(2024) 24:535
Lin et al. BMC Pediatrics
https://doi.org/10.1186/s12887-024-05009-1

BMC Pediatrics

RESEARCH

Open Access

Effect of dietary antioxidant quality score
on tobacco smoke exposure and asthma
in children and adolescents: a cross‑sectional
study from the NHANES database
Wei Lin1*†, Jinliang Lin1†, Fuhuang Lai1 and Jiaqiang Shi1

Abstract
Background Asthma is a common non-communicable disease in children, and airway inflammation is the main
pathological change of asthma. Tobacco smoke exposure (TSE) can cause systematic inflammation and oxidative
stress, which may further aggravate the progression of asthma. Dietary antioxidants can relieve the inflammationand oxidative stress in human body. This study aims to assess the effect of overall antioxidant capacity of dietaryintake, evaluating by dietary antioxidant quality score (DAQS), in the association between TSE and childhood asthma.
Methods Data of this cross-sectional study were extracted from the National Health and Nutrition Examination
Surveys (NHANES) 2007–2018. DAQS was calculated based on the daily dietary intake of selenium, zinc, magnesium,
vitamin A, C and E. TSE was measured by serum cotinine concentration. The weighted univariate and multivariate
logistic regression models were employed to evaluate the role of DAQS in the association between TSE and asthma
among children and adolescents. Subgroup analysis was conducted to further evaluate the association basedon gender.
Results Totally 11,026 children and adolescents were included, of whom 1,244 (11.28%) had asthma. After adjusted
all covariates, TSE was associated with the high odds of childhood asthma (OR = 1.26, 95%CI = 1.05–1.52). Amongchildren exposed to tobacco smoke, those with higher DAQS level (OR = 1.15, 95%CI: 0.88–1.50) had a reduced
risk of asthma compared with those children with lower DAQS level (OR = 1.43, 1.08–1.89), especially among girls(OR = 1.42, 95%CI: 0.93–2.17).
Conclusion High DAQS may have a moderating effect on asthma in children; that is, the higher DAQS, the lower
the odds of asthma in children who exposed to tobacco smoke. Our study provides a reference for developing moretargeted strategies for prevention and treatment of asthma in children.
Keywords Dietary antioxidant quality score, Asthma, Tobacco smoke exposure, Children and adolescents†

Wei Lin and Jinliang Lin contributed equally to this work.

*Correspondence:
Wei Linlw15880393265@126.com
1
Pediatrics Department of Longyan First HospitalAffiliated to, Fujian
Medical University, No. 105 Zhongcheng Jiuyi North Road, Xinluo District,Longyan, Fujian Province 364000, China

Background
Asthma, a common chronic non-communicable disease in children, is featured by airway obstruction due
to airway inflammation and increased mucus secretion
production [1]. It is estimated by the US Centers for Disease Control and Prevention (CDC) that 1 in 12 children
between ages of 0–17 have asthma [2]. Although the© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To
view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Lin et al. BMC Pediatrics(2024) 24:535

