Sex-Specific Effects of Relative Fat Mass on Attention-Deficit/Hyperactivity Disorder: Insights from the 1999–2004 National Health and Nutrition Examination Survey

Abstract

Background:

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in childhood. Conventional adiposity indicators have been linked to ADHD. Relative fat mass (RFM), a novel adiposity measure, has demonstrated advantages in predicting metabolic and cardiovascular disease risk, conditions that share overlapping pathophysiological mechanisms with ADHD. This study aimed to investigate the association between RFM and ADHD, with a particular focus on sex-specific differences.

Methods:

Data were obtained from the 1999–2004 National Health and Nutrition Examination Survey, including 5089 children aged 6–14 years. RFM was calculated using height and waist circumference, and ADHD was defined based on physician diagnosis reported in the questionnaire. Multivariable logistic regression models were applied to assess the association between RFM and ADHD. Smooth curve fitting was conducted to validate the results, and subgroup analyses were performed separately for boys and girls.

Results:

A significant sex-specific association between RFM and ADHD was observed. Among boys, higher RFM levels were inversely associated with ADHD (Model 3: odds ratio [OR] = 0.967, 95% confidence interval [CI]: 0.946–0.989), whereas among girls, higher RFM levels were positively associated with ADHD (Model 3: OR = 1.043, 95% CI: 1.007–1.081). Smooth curve fitting confirmed these opposite linear trends in both sexes. Subgroup analyses further demonstrated that this sex-specific pattern was consistent across strata defined by age, health insurance status, maternal smoking during pregnancy, and birth weight.

Conclusions:

There is a significant sex-specific association between RFM and ADHD, showing an inverse relationship in boys and a positive association in girls. These findings suggest that the influence of adiposity distribution on ADHD differs by sex, highlighting the importance of considering sex differences when evaluating risk factors for ADHD.

Keywords

relative fat mass ADHD NHANES sex-specific children

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood, characterized by inattention and hyperactivity/impulsivity, often accompanied by functional impairments. Epidemiological data show that in U.S. children aged 3–17 years, the diagnostic prevalence of ADHD was 11.4% and the current prevalence was 10.5% in 2022 (Danielson et al., 2024). ADHD may persist into adulthood, imposing a burden on learning and mental health, while also generating substantial economic costs. It is estimated that the annual excess societal cost attributable to ADHD among children and adolescents in the United States reaches as high as USD 33.2 billion (Henning et al., 2024; Wolraich et al., 2019; Schein et al., 2022). Symptoms of ADHD typically emerge during school age, and ages 6–14 represent a critical period of neurodevelopment, making this age group an important research window for investigating epidemiological features and associated factors (Gilboa and Helmer, 2020). Therefore, identifying potential risk factors associated with ADHD is essential for disease prevention and for alleviating both individual and societal burdens.

In recent years, increasing attention has been paid to the relationship between anthropometric indicators and ADHD. Traditional adiposity measures, such as body mass index and waist circumference, have been linked to the risk of ADHD. Some studies suggest that obesity and ADHD share certain neurobiological mechanisms, including functional abnormalities or structural alterations in the hypothalamus, executive control centers, and reward circuitry, which may partially or fully manifest as symptoms of both conditions, thereby contributing to disease onset (Bowling et al., 2018; O’Hara et al., 2020). However, traditional anthropometric indicators are limited in capturing body fat distribution. Relative fat mass (RFM) is a new body fat estimation index based on height and waist circumference. Studies have shown that it has significant advantages in predicting obesity and metabolic-related risks. For instance, RFM has shown strong associations with metabolic syndrome and cardiovascular disease risk (Wang et al., 2024; Zheng et al., 2024), conditions that share overlapping pathophysiological mechanisms with ADHD. This suggests that RFM may also play a potential role in the development of ADHD. Nevertheless, current evidence on the relationship between RFM and neuropsychiatric disorders—particularly ADHD—remains scarce, necessitating further research.

