Child predisposition to attention-deficit/hyperactivity disorder, autism spectrum disorder, and obesity Predictive variables in the first year of life and growth patterns in a population-based context Karin Fast Department of Paediatrics Institute of Clinical Sciences Sahlgrenska Academy, University of Gothenburg Gothenburg 2025 Gothenburg 2025 Illustration: Cecilia Kullberg Citation: Astrid Lindgren Child predisposition to attention-deficit/hyperactivity disorder, autism spectrum disorder, and obesity – Predictive variable in the first year of life and growth patterns in a population-based context © Karin Fast 2025 karin.fast@gu.se karin.h.fast@gmail.com ISBN 978-91-8115-244-9 (PRINT) ISBN 978-91-8115-245-6 (PDF) http://hdl.handle.net/2077/85347 Printed in Borås, Sweden 2025 NENMÄRVA KE Printed by Stema Specialtryck AB Trycksak 3041 0234 S T “Give the children love, more love and still more love – and the common sense will come by itself.” Child predisposition to attention-deficit/hyperactivity disorder, autism spectrum disorder, and obesity Predictive variables in the first year of life and growth patterns in a population-based context Karin Fast Department of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg ABSTRACT The prevalence of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) has increased in the last decades, together with childhood obesity. These conditions often co-occur and may share early biological, environmental, and behavioural risk factors. This thesis explores early predictors and growth patterns to improve early identification and prevention. Four studies are included: Paper I is a clinical review of changes in body mass index (BMI) in 118 children treated with stimulants for ADHD. Papers II–IV use data from a Swedish population-based birth cohort (n=2,666) to analyse the prevalence and predictors of ADHD and ASD, as well as the impact of breastfeeding. Paper IV also report on early growth in relation to later neurodevelopmental and BMI outcomes. Key findings include that nearly half of children with ADHD and comorbid overweight or obesity reached normal weight after stimulant treatment was initiated. In the population-based birth cohort, the prevalence of ADHD and ASD at age twelve was 7.6% and 1.1%, respectively. Preterm birth, maternal smoking, high maternal BMI, and lack of breastfeeding at three and six months were associated with ADHD in offspring. An earlier adiposity rebound was observed in children with higher BMI at age ten. No statistically significant different growth patterns were associated to ADHD nor ASD. Conclusion: ADHD, ASD, and obesity appear to share early-life risk factors and developmental pathways. Stimulant treatment may offer dual benefits in children with ADHD and obesity. Growth monitoring and early behavioural indicators, such as feeding patterns and self-regulation difficulties, may help identify children at risk. These results emphasise the need for integrated, family-centred, and interdisciplinary approaches in early child health surveillance and intervention. Keywords: attention-deficit/hyperactivity disorder, autism spectrum disorder, childhood obesity, early growth, breastfeeding, stimulant treatment ISBN 978-91-8115-244-9 (PRINT) ISBN 978-91-8115-245-6 (PDF) http://hdl.handle.net/2077/85347 i SAMMANFATTNING PÅ SVENSKA Allt fler barn får idag neuropsykiatriska diagnoser som ADHD och autism samtidigt som andelen barn med övervikt och obesitas har ökat. I den här avhandlingen har jag undersökt hur dessa tillstånd hänger ihop och om det finns gemensamma faktorer tidigt i livet som kan påverka barns neurologiska utveckling och hälsa. Avhandlingen bygger på två olika grupper av barn: en klinisk grupp som fått ADHD-diagnos och behandlats med läkemedel samt en populationsbaserad grupp bestående av drygt 2 600 barn i Halland, som har följts från födseln till tolv års ålder. Resultaten visar att många barn med ADHD som samtidigt var överviktiga eller hade obesitas gick ner i vikt under behandling med centralstimulerande läkemedel. Nästan hälften av dessa blev normalviktiga. I den stora befolkningsstudien såg vi att barn som hade fötts för tidigt, vars mammor rökte under graviditeten eller hade högt BMI tidigt under graviditeten, hade ökad risk att senare få en ADHD-diagnos. Vi såg också att amning hade betydelse; barn som inte ammades vid tre eller sex månaders ålder löpte en ökad risk för att utveckla ADHD. Slutligen fann vi, i linje med tidigare forskning, att barn som hade högre BMI vid tio års ålder oftare hade haft en så kallad tidig ”adiposity rebound", dvs. den BMI-uppgång som normalt sker mellan 5-7 år har börjat tidigare än förväntat. Vi kunde däremot inte påvisa ett annorlunda tillväxtmönster hos barn med ADHD. Sammanfattningsvis visar avhandlingen att faktorer tidigt i livet – både biologiska faktorer och förhållanden i omgivningen – spelar en viktig roll för barns utveckling. ADHD, autism och övervikt kan ibland ha gemensamma orsaker där både arv och miljö har betydelse. En bättre förståelse av dessa samband kan hjälpa oss att tidigare identifiera barn som är i behov av extra stöd så att de kan få insatser i rätt tid. ii LIST OF PAPERS This thesis is based on the following studies, referred to in the text by their Roman numerals. I. Fast K, Björk A, Strandberg M, Johannesson E, Wentz E, Dahlgren J. Half of the children with overweight or obesity and attention-deficit/hyperactivity disorder reach normal weight with stimulants. Acta Paediatr. 2021;110:2825–2832. II. Fast K, Wentz E, Roswall J, Strandberg M, Bergman S, Dahlgren J. Prevalence of attention-deficit/hyperactivity disorder and autism in 12-year-old children: A population- based cohort. Dev Med Child Neurol. 2023;00:1–8. III. Fast K, Dahlgren J, Wentz E, Roswall J, Strandberg M, Bergman S, Almquist-Tangen G. Breastfed children have a lower prevalence of attention-deficit/hyperactivity disorder at twelve years of age. Submitted to Dev Med Child Neurol. 2025. IV. Fast K, Roswall J, Bergman S, Strandberg M, Wentz E, Johansson Fast B, Anderson-Conway L, Dahlgren J. Early growth patterns in children with attention-deficit/ hyperactivity disorder, autism spectrum disorder and childhood obesity. Manuscript. iii iv CONTENTS Abbreviations .................................................................................................. ix Definitions in short ......................................................................................... xi 1 Introduction ................................................................................................ 1 1.1 Neurodevelopmental disorders .............................................................. 1 1.1.1 Attention-deficit/hyperactivity disorder ........................................ 2 1.1.1.1 Prevalence and diagnostic criteria ..................................... 3 1.1.1.2 Aetiology and gender differences ..................................... 3 1.1.2 Autism spectrum disorder .............................................................. 3 1.1.2.1 Aetiology and treatment .................................................... 5 1.1.3 Comorbidity ................................................................................... 5 1.1.3.1 ESSENCE ......................................................................... 6 1.1.3.2 AuDHD ............................................................................. 6 1.2 Childhood .............................................................................................. 7 1.2.1 Health ............................................................................................. 