Abstract
Background
Childhood obesity has a rising prevalence, and the function of oxidative stress is of increasing interest. However, few studies are available on the association of composite dietary antioxidant index (CDAI) with childhood obesity. Therefore, this study intends to investigate the relationship between CDAI and childhood obesity, and to further examine the potential mediating role of the systemic immune-inflammation index (SII) on this association.
Methods
Using data from the National Health and Nutrition Examination Survey (NHANES) 2011–2018, restricted cubic spline (RCS) and logistic regression analyses were performed to explore the relationships of CDAI, SII, and childhood obesity. Additionally, mediation analysis was used to determine the mediating role of SII.
Results
We finally included 4,134 participants, with an obesity rate of 29.05%. The fully adjusted regression model showed a significant inverse relationship between CDAI and childhood obesity (OR 0.93; 95% CI 0.89–0.97, P = 0.001). The RCS analysis found a linear association between the two (P for nonlinear = 0.816). Mediation analysis results indicated that the proportion mediated by SII was 15.46% in the association between CDAI and obesity in children and adolescents.
Conclusions
An association exists between CDAI and childhood obesity, with SII acting as a partial mediator.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12944-025-02798-x.
Keywords: Composite dietary antioxidant index, Systemic Immune-Inflammation index, Childhood obesity, Child and adolescent
Background
With a rising prevalence, childhood obesity has become an urgent public health issue globally. As reported by the World Health Organization and World Obesity Atlas, the childhood obesity rate has doubled in the past thirty years. By 2022, approximately 160 million children and adolescents will be disturbed by obesity, and this figure, if no effective interventions are made, is expected to reach 254 million by 2030 [1, 2]. Obesity affects many organs in children and adolescents, and its effects can partially persist into adulthood and even cause premature death [3]. Moreover, obesity has a strong association with an elevated risk of complications such as metabolic dysfunction-associated fatty liver disease, hypertension, dyslipidemia, diabetes, sleep disorders, polycystic ovary syndrome, depression, and anxiety [4]. Therefore, effective prevention, treatment, and interventions are urgently needed to alleviate the burden of disease related to childhood obesity.
Given this, lifestyle interventions are central strategies for preventing and treating obesity in children and adolescents, with dietary management being a crucial component. Recently, composite health indicator systems, such as the Life’s Essential 8 covering eight dimensions [diet, physical activity, nicotine exposure, sleep health, body mass index (BMI), blood lipids, blood glucose, and blood pressure], have been developed to provide a vital framework for assessing and promoting children’s overall cardiometabolic health [5, 6]. In this macro backdrop, deeply investigating specific lifestyle factors, especially mechanisms for intervention in obesity via particular dietary components and their biological pathways, is crucial for developing precise strategies for prevention and treatment. It is generally recognized that obesity is fundamentally defined as excessive abnormal accumulation of adipose tissues, which persistently release pro-inflammatory substances and induce chronic oxidative stress, creating a vicious cycle of “low-grade inflammation-oxidative damage” [7]. This vicious cycle not only drives obesity progression but is also closely associated with a variety of metabolic disorders, including cardiovascular disease, insulin resistance, diabetes, and atherosclerosis [8]. Research suggests that dietary antioxidants are effective interventions in this vicious cycle [9]. For instance, vitamin C (VC) intake negatively associates with BMI in adolescents, and appropriate VC supplementation may aid weight management [10]. Trace elements (e.g., selenium) may buffer obesity-related early oxidative damage during adolescence through multiple antioxidant and anti-inflammatory pathways [11]. Composite dietary antioxidant index (CDAI) is an indicator integrating six major dietary antioxidants [vitamin A (VA), VC, vitamin E (VE), carotenoids, selenium, and zinc], and it is used to assess the overall antioxidant ingredients of the diet [12, 13]. Wang, Z. et al. [14] found that CDAI is negatively associated with the risk of both systemic obesity and abdominal obesity in the US adult population. After extending outcomes to metabolic syndrome and all-cause mortality, Zhou, Q. et al. [15] identified a negative dose-response relationship of CDAI with the prevalence of metabolic syndrome, particularly with central obesity and dyslipidemia; however, no significant association of CDAI with mortality was observed in patients with established metabolic syndrome. These findings suggest that adopting high-antioxidant dietary patterns during early life (e.g., childhood and adolescence) may be crucial for preventing obesity and related metabolic disorders. However, the potential impact of total dietary antioxidant capacity on obesity risk in children and adolescents and its underlying mechanisms remain understudied.
