Exploring the bidirectional associations of ADHD symptomatology, nutritional status, and body composition in childhood: evidence from a Brazilian Birth Cohort Study.

doi:10.21203/rs.3.rs-4619563/v1

Download PDF

Article

Exploring the bidirectional associations of ADHD symptomatology, nutritional status, and body composition in childhood: evidence from a Brazilian Birth Cohort Study.

https://doi.org/10.21203/rs.3.rs-4619563/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Attention deficit hyperactivity disorder (ADHD) has been linked to excessive weight; however, the underlying mechanisms of this association are not well understood. To date, the bidirectional associations between ADHD and nutritional status in childhood have been explored in a limited number of studies, with particularly few of those incorporating body composition data. This study aims to evaluate the associations of ADHD symptoms, nutritional status, and body composition in childhood.

Methods: We analyzed data from 3940 children from the 2015 Pelotas (Brazil) Birth Cohort at 4 and 6-7 years of age. Linear regression was performed to evaluate the association between ADHD symptoms and nutritional status (weight, height, and body mass index [BMI]) at ages 4 and 6-7, as well as body composition, specifically fat mass (FF) and fat-free mass (FFM) at ages 6-7. Moreover, a cross-lagged panel model (CLPM) analysis between ADHD symptoms and BMI was performed to explore the bidirectional associations.

Results: ADHD symptoms were associated with increased height (β 0.01, 95%CI 0.001, 0.026) and FFM (β 0.02, 95%CI 0.008 - 0.035) at age 4, and increased BMI (β0.02, 95%IC 0.002, 0.038), weight (β0. 02, 95%CI 0.005, 0.039), height (β 0.01, 95%CI 0.000, 0.024), and FFM (β 0.02, 95%CI 0.012, 0.040) at ages 6-7. Although the effects observed in the CLPM suggest a bidirectional relationship between ADHD symptoms and BMI, the association did not reach statistical significance.

Conclusion: Children with higher ADHD symptoms showed increased growth in weight, height, and BMI. The observed increase in weight and BMI was attributed to greater FFM in these children.

Health sciences/Diseases/Nutrition disorders/Obesity

Health sciences/Health care/Paediatrics

Health sciences/Risk factors

Health sciences/Health care/Nutrition

Health sciences/Medical research/Epidemiology

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in childhood, characterized by a persistent pattern of inattention, hyperactivity, and impulsivity at a dysfunctional level [1]. Its etiology is multifactorial, involving a complex interaction between genetic and environmental factors [2, 3].

Numerous studies have explored the association between ADHD and obesity, focusing on their co-occurrence. A meta-analysis revealed a 40% higher prevalence of obesity in children with ADHD compared to those without the disorder, suggesting a substantial link between ADHD and the risk of developing obesity during childhood [4, 5]. In addition, studies of children with ADHD revealed that those with a higher body weight exhibit more severe symptoms of the disorder [6–10].

The direction of the causal relationship between ADHD and obesity in childhood remains unclear and the findings in the literature published to date suggest bidirectionality. Some longitudinal studies indicate that childhood ADHD symptoms may precede obesity during childhood and adolescence [6, 11], and similar evidence has been observed in studies involving adults [12–14]. Conversely, other studies suggest that obesity may have a causal effect on ADHD [15, 16].

The biological plausibility of the relationship between ADHD and obesity has been linked to genetic factors, dysfunctional behavior, and neuropsychological deficits associated with ADHD [17, 18]. A great number of studies agree that impaired reward circuits are a potential mechanism underlying ADHD symptoms and the pathophysiology of obesity, with binge eating identified as one of the main contributing factors [19–21]. On the other hand, obesity's potential to exacerbate ADHD may stem from excess adipose tissue, recognized as an active endocrine organ [15] capable of initiating pro-inflammatory cascades and influencing neurological function [15]. This hypothesis is supported by epidemiological evidence [16, 22].

The Body Mass Index (BMI) is a widely used and easily applied measure that captures obesity, but its ability to accurately assess body composition is limited. This limitation is particularly relevant considering the significant role adipose tissue may play in the etiology and symptomatology of ADHD [16]. Therefore, studies with more precise and sensitive methods to evaluate body composition are needed to enable a more accurate assessment of adipose tissue and its relationship with ADHD in childhood [6]. Current research on body composition in children with ADHD has yielded divergent results: one study indicated that ADHD symptoms at age 6 predicted increased fat at age 9 [6], while another found that higher hyperactivity/inattention scores were associated with a lower body fat percentage [23]. These inconsistencies underscore the necessity for further research to elucidate the role of body fat in ADHD.