7 1.2.1.1 Physical health .................................................................. 7 1.2.1.2 Mental health ..................................................................... 8 1.2.1.3 Social health ...................................................................... 9 1.2.2 Factors affecting child health ......................................................... 9 1.2.3 Growth ......................................................................................... 10 1.2.3.1 Infancy peak .................................................................... 11 1.2.3.2 Adiposity rebound ........................................................... 11 1.2.3.3 Childhood obesity ........................................................... 12 1.3 Models of explanations ....................................................................... 13 1.3.1 Heritability and genetic influence on development ..................... 13 1.3.1.1 Genotype and phenotype ................................................. 14 1.3.1.2 SNPs and genetic variability ........................................... 14 1.3.2 The bridge between genes and environment ............................... 15 1.3.2.1 Perinatal factors ............................................................... 15 1.3.2.2 HPA axis and cortisol ...................................................... 16 v 1.3.2.3 Hormonal Influences ....................................................... 16 1.3.2.4 Neuropeptides and neurotransmitters .............................. 17 1.3.2.5 Gut microbiota and neurodevelopment ........................... 17 1.3.3 The interplay of biology and environment in early development 17 1.3.3.1 Attachment and early social interactions ........................ 18 1.3.3.2 A key skill for learning and adaptation ........................... 18 1.3.3.3 Environmental influences on development ..................... 19 2 Aim ........................................................................................................... 21 3 Participants and methods .......................................................................... 23 3.1 Paper I ................................................................................................. 23 3.1.1 Study participants ........................................................................ 23 3.1.2 Variables ...................................................................................... 23 3.1.2.1 BMI standard deviation score (BMI SDS) ...................... 23 3.1.2.2 Stimulants ........................................................................ 23 3.1.3 Ethics ........................................................................................... 24 3.2 Paper II-IV ........................................................................................... 24 3.2.1 Study participants ........................................................................ 24 3.2.2 Variables ...................................................................................... 24 3.2.2.1 Prevalence ADHD and ASD ........................................... 24 3.2.2.2 Perinatal factors ............................................................... 25 3.2.2.3 Parental factors ................................................................ 25 3.2.2.4 Socioeconomic factors .................................................... 26 3.2.2.5 Breastfeeding .................................................................. 26 3.2.2.6 Overweight and obesity .................................................. 26 3.2.2.7 Growth patterns ............................................................... 26 3.2.3 Ethics ........................................................................................... 27 3.3 Statistics .............................................................................................. 27 3.3.1.1 Paper I ............................................................................. 27 3.3.1.2 Paper II ............................................................................ 27 3.3.1.3 Paper III ........................................................................... 28 3.3.1.4 Paper IV .......................................................................... 28 vi 4 Results ...................................................................................................... 29 4.1 Paper I .................................................................................................. 29 4.1.1 BMI shift and ADHD treatment .................................................. 29 4.2 Paper II-IV ........................................................................................... 31 4.2.1 Prevalence of ADHD and ASD ................................................... 31 4.2.2 Perinatal factors ........................................................................... 32 4.2.3 Parental factors ............................................................................ 32 4.2.3.1 Socioeconomic factors .................................................... 33 4.2.3.2 Breastfeeding and ADHD ............................................... 33 4.2.3.3 BMI and ADHD .............................................................. 35 4.2.3.4 Growth trajectories .......................................................... 36 5 Discussion ................................................................................................. 37 5.1 Clinical impact of stimulant treatment on weight status ..................... 37 5.1.1 Growth monitoring in clinical practice ........................................ 37 5.1.2 Collaborative care ........................................................................ 38 5.2 Prevalence and early-life predictors of ADHD and ASD ................... 39 5.2.1 Prevalence rates in the context of previous research ................... 39 5.2.2 Sex differences in ADHD and ASD ............................................ 40 5.3 Neurodevelopmental benefits of breastfeeding ................................... 41 5.3.1 Breastfeeding as an early indicator .............................................. 42 5.4 Growth patterns in early childhood ..................................................... 43 5.4.1 Early identification ...................................................................... 44 5.4.2 Targeted interventions ................................................................. 44 5.5 Methodological considerations and limitations ................................... 47 5.5.1 Growth data limitations ............................................................... 47 5.5.2 Diagnostic data and reporting bias .............................................. 48 5.5.3 Socioeconomic measures and generalisability ............................ 48 5.6 Socioeconomic status .......................................................................... 49 5.6.1 The interplay with heritability ..................................................... 50 5.7 Findings across papers ......................................................................... 51 vii 5.8 Chicken or egg .................................................................................... 51 5.9 The child behind the data .................................................................... 52 6 Conclusion ................................................................................................ 53 6.1 Toward early preventive models ......................................................... 54 7 Future perspectives ................................................................................... 