Importantly, ADHD exhibits distinct sex differences in its clinical presentation. Boys are more likely to display hyperactive/impulsive symptoms, whereas the diagnosis rate of ADHD in girls remains consistently underestimated worldwide (Sayal et al., 2018), suggesting that sex may serve as a key moderating factor in ADHD pathogenesis. Based on this premise, this study utilized data from the 1999–2004 National Health and Nutrition Examination Survey (NHANES) to systematically examine the association between RFM and ADHD in children, with a particular emphasis on exploring potential sex-specific differences.

Materials and Methods

Study design and participants

NHANES, conducted by the National Center for Health Statistics under the Centers for Disease Control and Prevention (CDC), is a continuous, nationwide surveillance program designed to monitor the health and nutritional status of the U.S. population. By conducting periodic health examinations and nutritional assessments across diverse age, racial, and regional groups, NHANES collects comprehensive health-related data to inform public health policy, disease prevention, and health promotion. NHANES data are publicly accessible via the CDC/National Center for Health Statistics website.

The initial study population included 31,126 participants from the 1999–2004 survey cycles. To ensure representativeness and data completeness, the authors applied the following inclusion and exclusion criteria:

Inclusion criteria: (1) children aged 6–14 years; (2) complete data on height and waist circumference; and (3) complete ADHD questionnaire data.

Exclusion criteria: (1) participants outside the target age range (n = 24,942); (2) missing data on height, waist circumference, or ADHD questionnaire (n = 368); and (3) missing covariate data (n = 727). After applying these criteria, a total of 5089 eligible participants were included in the final analysis (Fig. 1).

FIG. 1.

The flowchart of sample selection in NHANES 1999–2004. NHANES, National Health and Nutrition Examination Survey.

Calculation of RFM

RFM was calculated using the following formula:

RFM = 64 - 20 \times \frac{Height}{Waist} + 12 \times Gender

where male = 0 and female = 1. Height and waist circumference were measured by trained professionals at the Mobile Examination Center (MEC). Waist circumference was measured at the level of the iliac crest, and height was assessed using a stadiometer specifically designed for MEC examinations. Both measurements were recorded in centimeters (Woolcott and Bergman, 2018).

Assessment of ADHD

ADHD was determined based on interview data from the NHANES questionnaire, defined as a positive response to the question: “Has a doctor or health professional ever told you that you have attention deficit disorder?”

Other covariates

Based on prior studies (Xie, 1985; Li et al., 2025), potential confounders included race/ethnicity, poverty–income ratio (PIR), age, maternal age at delivery, maternal smoking during pregnancy, health insurance coverage, and birth weight. In this study, age was restricted to children aged 6–14 years and categorized into <10 and ≥10 years. Race/ethnicity was classified as Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races. Maternal age at delivery, maternal smoking during pregnancy, and birth weight were derived from personal interview data of children aged ≤15 years: (1) maternal age at delivery was based on the question, “How old was your biological mother when you were born?”; (2) maternal smoking during pregnancy was assessed by the question, “Did your biological mother smoke during pregnancy?”; (3) birth weight was categorized as low birth weight (<2.49 kg) or normal birth weight (≥2.49 kg). Health insurance coverage was defined according to whether participants reported having health insurance or other types of health care plans (yes/no).

Statistical analysis

Participants were stratified into ADHD and non-ADHD groups, and baseline characteristics were summarized in descriptive tables. Categorical variables were expressed as n (%) and continuous variables as mean ± standard deviation. Multivariable logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between RFM and ADHD. Three regression models were constructed using a stepwise adjustment strategy: Model 1 was unadjusted; Model 2 was adjusted for age and race/ethnicity; and Model 3 was further adjusted for PIR, maternal age at delivery, maternal smoking during pregnancy, health insurance coverage, and birth weight. To further explore potential nonlinear relationships, smooth curve fitting was applied to visualize the association between RFM and ADHD across its range of values. In addition, subgroup analyses were conducted according to age (<10 years vs. ≥10 years), maternal smoking during pregnancy, health insurance coverage, and birth weight, to assess the robustness of the associations and examine potential effect modification. All statistical analyses were performed using EmpowerStats (version 4.2) and R software, with a two-sided p value <0.05 considered statistically significant. The statistical analysis did not use sampling weights or take into account the complex probability sampling design of NHANES. Therefore, the tables and results presented in this study are all based on unweighted data (Liu et al., 2025).