A significant limitation in comprehending the relationship between childhood overweight and ADHD is the predominance of cross-sectional studies. The paucity of longitudinal research examining the connection between ADHD and detailed body composition measurements in children represents a notable gap in the scientific literature. This lack of longitudinal studies impedes understanding the potential bidirectional nature of this relationship and prevents determining the temporal sequence of weight contributing to the onset of ADHD symptoms and vice versa. [11, 24]. Moreover, most studies have been conducted in European and other high-income countries, highlighting another limitation in the absence of research from low- and middle-income countries. This gap is significant, as differing lifestyles, access to education, information, and healthcare services in these regions play a crucial role in managing ADHD and controlling excessive weight in children [11, 24, 25].

This study aims to investigate the relationship between ADHD symptoms and children's nutritional status and body composition within the 2015 Pelotas (Brazil) Birth Cohort. We specifically tested: (i) the association between ADHD symptoms and various anthropometric measures (weight, height, and BMI-for-age) at ages 4 and 6–7; (ii) the association of ADHD symptoms with fat mass (FM) and fat-free mass (FFM) at age 6–7; and (iii) the bidirectional relationship between ADHD symptoms and BMI-for-age at both ages using a cross-lagged panel model.

We used data from the 2015 Pelotas Birth Cohort, placed in a city in southern Brazil with approximately 326,000 inhabitants [26]. In 2015, all five maternity hospitals in Pelotas were monitored from January 1st to December 31st. Each birth was recorded by the research team. During that year, 4333 children were born to mothers residing in the city's urban area and were thus eligible. Considering the 54 stillborns, the cohort's final sample includes 4275 live births. The rate of losses and refusals was 1.3%. Data for the current analyses were collected in the perinatal follow-up, where mothers were interviewed between 24 and 48 hours after delivery, and in the 4- and 6-7-year follow-ups. The follow-up rate at 4 years was 95.4% (n = 4010), and at 6–7 years, 92% (n = 3867). More information is available in the 2015 cohort profiles [27, 28].

Nutritional status

In the perinatal assessment, the weight and length of the baby were measured shortly after birth. Weight and height data were collected by trained interviewers at 4 and 6–7 years of age. For both follow-ups, a TANITA® 17 model UM-080 scale was used to measure the child's weight, with a maximum capacity of 150 kg and precision of 100g, and a fixed Harpenden® stadiometer was used to measure height, with a maximum height of 2.06 m and precision of 1 mm. Nutritional status was assessed using weight-for-age, height-for-age, and BMI-for-age (BMI/A), classified as a z-score using the softwares Anthro^® e AnthroPlus^®.

Body composition

Body composition was assessed at 6–7 years by measuring fat-free mass (FFM) and fat mass (FM). These measurements were obtained using the DXA (dual X-ray absorptiometry) with GE Healthcare's Lunar Prodigy equipment®. The equipment was calibrated daily, following the manufacturer's recommendations. The participants remained supine, barefoot, and wearing light, tight-fitting clothing without adornments or metal objects to obtain these measurements. A smaller number of participants have complete body composition data, totaling 2271. A comparison between participants with body composition data and those without is shown in Supplementary Table 1. The measurements of FM and FFM were expressed in kilograms (kg) and by their respective indices (kg/m²), calculated from the ratio of each variable (kg) to height squared (m²), representing the fat mass (FMI) and the fat-free mass (FFMI) index, respectively.

ADHD

ADHD symptoms were assessed at 4 and 6–7 years of age using the Strengths and Difficulties Questionnaire (SDQ), applied with parents or caregivers during follow-up and administered by trained interviewers. The SDQ consists of 25 items, divided into 5 subscales, that assess various aspects of behavior, including inattention/hyperactivity symptoms, conduct problems, emotional symptoms, peer relationship problems, and prosocial behavior. The questionnaire was adapted and validated for the Brazilian population of children and adolescents aged between 4 and 16 years [29]. In this study, the five questions comprising the hyperactivity and inattention symptoms subscale were treated as continuous variables (scores ranging from 0 to 10). For descriptive purposes, the variable was dichotomized for ease of presentation, with scores of 7 or higher considered indicative of ADHD cases.

Covariates

Potential confounding factors associated with nutritional status, body composition, and ADHD were selected through a literature review. The following variables were considered as covariates: Maternal characteristics, including pre-pregnancy BMI (Kg/m²), smoking (yes or no) and alcohol consumption (yes or no) during pregnancy, schooling (collected in complete years of study and categorized as 0–4, 5–8, 9–11, and ≥ 12 years), and a socioeconomic level classified according to the indicators of the Brazilian Association of Research Companies (ABEP – A, B, C, D-E [30]), maternal height (m), and from perinatal follow-up; and sex (male or female), skin color (white, black, or brown), low birth weight (< 2500g; yes or no), type of delivery (normal or cesarean), birth weight in z-score (continuous in grams).