Page 2 of 9

majority of children with asthma can be controlled withinhaled corticosteroids, a part of children may experiencefrequent and severe asthma attacks which will lead todeterioration of lung function [3]. Control of asthma inchildren is becoming a major challenge in primary healthcare.
The most important pathological change in asthma ischronic inflammation of the airways and clinical studies have shown that airway inflammation occurred evenin mild asthma [4]. Environment exposure and dietaryintake are two modifiable factors affecting airway inflammation and asthma [5–8]. Tobacco smoke exposure(TSE), a major source of indoor air pollution, has beenshown to directly cause airway inflammation and is associated to poorer asthma control [9]. Cotinine, as a direct
metabolite of nicotine, is a specific and sensitive marker
of TSE [10]. Clinical studies suggested that high serum
cotinine concentrations are independently related to thehigh risk of asthma in children [11]. Moreover, dietary
antioxidants play an anti-inflammatory role by participating in oxidative stress and clearing oxidative free radicals, thereby reducing the risk of asthma [12, 13]. It isthought that evaluating the overall antioxidant quality of
an individual’s diet rather than a single antioxidant dietary factor might provide a more comprehensive pictureof the relationship between dietary antioxidants healthoutcome [14]. Hence, dietary antioxidant quality score(DAQS) is proposed, which represents the overall dietary
antioxidant capacity by adding the common dietary antioxidant: vitamin A, C, E, magnesium (Mg), zinc (Zn) andselenium (Se) [15]. Luu et al. [16] reported a significant
inverse correlation between DAQS and the levels of oxidative stress and inflammatory markers in vivo. Mendes
et al. [17] found the quality of diet might affect the relationship between indoor air pollution and asthma in children. Compared to the anti-inflammatory diet, children
exposed to particulate matter 2.5/10 and with the inflammatory diet have a high risk of asthma.
Based on the above studies about relationships
between dietary antioxidants, environmental exposureand asthma, we hypothesized that high DAQS may havea protective effect on the risk of asthma in children who
exposed to tobacco smoke. Herein, the study was toexplore the effect of DAQS on TSE and asthma in thegeneral U.S. children and adolescents.

health and nutrient status of noninstitutionalized U.S.
population [18]. This survey uses complex, multistage,
probability sampling methods based on broad population distribution. NHANES protocols are approved by
the NCHS Ethics Board of the US CDC. All individuals
provided written informed consent during the survey.
According to the Ethics Review Board of First Hospital
Affiliated to Fujian Medical University Hospital, crosssectional studies have been exempted from the ethicalreview.
The included criteria were: (1) participants aged
1–17 years old; (2) participants with complete dietaryintake information; (3) participants with the serum cotinine measurement. The excluded criteria were: (1) missing body mass index (BMI) data; (2) missing the asthma
assessment information.

Methods

Serum cotinine is a primary nicotine metabolite and its
short-term level was used as a marker of active smoking
and as an indicator of exposure to secondhand smoke[21]. Isotope dilution high-performance liquid chromatography and atmospheric pressure chemical ionization
tandem mass spectrometry were used to measure theserum cotinine levels. This assay has good accuracy, with

Study design and population

Data of this cross-sectional study were extracted fromthe National Health and Nutrition Examination Survey (NHANES) 2007–2018. NAHNES is a major project conducted by National Center for Health Statistics(NCHS), a part of CDC and aimed to evaluate the

DAQS assessment

In this study, dietary intake information was obtained
through two days 24-h dietary recall interviews. The first24-h dietary recall interview was administered during theexamination at the mobile examination center (MEC). In
this interview, all foods and nutritional supplements consumed in the 24-h prior to the interview, the quality offood reported, and a detailed description of the food. The
second 24-h interview was administered by telephone
3–4 day after the MEC exam [19]. The dietary intake wascalculated as the total intake of dietary and nutritional
supplements.
DAQS was obtained from some vitamins and minerals
that have antioxidant function including vitamin A, C, E,
zinc, Mg and selenium [20]. We compared each above six
vitamins or minerals to their recommended daily intake(RDI) for US adults 2015-2020_Dietary_Guidelines.
pdf (health.gov). Each of the vitamins or minerals wasassessed and then we allocated a value of 0 or 1, respectably. When the intake was lower than 2/3 of the RDI, it
was assigned a value of 0. Similarity, when the vitamins or
minerals was higher than 2/3 of the RDI, it was assigned
a value of 1. Finally, the total DAQS ranged from 0 (poorquality) to 6 (high quality). In present study, the DAQS
was then classified into the two groups by media level of
population included in our study: < 5 (low quality) and ≥ 5(high quality).
TSE assessment