55 7.1 Nature and nurture – reflections and research ideas ........................... 56 Acknowledgement ......................................................................................... 57 References ..................................................................................................... 59 viii ABBREVIATIONS ADHD Attention-Deficit/Hyperactivity Disorder ARFID Avoidant/Restrictive Food Intake Disorder ASD Autism Spectrum Disorder CHS Children’s Health Services DNA Deoxyribonucleic Acid DSM Diagnostic and Statistical Manual of Mental Disorders FTO Fat mass and Obesity-associated gene HPA Hypothalamic-Pituitary-Adrenal ICP Infancy-Childhood-Puberty RNA Ribonucleic Acid SDS Standard Deviation Score SGA Small for Gestational Age SNP Single Nucleotide Polymorphism WHO World Health Organization ix x DEFINITIONS IN SHORT Adiposity rebound Refers to the second rise in BMI that typically occurs between five and seven years of age after an initial decline in early childhood. An earlier adiposity rebound has been associated with an increased risk of obesity and metabolic disorders later in life. Apgar score A quick assessment of a newborn's health performed at one, five and ten minutes after birth. It evaluates five criteria: appearance (skin colour), pulse (heart rate), grimace (reflexes), activity (muscle tone), and respiration (breathing effort), with each scored from 0-2. AuDHD Refers to the co-occurrence of ASD and ADHD, combining traits like inattention, impulsivity, social difficulties, and sensory sensitivities. ESSENCE Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations is referring to the overlapping symptoms of neurodevelopmental conditions such as ADHD, ASD, learning disabilities, speech disorders, and motor coordination issues, which often appear early in childhood and require comprehensive assessment. Infancy peak Infancy peak refers to the rapid increase in BMI during the first months of life, reaching a maximum before gradually declining toward early childhood. It is a key phase in early growth patterns and may be linked to later metabolic and health outcomes. xi Karin Fast 1 INTRODUCTION With each birth, a new life begins — a baby takes its first breath, and the room is filled with quiet awe and eyes brimming with joy. Families come in many shapes, and every pregnancy carries its own story. Labour might last minutes or stretch across days, but all journeys lead to this moment. Sometimes, however, clinical signs at birth or details from the mother’s records quietly hint that this child may face more challenges ahead (1, 2). In others, concerns arise later, either through parental reports at Child Health Services (CHS) or teachers’ observations regarding a child's special needs. Sometimes, these challenges are not obvious at first, but as the child grows, he or she may begin to feel different or out of place — feelings that can later be linked to neurodevelopmental difficulties. This thesis aims to contribute to the understanding of factors influencing the risk of developing neurodevelopmental conditions by integrating existing scientific knowledge and providing new insights. By identifying who is at risk and what factors contribute to that risk, we can work towards preventive strategies, early identification, and improved interventions (3). 1.1 NEURODEVELOPMENTAL DISORDERS Neurodevelopmental disorders encompass a broad range of conditions, with Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) being among the most prevalent (4, 5). Both ADHD and ASD exist on a spectrum, meaning that traits associated with these disorders are present in the general population to varying degrees. However, a diagnosis is made when these traits are significantly pronounced, present from early childhood, and cause substantial functional impairment (6). Neurodevelopmental disorders have complex, multifactorial causes, with both genetic and environmental influences contributing to an individual’s susceptibility (7). Having a neurodevelopmental condition not only increases the risk of other neurodevelopmental diagnoses but is also associated with a higher risk of co-occurring physical and mental health conditions (8). 1 Child predisposition to ADHD, ASD, and obesity 1.1.1 ATTENTION-DEFICIT/HYPERACTIVITY DISORDER ADHD has been recognised for centuries. One of the earliest descriptions dates back to 1798, when Scottish physician Sir Alexander Crichton introduced the concept of attention regulation (9). He described it as “the incapacity of attending with a necessary degree of constancy to any one object” (9, 10). ADHD is a neurodevelopmental condition primarily characterised by difficulties with attention, impulsivity, and hyperactivity, often accompanied by challenges in emotional regulation, behavioural control, learning, and sleep (11). See Figure 1 for an illustrative depiction of ADHD in a child. Figure 1. Visual representation of how ADHD may feel for a child. Illustration: Cecilia Kullberg, created for this thesis. 2 Karin Fast 1.1.1.1 PREVALENCE AND DIAGNOSTIC CRITERIA ADHD has been estimated to affect approximately 6% of children worldwide (12). While many individuals learn to manage their symptoms as they grow, about 3% of adults continue to meet diagnostic criteria (13). The condition is diagnosed based on criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM). The criteria have been revised periodically, a notable change in DSM-5 (2013) is the raising of the upper age limit for symptom onset from seven to twelve years (14). Treatment of ADHD in children involves a multimodal approach, including parental education, lifestyle modifications, and tailored educational support (15). Pharmacological treatment, primarily with stimulant medications, can be beneficial in improving focus, impulse control, and mental endurance (16). However, side effects – such as appetite suppression, elevated heart rate, dry mouth, and sleep disturbances – can pose challenges, particularly during initial dose adjustments (17). 1.1.1.2 AETIOLOGY AND GENDER DIFFERENCES ADHD is more frequently diagnosed in boys than in girls, although the core symptoms such as inattention, hyperactivity, and impulsivity remain the same (13). However, the manifestation of symptoms can differ based on biological, social, and environmental factors (18). The DSM-5 update aimed to improve recognition of ADHD in females - often presenting more subtle symptoms and therefore tending to be diagnosed later than in males (19). How ADHD traits are expressed can also be influenced by the individual's cognitive ability and environmental context (20). ADHD has a strong hereditary component, with genetic variants playing a significant role in susceptibility (21). However, environmental factors interact with the genetic predispositions, influencing the severity and presentation of symptoms (22). 1.1.2 AUTISM SPECTRUM DISORDER ASD is a neurodevelopmental condition defined by deviations in cognitive development, social communication skills, and behavioural patterns (14). The term 'spectrum' reflects the wide diversity in how autism presents, including variations in communication, behaviour, and support needs. Among individuals with autism, approximately 30-40% have co-occurring intellectual or developmental disabilities, while others have average or above-average intelligence (14, 23, 24). See Figure 2 for an illustrative depiction of ASD. 3 Child predisposition to ADHD, ASD, and obesity Figure 2. Illustration depicting of a child with ASD. Illustration: Cecilia Kullberg, created for this thesis. 