The specific etiology and mechanism of childhood obesity are still unclear, but inflammation is indeed crucial in the development and progression of obesity. Obesity causes fat over-accumulation to trigger cell dysfunction (e.g., hypoxia, hypertrophy, and increased death); as a result, adipocytes and immune cells secrete pro-inflammatory factors (e.g., tumor necrosis factor-α and interleukin-6), creating a pro-inflammatory microenvironment, inducing immune cell infiltration, and aggravating inflammatory responses [16, 17]. Meanwhile, an inflammatory imbalance further occurs due to a weakened secretion of anti-inflammatory factors (e.g., interleukin-10 and interleukin-4) and an enhanced secretion of cytokines (e.g., leptin and adiponectin). All these inflammatory factors not only act on local adipose tissues but also enter the systemic circulation, elevating the risk of obesity-related metabolic disturbance [7, 17, 18]. Therefore, searching for reliable hematological markers able to fully reflect systemic inflammatory status is crucial for early identification and intervention of obesity and its complications. Integrating three types of inflammatory cells (platelets, neutrophils, and lymphocytes), the systemic immune-inflammation index (SII) can provide a clearer picture of the inflammatory response [19]. In the field of cardiovascular disease, SII has been recognized as an independent predictor of in-stent restenosis and vulnerable plaques, whose predictive efficacy is even superior to traditional markers such as C-reactive protein [20]. Furthermore, studies on inflammatory markers and erectile dysfunction further verified the link between systemic inflammation and endocrine and metabolic disorders [21]. To sum up, these findings indicate that SII serves as a crucial biological bridge between obesity-related inflammation and metabolic and cardiovascular complications. Although some studies suggest SII may be a partial mediator of the association between CDAI and obesity-related diseases, less subsequent supportive evidence is available in the pediatric population [22].
Based on these findings, it is hypothesized that higher CDAI is associated with lower obesity odds in children and adolescents, in which SII serves as a partial mediator. Therefore, this study intends to explore the association of CDAI with childhood obesity and further analyze the mediating role of SII in this association based on a national sample. The findings will help deepen the understanding of the complex pathogenesis of childhood obesity and offer a scientific basis for obesity management and prevention.
Methods
Study design and population
The National Health and Nutrition Examination Survey (NHANES) is a survey of a nationally representative U.S. population, which utilizes a complex, multistage probability sampling design to offer large amounts of information on the nutritional and health status of the general U.S. residents. The NHANES data are collected through interviews, physical and laboratory examinations. Its protocol was approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants gave their written informed consent. This study was conducted based on the NHANES 2011–2018, with 39,156 participants enrolled over four cycles. Participants with the following characteristics were excluded: aged > 19 years, with missing data on dietary interview, BMI, neutrophil count, platelet count, and lymphocyte count, and with missing covariates (race, age, gender, serum cotinine concentration, family monthly poverty level index, total number of household smokers, maternal smoking, protein intake, and fat intake). Ultimately, 4,134 participants were included (Fig. 1).
Fig. 1.
Flowchart of participant selection from NHANES 2011–2018
CDAI
The CDAI was calculated using the data from two non-consecutive 24-h dietary recall interviews in NHANES. The first interview was conducted face-to-face at a mobile examination center, while the second was completed by telephone 3–10 days later. Each participant’s daily nutrient intake was reported by the mean of the two recall results. The CDAI incorporated six antioxidants derived solely from food: VA, VC, VE, zinc, selenium, and carotenoids, with antioxidant components from dietary supplements, medications, or drinking water excluded. The CDAI was calculated by the following equation developed by Wright et al. [12]:
 |
Where xi represents daily antioxidant intake, mean(xi) represents the mean amount of these antioxidants in the study population, and std(xi) represents the standard deviation.
Based on the distribution of CDAI values across the entire study sample, it was assigned into three groups (by tertiles): CDAI < −1.80 (T1); −1.80 ≤ CDAI < 1.13 (T2); CDAI ≥ 1.13 (T3).
Definition of obesity
In the Centers for Disease Control and Prevention (CDC) Growth Charts, obesity refers to a BMI (kg/m2) ≥ 95th percentile of BMI for the same age and gender.
SII
Complete blood counting was performed, comprising neutrophils (×103 cells/µL), lymphocytes (×103 cells/µL), and platelets (×103 cells/µL), using an automated hematology analyzer (Kurt® Dx H 800), based on which SII was calculated (platelet count × neutrophil count/lymphocyte count) [23].