Statistical analysis

The analytical sample includes participants with SDQ and BMI information at the 4- or 6-7-year follow-ups, totaling 3940 participants (92.2% of the original cohort). Bivariate analyses were performed to describe, in absolute values (n) and relative frequencies (%), characteristics of the participants included in the analytical sample according to the covariates. The differences in characteristics between the analytical sample and those not included in the study were calculated using Pearson's chi-square test.

Cross-sectional analyses to investigate the association between ADHD and nutritional status were performed using crude and adjusted linear regression models (including BMI-for-age, weight-for-age, and height-for-age) at age 4. Similar analyses were conducted with data from the 6–7-year follow-up. For the adjusted analysis, we considered two models: model 1, including maternal schooling, ABEP classification, smoking and alcohol use during pregnancy, type of delivery, birth weight, gender, and skin color; and model 2, which included all variables from model 1 plus mother's height variable (meters). The results were presented using the beta coefficient (β) and their respective 95% confidence intervals (95%CI), with variables associated with the outcome being those with a p-value of < 0.05.

The body composition variables, FM and FMI exhibited non-normal distributions, requiring rank-normal transformations of these variables. To ensure consistency in variable standardization, the same transformations were applied to FFM and FFMI. (Supplementary Figs. 1 and 2). Linear regression was subsequently conducted to assess the association between childhood ADHD symptoms (at each of ages 4 and 6–7 years) and body composition data at 6–7 years of age, employing both crude and adjusted analyses (model 1). FM and FFM were examined measured in kilograms as well as in index format. These analyses also included FM and FFM variables in their original scales, as detailed in Supplementary Table 2.

Bidirectional associations between ADHD symptoms and BMI was examined in a cross-lagged panel model (CLPM). This model tests whether the average exposure score (e.g., ADHD symptoms) at "time 1" predicts a behavior (e.g., BMI/A) at "time 2" (i.e., cross-lagged effect) and vice versa [31] – in which a significant effect is often interpreted as an indication of causality between mean exposure scores at “time 1” and behavior at “time 2” (and vice versa). For the adjusted analyses (model 1), the model was standardized through linear regression and its residuals were incorporated into the analysis model, and the results were expressed through the beta coefficient (β) and their respective 95% confidence intervals (95%CI).

Statistical analyses were conducted using the Stata 15.0 statistical software (Stata Corporation, CollegeStation, USA) and the cross-lagged panel analysis model using the Lavaan statistical package in R.

Ethical aspects

The 2015 Pelotas Birth Cohort project was submitted to and approved by the Research Ethics Committee Board of Federal University of Pelotas. At all stages of follow-up, the mother or legal guardian signed an Informed Consent Form agreeing to take part in the research.

The maternal and birth characteristics of the original cohort and the analytical sample are described in Table 1. For the participants in the analytical sample, around 19% of the mothers were classified as having obesity in the period prior to pregnancy, 16.5% reported smoking and 7.3% alcohol use during pregnancy, about 9% of the mothers had up to 4 years of schooling, and almost 20% belonged to the lowest economic class in the ABEP classification (D-E). Regarding children's birth characteristics, 50.7% were male, 72.5% had white skin, 9.5% had low birth weight, 14.5% were premature, and 64.7% were born by cesarean section.

At the age of 4, the median score for ADHD symptoms was 4.0 points (interquartile interval [IIQ] 2.0, 7.0), and at 6-7 years, it was 3.0 points (IIQ 1.0, 6.0). At 4 years of age, the median weight was 16.3 kg (IIQ 14.9, 18.7), height 1.0 m (IIQ 0.9, 1.0), BMI 16.1 kg/m²(IIQ 15.2, 17.3). At 6-7 years old, the median weight was 24.7 kg (IQR 21.8, 29.4), height 1.2 m (IIQ 1.2, 1.3), BMI 16.5 kg/m² (IIQ 15.1, 18. 9), FFM was 17.9 kg (IIQ 16.3, 19.5), FFMI 12.0 kg/m² (IIQ 11.4, 12.7), FM 5.1 kg (IIQ 3.3, 8.7) and FMI 3.5 kg/m² (2.3, 5.7) (Table 2). The prevalence of ADHD according to maternal and birth characteristics is presented in Supplementary Table 3. Furthermore, the variations in ADHD symptom scores, nutritional status at 4 and 6-7 years, and body composition at 6-7 years among participants with high ADHD symptom scores are detailed in Supplementary Table 4.