4 Karin Fast The first documented case of autism dates back to Donald Triplett, born in 1933 in Mississippi. His diagnosis, assigned by the child psychiatrist Leo Kanner, was instrumental in establishing autism as a distinct condition and laying the groundwork for modern diagnostic criteria (25). Diagnosis of ASD is based on clinical assessments, including developmental history, behavioural observations, and standardised diagnostic tools. Key characteristics of ASD include difficulties in social communication and interaction, restricted and repetitive behaviours, and atypical sensory processing (14). 1.1.2.1 AETIOLOGY AND TREATMENT The aetiology of ASD is complex and involves genetic, epigenetic, and environmental factors. While the genetic contribution is substantial, no single gene has been identified as solely responsible for ASD. Instead, multiple genetic variants, combined with environmental influences such as preterm birth contribute to an individual's likelihood of developing the condition (23, 26). Although there is no specific medical treatment for ASD, early diagnosis can provide significant benefits for children and their families by offering an explanatory model for their challenges. Increased understanding, targeted support, and interventions can help mitigate difficulties and reduce the risk of secondary mental health issues (27). 1.1.3 COMORBIDITY Neurodevelopmental disorders such as ADHD and ASD are not classified as diseases but rather as variations in brain development that can lead to challenges in daily functioning. Neurodevelopmental disorders often co-occur and are associated with a heightened risk of additional somatic and psychological difficulties (28, 29). ADHD and ASD in childhood have been linked to an increased prevalence of obesity (30-33), sleep disturbances, headache, and gastrointestinal symptoms, such as constipation and stomach pain. Injuries are also more prevalent, likely reflecting a combination of impulsivity, attentional difficulties, motor coordination impairments, and challenges with risk assessment (34, 35). In addition, individuals with ADHD or ASD have an increased risk of developing anxiety, depression, gender dysphoria and behavioural challenges, including aggression and self-harm (29, 36). Adolescents with ADHD and/or ASD face 5 Child predisposition to ADHD, ASD, and obesity a higher likelihood of forming suicidal ideas and, in some cases, develop a substance abuse (37, 38). 1.1.3.1 ESSENCE Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations (ESSENCE), is an acronym coined by Christopher Gillberg in 2010 (39). It is a collective term for conditions characterised by early-onset behavioural difficulties and/or cognitive challenges that prompt consultations with various specialists, who often work independently of one another (40). The framework emphasises the importance of early identification of neurodevelopmental difficulties, as symptoms typically emerge within the first three years of life. By recognising ESSENCE at an early stage, early interventions can be implemented, reducing the risk of more severe mental health symptomatology later in life and the risk of secondary challenges such as academic struggles (41). 1.1.3.2 AUDHD AuDHD, the co-occurrence of ASD and ADHD, is well-documented, with studies indicating that approximately 30-50% of children with ASD also fall within the diagnostic frame for ADHD (42). Conversely, approximately 10% of children diagnosed with ADHD have co-occurring ASD (43). ASD and ADHD share overlapping features, such as difficulties with attention, impulse control, social interaction, and executive functions, which can complicate diagnostics and treatment. Despite these similarities, the underlying mechanisms of ASD and ADHD differ. As summarised on the educational platform “Psychiatric Advisor” (44), individuals with ASD may show attention difficulties due to intense focus on specific interests, while those with ADHD often struggle to maintain attention due to hyperactivity and distractibility – although some individuals with ADHD may also experience episodes of hyperfocus, particularly during highly stimulating or rewarding tasks. The overlap between the two disorders can lead to misdiagnosis or delayed diagnostics, especially when the two conditions are not actively considered during the clinical assessment (44). Early identification and a comprehensive, individualised approach to treatment – including behavioural interventions, medication, and educational support – are essential for addressing the unique needs of children with AuDHD (45). 6 Karin Fast 1.2 CHILDHOOD Childhood provides the foundation for lifelong health and well-being. The physical, emotional, and social experiences during these early years influence a child's ability to learn, thrive and adapt to changes (46). 1.2.1 HEALTH The World Health Organization (WHO) describes health as a state of complete physical, mental, and social well-being, extending beyond absence of disease (47). Childhood represents a pivotal period for growth and development, making it essential to ensure good health for fostering both immediate and future well-being. Research exploring parental perspectives on "child health" in infancy, through qualitative interviews conducted in Sweden, reveals that parents view health as a multifaceted concept. Among various factors, feeding issues are regarded as a primary concern and a critical indicator of a child’s health by parents (48). 1.2.1.1 PHYSICAL HEALTH Healthy physical development encompasses adequate growth in height and weight, as well as the progression of motor skills such as crawling followed by walking. These milestones are routinely tracked through visits to CHS. To maintain health and prevent disease, nutrition, adequate sleep, physical activity, and vaccinations, all play vital roles (49). Nutrition: Proper nutrition is essential for promoting growth and maintaining immune function. A well-balanced diet, rich in vitamins, minerals, protein, and essential proteins and fatty acids, supports brain development, muscle and skeletal growth, and energy needs. The nutritional requirements of children change with age, with infants, toddlers, and school-age children having different needs (50). Sleep: Sufficient sleep is vital for children’s health. The required amount of sleep varies by age; infants typically need between 11-17 hours daily, school- aged children need approximately 9-11 hours, while adolescents generally require 8-10 hours of sleep per day (51, 52). Physical activity: Consistent physical activity is critical for promoting bone density, muscle strength, and cardiovascular health. Parents should aim for the child participating in at least one hour of moderate-to-vigorous physical activity daily, which involve activities such as running, playing, dancing, or 7 Child predisposition to ADHD, ASD, and obesity participating in sports (52). Beyond its physical benefits, regular exercise has been shown to enhance cognitive functions like concentration, memory, and creativity (53). This improvement is attributed to elevated levels of growth hormone (GH) and insulin-like growth factor 1 (IGF-1), which foster neurogenesis and synaptic plasticity in the hippocampus, a vital brain region for learning and memory (54, 55). Additionally, physical activity helps reduce stress, anxiety, and depressive symptoms, while improving overall sleep quality (56). 