Covariates
Based on clinical rationality and previously published articles [24–27], a directed acyclic graph (DAG) was generated by DAGitty software (v3.1) [28] (Fig. 2). Covariates included gender, age (coinciding with key educational stages: 2–5 years for preschool, 6–12 years for primary school, and 13–19 years for middle school), race (Mexican American, non-Hispanic Asian, non-Hispanic white, non-Hispanic black, and others), family monthly poverty level index [≤ 1.3 (low income), 1.30–1.85 (low-middle income), >1.85 (high-middle income)], total number of household smokers, maternal smoking (a question on the NHANES questionnaire for pregnant women: Did you smoke at any time during pregnancy?), serum cotinine concentration (limit of detection: 0.011 ng/mL), protein intake, and fat intake. Total energy, protein, and fat intake (from both food and supplements) were derived from the first and second 24-h dietary recall interviews.
Fig. 2.
Directed acyclic graph of the association between CDAI and obesity Note: Green variables with the symbol “►” in rectangular boxes are exposure variables; blue variables with the letter “I” in rectangular boxes are outcome variables; blue variables are antecedents of outcome variables; red variables are antecedents of both exposure and outcome variables
Statistical analysis
Considering that a complex multistage probability sampling design is utilized by NHANES to select representative samples, sample weights were incorporated in all statistical analyses, and the clustering effects at the primary sampling unit and stratified variables were adjusted for to ensure that the results could be validly generalizable to the overall U.S. children and adolescents. Continuous variables underwent normality tests, and then variables of normal distribution were presented by mean ± standard deviation, while those of abnormal distribution were presented by median (interquartile range). Categorical variables were presented by [n(%)]. T-tests and Mann-Whitney U tests were conducted on continuous variables, and chi-square tests were conducted on categorical variables. To avoid multicollinearity, the variance inflation factor (VIF) was utilized to assess covariates, and it was less than 10 for all covariates, indicating no severe multicollinearity. To assess the association of CDAI with obesity in children and adolescents, three weighted multivariable logistic regression models were established: Model 1: unadjusted; Model 2: adjusted for age, gender, and race; Model 3: further adjusted for family monthly poverty level index, total number of household smokers, maternal smoking, serum cotinine concentration, protein intake, and fat intake. We assessed the potential nonlinear association between the two using the restricted cubic spline (RCS) with three knots (10th/50th/90th percentiles). Furthermore, we conducted subgroup analyses to examine the presence or absence of heterogeneity and interactions in specific populations. Considering the possibility of Type I errors, Bonferroni correction for multiple testing was applied in subgroup analyses to reduce the likelihood of false positives. The Bonferroni-corrected significance level was α’=0.05/n, where n represents the number of subgroups to be compared (n = 21 in this study, resulting in a corrected significance level of α’=0.00238). An unadjusted P-value less than the Bonferroni-corrected threshold of 0.00238 was considered statistically significant. Equivalently, an adjusted P-value (unadjusted P-value × 21) less than 0.05 was considered statistically significant. To further explore potential mechanisms, the associations between CDAI and SII and between SII and obesity were also examined using weighted multivariable linear regression and logistic regression models. Mediation analyses were then conducted to assess whether SII mediates the association between CDAI and obesity, with the estimated proportion mediated and 95% CI calculated by the Bootstrap method (1,000 resamples). Additionally, two sensitivity analyses were conducted. With the participants with extreme values in key variables (such as CDAI, SII, or BMI) excluded, the first sensitivity analysis repeated the primary analysis. The second sensitivity analysis further incorporated other potential confounders (energy intake, carbohydrate intake, physical activity, and sedentary time) to verify the robustness of the results. R4.4.3 was utilized for analyses, and statistical significance was considered at P < 0.05 (two-tailed).
Results
Baseline characteristics
We finally included 4,134 participants, with an obesity rate of 29.05%, including 2,137 males (52.92%) and 1,997 females (47.08%). Most participants were primary school-aged children (63.89%). Non-Hispanic whites accounted for 52.48% and Mexican Americans for 15.81%. Based on the diagnostic criteria for obesity in children and adolescents, this study encompassed 2,888 non-obese participants and 1,246 obese participants. We observed statistical differences in age, race, gender, serum cotinine concentration, family monthly poverty level index, total number of household smokers, and maternal smoking between obese and non-obese participants. Obese participants had significantly higher platelet and neutrophil counts, but significantly lower VA and alpha-carotene intake, compared to non-obese participants. Overall, Obese participants had significantly higher SII scores (P < 0.001) and significantly lower CDAI scores (P = 0.045) than non-obese participants (Table 1).