Crude and adjusted linear regression models were employed to assess the cross-sectional associations between ADHD symptoms and nutritional status measures, at two time points (ages 4 and 6-7) as detailed in Table 3. At age 4, after adjustment, the findings indicated that a one-point increase in the ADHD scale corresponded to an average higher than of 0.014 points in height-for-age (95% CI 0.001, 0.026). No significant associations were observed between ADHD and BMI-for-age or weight-for-age. In contrast, at ages 6-7, ADHD showed associations with all collected measures of nutritional status. Following adjustment, an increase in ADHD was associated with an average increase of 0.02 points in BMI/A (95% CI 0.002, 0.038), 0.02 points in weight (95% CI 0.005, 0.039), and 0.01 points in height (95% CI 0.000, 0.024).

Crude and adjusted linear regression models examining the association between ADHD at each age, 4 and 6-7, and body composition at ages 6-7 are presented in Table 4. In the adjusted models, significant associations were observed between ADHD symptoms at age 4 and FFM and FFMI at age 6-7. Specifically, each one-point increase in the ADHD scale was associated with an average increase of 0.02 kg in FFM (95% CI 0.008, 0.035) and 0.02 in FFMI (95% CI 0.005, 0.031) at age 6-7. Similar associations were found for ADHD at age 6-7, showing an increase in FFM (β=0.02, 95% CI 0.012, 0.040) and FFMI (β=0.02, 95% CI 0.005, 0.032). However, no significant associations were observed for FM and FMI outcomes.

CLPM analyses examining bidirectional associations between ADHD and BMI/z-score are depicted in Figure 1. Following adjustment for covariates, ADHD symptoms (β 0.484, p < 0.001) and BMI/z-score (β 0.716, p < 0.001) remained stable from ages 4 to 6-7. Longitudinal findings indicated that higher ADHD scores at age 4 predicted a slight increase in BMI/A at age 6-7 (β 0.003, 95% CI: -0.026, 0.020), while a modest increase in BMI/A predicted higher ADHD scores at age 6-7 (β = 0.013; 95% CI -0.018, 0.044) (Figure 1; Supplementary Table 5). However, the model tested did not achieve statistical significance.

This study investigated the association between ADHD and nutritional status, as well as body composition, using multiple approaches. The findings revealed that at 4 years of age, children with higher ADHD symptom scores exhibited higher z-scores for height. At ages 6–7, children with higher ADHD also demonstrated higher z-scores for BMI, weight, and height. In terms of body composition, higher ADHD symptom scores (measured at both ages 4 and 6–7 years) were associated with higher FFM and FFMI at 6–7 years of age. However, no significant associations were observed between ADHD symptoms and FM or FMI at either age. Furthermore, the CLPM analysis did not provide strong evidence of a bidirectional association between ADHD symptoms and BMI between the ages of 4 and 6–7.

Our findings reveal an association between ADHD symptoms and BMI at ages 6–7. Consistent with these results, previous studies have also documented a positive correlation between ADHD symptoms and higher BMI in children of a similar age [4, 5, 7, 9, 32]. While the increase in BMI observed in our study is modest, even slight changes during childhood are clinically significant, given the critical period for shaping body composition [33]. The literature posits several pathways linking ADHD, weight, and elevated BMI. Genetic factors, dysfunctional behaviors, and neuropsychological deficits are suggested as potential mechanisms, suggesting that symptoms of impulsivity, inattention, or hyperactivity may contribute to weight gain. Moreover, these symptoms can hinder effective BMI reduction efforts [12]. Notably, there was no association between ADHD symptoms and BMI at 4 years of age; the association emerged later, at 6–7 years.

In addition, we showed that at 6–7 years of age, the children had greater weight and height. Other studies have also found that ADHD symptoms were associated with an increase in body weight in childhood [4, 22, 34]. One plausible explanation for weight gain is the presence of impulsivity and inattention symptoms, which are intimately related to binge eating. This reinforces the hypothesis that the reward system, identified through binge eating, is a mechanism shared between ADHD and excess weight [19, 20]. There was no association between ADHD symptoms and weight at 4 years. On the other hand, an increase in height was associated with higher ADHD symptom scores at both ages. Prior studies that examined the association between ADHD and linear growth have produced inconsistent results. A recent study conducted on preschool children revealed that ADHD children with hyperactive/impulsive symptoms were taller, while children with the combined type were shorter compared to children without the disorder [35]. Other studies, available in the literature, have found reported that children with ADHD are shorter in stature [34, 36–38], while others find no association [39–43], the effect of ADHD symptoms on stature remains inconclusive.

Studies suggest that shorter height observed among children with ADHD may reflect higher levels of stressful events, leading to increased cortisol release. This hormone participates in the physiology of bone, muscle, and fat mobilization processes, as well as increasing the secretion of somatostatin, which is responsible for inhibiting the secretion of growth hormone by the pituitary gland [37]. Genetic and environmental factors are known to account for 65–85% of height variation, while environmental factors, including diet, psychosocial stress, and lifestyle, contribute an additional 5–23% [44]. Therefore, the precise mechanisms by which ADHD might influence growth are not fully understood.