1.2.1.2 MENTAL HEALTH Mental health in childhood includes emotional, cognitive, and social functioning. A strong foundation in mental well-being supports a child’s capacity to manage stress, form healthy relationships, and make thoughtful decisions. Research suggests that caregivers are generally reliable in assessing their child’s overall development, particularly in early infancy. This underscores the importance of taking caregivers’ concerns seriously in clinical settings, as their observations can provide valuable input for decisions regarding further developmental assessments (57). In childhood, mental health plays a crucial role in overall health and well- being, influencing how children think, feel, and behave. It also influences their ability to interact with others and cope with stress. Struggles with mental health can interfere with the child’s school performance, social engagement, and overall development in children. The WHO reports that mental health disorders are a major contributor to the global burden of childhood illnesses (58). Parental involvement, by both mothers and fathers early in a child's life, has been shown to positively impact long-term developmental and educational outcomes. Studies by Flouri and Buchanan (59), as well as a systematic review by Sarkadi et al. (60), highlight that active engagement from fathers is associated with better emotional regulation, social competence, and academic achievement in children. Various mental health conditions can emerge during childhood, including anxiety, depression, ADHD, and ASD, with a subset individual later developing psychotic or bipolar disorder. Early identification and intervention play a vital role in managing these conditions and promoting healthy emotional and social development (61). 8 Karin Fast 1.2.1.3 SOCIAL HEALTH Social health refers to the quality of a child’s relationships with family, peers, and the wider community. It involves elements such as social interactions, emotional support, and sense of belonging. Healthy social connections are key to emotional development and help children manage stress and overcome challenges. Inadequate social health can result in feelings of isolation, loneliness, and contribute to mental health difficulties. Families play a vital role in nurturing social health by creating a safe, supportive environment that promotes positive relationships (62). A child's social health is strongly influenced by its family environment. Supportive and nurturing family relationships, parenting that provides guidance, emotional support, and appropriate boundaries, and a stable home life contribute to overall well-being. In contrast, exposure to neglect, abuse, or family dysfunction can negatively impact a child's development and long-term health (63, 64). Routine health check-ups within CHS play a crucial role in monitoring growth and developmental milestones, allowing for early detection and management of potential health concerns. Deviations in growth patterns may serve as early indicators of underlying medical conditions, such as thyroid disorders, craniopharyngioma, or hypercortisolism. Gastrointestinal symptoms could signal conditions like celiac disease or inflammatory bowel disease. Feeding and eating disorders, such as Avoidant/Restrictive Food Intake Disorder (ARFID) or early-onset anorexia nervosa, should also be considered when significant deviations in growth or nutritional status are observed. Additionally, in some cases, abnormal growth trajectories may raise concerns about possible physical or sexual abuse (65). 1.2.2 FACTORS AFFECTING CHILD HEALTH Several factors contribute to child health, interacting in complex ways. These include genetics, environment, socioeconomic status, healthcare access, and lifestyle choices. Children living in poverty often struggle with limited access to nutritious food, healthcare, and safe living conditions, which can negatively impact their health. In contrast, children from higher socioeconomic backgrounds generally have greater access to resources that promote health. The factors contributing to child health can have long-term effects on a child’s physical, mental, and social well-being (66). To maintain child health, both disease prevention and health promotion are both vital. Public health strategies that prevent illness and promote healthy 9 Child predisposition to ADHD, ASD, and obesity behaviours are key to reducing the incidence of diseases among children. These strategies include vaccination programs, nutrition education, promoting physical activity, good oral health, ensuring access to clean water and sanitation, and minimising exposure to toxins and pollutants. Health promotion initiatives also focus on increasing awareness of mental health, fostering healthy family environments, and tackling social determinants of health, such as poverty and inequality (49). Early intervention has a pivotal role in enhancing long-term health outcomes for children. Recognising and addressing health and developmental concerns at an early stage can prevent more severe issues in the future. Early intervention services encompass screenings for developmental delays, vaccinations, mental health support, and healthcare access. The sooner children receive appropriate care, the more effectively their needs can be addressed (67). Parents fundamentally influence the health and overall well-being of their children. They are responsible for ensuring proper nutrition, creating a safe environment, promoting healthy behaviours and obtaining necessary medical care. Parental involvement also extends to offering emotional support, teaching coping strategies, and encouraging social engagement. Parents are also role models, their health behaviours and attitudes shaping the health habits of their children (68). 1.2.3 GROWTH Childhood growth is shaped by a complex interaction of genetic, biological, and environmental influences. From infancy to early childhood, distinct phases of growth emerge that play crucial roles in long-term health outcomes. Two particularly important milestones are the infancy peak in BMI and the adiposity rebound, both of which have been associated with later obesity and metabolic risk (69, 70). During infancy, rapid physical development is typical, marked by increases in both weight and length. Following this period, BMI typically declines until the adiposity rebound phase occurs. The timing of these changes – notably, the age at which BMI peaks and begins to rise again – has been identified as predictors of obesity risk later in life (71, 72). Behind these patterns lie genetic and epigenetic mechanisms. While genetic factors determine a child's inherent growth potential, epigenetic processes – such as those influenced by prenatal conditions, early nutrition, and environmental exposures – can shape how these traits are expressed. This concept, known as developmental programming, suggests that early growth 10 Karin Fast trajectories are closely linked to long-term risks for obesity, cardiovascular disease, and diabetes (73, 74). In addition to biological determinants, growth is also shaped by social and environmental factors, including socioeconomic status, access to nutritious food, healthcare, and opportunities for physical activity. Children in economically disadvantaged settings may experience both undernutrition and overnutrition, highlighting the complex relationship between biology and environmental circumstances (75, 76). Understanding these early growth phases is essential for identifying children at risk for adverse health outcomes. Early interventions that promote healthy development – particularly in nutrition and lifestyle – can play a crucial role in preventing later complications (71). 