Table 1.
Baseline characteristics
| Characteristic | N | Overall | Normal | Obesity | P-value |
|---|---|---|---|---|---|
| Gender (%) | 4134 | 0.023 | |||
| Female | 1997 (47.08%) | 1389 (48.59%) | 608 (43.39%) | ||
| Male | 2137 (52.92%) | 1499 (51.41%) | 638 (56.61%) | ||
| Age (%) | 4134 | 0.008 | |||
| Preschool | 304 (6.84%) | 251 (7.85%) | 53 (4.36%) | ||
| Primary school | 2689 (63.89%) | 1851 (63.47%) | 838 (64.91%) | ||
| Middle school | 1141 (29.27%) | 786 (28.68%) | 355 (30.73%) | ||
| Race (%) | 4134 | < 0.001 | |||
| Mexican American | 873 (15.81%) | 555 (13.93%) | 318 (20.40%) | ||
| Non-Hispanic Asian people | 308 (3.80%) | 250 (4.31%) | 58 (2.55%) | ||
| Non-Hispanic black people | 994 (13.44%) | 692 (12.96%) | 302 (14.61%) | ||
| Non-Hispanic white people | 1200 (52.48%) | 879 (55.43%) | 321 (45.26%) | ||
| Other races | 759 (14.47%) | 512 (13.37%) | 247 (17.17%) | ||
| Cotinine (%) | 4134 | 0.003 | |||
| <0.05 ng/mL | 2407 (62.18%) | 1726 (64.67%) | 681 (56.09%) | ||
| 0.05–2.99 ng/mL | 1533 (32.83%) | 1017 (30.18%) | 516 (39.29%) | ||
| ≥3 ng/mL | 194 (5.00%) | 145 (5.16%) | 49 (4.62%) | ||
| Family monthly poverty level index (%) | 4134 | 0.020 | |||
| Low income | 2062 (39.72%) | 1396 (37.75%) | 666 (44.53%) | ||
| Low-middle income | 635 (15.27%) | 428 (14.86%) | 207 (16.26%) | ||
| High-middle income | 1437 (45.01%) | 1064 (47.38%) | 373 (39.21%) | ||
| The Total number of smokers at home (%) | 4134 | < 0.001 | |||
| 0 | 2840 (69.73%) | 2030 (72.27%) | 810 (63.53%) | ||
| 1 | 802 (17.37%) | 537 (15.76%) | 265 (21.30%) | ||
| ≥2 | 492 (12.90%) | 321 (11.97%) | 171 (15.17%) | ||
| Whether the mother smoked during pregnancy (%) | 4134 | 0.012 | |||
| No | 3576 (84.75%) | 2518 (86.03%) | 1058 (81.62%) | ||
| Yes | 558 (15.25%) | 370 (13.97%) | 188 (18.38%) | ||
| Protein (gm) | 4134 | 64.78 (50.01, 81.74) | 64.04 (49.79, 81.39) | 65.68 (50.44, 82.90) | 0.400 |
| Fat (gm) | 4134 | 68.35 (51.80, 88.68) | 68.72 (52.47, 89.37) | 67.31 (51.10, 87.82) | 0.140 |
| Vitamin A (mcg) | 4134 | 558.50 (363.50, 804.00) | 563.50 (372.00, 836.00) | 553.00 (342.50, 751.00) | 0.011 |
| Vitamin C (mg) | 4134 | 53.95 (27.50, 95.30) | 56.05 (27.90, 97.90) | 51.05 (25.65, 89.70) | 0.084 |
| Vitamin E (mg) | 4134 | 6.46 (4.65, 8.87) | 6.52 (4.69, 9.02) | 6.38 (4.58, 8.49) | 0.065 |
| Selenium (mcg) | 4134 | 90.60 (69.50, 117.20) | 90.40 (69.75, 116.75) | 91.00 (69.30, 119.00) | > 0.900 |
| Zinc (mg) | 4134 | 9.20 (6.86, 12.37) | 9.19 (6.96, 12.42) | 9.23 (6.71, 12.28) | 0.400 |
| Alpha-carotene (mcg) | 4134 | 29.00 (8.00, 177.50) | 30.50 (8.50, 198.50) | 26.00 (7.00, 125.50) | 0.021 |
| Beta-carotene (mcg) | 4134 | 553.00 (251.50, 1445.00) | 561.00 (254.50, 1498.00) | 515.50 (245.00, 1291.00) | 0.089 |
| Beta-cryptoxanthin (mcg) | 4134 | 34.00 (11.50, 91.00) | 35.50 (11.00, 91.00) | 31.50 (11.50, 91.50) | 0.500 |
| Lycopene (mcg) | 4134 | 2653.00 (771.00, 6189.50) | 2641.00 (696.00, 6285.50) | 2679.00 (884.50, 5775.00) | 0.800 |
| Lutein + zeaxanthin (mcg) | 4134 | 548.00 (334.50, 920.50) | 557.50 (343.00, 934.00) | 529.50 (327.00, 862.50) | 0.083 |
