermatitis than underweight.

Conclusion

Increased degree of obesity may contribute to the development of atopic dermatitis. The normal-weight and obese adolescents had higher likelihood of developing atopic dermatitis compared with the underweight adolescents.

Introduction

Atopic dermatitis (AD), also known as atopic eczema, is a common chronic inflammatory skin disease characterized by relapsing eczematous lesions and intense itching [1]. Internationally, the incidence rates of AD are approximately 15%–20% in children and 1%–3% in adults [2]. The Korean Disease Control and Prevention Agency (KDCA) conducted a survey on the prevalence of AD in adolescents, revealing a rate of 16.4% among individuals aged 12–18 years [3]. It typically develops in early infancy and often persists until adulthood [4]. Considering the “atopic march,” which begins with AD and progresses to food allergy, allergic asthma, and allergic rhinitis in adulthood, it is crucial to administer treatment at the appropriate time in childhood [5]. Although AD is not a fatal disease, it causes a significant psychosocial burden, which can worsen the quality of life and mental health of patients and their caregivers (such as family and relatives) [6]. Moreover, AD imposes an economic burden, which involves direct costs for treatment and indirect costs such as absenteeism from work and school, leading to avoidance of social interactions [7].

The global prevalence of childhood obesity has substantially risen in recent decades [8], and it is linked to the emergence of various comorbidities [9]. Several studies have examined the association of AD with childhood overweight and obesity, but it is still unclear whether AD is one of these comorbidities. Some studies have reported no association between these factors [10, 11], while others have found a positive association [12–15]. Additionally, there are studies that have reported a positive association in only one sex [16–18]. In the meantime, the majority of those studies utilized body mass index (BMI) as the measure for obesity, with a limited number of studies examining the relationship with underweight. Among those studies, a large number of them did not define underweight, comparing the obese group with the non-obese group. Alternatively, the findings for underweight individuals were not statistically significant due to the limited sample size, or were not meaningfully interpreted, even though significant results were determined [19–21]. Given that BMI may not provide an accurate representation of body fat [12–14, 17], it is imperative to conduct an inquiry into the potential existence of risks within the realm of normal weight, which may present a threat to this correlation.

Furthermore, AD management aims to avoid or minimize events that can trigger itchiness and to provide continuous treatment of the disease. However, certain treatments for AD, such as phototherapy and pharmaceutical interventions, pose potential risks that may give rise to additional complications, prompting inquiries into their efficacy in managing the condition [22]. If the degree of obesity has a positive impact on AD, then weight reduction could be used as a non-pharmacological measure to support adolescent AD treatment on a broader scale. Therefore, the primary objective of this cross-sectional study was to investigate the association between the degree of obesity as measured by BMI and the morbidity of AD in a representative sample of Korean adolescents.

Materials and methods

Data

The study used data from the Korea Youth Risk Behavior Web-based Survey (KYRBS) conducted by the KDCA. The KYRBS is aimed at investigating the current risk behavior of Korean adolescents and identifying the health indicators in adolescents that can be used in health promotion program planning, assessment, and international comparison. The multistage stratified random cluster sampling technique was employed by the KYRBS to produce a sample that was nationally representative. To reduce sampling error, the sample was stratified by school type and geographic location (17 provinces). 400 middle schools and 400 high schools were selected annually for the sample allocation using proportional sampling in order to match the population. Previous studies demonstrated the validity and reliability of this survey [23, 24]. All participants answered the self-report online survey anonymously.

Participants

This study utilized KYRBS data collected during a 3-year period (2019–2021). From the 3-year data, all 167,099 participants were initially considered eligible. After excluding adolescents who were missing in height and weight (N = 4,372) and adolescents who were missing in other variables (N = 18,589), a total of 144,138 participants were finally included in this study (50,445 in 2019, 45,351 in 2020, and 48,387 in 2021). Of the total participants, 74,704 were male adolescents, and 69,479 were female adolescents, with ages ranging from 12–18 years old. Ethics approval for the KYRBS was waived by the KDCA institutional review board (IRB) under the Bioethics & Safety Act and opened to the public for academic use. When participating in the KYRBS, all participants, as well as their parents or legal guardians, completed an informed consent.

Variables

To measure the degree of obesity, we used the BMI, which is calculated by dividing an individual’s weight (in kg) by their height squared (in meters). These were obtained by the response that adolescents who participated in the survey recorded their height and weight to the first decimal place. Based on the KDCA guidelines, the adolescent BMI was classified as underweight (under 5th percentile [pct]: BMI<5th), normal weight (5th–85th pct: 5th ≤BMI<85th), overweight (85th–95th pct: 85th ≤BMI<95th), and obesity (over 95th pct: 95th ≤BMI) [25]. In this study, the BMI levels were categorized as underweight (under 5th pct: BMI<5th), normal weight (5th–85th pct: 5th≤BMI<85th), and overweight (over 85th pct: 85th ≤BMI). For subgroup, BMI were categorized into six distinct groups: under 5th pct, 5th–25th pct, 25th–50th pct, 50th–75th pct, 75th–95th pct, and over 95th pct.