1.2.3.1 INFANCY PEAK The infancy peak refers to the period in which an infant’s BMI reaches its highest point, typically between six and twelve months of age, before gradually declining. This temporary elevation in BMI is driven by fat accumulation, which is believed to serve important roles in cognitive development and energy storage during this rapid growth phase (77). This early peak is a normal and essential part of development, but its timing and trajectory can offer insights into a child’s metabolic programming. Monitoring the infancy peak allows healthcare professionals to assess whether infants are receiving adequate nutrition, and whether growth patterns are aligning with typical developmental expectations. Identifying deviations early may help prevent later weight-related concerns and support optimal long-term outcomes (70, 71, 78). 1.2.3.2 ADIPOSITY REBOUND The term adiposity rebound refers to the stage in early childhood when body fat levels begin to increase again following a natural decline after infancy and toddlerhood. Although there is individual variation, this phase usually occurs between the ages of five and seven. The timing of the rebound is considered a key predictor of future weight status, with an early rebound – before the age of five – being associated with an increased risk of overweight, obesity, and related metabolic complications in adolescence and adulthood (71, 72). An early adiposity rebound may reflect a mismatch between energy intake and expenditure or indicate broader genetic, epigenetic, or environmental 11 Child predisposition to ADHD, ASD, and obesity vulnerabilities. Children with early rebound often exhibit accelerated fat gain, which may be compounded by behavioural and lifestyle factors such as sedentary habits or poor dietary patterns. Recognising the timing of adiposity rebound through regular growth monitoring can help guide early, preventive strategies to reduce the risk of obesity (69, 79). 1.2.3.3 CHILDHOOD OBESITY Childhood obesity is a serious challenge in public health, posing significant health risks. It is one of the most common chronic conditions affecting children and has both immediate and long-term health consequences. Childhood obesity is diagnosed using BMI percentiles or z-scores, both of which take into account age and gender differences in growth patterns (80, 81). The development of obesity is influenced by multiple factors, including genetic predisposition, poor dietary habits, insufficient physical activity, socioeconomic conditions, and psychological factors such as stress (64). Additionally, certain medical conditions, including craniopharyngioma and polycystic ovary syndrome, as well as the use of specific medications, can contribute to excessive weight gain (82, 83). In the short term, children with obesity face increased risks for type 2 diabetes, hypertension, elevated cholesterol levels, respiratory issues such as asthma and sleep apnoea, and joint discomfort due to excess weight. Long-term implications include an increased likelihood of developing cardiovascular disease, stroke, metabolic syndrome, and persistent obesity into adulthood (84, 85). Psychological effects, such as low self-esteem, bullying, depression, anxiety, and social stigmatisation further exacerbate the challenges faced by children with obesity (86, 87). Neurodevelopmental conditions such as ADHD and ASD are markedly more common among children with obesity, as reported by a previous Swedish cohort study (88, 89). Effective prevention and management strategies are essential in addressing childhood obesity (90). These include: Healthy eating habits: Encouraging a well-balanced diet, reducing the intake of added sugars, and promoting regular family meals (91). Physical activity: Daily engagement in moderate to vigorous physical activity for 60 minutes (92). 12 Karin Fast Behavioural interventions: Implementing school- and community-based programs that foster healthier lifestyles through education and structured activities (93). Medical interventions: In severe cases, pharmacological treatments or even bariatric surgery may be considered under medical supervision (94, 95). Emotional and professional support: Providing psychological counselling and professional guidance to assist families in managing obesity-related challenges (64). To address the multifaceted causes of childhood obesity, mitigating its long- term impact and improving overall child health outcomes, it is important that healthcare professionals, caregivers and policymakers collaborate (86, 96). 1.3 MODELS OF EXPLANATIONS The co-occurrence of neurodevelopmental disorders and obesity arises from a complex interplay of biological, psychological, and environmental influences. While ADHD is primarily classified as a neurodevelopmental condition and obesity is a metabolic disorder, emerging research highlights shared mechanisms that contribute to their frequent overlap. These mechanisms encompass genetic predispositions, early-life environmental factors, hypothalamic dysregulation, and metabolic processes, emphasising the intricate balance between inherited traits and external influences (28, 29, 82, 97). 1.3.1 HERITABILITY AND GENETIC INFLUENCE ON DEVELOPMENT Within a given population, heritability reflects how much of the variation in a trait is due to genetic influences. While genetic information provides the foundational blueprint for individual development, traits such as cognition, metabolism, and behaviour emerge through dynamic interactions between genes, epigenetics, and environmental exposures (98). Rather than being solely determined by genetic inheritance, heritability represents an ongoing interplay between genotype, phenotype, and external influences. Single nucleotide polymorphisms (SNPs) contribute to genetic variability, yet their expression and impact are often shaped by epigenetic modifications and life experiences (21). Understanding these relationships can provide valuable insights into the development of ADHD and obesity, paving 13 Child predisposition to ADHD, ASD, and obesity the way for more targeted interventions that consider both genetic and environmental risk factors (99). 1.3.1.1 GENOTYPE AND PHENOTYPE An individual’s genotype consists of the complete set of genetic instructions inherited from their parents, whereas the phenotype encompasses the observable traits that result from gene-environment interactions (100). While genetic inheritance plays a crucial role in shaping cognitive abilities, metabolic processes, and behavioural tendencies, environmental factors – such as diet, gut microbiota, stress, and lifestyle – further influence how these traits manifest (101, 102). For instance, specific genetic variants may predispose an individual to impulsivity or metabolic dysregulation; however, the extent to which these traits are expressed depends on additional factors such as premature birth, early nutrition, or early antibiotic use (102, 103). 