Results

Baseline characteristics of participants

A total of 5089 participants were included, comprising 385 children with ADHD and 4704 without ADHD (Table 1). The proportion of males was significantly higher in the ADHD group compared with the non-ADHD group (p < 0.001). Significant between-group differences were also observed in age distribution, racial composition, maternal smoking during pregnancy, maternal age at delivery, and health insurance coverage (p < 0.001 for all). Children with ADHD were significantly taller than those without ADHD (p < 0.001), while their RFM was significantly lower (p < 0.001). PIR and birth weight were balanced between groups (p > 0.05).

Table 1.

Characteristics of NHANES Participants Between 1999 and 2004

	Total	Non-ADHD	ADHD	p Value
	N = 5089	N = 4704	N = 385	p Value
PIR	1.99 ± 1.49	1.99 ± 1.50	2.00 ± 1.46	0.981
Height (cm)	146.71 ± 16.79	46.47 ± 16.86	149.68 ± 15.52	<0.001
Waist (cm)	70.82 ± 14.45	70.81 ± 14.43	70.91 ± 14.61	0.899
RFM	27.79 ± 8.63	28.11 ± 8.53	23.77 ± 8.91	<0.001
Maternal age at childbirth (years)	25.50 ± 6.01	25.58 ± 6.04	24.51 ± 5.52	<0.001
Gender				<0.001
Male	2473 (48.60%)	2185 (46.45%)	288 (74.81%)
Female	2616 (51.40%)	2519 (53.55%)	97 (25.19%)
Age (years)				<0.001
<10	1847 (36.29%)	1740 (36.99%)	107 (27.79%)
≥10	3242 (63.71%)	2964 (63.01%)	278 (72.21%)
Race, n (%)				<0.001
Mexican American	1677 (32.95%)	1615 (34.33%)	62 (16.10%)
Other Hispanic	209 (4.11%)	194 (4.12%)	15 (3.90%)
Non-Hispanic White	1381 (27.14%)	1230 (26.15%)	151 (39.22%)
Non-Hispanic Black	1610 (31.64%)	1474 (31.34%)	136 (35.32%)
Other races	212 (4.17%)	191 (4.06%)	21 (5.45%)
Smoking during pregnancy				<0.001
Yes	717 (14.09%)	614 (13.05%)	103 (26.75%)
No	4372 (85.91%)	4090 (86.95%)	282 (73.25%)
Birth weight				0.298
Low birth weight	616 (12.10%)	563 (11.97%)	53 (13.77%)
Normal birth weight	4473 (87.90%)	4141 (88.03%)	332 (86.23%)
Health insurance				<0.001
Yes	4265 (83.81%)	3914 (83.21%)	351 (91.17%)
No	824 (16.19%)	790 (16.79%)	34 (8.83%)

ADHD, attention-deficit/hyperactivity disorder; NHANES, National Health and Nutrition Examination Survey; PIR, poverty–income ratio; RFM, relative fat mass.

Association between RFM and ADHD

Multivariable logistic regression analyses revealed that the association between RFM and ADHD differed by sex (Table 2). Among boys, RFM was inversely associated with ADHD across all models. In Model 3, each one-unit increase in RFM was associated with a 3.3% reduction in the odds of ADHD (OR = 0.967, 95% CI: 0.946–0.989, p = 0.003). Compared with those in the lowest RFM quartile (Q1), boys in the highest quartile (Q4) had a 38.3% lower risk of ADHD (OR = 0.617, 95% CI: 0.427–0.893, p = 0.010), with a significant trend across quartiles (p for trend < 0.05).