To determine the presence of physician-diagnosed AD as the dependent variable, we used two questions. The survey first inquired whether the participants had ever been diagnosed with AD. Second, it asked if they had received an AD diagnosis within the past year. Participants who answered "yes" to both questions were classified into the AD "yes" group. For those who responded "no" to the first question, the survey instructed them to skip the second question. The second question was only answered by those who responded "yes" to the first question. Eventually, those who did not answer “yes” to the second question were placed in the AD "no" group. For subgroups, AD diagnosis status were categorized into three distinct groups: never diagnosed, past diagnosed, and recent diagnosed.

The following factors based on previous studies were considered as covariates [26, 27]: sociodemographic factors (sex [male and female], grade [7th, 8th, 9th, 10th, 11th, and 12th], area of residence [metropolitan, urban, or rural], household income level [high, middle, or low]). Health-related factors (secondhand smoke experience in a week [yes or no], frequency of physical activity [no exercise, 1–3 times a week, 4–6 times a week, or every day], sleeping time [≥8 hours a day: sufficient or <8 hours a day: insufficient], subjective stress level (a lot, a little, or free), subjective health (healthy, normal, or unhealthy), frequency of soda/sweet beverage/fast-food intake (no, several times a week, or several times a day), lifetime diagnosis experience of asthma/rhinitis (yes or no), and year (2019, 2020, and 2021).

Statistical analyses

All analyses were stratified by sex [16–18], since some studies reported sex differences in the incidence of AD. To explore the distribution of the adolescents’ general characteristics, a chi-square test was used. To determine the association between BMI and AD, multiple logistic regression analysis was performed after adjusting for all covariates [28]. Moreover, multiple and multinomial logistic regression analyses of subgroups were conducted to evaluate the association between the stratified sections of AD diagnosis and BMI. The odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to identify the associations among variables. SAS version 9.4M6 (SAS Institute, Cary, NC, USA) was used to perform all statistical analyses.

Results

Descriptive analyses

Table 1 shows a summary of the general characteristics of the study population by sex. Over the past year, 4,014 of the 74,704 male participants (5.4%) and 5,092 of the 69,479 female participants (7.3%) were diagnosed with AD. As the BMI levels increased (in the order of underweight, normal weight, and overweight), the proportion of patients diagnosed with AD increased in both male and female groups. The results of the chi-square test between BMI and AD in adolescents were significant (both p < .0001).

Table 1. General characteristics of the study population.