| Carotenoids (mcg) | 4134 | 4856.00 (2270.50, 9492.50) | 4972.50 (2260.00, 9712.50) | 4551.00 (2316.00, 8980.00) | 0.300 |
| Platelet count (1000 cells/uL) | 4134 | 271.00 (237.00, 312.00) | 267.00 (234.00, 307.00) | 283.00 (243.00, 324.00) | < 0.001 |
| Segmented neutrophils count (1000 cell/uL) | 4134 | 3.40 (2.60, 4.40) | 3.20 (2.50, 4.30) | 3.80 (3.00, 4.90) | < 0.001 |
| Lymphocyte count (1000 cells/uL) | 4134 | 2.60 (2.10, 3.10) | 2.50 (2.10, 3.10) | 2.60 (2.20, 3.10) | 0.300 |
| SII | 4134 | 355.71 (255.15, 492.00) | 336.25 (240.59, 460.26) | 409.55 (310.48, 549.82) | < 0.001 |
| CDAI | 4134 | −0.48 (−2.55, 2.08) | −0.41 (−2.44, 2.30) | −0.62 (−2.82, 1.69) | 0.045 |
Data were presented as mean ± standard deviation for normally distributed variables, median (interquartile range) for non-normally distributed variables, and n (%) for categorical variables
T-tests and Mann-Whitney U tests were conducted on continuous variables, and chi-square tests were conducted on categorical variables
Association of CDAI with obesity
The association of CDAI with obesity in children and adolescents was assessed by multivariable logistic regression (Table 2). A significant negative association was found in Model 1 (OR 0.97, 95% CI 0.94–0.99), Model 2 (OR 0.96, 95% CI 0.93–0.99), and Model 3 (OR 0.93, 95% CI 0.89–0.97). CDAI was divided into T1 (a reference), T2, and T3, and Model 3 displayed a significant negative association of T3. Moreover, the trend test showed that the rate of obesity in children and adolescents declined with increasing CDAI (P for trend = 0.003), and a linear association between the two was found by the RCS analysis (P for nonlinear = 0.816) (Fig. 3).
Table 2.
Association of CDAI with obesity in children and adolescents
| Participants | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95%CI) | P-value | OR (95%CI) | P-value | OR (95%CI) | P-value | |
| CDAI | ||||||
| Continuous | 0.97(0.94,0.99) | 0.020 | 0.96(0.93,0.99) | 0.003 | 0.93(0.89,0.97) | 0.001 |
| T1 (CDAI < −1.80) | ref. | ref. | ref. | |||
| T2 (−1.80 ≤ CDAI < 1.13) | 0.96(0.77,1.21) | 0.800 | 0.91(0.72,1.15) | 0.400 | 0.87(0.67,1.13) | 0.300 |
| T3 (CDAI ≥ 1.13) | 0.76(0.59,0.97) | 0.026 | 0.69(0.53,0.89) | 0.005 | 0.62(0.46,0.84) | 0.003 |
| P for trend | 0.050 | 0.010 | 0.003 | |||
Model 1: No covariate was adjusted
Model 2: Gender, age and race were adjusted
Model 3: Gender, age, race, cotinine, family monthly poverty level index, the total number of smokers at home, whether the mother smoked during pregnancy, protein and fat were adjusted
Fig. 3.
Association between CDAI and obesity in children and adolescents
Association of CDAI with SII
The association of CDAI with SII was explored by linear regression models (Supplementary Table 1). The results revealed a significant negative association between higher CDAI and lower SII (β = −3.82, 95% CI −6.15 to −1.49). This negative association persisted and became stronger after adjusting for covariates.