Table 2.

Multivariable Logistic Regression Analyses of the Association Between RFM and ADHD

RFM	Model 1OR(95% CI)p Value	Model 2OR(95% CI)p Value	Model 3OR(95% CI)p Value
Male	0.956 (0.936, 0.977) <0.001	0.968 (0.947, 0.990) 0.004	0.967 (0.946, 0.989) 0.003

Quartiles of RFM
Q1 <16.78	Ref.	Ref.	Ref.
Q2 16.78–20.07	0.764 (0.554, 1.055)	0.855 (0.614, 1.191)	0.896 (0.642, 1.252)
Q3 20.07–25.41	0.578 (0.410, 0.814)	0.696 (0.486, 0.995)	0.682 (0.475, 0.979)
Q4 >25.41	0.513 (0.361, 0.731)	0.616 (0.428, 0.888)	0.617 (0.427, 0.893)
p for trend	<0.001	0.005	0.004
Female	1.043 (1.007, 1.079) 0.019	1.049 (1.014, 1.086) 0.007	1.043 (1.007, 1.081) 0.018

Quartiles of RFM
Q1 <29.86	Ref.	Ref.	Ref.
Q2 29.86–33.18	0.846 (0.439, 1.630)	0.975 (0.504, 1.889)	0.953 (0.490, 1.853)
Q3 33.18–33.75	1.524 (0.856, 2.712)	1.769 (0.987, 3.170)	1.732 (0.960, 3.126)
Q4 >33.76	1.524 (0.856, 2.712)	1.739 (0.972, 3.111)	1.607 (0.889, 2.905)
p for trend	0.048	0.018	0.038

Model 1: unadjusted.

Model 2: adjusted for age and race/ethnicity.

Model 3: adjusted for age, race/ethnicity, PIR, maternal age at delivery, maternal smoking during pregnancy, health insurance coverage, and birth weight.

ADHD, attention-deficit/hyperactivity disorder; CI, confidence interval; OR, odds ratio; PIR, poverty–income ratio; RFM, relative fat mass.

In contrast, among girls, RFM was positively associated with ADHD risk. In Model 3, each one-unit increase in RFM corresponded to a 4.3% higher risk of ADHD (OR = 1.043, 95% CI: 1.007–1.081, p = 0.018). Although the ORs for higher quartiles compared with Q1 did not reach statistical significance, a consistent trend toward increased risk was observed, with a significant trend test (p for trend = 0.038).

To further verify the shape and direction of the association, smooth curve fitting was performed to examine the relationship between RFM and ADHD (Fig. 2). The results were consistent with those of the multivariable regression models. Among boys, RFM showed a linear inverse association with ADHD risk (Fig. 2A), with the risk of ADHD steadily decreasing as RFM increased. In contrast, among girls, RFM exhibited a linear positive association with ADHD risk (Fig. 2B), with higher RFM corresponding to an increased likelihood of ADHD.

FIG. 2.

Smooth curve fitting of the association between RFM and ADHD, adjusted for age, race/ethnicity, PIR, maternal age at delivery, maternal smoking during pregnancy, health insurance coverage, and birth weight. (A) Linear association between RFM and ADHD in boys. (B) Linear association between RFM and ADHD in girls. ADHD, attention-deficit/hyperactivity disorder; PIR, poverty–income ratio; RFM, relative fat mass.

Subgroup analyses

Subgroup analyses (Table 3) showed that among boys, higher RFM was significantly associated with a reduced risk of ADHD (p < 0.05). This association remained significant in subgroups defined by age, health insurance coverage, maternal nonsmoking during pregnancy, and normal birth weight (all p < 0.05). However, no significant interactions were detected across subgroups (all p for interaction > 0.05).