Variables Atopic dermatitis
Male Female
Total Yes No P-value Total Yes No P-value
N % N % N % N % N % N %
Total (N = 144,138) 74,704 100.0 4,014 5.4 70,690 94.6 69,479 100.0 5,092 7.3 64,387 92.7
Body Mass Index < .0001 < .0001
 Underweight 5,210 7.0 233 4.5 4,977 95.5 5,547 8.0 334 6.0 5,213 94.0
 Normal 49,242 65.9 2,546 5.2 46,696 94.8 51,759 74.5 3,755 7.3 48,004 92.7
 Overweight 20,252 27.1 1,235 6.1 19,017 93.9 12,173 17.5 1,003 8.2 11,170 91.8
Age 0.0533 0.0003
 7th 13,294 17.8 653 4.9 12,641 95.1 12,571 18.1 817 6.5 11,754 93.5
 8th 12,921 17.3 663 5.1 12,258 94.9 12,250 17.6 861 7.0 11,389 93.0
 9th 12,981 17.4 719 5.5 12,262 94.5 11,844 17.0 868 7.3 10,976 92.7
 10th 12,116 16.2 668 5.5 11,448 94.5 11,007 15.8 851 7.7 10,156 92.3
 11th 11,863 15.9 673 5.7 11,190 94.3 11,020 15.9 857 7.8 10,163 92.2
 12th 11,529 15.4 638 5.5 10,891 94.5 10,787 15.5 838 7.8 9,949 92.2
Region 0.4416 0.2636
 Metropolitan 33,120 44.3 1,743 5.3 31,377 94.7 30,065 43.3 2,151 7.2 27,914 92.8
 Urban 35,720 47.8 1,958 5.5 33,762 94.5 34,099 49.1 2,554 7.5 31,545 92.5
 Rural 5,864 7.8 313 5.3 5,551 94.7 5,315 7.6 387 7.3 4,928 92.7
Household income < .0001 < .0001
 High 31,193 41.8 1,619 5.2 29,574 94.8 25,580 36.8 1,839 7.2 23,741 92.8
 Middle 34,572 46.3 1,828 5.3 32,744 94.7 35,528 51.1 2,489 7.0 33,039 93.0
 Low 8,939 12.0 567 6.3 8,372 93.7 8,371 12.0 764 9.1 7,606 90.9
Secondhand smoke experience < .0001 < .0001
 Yes 38,635 51.7 2,305 6.0 36,330 94.0 44,916 64.6 3,583 8.0 41,333 92.0
 No 36,069 48.3 1,709 4.7 34,360 95.3 24,563 35.4 1,509 6.1 23,054 93.9
Physical activity 0.6851 0.0110
 No exercise 19,527 26.1 1,019 5.2 18,508 94.8 31,117 44.8 2,205 7.1 28,912 92.9
 1–3 time(s) in a week 32,512 43.5 1,752 5.4 30,760 94.6 29,770 42.8 2,191 7.4 27,579 92.6
 4–6 times in a week 15,722 21.0 863 5.5 14,859 94.5 6,695 9.6 533 8.0 6,162 92.0
 Everyday 6,943 9.3 380 5.5 6,563 94.5 1,897 2.7 163 8.6 1,734 91.4
Sleeping time 0.0595 < .0001
 Sufficient 54,557 73.0 2,983 5.5 51,574 94.5 56,479 81.3 4,254 7.5 52,225 92.5
 Insufficient 20,147 27.0 1,031 5.1 19,116 94.9 13,000 18.7 838 6.4 12,162 93.6
Stress < .0001 < .0001
 A lot 22,412 30.0 1,473 6.6 20,939 93.4 30,800 44.3 2,624 8.5 28,176 91.5
 A little 33,103 44.3 1,729 5.2 31,374 94.8 28,890 41.6 1,927 6.7 26,963 93.3
 Free 19,189 25.7 812 4.2 18,377 95.8 9,789 14.1 541 5.5 9,248 94.5
Subjective health < .0001 < .0001
 Healthy 55,608 74.4 2,747 4.9 52,861 95.1 44,151 63.5 2,851 6.5 41,300 93.5
 Normal 14,553 19.5 891 6.1 13,662 93.9 19,080 27.5 1,578 8.3 17,502 91.7
 Unhealthy 4,543 6.1 376 8.3 4,167 91.7 6,248 9.0 663 10.6 5,585 89.4
Soda frequency 0.1984 0.0061
 No soda 12,670 17.0 680 5.4 11,990 94.6 19,386 27.9 1,414 7.3 17,972 92.7
 Several in a week 55,861 74.8 2,972 5.3 52,889 94.7 47,259 68.0 3,427 7.3 43,832 92.7
 Several in a day 6,173 8.3 362 5.9 5,811 94.1 2,834 4.1 251 8.9 2,583 91.1
Sweet beverage frequency 0.0305 0.0018
 No sweet beverage 9,993 13.4 488 4.9 9,505 95.1 11,192 16.1 789 7.0 10,403 93.0
 Several in a week 56,196 75.2 3,038 5.4 53,158 94.6 52,423 75.5 3,807 7.3 48,616 92.7
 Several in a day 8,515 11.4 488 5.7 8,027 94.3 5,864 8.4 496 8.5 5,368 91.5
Fast-food frequency 0.1776 0.2554
 No fast-food 12,759 17.1 681 5.3 12,078 94.7 13,016 18.7 977 7.5 12,039 92.5
 Several in a week 60,490 81.0 3,239 5.4 57,251 94.6 55,633 80.1 4,044 7.3 51,589 92.7
 Several in a day 1,455 1.9 94 6.5 1,361 93.5 830 1.2 71 8.6 759 91.4
Asthma < .0001 < .0001
 Yes 5,055 6.8 584 11.6 4,471 88.4 3,775 5.4 604 16.0 3,171 84.0
 No 69,649 93.2 3,430 4.9 66,219 95.1 65,704 94.6 4,488 6.8 61,216 93.2
Rhinitis < .0001 < .0001
 Yes 25,123 33.6 2,278 9.1 22,845 90.9 24,713 35.6 2,924 11.8 21,789 88.2
 No 49,581 66.4 1,736 3.5 47,845 96.5 44,766 64.4 2,168 4.8 42,598 95.2
Year 0.3774 0.2923
 2019 26,014 34.8 1,430 5.5 24,584 94.5 24,431 35.2 1,824 7.5 22,607 92.5
 2020 23,639 31.6 1,276 5.4 22,363 94.6 21,712 31.2 1,608 7.4 20,104 92.6
 2021 25,051 33.5 1,308 5.2 23,743 94.8 23,336 33.6 1,660 7.1 21,676 92.9
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Regression analyses

Table 2 shows the results of the multiple logistic regression analysis between BMI and AD. The male adolescents with normal weight (OR: 1.19, CI: 1.02–1.38) and overweight (OR: 1.37, CI: 1.16–1.61) had higher odds of developing AD compared with those who were underweight. Similarly, the female adolescents with normal weight (OR: 1.26, CI: 1.10–1.43) and overweight (OR: 1.37, CI: 1.19–1.58) also had higher odds of developing AD compared with those who were underweight.