When evaluating body composition, we found that at both 4 and 6–7 years of age, children with higher ADHD symptom scores have more FFM, suggesting that the increased weight and BMI scores are not due to excess FM, but rather to a greater quantity of FFM (muscle and bone tissue). These findings align with those from a study with children from the Generation R cohort [32], which also used DXA to assess body composition. The authors concluded that a higher amount of lean mass at age 6 was predictive of more severe ADHD symptoms, while a higher amount of fat at age 6 predicted lower ADHD symptoms at age 9. Additionally, they found that more severe ADHD symptoms at age 6 led to a 0.22 kg increase in fat mass at age 9, suggesting that childhood ADHD symptoms can cause both weight and FM increase [32].

Another study, carried out [22] with children aged between 4 and 6 found that higher hyperactivity/inattention scores were associated with a lower percentage of body fat, but this effect was attenuated after adjusting for physical activity. The authors suggest that the lower percentage of fat is partially explained by the higher levels of daily physical activity (performed by children with higher hyperactivity scores), since hyperactivity involves not only agitated movements, but also an increase in general motor activity, justifying the increase in FFM in these children [23]. In children and adolescents with typical development, increased physical activity (both in terms of time and intensity) is associated with lower FM and higher FFM [23, 45, 46]. However, no studies were found evaluating this relationship in children with ADHD[5].

Studies establishing reference values for body composition in children with TD indicate significant variations with advancing age, observing a higher FFM has been observed in younger children [47–49] and an increase in FM during adolescence and adulthood [47–50]. Due to the typical characteristics of children's body composition, with lower FM and higher FFM, excess adipose tissue may not influence ADHD symptoms in childhood [23], starting in adolescence and adulthood [11, 12, 14]. However, the literature presents limited evidence on the relationship between body composition and ADHD symptoms in children, demonstrating a considerable gap that requires further research.

In the present study, we utilized the cross-lagged panel model (CLPM) to understand potential bidirectionality of the association between ADHD and nutritional status. Our findings indicated that a slight increase in ADHD symptom score at age 4 predict a higher BMI/A at age 6–7. Similarly, a minor increase in the BMI/A z-score at age 4 predict a higher score on the ADHD symptom scale at age 6–7, though these results were not statistically significant. However, few studies have longitudinally assessed these two conditions to elucidate the direction of this relationship [10, 11, 22, 32, 51].

The study [32] conducted with children participating in the Generation R cohort analyzed the bidirectionality of ADHD with body composition data, also using a CLPM. The authors found no evidence that body composition at age 6 was able to predict changes in the severity of ADHD symptoms at age 9, indicating that fat mass does not appear to have a protective or prejudicial effect on the risk of ADHD in this age group [32]. Still, they found that more severe ADHD symptoms at age 6 predicted greater fat mass at age 9.

In a longitudinal study carried out in Spain with preschoolers, the influence of BMI began at 3 years of age, predicting higher ADHD scores at 4 years[9], while in a study carried out with girls in the United States, no association was found between ADHD and BMI in early childhood, only in adolescence [11], the increase in BMI was significantly throughout development, resulting in higher levels in adolescence and adulthood, with a prevalence of obesity of 40.2% in those with ADHD in contrast to only 15.4% in the comparison group [11]. These results reinforce the idea that in very young children, even though they already show an increase in body weight and BMI, its effect is still small on ADHD symptoms [2, 5, 7, 22], mainly because most of these studies do not have body composition data [22], preventing the conclusion that this increase is really due to excess FM, and not FFM, as found in our results.

Findings should be interpreted considering some study limitations. The assessment of ADHD symptoms in population-based studies differs from the gold standard for diagnosing the disorder, clinical assessment. We used data from the SDQ, a brief and validated screening tool for assessing levels of hyperactivity/inattention, with high predictive sensitivity [29]. In the analysis that investigated the association between ADHD symptoms and height, there was no adjustment for the father's height because this information was not collected. Another limitation is the lack of body composition data (DXA) at 4 years of age, which prevents us from conducting a bidirectional assessment of body composition data, in addition to BMI. In addition, at the 6–7-year follow-up, approximately 53% of the cohort were able to undergo DXA examinations due to the change at the start of the follow-up, affected by the COVID-19 pandemic, and many interviews were conducted by telephone.

As strengths of this study, we highlight the longitudinal design of the analyses, using data from a population cohort with a large sample size and high follow-up rates, where data collection was carried out by trained and standardized staff, minimizing the risk of information bias, allowing us to investigate the association between ADHD symptoms and nutritional status and body composition in childhood and explore the bidirectionality of this association. In addition, we used complex data to measure body composition, using DXA, which has so far been little explored in children with ADHD, and which is unheard of in low- and middle-income countries such as Brazil.