Table 3.

Subgroup Analyses of the Association Between RFM and ADHD Among Boys and Girls

	Male	Female
Age
<10 years	0.930 (0.880, 0.983) 0.010	1.084 (1.000, 1.174) 0.050
≥10 years	0.975 (0.951, 0.999) 0.043	1.040 (1.000, 1.082) 0.052
p for interaction	0.138	0.571
Health insurance
Yes	0.967 (0.945, 0.990) 0.005	1.045 (1.007, 1.085)0.021
No	0.967 (0.890, 1.050) 0.419	1.056 (0.944, 1.181)0.344
p for interaction	0.527	0.752
Smoking during pregnancy
Yes	0.981 (0.938, 1.027) 0.411	1.069 (1.001, 1.142)0.046
No	0.963 (0.938, 0.988) 0.004	1.035 (0.992, 1.080)0.111
p for interaction	0.459	0.652
Birth weight
Low birth weight	0.989 (0.930, 1.051) 0.717	1.019 (0.908, 1.144)0.750
Normal birth weight	0.964 (0.941, 0.987) 0.003	1.042 (1.004, 1.082)0.032
p for interaction	0.356	0.977

ADHD, attention-deficit/hyperactivity disorder; RFM, relative fat mass.

Among girls, RFM was positively associated with ADHD risk. The association reached statistical significance in the subgroup of children aged <10 years, those with health insurance coverage, those whose mothers smoked during pregnancy, and those with normal birth weight (all p < 0.05). Similarly, no significant interactions were observed across subgroups (all p for interaction > 0.05).

Discussion

This study, based on NHANES 1999–2004 data, systematically investigated the sex-specific association between RFM and ADHD. The results demonstrated that in male children, higher RFM levels were associated with lower odds of ADHD, whereas in female children, higher RFM levels were linked to higher odds of ADHD. Subgroup analyses further confirmed that the association between RFM and ADHD was consistent across different population subgroups, showing an inverse association in males and a positive association in females. This sex-specific pattern was validated across key subgroups, including birth weight, health insurance coverage, and maternal smoking during pregnancy. These findings suggest that RFM may be a sex-specific influencing factor for ADHD, potentially operating through distinct biological or psychosocial mechanisms in boys and girls. This study provides a new perspective for understanding the relationship between body fat distribution and neurodevelopmental disorders, particularly with regard to sex differences.

In recent years, the association between obesity and ADHD has become an important topic in pediatric neurodevelopmental research. Previous studies have shown that the prevalence of ADHD symptoms is higher among obese children and adolescents compared with their nonobese counterparts, and that peer relationship problems are significantly associated with obesity risk (Sönmez et al., 2019). From a neurobiological perspective, both conditions may share a common pathological basis of dopaminergic dysfunction. In obese individuals, chronic high-calorie intake may reduce dopamine receptor sensitivity, alter the reward circuitry, and consequently impair behavioral control and attentional regulation (Patte et al., 2020; Wallace and Fordahl, 2022). Similarly, in individuals with ADHD, congenital dopaminergic dysfunction leads to deficits in attention control and behavioral inhibition (van der Meer et al., 2017). Furthermore, shared biological abnormalities may link adiposity to neurodevelopmental outcomes. For instance, HPA axis dysregulation and chronic inflammation have been observed in children with ADHD, and these metabolic disturbances are also well-documented features of obesity (Chang et al., 2020). From a lifestyle perspective, unhealthy behaviors commonly observed among obese children—such as high sugar/fat dietary patterns, insufficient physical activity, and disrupted sleep rhythms—interact complexly with ADHD symptoms. For example, sugar-induced fluctuations in blood glucose may directly impair energy supply to the prefrontal cortex, thereby disrupting executive function (Beecher et al., 2021); lack of exercise reduces neural plasticity and impairs neurotransmitter release such as dopamine (Ghanayim et al., 2025); and poor sleep quality disrupts neural repair processes, further aggravating attentional deficits (Zhong et al., 2022).