1.3.1.2 SNPS AND GENETIC VARIABILITY Genetic variations, particularly SNPs, play a significant role in determining heritable traits. SNPs are small changes in a single deoxyribonucleic acid (DNA) base pair that can influence gene expression and protein function, affecting cognitive abilities, reward processing, metabolism, and stress responses. Several SNPs have been associated in both ADHD and obesity (104): Dopaminergic genes: Variations in these genes, including those affecting dopamine receptors and dopamine transporters such as DAT1, can impact dopamine receptor sensitivity, transporter function, and neurotransmitter availability. These changes may influence affecting impulsivity, attention regulation, and reward-seeking behaviours – factors associated with both ADHD and overeating (105). Fat mass and obesity-associated gene (FTO): Certain SNPs in this gene are strongly linked to higher BMI, increased appetite, and alterations in fat metabolism, suggesting a genetic connection between obesity and brain function (106). Leptin and ghrelin-related genes: Variants affecting these appetite- regulating hormones can influence hunger cues and hypothalamic networks, contributing to challenges in weight regulation (82). 14 Karin Fast Although SNPs provide a genetic foundation for trait variability, environmental factors often change how these genes are expressed through epigenetic processes. 1.3.2 THE BRIDGE BETWEEN GENES AND ENVIRONMENT Epigenetics refers to modifications in gene expression that do not alter the DNA sequence but are influenced by environmental factors such as stress, diet, prenatal conditions, and early-life experiences. These modifications play a crucial role in shaping cognitive, metabolic, and behavioural outcomes. Common epigenetic mechanisms include (98): DNA methylation: The adding of methyl groups to DNA, which can either activate or silence genes, influencing brain function, metabolism, and behaviour. Histone modification: Alterations to proteins that package DNA, affecting how accessible specific genes are for transcription. Non-coding ribonucleic acids (RNAs): Molecules that regulate gene expression without directly modifying the genetic code. In the context of ADHD and obesity, epigenetic changes may impact stress regulation, impulse control and metabolic processes, altering the risk profile of an individual with a given genetic predisposition. For example, prenatal stress or early childhood adversity can lead to modifications in the hypothalamic- pituitary-adrenal (HPA) axis, which regulates cortisol production, thereby increasing susceptibility to both ADHD and obesity (107, 108). 1.3.2.1 PERINATAL FACTORS Factors such as preterm birth, intrauterine growth restriction, and pregnancy complications have been associated with increased risk for neurodevelopmental conditions, including ADHD. In a large Swedish population-based study, Beer et al. 2022 demonstrated that children born preterm, small for gestational age (SGA), or exposed to preeclampsia or placental abruption had a significantly higher likelihood of being diagnosed with ADHD (109). Perinatal factors have also been linked to later metabolic outcomes. Birth weight and gestational age influence the risk of childhood obesity, though the 15 Child predisposition to ADHD, ASD, and obesity relationship appears complex. Although some studies suggest that SGA infants may be more prone to metabolic dysregulation (110). 1.3.2.2 HPA AXIS AND CORTISOL Dysregulation of the HPA axis, which plays a key role in the body’s stress response, can contribute to altered cortisol levels. Children with ADHD often exhibit atypical stress responses, which may heighten impulsivity, emotional dysregulation, and food-seeking behaviours (111). Long-term stress and increased cortisol levels may also promote fat accumulation, particularly increased visceral adipose tissue, further strengthening the link between ADHD symptoms and obesity risk (112). 1.3.2.3 HORMONAL INFLUENCES Hormonal imbalances also contribute to the connection between ADHD and obesity. Regulatory hormones such as leptin and ghrelin, controlling hunger and satiety, may function abnormally in both conditions (113). Individuals with ADHD frequently experience disruptions in reward processing, leading to poor impulse control and a preference for highly palatable, energy-dense foods. This is supported by studies indicating that children with ADHD consume more processed meat products, milk-based desserts, and chocolate- sweets than their non-ADHD peers, with a positive correlation between ADHD symptom severity and the intake of such snacks. Additionally, the dopaminergic system, which plays a crucial role in the brain's reward pathways, is implicated in the motivation to obtain rewarding stimuli, such as high-calorie foods. Alterations in dopamine function are associated with both ADHD and the regulation of food intake, further indicating the link between ADHD and a preference for energy-dense foods (114-116). However, eating behaviours in ADHD can vary significantly. While some children with ADHD exhibit overeating tendencies, others – particularly those with co-occurring ARFID – may display selective eating patterns, sensory sensitivities, or food aversion, potentially leading to nutritional deficiencies and growth concerns (14, 117, 118). These contrasting dietary behaviours underscore the diverse ways in which ADHD traits, such as impulsivity and sensory processing differences, can influence metabolic outcomes (119). Additionally, metabolic disturbances such as insulin resistance, common in obesity, may exacerbate cognitive symptoms in ADHD, reinforcing a bidirectional relationship (120). 16 Karin Fast 1.3.2.4 NEUROPEPTIDES AND NEUROTRANSMITTERS Neuropeptides and neurotransmitters are signalling molecules that modulate various physiological and behavioural functions, including mood, appetite, and cognitive processes. While neurotransmitters facilitate synaptic communication within the nervous system, hormones regulate distant targets through the endocrine system (121). One neuropeptide of interest is oxytocin, which plays a role in social bonding, emotional regulation and stress response. Recent research suggests that oxytocin dysregulation may be implicated in both ADHD, ASD and obesity. Lower oxytocin levels or receptor dysfunction could contribute to impulsivity, social difficulties, emotional dysregulation, and appetite control issues. Future studies may explore whether oxytocin-based treatments, such as intranasal oxytocin, could help regulate eating behaviours and emotional processing in individuals with ADHD, ASD, obesity, or ARFID (122, 123). 1.3.2.5 GUT MICROBIOTA AND NEURODEVELOPMENT The gut microbiome has emerged as a potential link between ADHD and obesity. Gut bacteria play a role in neurotransmitter production, immune function, and metabolism, all of which are relevant to both conditions. The composition of intestinal flora, shaped by factors such as diet, antibiotic use, and early-life environmental exposures, may influence dopamine regulation and energy balance (102, 103, 124). Research suggests that variations in gut microbiota may interact with genetic predispositions, including SNPs, affecting both neurodevelopmental processes and metabolic pathways (125). By modulating inflammation, neurotransmitter signalling, and metabolic homeostasis, the gut microbiome may serve as a crucial factor in the shared aetiology of ADHD and obesity (104). 1.3.3 THE INTERPLAY OF BIOLOGY AND ENVIRONMENT IN EARLY DEVELOPMENT The first 1,000 days of life, from conception to the second birthday, represents a crucial period for development, where genetic predispositions interact with environmental factors to shape long-term health, cognitive abilities, and emotional resilience (126). Several key aspects – attachment, caregiver responsiveness, and early interactions – play a significant role in shaping developmental trajectories. This dynamic relationship between biological 17 Child predisposition to ADHD, ASD, and obesity predispositions and early life experiences reflects what has long been described as the interplay between nature and nurture. 