Table 2. Results of factors associated between Body mass index and Atopic dermatitis.

Variables Atopic dermatitis
Male Female
OR 95% CI OR 95% CI
Body Mass Index
 Underweight 1.00 1.00
 Normal 1.19 (1.02 - 1.38) 1.26 (1.10 - 1.43)
 Overweight 1.37 (1.16 - 1.61) 1.37 (1.19 - 1.58)
Age
 7th 1.00 1.00
 8th 1.05 (0.92 - 1.20) 1.08 (0.97 - 1.20)
 9th 1.06 (0.93 - 1.21) 1.06 (0.94 - 1.19)
 10th 1.02 (0.89 - 1.16) 1.09 (0.97 - 1.21)
 11th 1.06 (0.93 - 1.21) 1.09 (0.98 - 1.22)
 12th 1.02 (0.89 - 1.17) 1.06 (0.94 - 1.18)
Region
 Metropolitan 1.00 1.00
 Urban 1.05 (0.97 - 1.13) 1.04 (0.98 - 1.11)
 Rural 1.10 (0.96 - 1.26) 1.14 (0.99 - 1.30)
Household income
 High 1.00 1.00
 Middle 1.03 (0.96 - 1.11) 0.99 (0.92 - 1.06)
 Low 1.20 (1.07 - 1.34) 1.20 (1.08 - 1.34)
Secondhand smoke experience
 Yes 1.18 (1.10 - 1.26) 1.27 (1.18 - 1.36)
 No 1.00 1.00
Physical activity
 No exercise 0.89 (0.78 - 1.02) 0.83 (0.69 - 1.00)
 1–3 time(s) in a week 0.90 (0.79 - 1.01) 0.88 (0.74 - 1.06)
 4–6 times in a week 0.97 (0.85 - 1.11) 0.97 (0.79 - 1.20)
 Everyday 1.00 1.00
Sleeping time
 Sufficient 1.01 (0.92 - 1.11) 0.96 (0.87 - 1.05)
 Insufficient 1.00 1.00
Stress
 A lot 1.31 (1.18 - 1.45) 1.25 (1.12 - 1.40)
 A little 1.16 (1.05 - 1.27) 1.12 (1.00 - 1.26)
 Free 1.00 1.00
Subjective health
 Healthy 1.00 1.00
 Normal 1.15 (1.05 - 1.26) 1.16 (1.08 - 1.25)
 Unhealthy 1.41 (1.24 - 1.61) 1.33 (1.20 - 1.48)
Soda frequency
 No soda 1.00 1.00
 Several in a week 0.99 (0.89 - 1.09) 1.00 (0.93 - 1.08)
 Several in a day 1.09 (0.93 - 1.28) 1.14 (0.97 - 1.34)
Sweet beverage frequency
 No sweet beverage 1.00 1.00
 Several in a week 1.14 (1.02 - 1.27) 1.00 (0.92 - 1.10)
 Several in a day 1.13 (0.97 - 1.31) 1.05 (0.92 - 1.20)
Fast-food frequency
 No fast-food 1.00 1.00
 Several in a week 0.97 (0.88 - 1.06) 0.95 (0.87 - 1.03)
 Several in a day 1.04 (0.80 - 1.34) 0.90 (0.68 - 1.20)
Asthma
 Yes 1.83 (1.65 - 2.03) 1.86 (1.68 - 2.06)
 No 1.00 1.00
Rhinitis
 Yes 2.49 (2.33 - 2.67) 2.45 (2.30 - 2.61)
 No 1.00 1.00
Year
 2019 1.02 (0.93 - 1.11) 1.01 (0.93 - 1.09)
 2020 1.03 (0.95 - 1.13) 1.08 (1.00 - 1.16)
 2021 1.00 1.00
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Subgroup analyses