Lin et al. BMC Pediatrics(2024) 24:535

mean values within 9% of theoretical values at all levels
expect the lower limit of quantification, where it waswithin 14% of the theoretical values. All samples wereanalyzed at the Division of Laboratory Sciences, National
Center for Environmental Health, Centers for Disease
Control and Prevention in Atlanta, Georgia. In present
study, serum cotinine levels ≥ 0.05 ng/mL were considered as TSE, and < 0.05 ng/mL were considered as nonTSE [22].
Definition of current asthma

Current asthma was defined using self-reported questionnaire responses. Participants were defined to haveasthma if they answered the question “Has a doctor orother health professional ever told you that you haveasthma?” positivity. For asthma patients, the following
question were asked for further analysis: (1) “During thepast 12 months, have you had an episode of asthma or
an asthma attack?” (2) “During the past 12 months, haveyou had to visit an emergency room or urgent care center
because of asthma?” If participants answered the question “don’t know”, s/he was not considered as current
asthma patient and was excluded from this study [23].
For children aged 1–6 years, all of these questions wereanswered by their guardians; subjects aged 7–11 years
old were accompanied by their guardian to assist in
responding, while subjects aged 12–17 years old were
responded by themselves.
Potential covariates

Demographic information, physical information, laboratory values and dietary intake were extracted from the
NHANES database. Age was divided into three groups:
1–5 years old, 6–11 years old and 12–17 years old. Physical activity levels of children aged 2–11 years old wereassessed by the question “During the past 7 days, on how
many days was physical active for a total of least 60 min
per day?” Then, add up all the time spent in any kind ofphysical activity that increased him/her heart rate andmade him/her breathe hard some of the time. For adolescent aged 12–17 years old, physical activity was expressedas the metabolic equivalent task (MET) and calculate as
follows: physical activity (met·min) = recommended MET× exercise time for corresponding activities (min/day) ×
the number of exercise days per week day (day) [24]. Ideal
physical activity was defined as ≥ 180 met·min/day for
12–17 years old or ≥ 60 met·min/in/day for 2–11 years
old. Sedentary time for children aged 2–11 years old
was the sum of time spent watching television or videos and using computer per day; while sedentary time
for adolescent aged 12–17 years old, sedentary time wasassessed by the NHANES through the individual’s daily
hours of television, video, or computer use according toPage 3 of 9

the in-person interview data and was divided into three
categories: < 3, 3–6 h, and ≥ 6 h [25]. Hay fever and family history of asthma were assessed by medical conditions
questionnaire. Hay fever was assessed by the question“During the past 12 months, have you had an episodeof hay fever?” (yes/no). Family history of asthma wasassessed by the question “Including living and deceased,
were any of your close biological that is, blood relatives
including father, mother, sisters or brothers, ever told bya health professional that they had asthma?” (yes/no).
BMI was converted to a BMI Z-score accounting for age
and gender using recommended CDC percentiles. A BMI
Z-score of ≥ 85th percentile and < 95th percentile indicates overweight status, and a BMI Z-score of ≥ 95th percentile indicates obesity [26]. Smoking during pregnancy
was assessed by the question “Did (participant’s name)biological mother smoke at any time while she was pregnant with him/her?” (yes/no).
Statistical analysis

Continuous data were expressed as mean and standard
error (S.E.), and the weighted t-test was used for comparison between groups. Categorical variables were
described as the number and percentage [N (%)], andcomparisons between groups used the weighted RaoScott χ2 test. The weighted univariate and multivariate logistic regression models were used to utilized to
explore the association between the role of DAQS in theassociation between TSE and asthma among children,with odds ratios (ORs) and 95% confidence intervals(CIs). Model 1 was a crude model without adjusting any
covariates. Model 2 adjusted age, gender, family historyof asthma, energy and BMI. Subgroup analysis were conducted to further explore the association based on gender. All statistical analyzes were performed using R v 4.20
(R Foundation for Statistical Computing, Vienna, Austria) and SAS v 9.4 (SAS Institute, Cary, North Carolina)
software. Two-sided P-value < 0.05 was considered statistically significant.