In conclusion, our study observed that children with higher ADHD symptom scores exhibited greater body growth (weight, height, and BMI). Analysis of body composition indicated that the increase in weight and BMI was attributable to gains in fat-free mass rather than excess fat mass. However, literature suggests that accumulation of body fat typically occurs during adolescence, implying that at ages 6–7, fat mass may not yet influence ADHD symptomatology. Given the consistent findings linking ADHD symptoms with obesity in adulthood, our study underscores the importance of promoting healthy lifestyle habits in childhood, including physical activity and a balanced diet, as part of ADHD management. Future longitudinal investigations focusing on the transition from childhood to adolescence may elucidate when the relationship between ADHD symptoms and excess fat mass begins to manifest.

Conflict of interest

The authors declare that they have no conflict of interest

APA AAP (2014) DSM-5: Manual Diagnóstico e Estatístico de Transtornos Mentais. Artmed Editora
Faraone SV (2018) The pharmacology of amphetamine and methylphenidate: Relevance to the neurobiology of attention-deficit/hyperactivity disorder and other psychiatric comorbidities. Neurosci Biobehav Rev 87:255–270
Swanson JM, Arnold LE, Molina BSG, et al (2017) Young adult outcomes in the follow-up of the Multimodal Treatment Study of Attention-Deficit/Hyperactivity Disorder: symptom persistence, source discrepancy and height Suppression. J Child Psychol Psychiatry 58:663–678
Cortese S, Moreira-Maia CR, St Fleur D, Morcillo-Peñalver C, Rohde LA, Faraone SV (2016) Association Between ADHD and Obesity: A Systematic Review and Meta-Analysis. Am J Psychiatry 173:34–43
Turan S, Tunctürk M, Çıray RO, Halaç E, Ermiş Ç (2021) ADHD and Risk of Childhood Adiposity: a Review of Recent Research. Curr Nutr Rep 10:30–46
Bowling AB, Tiemeier HW, Jaddoe VWV, Barker ED, Jansen PW (2018) ADHD symptoms and body composition changes in childhood: a longitudinal study evaluating directionality of associations. Pediatr Obes 13:567–575
Kim EJ, Kwon HJ, Ha M, Lim MH, Oh SY, Kim JH, Yoo SJ, Paik KC (2014) Relationship among attention-deficit hyperactivity disorder, dietary behaviours and obesity. Child Care Health Dev 40:698–705
van Egmond-Froehlich A, Bullinger M, Holl RW, Hoffmeister U, Mann R, Goldapp C, Westenhoefer J, Ravens-Sieberer U, de Zwaan M (2012) The hyperactivity/inattention subscale of the Strengths and Difficulties Questionnaire predicts short- and long-term weight loss in overweight children and adolescents treated as outpatients. Obes Facts 5:856–868
van Egmond-Fröhlich AWA, Widhalm K, de Zwaan M (2012) Association of symptoms of attention-deficit/hyperactivity disorder with childhood overweight adjusted for confounding parental variables. Int J Obes 2005 36:963–968
van Mil NH, Steegers-Theunissen RPM, Motazedi E, Jansen PW, Jaddoe VWV, Steegers EAP, Verhulst FC, Tiemeier H (2015) Low and high birth weight and the risk of child attention problems. J Pediatr 166:862-869.e1–3
Porter PA, Henry LN, Halkett A, Hinshaw SP (2022) Body Mass Indices of Girls with and without ADHD: Developmental Trajectories from Childhood to Adulthood. J Clin Child Adolesc Psychol Off J Soc Clin Child Adolesc Psychol Am Psychol Assoc Div 53 51:688–700
Cortese S, Ramos Olazagasti MA, Klein RG, Castellanos FX, Proal E, Mannuzza S (2013) Obesity in men with childhood ADHD: a 33-year controlled, prospective, follow-up study. Pediatrics 131:e1731-1738
Khalife N, Kantomaa M, Glover V, Tammelin T, Laitinen J, Ebeling H, Hurtig T, Jarvelin M-R, Rodriguez A (2014) Childhood attention-deficit/hyperactivity disorder symptoms are risk factors for obesity and physical inactivity in adolescence. J Am Acad Child Adolesc Psychiatry 53:425–436
Martins-Silva T, Dos Santos Vaz J, Schäfer JL, et al (2022) ADHD in childhood predicts BMI and body composition measurements over time in a population-based birth cohort. Int J Obes 2005 46:1204–1211