Table 3 shows the significant results of subgroup analysis stratified by independent variables. In the health-related factors, adolescents who did not exercise had higher odds of developing AD compared with those who were underweight [male: normal weight (OR: 1.55 CI: 1.16–2.07) and had overweight (OR: 1.62, CI: 1.18–2.21); female: normal weight (OR: 1.23, CI: 1.02–1.47) overweight (OR: 1.45, CI: 1.19–1.77)]. Adolescents who had insufficient sleeping time had higher odds of developing AD compared with those who were underweight [male: normal weight (OR: 1.23, CI: 1.03–1.47) and had overweight (OR: 1.38, CI: 1.14–1.67); female: normal weight (OR: 1.29, CI: 1.12–1.49) and overweight (OR: 1.46, CI: 1.24–1.71)]. Adolescents with gender difference in subjective stress levels and subjective health perception showed variations in the odds of developing AD compared with the underweight groups. Subjective stress level: a lot [male: normal weight (OR: 1.55, CI: 1.18–2.03) and overweight (OR: 1.62, CI: 1.23–2.14)]; free [female: normal weight (OR: 1.40, CI: 0.96–2.03) and overweight (OR: 1.82, CI: 1.22–2.86)]. Subjective health perception: unhealthy [male: normal weight (OR: 2.04, CI: 1.28–3.23) and overweight (OR: 1.98, CI: 1.26–3.12)]; healthy [female: normal weight (OR: 1.44, CI: 1.19–1.73) and overweight (OR: 1.60, CI: 1.31–1.96)].

Table 3. Results of subgroup analysis stratified by independent variables.

Variables Atopic dermatitis
Male Female
Underweight Normal Overweight Underweight Normal Overweight
OR OR 95% CI OR 95% CI OR OR 95% CI OR 95% CI
Physical activity
 No exercise 1.00 1.55 (1.16 - 2.07) 1.62 (1.18 - 2.21) 1.00 1.23 (1.02 - 1.47) 1.45 (1.19 - 1.77)
 1–3 time(s) in a week 1.00 1.08 (0.87 - 1.34) 1.24 (0.99 - 1.55) 1.00 1.30 (1.07 - 1.59) 1.32 (1.06 - 1.64)
 4–6 times in a week 1.00 0.93 (0.65 - 1.34) 1.13 (0.78 - 1.65) 1.00 1.42 (0.90 - 2.24) 1.50 (0.92 - 2.45)
 Everyday 1.00 1.48 (0.75 - 2.92) 2.03 (1.02 - 4.05) 1.00 0.86 (0.47 - 1.54) 1.10 (0.55 - 2.20)
Sleeping time
 Sufficient 1.00 1.07 (0.80 - 1.43) 1.34 (0.99 - 1.82) 1.00 1.09 (0.84 - 1.43) 0.97 (0.70 - 1.34)
 Insufficient 1.00 1.23 (1.03 - 1.47) 1.38 (1.14 - 1.67) 1.00 1.29 (1.12 - 1.49) 1.46 (1.24 - 1.71)
Stress
 A lot 1.00 1.55 (1.18 - 2.03) 1.62 (1.23 - 2.14) 1.00 1.13 (0.95 - 1.36) 1.24 (1.03 - 1.49)
 A little 1.00 1.03 (0.82 - 1.29) 1.21 (0.95 - 1.54) 1.00 1.39 (1.12 - 1.71) 1.44 (1.14 - 1.81)
 Free 1.00 1.07 (0.78 - 1.46) 1.38 (0.99 - 1.92) 1.00 1.40 (0.96 - 2.03) 1.86 (1.22 - 2.86)
Subjective health
 Healthy 1.00 1.15 (0.94 - 1.39) 1.32 (1.07 - 1.63) 1.00 1.44 (1.19 - 1.73) 1.60 (1.31 - 1.96)
 Normal 1.00 1.04 (0.79 - 1.36) 1.28 (0.96 - 1.71) 1.00 1.10 (0.88 - 1.37) 1.27 (1.00 - 1.61)
 Unhealthy 1.00 2.04 (1.28 - 3.23) 1.98 (1.26 - 3.12) 1.00 1.10 (0.81 - 1.49) 1.00 (0.70 - 1.43)
Soda frequency
 No soda 1.00 1.35 (0.92 - 1.97) 1.59 (1.08 - 2.35) 1.00 1.48 (1.15 - 1.91) 1.57 (1.19 - 2.07)
 Several in a week 1.00 1.12 (0.94 - 1.33) 1.31 (1.09 - 1.58) 1.00 1.18 (1.00 - 1.38) 1.32 (1.10 - 1.57)
 Several in a day 1.00 1.48 (0.90 - 2.45) 1.41 (0.82 - 2.41) 1.00 1.35 (0.86 - 2.12) 1.14 (0.64 - 2.06)
Sweet beverage frequency
 No sweet beverage 1.00 1.19 (0.79 - 1.81) 1.25 (0.81 - 1.95) 1.00 1.20 (0.86 - 1.66) 1.19 (0.82 - 1.72)
 Several in a week 1.00 1.13 (0.95 - 1.35) 1.33 (1.10 - 1.60) 1.00 1.24 (1.06 - 1.44) 1.39 (1.17 - 1.64)
 Several in a day 1.00 1.62 (1.03 - 2.54) 1.85 (1.14 - 3.00) 1.00 1.43 (1.01 - 2.02) 1.46 (0.96 - 2.23)
Fast-food frequency
 No fast-food 1.00 1.19 (0.82 - 1.73) 1.58 (1.08 - 2.31) 1.00 1.29 (0.97 - 1.71) 1.38 (1.01 - 1.89)
 Several in a week 1.00 1.19 (1.00 - 1.41) 1.32 (1.10 - 1.58) 1.00 1.24 (1.07 - 1.44) 1.37 (1.16 - 1.61)
 Several in a day 1.00 1.09 (0.44 - 2.70) 1.54 (0.59 - 4.02) 1.00 1.83 (0.68 - 4.92) 1.12 (0.34 - 3.72)
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In the dietary factors, adolescents with frequency difference in soda, sweet beverage, and fast-food consumption showed variations in the odds of developing AD compared with the underweight groups. Soda frequency: no [male: normal weight (OR: 1.35, CI: 0.92–1.97) and overweight (OR: 1.59, CI: 1.08–2.35); female: normal weight (OR: 1.48 CI: 1.15–1.91) and overweight (OR: 1.57, CI: 1.19–2.07)]. Sweet beverage frequency: several in a day [male: normal weight (OR: 1.62, CI: 1.03–2.54) and overweight (OR: 1.85, CI: 1.14–3.00)]; healthy [female: normal weight (OR: 1.43, CI: 1.01–2.02) and overweight (OR: 1.46, CI: 0.96–2.23)]. Fast-food frequency: several in a week [male: normal weight (OR: 1.19, CI: 1.00–1.41) and overweight (OR: 1.32, CI: 1.10–1.58)]; female: normal weight (OR: 1.24, CI: 1.07–1.44) and overweight (OR: 1.37, CI: 1.16–1.61)].