RFM is closely related to metabolic and cardiovascular risk, and these conditions overlap with ADHD in terms of inflammatory mechanisms, insulin resistance, and neuroendocrine regulation (Uzun et al., 2023; Dehnavi et al., 2023). RFM can effectively capture body fat distribution and obesity status and has advantages over traditional measures by better distinguishing fat from lean mass, thereby providing a more accurate assessment of obesity-related health risks. Fat tissue functions not only as an energy reservoir but also as an active endocrine organ, secreting adipokines and proinflammatory cytokines such as leptin and adiponectin, which can cross the blood–brain barrier and influence neurodevelopment and brain function (Fain, 2006; Shao et al., 2016). For instance, leptin may regulate dopaminergic signaling pathways and thereby modulate attention and impulse control (Mancini et al., 2025), while proinflammatory cytokines such as tumor necrosis factor-α and interleukin-6 may impair prefrontal cortical function via neuroimmune mechanisms, contributing to ADHD symptomatology (Cortese et al., 2019; Kim et al., 2025). Importantly, these mechanisms may differ between males and females. Sex hormones such as estrogen and testosterone have been shown to modulate adipokine secretion and their neural effects (Jenks et al., 2017), providing a plausible biological explanation for the observed sex-specific associations between RFM and ADHD. Moreover, RFM may differentially capture visceral and subcutaneous fat compartments, which show distinct metabolic and endocrine profiles and develop differently between males and females across childhood and adolescence (Senkus et al., 2022). Visceral fat, which is more metabolically active and proinflammatory, tends to accumulate to a greater extent in males, whereas females generally exhibit higher levels of subcutaneous fat due to estrogen-related protective effects (Rodrigues et al., 2025). These sex-specific patterns of fat deposition may modify the extent to which RFM reflects biologically relevant risk pathways, such as systemic inflammation, insulin resistance, and hormone-regulated neuroendocrine signaling, thereby contributing to the differential associations observed between RFM and ADHD across sexes (Arner et al., 2024; Schuetz et al., 2024).

In the subgroup analyses, sex differences were particularly evident in relation to maternal smoking during pregnancy. Prior studies have reported that prenatal smoking more than doubles the risk of ADHD in offspring (Li et al., 2024). Mechanistically, harmful substances such as nicotine can cross the placenta and disrupt fetal neurodevelopment. Due to differences in hormonal levels and gene expression, male fetuses tend to be more sensitive to nicotine exposure, resulting in more pronounced neurodevelopmental impairments and higher ADHD risk during childhood (Chen et al., 2025; Deng et al., 2023). In contrast, female fetuses may benefit from the neuroprotective effects of estrogen, which partially buffers the neurotoxic effects of nicotine, thereby mitigating the impact of maternal smoking on the relationship between RFM and ADHD (Maher et al., 2022). In addition, health insurance coverage may influence this relationship indirectly by shaping access to prenatal care and the effectiveness of pediatric health interventions (Guldi and Hamersma, 2023). Birth weight also emerged as an important factor, as low birth weight has been strongly linked to later metabolic disturbances and obesity, particularly in males, further exacerbating their susceptibility to ADHD (Wei et al., 2022; Dooley et al., 2022). Collectively, these findings indicate that sex-specific physiological protective mechanisms may play a role in the comorbidity of ADHD and obesity.

The strengths of this study include the use of the nationally representative NHANES dataset with a relatively large sample size, which enhances generalizability. Unlike prior studies, the authors applied RFM, a novel and more accurate measure of body fat distribution. Anthropometric measures such as height and waist circumference were assessed by trained professionals, ensuring data quality. Furthermore, multiple regression models were employed to adjust for potential confounders, and extensive subgroup analyses were performed, lending robustness to the results.