Adamczak M, Wiecek A (2013) The Adipose Tissue as an Endocrine Organ. Adipose Tissue Endocr Organ 33:2–13
Özcan Ö, Arslan M, Güngör S, Yüksel T, Selimoğlu MA (2015) Plasma Leptin, Adiponectin, Neuropeptide Y Levels in Drug Naive Children With ADHD. J Atten Disord 22:896–900
Levitan RD, Masellis M, Lam RW, Muglia P, Basile VS, Jain U, Kaplan AS, Tharmalingam S, Kennedy SH, Kennedy JL (2004) Childhood Inattention and Dysphoria and Adult Obesity Associated with the Dopamine D4 receptor Gene in Overeating Women with Seasonal Affective Disorder. Neuropsychopharmacology 29:179–186
Choudhry Z, Sengupta SM, Grizenko N, Harvey WJ, Fortier M-È, Schmitz N, Joober R (2013) Body weight and ADHD: examining the role of self-regulation. PloS One 8:e55351
Comings DE, Blum K (2000) Reward deficiency syndrome: genetic aspects of behavioral disorders. In: Prog. Brain Res. Elsevier, pp 325–341
Martins-Silva T, Vaz J dos S, Genro JP, et al (2021) Obesity and ADHD: Exploring the role of body composition, BMI polygenic risk score, and reward system genes. J Psychiatr Res 136:529–536
Cortese S, Vincenzi B (2011) Obesity and ADHD: Clinical and Neurobiological Implications. In: Stanford C, Tannock R (eds) Behav. Neurosci. Atten. Deficit Hyperact. Disord. Its Treat. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 199–218
Pérez-Bonaventura I, Granero R, Ezpeleta L (2015) The relationship between weight status and emotional and behavioral problems in Spanish preschool children. J Pediatr Psychol 40:455–63
Ebenegger V, Marques-Vidal P-M, Munsch S, Quartier V, Nydegger A, Barral J, Hartmann T, Dubnov-Raz G, Kriemler S, Puder JJ (2012) Relationship of hyperactivity/inattention with adiposity and lifestyle characteristics in preschool children. J Child Neurol 27:852–858
Cortese S, Comencini E, Vincenzi B, Speranza M, Angriman M (2013) Attention-deficit/hyperactivity disorder and impairment in executive functions: a barrier to weight loss in individuals with obesity? BMC Psychiatry 13:286–286
Merrill BM, Morrow AS, Sarver D, Sandridge S, Lim CS (2021) Prevalence and Correlates of Attention-Deficit Hyperactivity Disorder in a Diverse, Treatment-Seeking Pediatric Overweight/Obesity Sample. J Dev Behav Pediatr JDBP 42:433–441
Pelotas (RS) | Cidades e Estados | IBGE. https://www.ibge.gov.br/cidades-e-estados/rs/pelotas.html. Accessed 14 May 2024
Hallal PC, Bertoldi AD, Domingues MR, Da Silveira MF, Demarco FF, Da Silva ICM, Barros FC, Victora CG, Bassani DG (2018) Cohort Profile: The 2015 Pelotas (Brazil) Birth Cohort Study. Int J Epidemiol 47:1048–1048h
Murray J, Leão OA de A, Flores TR, et al (2024) Cohort Profile Update: 2015 Pelotas (Brazil) Birth Cohort Study－follow-ups from 2 to 6–7 years, with COVID-19 impact assessment. Int J Epidemiol 53:dyae048
Fleitlich-Bilyk B, Goodman R (2004) Prevalence of child and adolescent psychiatric disorders in southeast Brazil. J Am Acad Child Adolesc Psychiatry 43:727–734
Associação brasileira de empresas de pesquisa | ABEP. https://www.abep.org/. Accessed 26 May 2024
Kearney M (2017) Cross-Lagged Panel Analysis.
Bowling AB, Tiemeier HW, Jaddoe VWV, Barker ED, Jansen PW (2018) ADHD symptoms and body composition changes in childhood: a longitudinal study evaluating directionality of associations. Pediatr Obes 13:567–575
Reilly JJ, Kelly J (2011) Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. Int J Obes 35:891–898
Hanc T, Slopien A, Wolanczyk T, Szwed A, Czapla Z, Durda M, Dmitrzak-Weglarz M, Ratajczak J (2015) Attention-Deficit/Hyperactivity Disorder is Related to Decreased Weight in the Preschool Period and to Increased Rate of Overweight in School-Age Boys. J Child Adolesc Psychopharmacol 25:691–700
Rojo-Marticella M, Arija V, Morales-Hidalgo P, Esteban-Figuerola P, Voltas-Moreso N, Canals-Sans J (2023) Anthropometric status of preschoolers and elementary school children with ADHD: preliminary results from the EPINED study. Pediatr Res 94:1570–1578
Sha’ari N, Manaf ZA, Ahmad M, Rahman FNA (2017) Nutritional status and feeding problems in pediatric attention deficit-hyperactivity disorder. Pediatr Int Off J Jpn Pediatr Soc 59:408–415