Fig 1 shows the results of subgroup analysis stratified by diagnosed time of AD. In both male and female adolescents, the normal weight and obese group had higher odds of developing AD compared with the underweight in the order of those who had never been diagnosed with AD, who had been diagnosed with AD but not in the past year [male: normal weight (OR: 1.09, CI: 0.99–1.20), overweight (OR: 1.04, CI: 0.94–1.16); female: normal weight (OR: 1.15, CI: 1.07–1.25), overweight (OR: 1.22, CI: 1.12–1.33)], and who had been diagnosed with AD in the past year [male: normal weight (OR: 1.20, CI: 1.03–1.40), overweight (OR: 1.38, CI: 1.17–1.62); female: normal weight (OR: 1.30, CI: 1.14–1.48), overweight (OR: 1.43, CI: 1.24–1.66)].

Fig 1. Results of subgroup analysis stratified by diagnosed time of atopic dermatitis.

Fig 1

Open in a new tab

Error bars: 95% confidence interval. *: p-value < .05 Reference: underweight Past: who have been diagnosed with atopic dermatitis but not in the past 1 year. Recent: who have been diagnosed with atopic dermatitis in the past 1-year.

Fig 2 shows the results of the subgroup analysis stratified by BMI. Compared with under 5th pct, as the pct section increased (higher BMI), the odds of developing AD increased [male: 5th–25th pct (OR: 1.12, CI: 0.95–1.32), 25th–50th pct (OR: 1.21, CI: 1.03–1.42), 50th–75th pct (OR: 1.22, CI: 1.03–1.44), 75th–95th pct (OR: 1.33, CI: 1.13–1.57), and over 95th pct (OR: 1.34, CI: 1.13–1.60); female: 5th–25th pct (OR: 1.22, CI: 1.06–1.40), 25th–50th pct (OR: 1.23, CI: 1.07–1.42), 50th–75th pct (OR: 1.34, CI: 1.16–1.55), 75th–95th pct (OR: 1.32, CI: 1.14–1.54), and over 95th pct (OR: 1.34, CI: 1.14–1.57)].

Fig 2. Results of subgroup analysis stratified by body mass index.

Fig 2

Open in a new tab

OR: odds ratio. CI: confidence interval.

Discussion

We found that the likelihood of developing AD were higher for adolescents with a higher BMI. The mechanisms underlying the association between obesity and AD could be explained by several hypotheses. Obesity could be linked to skin barrier dysfunction, which triggers the occurrence of chronic inflammation and thus leads to the development of AD. Heredity, epidermal dysfunction, skin microbiome abnormalities, and T-cell-induced inflammation (type 2 skewed immune dysregulation is dominant) are involved in the complicated AD pathophysiology [18, 29].