Results
Characteristics of study children

The flow chart of population screening was shown in
Fig. 1. Totally, 31,321 children were screened. Amongthem, 11,583 subjects missing serum cotinine measurement data, 3,211 subjects missing dietary two-day intake
information, 4 subjects missing DAQS calculation information, 87 subjects missing BMI data, and 5,410 subjects
missing asthma diagnosis information were excluded.
Table 1 shown the characteristics of the study population. Totally 11,026 eligible children were included, ofwhom, 1,244 (11.28%) had asthma. The proportion ofhigher DAQS in children and adolescents with asthma

Lin et al. BMC Pediatrics(2024) 24:535

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Fig. 1 The flow chart of population screening

was lower than in non-asthma group (58.97% vs. 62.98%).
Children and adolescents with asthma had higher rates
of exposure to tobacco than those non-asthma (47.35 vs.
39.21). Difference was found in age, the level of energy
intake, sedentary time and BMI, and the history of family
asthma and hay fevers between two groups (all P < 0.05).
Association between DAQS and TSE with current asthma

We explored two weighted logical regression models
to explore the association of DAQS and TSE with current asthma in children and adolescents, as presented
in Table 2. After adjusted age, gender, family historyof asthma, energy and BMI, TSE was associated withthe high odds of asthma in children (OR = 1.26, 95%CI:
1.05–1.52, P = 0.015). However, no significant association
was found in DAQS and asthma in our study population(P > 0.05).
Effect of DAQS on the association between TSE and current
asthmaThe effect of DAQS on the association between TSE andcurrent asthma in children and adolescents was shownin Table 3. In low quality of DAQS group, children andadolescents who exposed to tobacco smoke had a highodds of asthma (OR = 1.43, 95%CI: 1.08–1.89, P = 0.013).
While in high quality of DAQS group, no significant
effect of ADQS on the association between TSE andcurrent asthma was observed (P > 0.05). Taken together,high quality of DAQS may has a moderating effect on

the association between TSE and asthma in children andadolescents.
Moderating effect of DAQS on the association between TSEand current asthma in children and adolescents basedon gender

To further explore the moderating effect of DAQS on
the TSE and current asthma in children and adolescents,
subgroup analysis based on gender was conducted, aspresented in Fig. 2. After adjusted age, gender, family
history of asthma, energy and BMI in model 2, we foundthat the moderating effect of DAQS on the association of
TSE and childhood asthma remains robust, especially ingirls (OR = 1.42, 95%CI: 0.93–2.17).

Discussion
In the present study, using the moderating effect analysis,
we found that there was a moderating effect of DAQS to
the association between TSE and current asthma amongchildren and adolescents, especially in girls. That was, thehigher the DAQS, the smaller effect of TSE on the risk ofasthma in children and adolescents. Our study provides areference for developing more targeted strategies for prevention and treatment of current asthma in children andadolescents.
TSE is associated with lots of health hazards. While theproportion of adults who smoke and children exposed
to tobacco smoke continues to decline, the proportion of TSE children was still substantial at about 50%.

Lin et al. BMC Pediatrics(2024) 24:535

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Table 1 The characteristics of study children
Total (N = 11,026)

Non-asthma (N = 9782)

Asthma (N = 1244)

Low quality

4396 (37.36)

3865 (37.02)

531 (40.13)

High quality

6630 (62.64)5917 (62.98)

713 (59.87)

No6326 (59.90)

5741 (60.79)

585 (52.65)

Yes4700 (40.10)4041 (39.21)

659 (47.35)

8208 (71.67)

7308 (71.97)

900 (69.26)

Variables
DAQS, n (%)

Tobacco smoke exposure, n (%)

Smoking during pregnancy, n (%)
No

Statistics

P

1.602

0.209

11.385

0.001

2.003

0.138

Yes

1121 (11.23)

962 (10.91)

159 (13.92)