Association of SII with obesity
Furthermore, the association of SII with obesity was investigated by logistic regression models. SII displayed a positive association with the odds of obesity in children and adolescents in Model 1 (OR 1.11, 95% CI 1.04–1.18), Model 2 (OR 1.10, 95% CI 1.04–1.17), and Model 3 (OR 1.09, 95% CI 1.03–1.16). A nonlinear dose-response relationship between the two was revealed by the RCS analysis (P for nonlinear < 0.001). The SII cutoff value was 360.62. Further details are provided in Supplementary Tables 2 and Supplementary Fig. 1.
Interaction of childhood and adolescent obesity and CDAI across subgroups
Subgroup analyses were conducted by age, gender, race, serum cotinine concentration, family monthly poverty level index, total number of household smokers, and maternal smoking. The results were visualized by forest plots (Fig. 4). Obesity and CDAI exhibited a significant inverse association in the subgroups of female, male, primary school, middle school, non-Hispanic white, serum cotinine concentration < 0.05 ng/mL, low-middle income, high-middle income, total number of household smokers = 0, and no maternal smoking. After Bonferroni correction, none of the associations in subgroups retained statistical significance. Age displayed a significant interaction in the association between the two, and it retained statistical significance after Bonferroni correction (Bonferroni-corrected P for interaction = 0.021) (Supplementary Table 3).
Fig. 4.
Forest plots for subgroup analysis of the association between CDAI and obesity
Mediation analysis
The mediating role of SII in the association between obesity and CDAI was determined by a mediation analysis (Fig. 5). The direct and indirect effects of SII were − 0.0070 (95% CI −0.0107 to −0.0010, P = 0.014) and − 0.0013 (95% CI −0.0014 to −0.0003, P < 0.001), respectively. The proportion mediated by SII was 15.46%, suggesting its role as a partial mediator in the association of CDAI with obesity in children and adolescents. Furthermore, the mediation analysis also revealed the partial mediating role of SII in the middle school subgroup rather than in the preschool and primary school subgroups, with a proportion mediated of 9.91%.
Fig. 5.
Mediating role of SII in the association of CDAI with obesity Note: Mediating role of SII in all participants (A), preschool (B), primary school (C), and middle school (D)
Sensitivity analysis
The robustness of the main findings was verified by sensitivity analyses. First, after excluding participants with extreme values of CDAI, SII, and BMI, higher CDAI remained significantly associated with lower obesity odds (Supplementary Table 4). Second, this negative association remained robust after adjusting for multiple potential confounders: The association persisted after controlling for socioeconomic factors (family monthly poverty level index), smoking exposure (total number of household smokers, maternal smoking, and serum cotinine concentration), and macronutrient intake (protein, and fat); the negative association remained statistically significant after further introducing energy intake level, carbohydrate intake, physical activity, and sedentary time (Supplementary Table 5). To sum up, the findings possessed high robustness.
Discussion
This study ultimately included 4,134 children and adolescents from the NHANES (2011–2018), with an obesity rate of 29.05%. An association was found between higher CDAI and lower obesity odds in children and adolescents, which persisted after adjusting for covariates. Moreover, the RCS analysis results indicated a linear relationship between CDAI and obesity in children and adolescents. Additionally, age displayed a significant interaction in the association between CDAI and obesity, and SII served as a mediator in the association, with a proportion mediated of 15.46%. The robustness of these findings was further verified by sensitivity analyses.
Obesity is often accompanied by enhanced oxidative stress and immune cell infiltration in adipocytes, which is defined as a chronic low-grade inflammation [29]. Oxidative stress plays a pivotal role in obesity by regulating mitochondrial function, release of inflammatory mediators, lipogenesis, and hypothalamic energy balance [30]. The antioxidants (VA, VC, VE, carotenoids, selenium, and zinc) incorporated in CDAI can synergistically regulate these processes. With the combined intake of these micronutrients, visceral obesity is expected to be improved [31]. Zhang, H et al. [32] reported that pro-oxidative diets associate with elevated odds of cardiometabolic comorbidities and mortality. Retinol, β-carotene, and VE are negatively associated with metabolic obesity in children and adolescents [33]. However, supplementation of VE alone does not greatly improve lipid or adiponectin levels [34]. To sum up, combined intervention with multiple antioxidant nutrients may exert distinct effects from a single intervention.