Namimi-Halevi C, Dor C, Dichtiar R, Bromberg M, Sinai T (2023) Attention-deficit hyperactivity disorder is associated with relatively short stature among adolescents. Acta Paediatr 112:779–786
Davallow Ghajar L, DeBoer MD (2020) Children With Attention-Deficit/Hyperactivity Disorder Are at Increased Risk for Slowed Growth and Short Stature in Early Childhood. Clin Pediatr Phila 59:401–410
Heinonen K, Räikkönen K, Pesonen A-K, Andersson S, Kajantie E, Eriksson JG, Vartia T, Wolke D, Lano A (2011) Trajectories of growth and symptoms of attention-deficit/hyperactivity disorder in children: a longitudinal study. BMC Pediatr 11:84
Dubnov-Raz G, Perry A, Berger I (2011) Body mass index of children with attention-deficit/hyperactivity disorder. J Child Neurol 26:302–308
Tashakori A, Riahi K, Afkandeh R, Ayati AH (2011) Comparison of Height and Weight of 5-6 Year-old Boys with Attention Deficit Hyperactivity Disorder (ADHD) and Non-ADHD. Iran J Psychiatry Behav Sci 5:71–75
Hanc T, Cieslik J, Wolanczyk T, Gajdzik M (2012) Assessment of growth in pharmacological treatment-naïve Polish boys with attention-deficit/hyperactivity disorder. J Child Adolesc Psychopharmacol 22:300–6
Alpaslan AH, Ucok K, Coşkun KŞ, Genc A, Karabacak H, Guzel HI (2017) Resting metabolic rate, pulmonary functions, and body composition parameters in children with attention deficit hyperactivity disorder. Eat Weight Disord EWD 22:91–96
Tandon PS, Sasser T, Gonzalez ES, Whitlock KB, Christakis DA, Stein MA (2019) Physical Activity, Screen Time, and Sleep in Children With ADHD. J Phys Act Health 16:416–422
Skinner AM, Vlachopoulos D, Barker AR, et al (2023) Physical activity volume and intensity distribution in relation to bone, lean and fat mass in children. Scand J Med Sci Sports 33:267–282
Papadopoulou SK, Feidantsis KG, Hassapidou MN, Methenitis S (2021) The Specific Impact of Nutrition and Physical Activity on Adolescents’ Body Composition and Energy Balance. Res Q Exerc Sport 92:736–746
Escobar-Cardozo GD, Correa-Bautista JE, González-Jiménez E, Schmidt-RioValle J, Ramírez-Vélez R (2016) Percentiles of body fat measured by bioelectrical impedance in children and adolescents from Bogotá (Colombia): the FUPRECOL study. Arch Argent Pediatr 114:135–142
Kurtoglu S, Mazicioglu MM, Ozturk A, Hatipoglu N, Cicek B, Ustunbas HB (2010) Body fat reference curves for healthy Turkish children and adolescents. Eur J Pediatr 169:1329–1335
Plachta-Danielzik S, Gehrke MI, Kehden B, Kromeyer-Hauschild K, Grillenberger M, Willhöft C, Bosy-Westphal A, Müller MJ (2012) Body Fat Percentiles for German Children and Adolescents. Obes Facts 5:77–90
Amaral MA, Mundstock E, Scarpatto CH, Cañon-Montañez W, Mattiello R (2022) Reference percentiles for bioimpedance body composition parameters of healthy individuals: A cross-sectional study. Clinics 77:100078
Leventakou V, Herle M, Kampouri M, Margetaki K, Vafeiadi M, Kogevinas M, Chatzi L, Micali N (2022) The longitudinal association of eating behaviour and ADHD symptoms in school age children: a follow-up study in the RHEA cohort. Eur Child Adolesc Psychiatry 31:511–517

Tables 1 to 4 are available in the Supplementary Files section

There is NO conflict of interest to disclose

Download PDF

Reviewer #2 agreed at journal
13 Aug, 2024
Review #1 received at journal
29 Jul, 2024
Reviewer #1 agreed at journal
15 Jul, 2024
Reviewers invited by journal
29 Jun, 2024
Submission checks completed at journal
27 Jun, 2024
First submitted to journal
25 Jun, 2024
Unknown event
24 Jun, 2024
Editor assigned by journal
21 Jun, 2024

You are reading this latest preprint version

Exploring the bidirectional associations of ADHD symptomatology, nutritional status, and body composition in childhood: evidence from a Brazilian Birth Cohort Study.

Status:

Version 1

Abstract

Figures

Introduction

Methods

Nutritional status

Body composition

ADHD

Covariates

Statistical analysis

Ethical aspects

Results

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1