Unknown

1697 (17.09)

1512 (17.13)

185 (16.82)

Age, years, Mean (± SD)

10.64 (± 0.07)

10.57 (± 0.07)

11.17 (± 0.21)

2.783*

0.007

Energy, kcal, Mean (± SD)

1899.25 (± 12.13)

1890.60 (± 12.07)

1970.30 (± 37.42)

2.133*

0.036

5.310

0.006

1.116

0.294

3.055

0.052

0.674

0.560

0.412

0.660

5.313

0.002

8.239

< 0.001

88.818< 0.001

676.627< 0.001

Age, years, n (%)
1–5

1746 (14.83)

1600 (15.39)

146 (10.27)

6–114700 (39.74)

4152 (39.82)

548 (39.09)

12–174580 (45.43)

4030 (44.79)

550 (50.64)

Male5631 (51.07)

4934 (50.74)

697 (53.82)

Female5395 (48.93)

4848 (49.26)

547 (46.18)

6251 (45.62)

5475 (44.91)

776 (51.51)

Gender, n (%)

PIR, n (%)   < 2  ≥ 2
Unknown

3999 (48.34)

3606 (49.04)

393 (42.51)

776 (6.04)

701 (6.05)

75 (5.98)

2512 (16.39)

2264 (16.48)

248 (15.66)

Household person education level, n (%)
Below high school
High school/GED or equivalent

2060 (17.45)

1807 (17.20)

253 (19.50)

Above high school

4560 (46.80)

4025 (47.09)

535 (44.39)

Unknown

1894 (19.37)

1686 (19.24)

208 (20.45)

1569 (14.53)

1399 (14.37)

170 (15.88)

Physical activity, met·min/day, n (%)
Ideal physical activity
Non-ideal physical activity

8346 (75.98)

7392 (76.08)

954 (75.19)

Unknown

1111 (9.49)

991 (9.55)

120 (8.93)

3090 (27.47)

2804 (28.25)

286 (20.99)

Sedentary time, hours, n (%)
  < 3
3–5.9
  ≥ 6
Unknown

3532 (32.19)

3140 (32.25)

392 (31.65)

4210 (38.97)

3668 (38.17)

542 (45.56)

194 (1.38)

170 (1.33)

24 (1.81)

6851 (63.45)

6184 (64.37)

667 (55.94)

BMI, kg/m2, n (%)
NormalObesity

1876 (16.06)

1572 (15.35)

304 (21.95)

Overweight

2299 (20.48)

2026 (20.29)

273 (22.11)

6126 (56.46)

5779 (59.42)

347 (32.06)

Family history of asthma, n (%)
No
Yes2991 (27.07)

2267 (23.62)

724 (55.45)

Unknown

1909 (16.47)

1736 (16.96)

173 (12.49)

488 (4.05)

0 (0.00)

488 (37.34)

Hay favor, n (%)
No
Yes

139 (1.62)

0 (0.00)

139 (14.91)

Unknown

10,399 (94.33)

9782 (100.00)

617 (47.75)

SD Standard deviation, t t-test, DAQS Dietary antioxidant quality score, met metabolic equivalent task, BMI Body mass index

Lin et al. BMC Pediatrics(2024) 24:535

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Table 2 Association between DAQS and TSE with asthma
Variables

Model 1OR (95% CI)

Model 2
P value

OR (95% CI)

P value

DAQS
Low quality

Ref

High quality

0.88 (0.71–1.08)

Ref
0.209

0.99 (0.74–1.32)

0.919

TSE
No

Ref

Yes

1.39 (1.15–1.70)

Ref
0.001

1.26 (1.05–1.52)

0.015

Model 1: crude model
Model 2: adjustment for age, gender, family history of asthma, energy and BMI
OR Odds ratio, CI Confidence intervals, Ref Reference, DAQS Dietary antioxidantquality score, TSE Tobacco smoke exposure