Previous studies with conflicting findings could be attributed to variations in study designs, such as differences in sample sizes and measurement tools for AD and obesity [30]. Furthermore, considering the substantial variation in the prevalence of AD in relation to the age of infants and adolescents [31], the disparity in age among the participants in the study could also be a significant factor contributing to the incongruous results. Meanwhile, prior works have indicated that individuals who are underweight are less likely to develop AD compared to those of normal weight. Nevertheless, these studies have not offered a specific interpretation for this association [19–21].

One Ghanaian study that consistent with the findings of the current study expounds upon the notion that individuals who are underweight exhibit a lower prevalence of AD compared to those with normal weight [32]. This phenomenon can be attributed to the intricate mechanisms involving the reduction of inflammatory markers from underweight status, thereby bolstering the immune system’s resistance to foreign substances [32]. In a Japanese study, the fact that the prevalence of wheezing and asthma exhibited a U-shaped pattern, indicating a greater prevalence in individuals who are underweight or overweight than those of normal weight, but not in AD may uphold the linear correlation within this research [33]. These studies also showed slight differences when compared to the present study, as they are contingent upon several factors such as sample size, the range of ages included in the study population, and the specific measurement tools utilized to gather data and information.

In another potential explanation, according to a study conducted on mice, Sirtuin 1 (SIRT1) is essential for the maintenance of the skin barrier [34]. A separate investigation documented that the expression of SIRT1 exhibited a decline in individuals affected by AD, thereby implying a decrease in the capacity of the skin to regenerate [35]. Additionally, higher BMI was linked to lower expression of SIRT1 [36]; therefore, the pattern of SIRT1 expression is inversely associated with the percentage of fat mass. Regarding skin regeneration, a study found that there is an association between elevated BMI and a lower expression of SIRT1 [36], which is linked to AD. This suggests that obese adolescents, as well as those with normal BMI who are diagnosed with AD may benefit from weight management. BMI, which is known to be fluctuate during adolescence [37], may have influenced the outcome of recent AD diagnosis. In individuals diagnosed with AD throughout their lives but not within the last year, there may have been a difference in BMI between past and current assessments. Additionally, individuals with obesity may have sought medical attention in the past year due to the severity of AD [38].

In the other factors that contribute to the association between the degree of obesity and AD, individuals with lesser levels of physical activity and higher levels of BMI had increased likelihoods of developing AD. Patients with AD may refrained from performing exercise or vigorous physical activity due to the fact that intense physical activity can produce sweat and provoke itchiness [13]. Still, the symptoms of AD can be mitigated through exercise, making it a viable approach for managing weight [39]. Constant AD symptoms can disrupt in sleeping patterns, resulting in insufficient duration of sleep [40]. Additionally, inadequate sleep has been associated with obesity, exacerbating the severity of AD [41].

Gender modulating effects were also observed between these associations. With high BMI, elevated levels of subjective stress in males whereas lower levels of that in females were associated with greater likelihoods of AD. Research on stress levels and the atopic risk with similar results of the present study proposed young men exhibit more prominent cortisol responses to stress than young women [42]. Furthermore, subjective health perception may be a connected factor to the findings of the present study. Specifically, males in unhealthy groups demonstrated a higher likelihood of AD, whereas women in healthy groups exhibited a similar trend, highlighting the impact of stress on AD symptoms. Nonetheless, further investigation is required to better understand this association, particularly in terms of gender disparities. As for dietary factors, the implementation of a strategy to restrict the consumption of certain foods that have been indicated to exacerbate symptoms of AD as a preventive measure may associated with reduced intake of soft drinks [43]. Nevertheless, an increased frequency of consumption of sweet beverage or fast-food is associated with a heightened likelihood of developing AD, implying that such options may be considered restrictive in terms of weight management [44, 45].

This study has the strength of being capable of generalizing findings to South Korean adolescents by utilizing nationally representative KYRBS data. Furthermore, the multiple analyses conducted in this study is notable for examining the correlation with underweight, an issue that has received less attention in previous studies. However, this study has several limitations. First, this was a cross-sectional study and may be susceptible to reverse causality. Bidirectional situations can be possible; that is, having a higher BMI may predispose an individual to develop AD, and AD may cause an elevated BMI [46]. Second, the questionnaire used in this study conducted by KYRBS was a self-reported questionnaire. Thereby, the participants were likely to provide incorrect answers either intentionally or unintentionally. Third, due to the availability of surveys, adjusting for potential confounders, such as the severity of AD, other dietary compositions associated with AD, living environment, or allergic history may not be sufficient.

In conclusion, this study was conducted to examine the association between the degree of obesity measured by BMI and AD in Korean adolescents. The normal-weight and obese adolescents had higher likelihood of developing AD compared with the underweight adolescents both in males and females. These results may provide information about the feasibility of weight reduction as a non-pharmacological treatment for AD.