Recently, multiple studies have examined the association of various dietary antioxidant indicators with obesity in children and adolescents, but the results possess considerable heterogeneity. In a cross-sectional study of 1,580 students aged 10–12 years, Kokkou et al. [35] found that the dietary antioxidant index (DAI, including magnesium but excluding carotenoids) has a significant negative association with body weight. However, a larger-sample study using the dietary antioxidant quality score (DAQS, excluding magnesium and carotenoids) reported higher intakes of some antioxidant nutrients in overweight and obese children, suggesting that different nutrient compositions and population characteristics may influence study conclusions [36]. Magnesium is excluded from the CDAI primarily because it does not work as a direct antioxidant but rather more broadly involves energy metabolism and electrolyte balance, although it participates in multiple metabolic processes [37]. In contrast, carotenoids possess potent antioxidant activity, which can effectively scavenge free radicals and mitigate oxidative stress, so they are incorporated into the CDAI to more fully assess the total dietary antioxidant capacity [38]. Chen. J. et al. [13] employed CDAI and confirmed its significant negative association with overweight/obesity odds in adolescents; they also found that the association between selenium and obesity is influenced by energy adjustment methods, and the modified CDAI that excludes selenium consistently demonstrates protective effects across multiple energy-adjusted models. In this study, after adjusting for multiple covariates, the CDAI exhibited a significant negative association with obesity in children and adolescents, consistent with prior findings.
Additionally, after Bonferroni correction, none of the associations in subgroups retained statistical significance, but the negative association remained consistent across subgroups, suggesting that the potential protective effect of CDAI against obesity may be widely present in children and adolescents. Age displayed a significant interaction with the association of CDAI with obesity. This variation may be attributed to distinct growth, developmental, and metabolic characteristics, and behavioral patterns across age groups. Among middle school-aged adolescents, the puberty initiation is accompanied by significant increases in sex hormones and insulin-like growth factor-1 levels, which enhance control over oxidative stress and inflammation [11, 39, 40]. More importantly, dietary behaviors undergo significant changes during this period, with more autonomy in food choices and higher intake of pro-inflammatory diets rich in sugar and fat, potentially overwhelming the compensatory capacity of endogenous antioxidant systems [41]. Therefore, dietary adequacy of antioxidant intake has become an exogenous factor influencing the balance between oxidative stress and inflammation, with prominent protective effects. In contrast, preschool- and primary school-aged children have relatively stable growth rates and lower sex hormone levels, and their dietary patterns are more heavily influenced by family dietary structures, typically with significantly lower levels of oxidative stress and inflammatory activation than adolescents [11, 42, 43]. Therefore, the unique “high metabolic stress” and “unbalanced dietary behaviors” in adolescence may constitute a susceptible background, making the anti-inflammatory effects of dietary antioxidants particularly significant in this population.
As previously noted, inflammation is a crucial player in the onset and progression of obesity. This study analyzed the associations between CDAI and SII, and between SII and obesity. The results revealed a significant negative association between higher CDAI and lower SII, and a positive association of SII with obesity in children and adolescents. A nonlinear dose-response relationship between SII and obesity was revealed by the RCS analysis (P for nonlinear < 0.001), which possibly stemmed from complex interactions between inflammation and obesity, and population heterogeneity [44]. SII was selected as the mediating variable since it integrated three key immune cells (neutrophils, lymphocytes, and platelets) to more fully reflect the “immune-inflammation-thrombosis” interaction network [23, 45]. SII better reflects chronic low-grade inflammation than acute-phase markers like C-reactive protein [20, 46]. Compared with any single indicator (e.g., neutrophil-to-lymphocyte ratio), SII incorporates platelets, which play a crucial role in obesity-related inflammatory processes [47]. Recent studies also demonstrate that SII is superior to traditional inflammatory markers in forecasting obesity-related diseases [46]. This study showed that SII partially mediated the association of CDAI with obesity in children and adolescents. These findings offered preliminary, hypothetical cross-sectional evidence for the potential pathway: “dietary antioxidants → mitigation of inflammation → reduced obesity odds”. Although few studies directly examined the mediating effect of CDAI on childhood obesity via SII, we made a consistent conclusion with recent findings that inflammation is a mediator in the diet-obesity relationship. For instance, Yuguang et al. [48] found that SII mediates 37.5% of the association between the healthy eating index and metabolic syndrome, possibly because metabolic syndrome encompasses more inflammation-driven components (e.g., hyperglycemia, dyslipidemia), highlighting the inflammatory mediation pathway. Another study reported that leukocytes and neutrophils mediate 6.92% and 11.2% of the association between dietary inflammation index and all-cause mortality, respectively [49]. The strength of the mediating effect observed in this study fell within the range reported previously but exceeded that based on a single inflammatory cell count, possibly because SII, as a composite indicator, can more fully capture the status of chronic low-grade inflammation.