Nevertheless, several limitations should be acknowledged. First, due to the cross-sectional design, causal inference between RFM and ADHD cannot be established, and prospective studies are warranted to validate their findings. Specifically, it remains unclear whether altered RFM contributes to ADHD risk, or conversely, whether ADHD-related behavioral or metabolic changes influence body fat distribution, and the possibility of reverse causation cannot be excluded. Therefore, the interpretation of these associations should be made with caution. Second, ADHD diagnosis relied on parental report of physician diagnoses, without standardized clinical assessments, which may introduce misclassification bias (Cree et al., 2023). Such misclassification is most likely nondifferential, which would tend to attenuate the observed associations toward the null, potentially leading to an underestimation of the true effect (Yland et al., 2022). However, differential misclassification could occur if the likelihood of reporting a diagnosis differs by sex, with boys historically more likely to be diagnosed or reported with ADHD than girls (Babinski, 2024). This may partially contribute to the observed sex-specific differences in associations between RFM and ADHD, although the direction and magnitude of this effect cannot be precisely quantified without standardized diagnostic data (Hübel et al., 2019). Therefore, while the observed sex-specific associations remain informative, they should be interpreted with caution. Third, although the authors adjusted for multiple confounders, residual confounding by unmeasured factors such as genetics, diet, or psychosocial influences cannot be excluded. In particular, information on ADHD medication use was unavailable in NHANES 1999–2004. Because stimulant and nonstimulant treatments can affect appetite, metabolism, and body composition, the inability to adjust for medication exposure may introduce residual confounding (Deng et al., 2021). Such unmeasured treatment effects could influence RFM differently across subgroups, potentially biasing the observed associations, most likely toward attenuation rather than creating spurious findings. In addition, the complex survey design of NHANES was not accounted for in their analysis, as survey weights, strata, and primary sampling unit variables were not applied. Ignoring these design features may lead to biased standard errors and reduced precision of effect estimates. Therefore, the reported associations should be interpreted with consideration of this methodological limitation. Moreover, the number of female ADHD cases in their dataset was relatively small, which may reduce the stability and precision of the subgroup estimates. Although a positive association between RFM and ADHD was observed among girls, the limited sample size increases the likelihood of unstable results and lower statistical power. Therefore, the female subgroup findings should be interpreted with caution. Finally, because the data were drawn from U.S. children, caution is needed when generalizing these results to other populations. It is also worth noting that both diagnostic practices and obesity patterns have evolved since 1999–2004. Clinicians’ thresholds and approaches to identifying ADHD may have shifted over time, and childhood obesity rates have continued to rise, altering current adiposity profiles compared with those of earlier cohorts (Arnold, 2025). Although these secular trends may influence the extent to which their findings apply to contemporary populations, the underlying biological mechanisms linking adiposity and neurodevelopment remain consistent, supporting the relevance of the observed associations.

Conclusion

This study, using a nationally representative pediatric sample from NHANES, revealed a significant sex-specific association between RFM and ADHD. Higher body fat levels were associated with lower ADHD risk in boys but higher ADHD risk in girls, underscoring the importance of considering sex-stratified analyses in ADHD research. These findings highlight the potential role of distinct pathophysiological mechanisms in males and females. Future prospective studies are needed to verify causal relationships and to elucidate the underlying biological pathways, thereby providing a stronger scientific basis for the prevention and targeted intervention of ADHD.

Authors’ Contributions

(I) Conception and design: B.S.T. and L.H.J.; (II) Administrative support: B.S.T. and L.L.H.; (III) Provision of study materials: B.S.T. and X.X.Y.; (IV) Collection and assembly of data: B.S.T. and L.H.J.; (V) Data analysis and interpretation: B.S.T., X.X.Y., and L.L.H.; (VI) Article writing: All authors; (VII) Final approval of article: All authors.

Footnotes

Availability of Data and Materials

The data and materials in the current study are available from the corresponding author on reasonable request.

Disclosures

The authors declare that they have no potential conflicts of interest.

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