Data Availability

The third-party data underlying the results presented in the study is publicly available from the KYRBS website (https://www.kdca.go.kr/yhs/home.jsp).

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

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11 Apr 2024

PONE-D-24-07867Association between degree of obesity and atopic dermatitis among adolescents: Findings from a national cross-sectional study in KoreaPLOS ONE

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#1. KCDC -> KDCA

#2. To determine the association between BMI and AD, multiple logistic regression analysis was performed after adjusting for all covariates. -> Please cite the statistical guideline (DOI: https://doi.org/10.54724/lc.2022.e3).

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Reviewer #1: Partly

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: I Don't Know

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Reviewer #2: Yes

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Reviewer #1: This is a good manuscript. But I have some comments as followed.

1.The title might be revise as "Association between BMI and atopic dermatitis among adolescents: Findings from a national cross-sectional study in Korea".

2.Background part: I suggest describe the epidemical situation of atopic dermatitis at first.

3.Methods: part: Please describe the measure methods of the important variables. How to measure obesity and underweight?

4.Please add the inclusion criteria and exclusion criteria in the part of Participants.

5.Results part: Please add to the subtitle of different part.

6.Discussion part: please add to advantage of the research before the limitation of the research.

Reviewer #2: Through this manuscript, the authors have effectively demonstrated the relationship between the degree of obesity and atopic dermatitis. I appreciated how they tied this association to the clinical aspect of the disease, offering non-pharmacological interventions as a viable option. However, upon reviewing the manuscript, I encountered some conflicting points. I hope the authors will view this feedback positively.

1. In the Materials and Methods section, under the subsection "Data," the authors mentioned "sampling design method." I intend to question the appropriateness of this sampling technique.

2. Under the subheading "Variables," the study mentioned using BMI levels categorized as underweight, normal weight, and obesity. However, the World Health Organization classifies BMI into underweight, normal, overweight, and obese categories. Therefore, it would enhance the study's credibility to align with the globally accepted classification system.

3. Atopic dermatitis is a chronic skin condition. However, in this study, participants with a past history of atopic dermatitis (more than 1 year ago) were categorized into the AD "no" group. I believe this could potentially underestimate the prevalence of the disease, which might impact the subsequent analyses conducted in the study.

4.In the majority of cases, atopic dermatitis (AD) has an onset before the age of five years. However, this study targeted participants aged 12 to 18 years. This age group, coupled with the exclusion of participants with a history of AD, could have influenced the results, particularly in terms of prevalence.

5.The authors stated that "All participants signed an informed consent when they participated." However, it's worth noting that the majority of participants are minors. This raises ethical concerns regarding whether it is appropriate for a study to involve minors signing consent forms.

6.The data provided in the manuscript are publicly available on the website mentioned. However, it's important to note that all the data are in a language other than English. If feasible, it would be beneficial to have the data translated into English for wider accessibility and comprehension.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2024 Jul 19;19(7):e0307140. doi: 10.1371/journal.pone.0307140.r002

Author response to Decision Letter 0

22 Jun 2024

We express our gratitude for the opportunity to make revisions to our manuscript. Throughout the revision process, we have taken into careful consideration the feedback provided by the editor and reviewers, and have made every effort to integrate their suggestions appropriately. Following the given instructions, we have endeavored to elucidate the modifications implemented in response to all reviewer comments. The insights offered by the reviewers were beneficial, and we value the constructive criticism provided for our submission. Upon addressing the raised concerns, we believe that the quality of the paper has significantly improved, and we trust that you share this sentiment. Our detailed responses to each comment are outlined below, and we have enclosed a revised version of the manuscript with the tracked changes. Once again, we appreciate the insightful and constructive feedback.

Attachment

Submitted filename: Response to reviewers.docx

pone.0307140.s001.docx (45KB, docx)

Decision Letter 1

Dong Keon Yon

2 Jul 2024

Association between degree of obesity and atopic dermatitis among adolescents: Findings from a national cross-sectional study in Korea

PONE-D-24-07867R1

Dear Dr. Park,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Dong Keon Yon, MD, FACAAI, FAAAAI

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

This is an excellent paper.

Reviewers' comments:

Acceptance letter

Dong Keon Yon

9 Jul 2024

PONE-D-24-07867R1

PLOS ONE

Dear Dr. Park,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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on behalf of

Dr. Dong Keon Yon

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Attachment

    Submitted filename: Response to reviewers.docx

    pone.0307140.s001.docx (45KB, docx)

    Data Availability Statement

    The third-party data underlying the results presented in the study is publicly available from the KYRBS website (https://www.kdca.go.kr/yhs/home.jsp).

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