Table 3 Effect of DAQS on the association between TSE andasthmaVariables

Model 1OR (95% CI)

Model 2
POR (95% CI)

0.001

1.43 (1.08–1.89)

0.086

1.15 (0.88–1.50)

P

Low quality of DAQS
TSE
No

Ref

Yes

1.58 (1.20–2.09)

Ref
0.013

High quality of DAQS
TSE
No

Ref

Yes

1.27 (0.97–1.66)

Ref
0.295

Model 1: crude model
Model 2: adjustment for age, gender, family history of asthma, energy and BMI
OR Odds ratio, CI Confidence intervals, Ref Reference, DAQS Dietary antioxidantquality score, TSE Tobacco smoke exposure

Previous, various evidence suggested that individual
who exposed to tobacco smoke had a higher risk ofasthma [11, 27–29]. Akinbami et al. [11] focused on theFig. 2 The forest plot of TSE and asthma in children stratified by gender

environmental tobacco smoke and asthma in children
suggested that 53.3% of children aged 6–9 years old suffer from asthma were TSE exposed. Even TSE resulting in low serum cotinine concentrations was related torisks for children with asthma. An analyses from Phase
Three of the ISAAC programme reported that children
exposed to tobacco smoke had a higher risk of asthma inboth age groups of 6–7 and 13–14 years old, and there
was a clear dose relationship between maternal smoking
and asthma symptoms [27]. Wang et al. [28] concluded
that TSE associated with the more severe asthma attacks.
Children with asthma and TSE were twice as likely to behospitalized for asthma exacerbation and are more likely
to have the bad outcome of pulmonary function. Similar results were also reported by Andrews et al. [29] that
there was a significantly related to length of stay at both
institutions among children hospitalized for asthma.
Cohen et al. [30] showed that in utero TSE increased agerelated airway hyper responsiveness as well as reduced
the efficacy of inhaled corticosteroids among asthmaticchildren. Moreover, a study from Israel examined theassociation between cotinine concentration in serum,
urine and saliva and the severity of asthma. Based on
the Global Initiative for Asthma (GINA) classification,
percentage of severe asthmatic patients was significantly
higher in passive smoker group. In passive smoker group,
the concentration of cotinine in serum, urine and saliva
were higher than moderate ad mild asthma [31]. Thesefindings gave support to the results of our study, whichobserved that children exposed to tobacco smoke had ahigher risk of asthma. The potential mechanism for the
relationship between TSE and asthma might be Inflammatory response and oxidative stress. Oxidative stress is
an important feature of the pathophysiology of asthma,and chemicals such as nicotine in cigarettes can cause
elevated levels of reactive oxygen species (ROS) in vivo
[32]. The accumulation of ROS further enhances theLin et al. BMC Pediatrics(2024) 24:535