Notably, the mediating effect of SII was significant only in the middle school-aged adolescents, rather than in the preschool- or primary school-aged children. This finding suggests that the unique physiological environment of adolescence provides essential conditions for the “diet-inflammation-obesity” pathway, i.e., active metabolic changes and inflammatory backgrounds in middle school-aged adolescents make SII more sensitive to the regulatory effects of dietary antioxidants, so SII becomes a key bridge between CDAI and obesity [39, 40]. In contrast, preschool- or primary school-aged children, characterized by lower levels of baseline inflammation, may develop obesity through non-inflammatory mechanisms such as genetic factors, family dietary habits, and early feeding patterns, which may explain why the mediating effect of SII was less significant in younger children [42, 50, 51]. Thus, the age-specific differences in the association of CDAI with obesity not only reflect physiological and behavioral variations across age groups but also suggest that age-specific intervention is required for the prevention of obesity. For middle school-aged adolescents, a combination of foods rich in antioxidant nutrients should be prioritized in dietary interventions, including increasing the supply of dark-colored vegetables, fruits, and nuts in school meals, and incorporating selenium-rich foods like oats and mushrooms, and zinc-rich foods such as lean meats and seafood. Moreover, we can monitor SII to objectively assess the intervention effect.
Some limitations are worth noting in this study. First, CDAI is a composite indicator, but it does not fully account for synergistic interactions of nutrients (e.g., the cyclic interaction between VC and VE), potentially underestimating the overall dietary antioxidant effect. It should also be noted that the validity of CDAI in pediatric and adolescent populations requires further validation, particularly its applicability under the unique dietary patterns and metabolic variation in developmental stages. Second, SII is a composite inflammatory marker based solely on complete blood cell counts (neutrophils, lymphocytes, and platelets), excluding molecular inflammatory markers like interleukin-6 and tumor necrosis factor-α, restricting its ability to reflect systemic inflammatory status. Furthermore, the cross-sectional design precluded inference of causality between CDAI and obesity. Fourth, data were required by two 24-h dietary recall interviews, but recall bias could not be entirely ruled out. Although multiple confounders were adjusted for, unmeasured confounders may still exist. Finally, the study results were based on the U.S. population, so they could not be fully generalized to other populations. In the future, large-scale prospective cohort studies or randomized controlled trials are required to further verify the causal temporal relationships of CDAI, SII, and obesity odds in children and adolescents, as well as relevant mechanisms.
Conclusions
This study demonstrates a significant linear association between CDAI and obesity in children and adolescents. Higher CDAI associates with a lower grade of systemic inflammation, the latter of which is associated with reduced odds of obesity. Furthermore, SII partially mediates the association between CDAI and obesity in children and adolescents. These findings offer preliminary cross-sectional evidence for investigating the potential path of dietary interventions modulating inflammation to improve obesity in children and adolescents.
Supplementary Information
Acknowledgements
Not applicable.
Abbreviations
- BMI
Body mass index
- CDAI
Composite dietary antioxidant index
- CDC
Centers for Disease Control and Prevention
- DAG
Directed acyclic graph
- DAI
Dietary antioxidant index
- DAQS
Dietary antioxidant quality score
- NCHS
National Center for Health Statistics
- NHANES
National Health and Nutrition Examination Survey
- RCS
Restricted cubic spline
- SII
Systemic Immune-Inflammation Index
- VA
Vitamin A
- VC
Vitamin C
- VE
Vitamin E
- VIF
Variance inflation factor
Authors’ contributions
Conceptualization, A.Z. and F.Y.; methodology, F.Y.; formal analysis, A.Z. and P.L.; investigation, P.L. and L.F.; data curation, A.Z., P.L., L.F., and F.Y.; writing—original draft preparation, A.Z.; writing—review and editing, A.Z., P.L., L.F., and F.Y; supervision, F.Y.; project administration, F.Y. All authors have read and agreed to the published version of the manuscript.
Funding
The authors declare that they did not receive any funding from any source.
Data availability
The datasets analyzed during the current study are available in the NCHS, https://www.cdc.gov/nchs/nhanes/about/index.html.
Declarations
Ethics approval and consent to participate
All methods in our research were performed in accordance with the Declaration of Helsinki. This study was based on a public database and approved by the Research Ethics Review Committee of the NCHS. All participants gave written informed consent, and ethical review was exempted in this study since it utilized publicly available NHANES data.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets analyzed during the current study are available in the NCHS, https://www.cdc.gov/nchs/nhanes/about/index.html.