oxidative stress response, which can damage cellular andsubcellular targets such as lipids, proteins and nucleic
acids, thereby inducing airflow obstruction, airway
hyperresponsiveness and remodeling, and ultimately promoting the occurrence of asthma [33].
Dietary supplements are widely acknowledged to offer
the potential to improve health if appropriately targetedto those populations in need [34]. Airways in asthmaticpatients have specific inflammatory abnormalities associated with increased generation of ROS and the tissue
damage by free radicals [35]. Dietary antioxidants such asvitamin A, C and E may protect the respiratory systemfrom oxidants. Several studies have reported an association between selected dietary antioxidants and asthma.
A latent class analysis showed that dietary inflammatory
index (DII), an index indicated an individual’s diet on acontinuum, from the most anti-inflammatory to the mostpro-inflammatory, was associated with the high-burden
asthma [36]. In a Swedish birth cohort study, Rosenlund
et al. [12] showed that Mg intake was inversely related
to asthma. A cross-sectional study from the NHANES
database found that children with high intakes of vitamin C and E may be related to a reduced prevalence ofasthma [13]. A study in urban and rural Saudi Arabia
reported that after adjustment for all covariates, thelowest intakes of vitamin E, Mg and sodium related significantly and independently to the risk of asthma [37].
Recently, a study included 501 children from 20 publicschools located in Portugal showed that after adjustment,
the exposure effect of PM2.5/10 levels on children withasthma was higher for those having a pro-inflammatory
diet, highlighting the relevance of children’s diet as apotential protective factor to pollutant exposure in children with asthma [16]. In our study, we also observed themoderating effect of DAQS on the association between
TSE and asthma in children. Among children exposed totobacco smoke, the risk of asthma in children with highDAQS level was reduced from 43 to 15% compared withchildren with low DAQS levels. TSE can induce oxidative
stress and inflammation on the respiratory airways, andantioxidants plays a vital role in relieving the severity andcontrolling asthma. Possible pathways by which dietary
antioxidants modulate the association between TSE andchildhood asthma was through reducing oxidative stressand inflammation in the airway caused by TSE.
Herein, we provided reference for the prevention ofchildhood asthma and increase social well-being by
exploring the moderating effect of DAQS, a comprehensive index reflecting the antioxidant capacity of
dietary antioxidants, on the risk of asthma in children
exposed to tobacco smoke. Previous study has shownthat parental food habits and feeding strategies are thePage 7 of 9

most dominant determinants of a child’s eating behaviors and food choices [38]. Our study highlights thebenefits of a healthy diet rich in dietary antioxidants
in reducing the risk of asthma in children and provides
a reference for parents and pediatricians to manage ahealthy diet for children and adolescents. Nevertheless,several limitations need caution in interpreting our
findings. First, this was a cross-sectional study, only themoderating effect of DAQS on the relationship between
tobacco smoke exposure and the odds of asthma inchildren and adolescents could be found, and causal
association could not be inferred. Second, the information on physical activity time and family income
was self-reported by the study children, which may beaffected by recall bias. Further large-scale prospective
cohort studies are needed to conduct to explore themoderating effect of DAQS on the association between
tobacco smoke exposure and asthma in children andadolescents.

Conclusion
This study explored the effect of DAQS on the association between asthma and TSE among children and adolescents. The results found that DAQS has a moderating
effect on the relationship between TSE and the risk ofasthma in children and adolescents, especially among
girls. The findings suggested that children and adolescents exposed to tobacco smoke are recommended tokeep healthier diet to decrease their risk of asthma.
Abbreviations
CDC	Centers for Disease Control and Prevention
DAQS	Dietary antioxidant quality score
Mg	Magnesium
Zn	Zinc
Se	Selenium
NHANES	National Health and Nutrition Examination Survey
NCHS	National Center for Health Statistics
BMI	Body mass index
MEC	Mobile examination center
MET	Metabolic equivalent task
S.E.	Standard errorORs	Odds ratios
CIs	Confidence intervals
Acknowledgements
Not applicable.
Authors’ contributions
Wei Lin designed the study, Jinliang Lin wrote the manuscript, Fuhuang Lai
and Jiaqiang Shi collected, analyzed and interpreted the data, Wei Lin criticallyreviewed the manuscript, all authors read and approved the manuscript.
Funding
Not applicable.
Availability of data and materials
The datasets generated and/or analyzed during the current study are available
in the NHANES database, https://​wwwn.​cdc.​gov/​nchs/​nhanes/.

Lin et al. BMC Pediatrics(2024) 24:535

Declarations
Ethics approval and consent to participate
The requirement of ethical approval for this was waived by the Institutional
Review Board of Longyan First Hospital Affiliated to Fujian Medical University,
because the data was accessed from NHANES (a publicly available database).
The need for written informed consent was waived by the Institutional Review
Board of Longyan First Hospital Affiliated to Fujian Medical University due toretrospective nature of the study. All methods were performed in accordancewith the relevant guidelines and regulations.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 7 May 2024 Accepted: 13 August 2024

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