1.3.3.1 ATTACHMENT AND EARLY SOCIAL INTERACTIONS A child’s early relationships with caregivers provide a foundation for emotional security and self-regulation (127). When caregivers respond consistently and sensitively to a child's needs, secure attachment develops, fostering trust and confidence (128). Attachment theory, while influential, represents a theoretical framework and should be understood as such. In contrast, insecure attachment may emerge if caregiving is unpredictable or inconsistent, which can contribute to emotional and behavioural difficulties. A subset of children experiences disorganised attachment, where caregiving is simultaneously a source of comfort and fear, potentially leading to difficulties in emotional regulation and stress management later in life (129). Children with disabilities or developmental delays, including neurodevelopmental conditions such as ADHD and autism, show lower rates of secure attachment and higher rates of disorganised attachment compared to typically developing peers. Importantly, these differences are not necessarily due to poor caregiving but may instead be influenced by the child's intrinsic difficulties in social communication, emotional regulation, or responsiveness (130). Early interactions help shape a child’s understanding of social relationships and the predictability of their environment (131). Exposure to chronic stress, such as neglect, poverty, or unstable caregiving, can elevate stress hormone levels, impacting brain development and increasing the risk of self-regulation difficulties (132). Research indicates that interventions focusing on enhancing parental responsiveness, can improve attachment quality and support healthy development (60). 1.3.3.2 A KEY SKILL FOR LEARNING AND ADAPTATION Self-regulation encompasses impulse control, goal-directed behaviour, and emotional management, all of which are essential for navigating social interactions, maintaining focus, and managing food intake. Children with underdeveloped self-regulation skills may face challenges in academic settings and peer relationships, increasing their risk of long-term difficulties (133). A well-known study on self-regulation, often referred to as the “marshmallow test,” demonstrated that children who were able to delay gratification tended to achieve better academic and emotional outcomes in later life (134). However, subsequent research has highlighted that self-regulation is not solely an innate trait but is also shaped by early life experiences, including attachment 18 Karin Fast security and socioeconomic conditions (135, 136). A child’s ability to delay gratification may reflect not only self-control but also the extent to which they perceive their environment as predictable and supportive (137). 1.3.3.3 ENVIRONMENTAL INFLUENCES ON DEVELOPMENT Neuroplasticity describes the ability of the brain to change and restructure itself based on experiences (138, 139). Early exposure to language-rich environments, interactive play, and nurturing caregiving can support cognitive development (140), while adverse experiences – such as chronic stress, neglect and socioeconomic disadvantage – can have the opposite effect (141). Studies on early childhood development indicate that the parental education level is linked to brain structure, particularly in areas associated with language and executive functioning (142). Meanwhile, the relationship between family income and brain development follows a logarithmic pattern, meaning that even modest improvements in financial stability can have a significant positive impact on cognitive growth (143). By recognising the dynamic interaction between biology and environment, early interventions can be designed to optimise developmental outcomes, mitigating the impact of adverse experiences and promoting resilience in children (144). 19 Child predisposition to ADHD, ASD, and obesity 20 Karin Fast 2 AIM The overarching aim of this thesis is to elucidate the interplay between ADHD, ASD and childhood obesity. The focus is on exploring correlating factors, the extent of comorbidity, and the effect of early detection/intervention. The specific aims for each paper are: I. To investigate weight outcomes in children with ADHD after stimulant treatment. II. To assess the prevalence of ADHD and ASD in 12-year-old children in the Halland region and to evaluate the association with prenatal and perinatal factors. III. To explore whether breastfeeding is linked to a lower risk of ADHD at age twelve in the Halland region. IV. To investigate in children in the Halland region how early growth trajectories relate to obesity, ADHD, and ASD. The specific hypotheses for each paper were: I. We hypothesised that children with ADHD would experience similar weight loss from stimulant treatment whether they had overweight/obesity or normal weight. II. We expected that preterm birth, preeclampsia, and maternal overweight would increase the risk of ADHD or ASD. III. We hypothesised that non-breastfed children would have a higher prevalence of ADHD than breastfed children. IV. We hypothesised that the timing and magnitude of the BMI peak in infancy and the adiposity rebound before adolescence would significantly influence the weight status at age ten. 21 Child predisposition to ADHD, ASD, and obesity 22 Karin Fast 3 PARTICIPANTS AND METHODS This thesis includes two published papers and two manuscripts under preparation. Paper I is a retrospective medical record review of 118 children treated for ADHD in the Gothenburg area, examining their height and weight development during the first year of ADHD medication compared to the previous year. Papers II, III, and IV are based on the same Halland region birth cohort which has been followed prospectively. 3.1 PAPER I In this retrospective cohort study, medical records from several clinics, including both child psychiatric and paediatric clinics, were reviewed. The clinics, situated in the greater Gothenburg area, were responsible for diagnosing and treating children with ADHD during the study period. The timeframe was limited to the period in which a specific digital medical record system was in use, allowing authorised access to identify all children diagnosed with ADHD, including its subtypes. 3.1.1 STUDY PARTICIPANTS The participants were children born between 1998 and 2011, diagnosed with ADHD (ICD-10 F90) aged between 6 and 17 years at the start of stimulant treatment. Complete growth data were recorded at treatment start (±3 days), 12 months before (±270 days), and 12 months after (±30 days). 3.1.2 VARIABLES 3.1.2.1 BMI STANDARD DEVIATION SCORE (BMI SDS) The primary outcome was the change in BMI SDS over the twelve months before and after treatment. Participants were categorised by BMI status, ADHD subtype, and age. Growth data, stimulant type/dosage, and comorbid conditions were collected from medical records. Seven participants with data outside the inclusion window were retained based on stable growth trajectories. 3.1.2.2 STIMULANTS All participants were prescribed stimulant medications, with methylphenidate being the most common, followed by lisdexamfetamine. Atomoxetine was permitted as an adjunct but not as monotherapy. Dosages varied based on 23 Child predisposition to ADHD, ASD, and obesity clinical recommendations. Exclusion criteria included treatment noncompliance. 3.1.3 ETHICS The Regional Ethical Review Board in Gothenburg approved the study (133– 17), with consent obtained from the department heads. Due to the study's retrospective nature and the use of anonymised data, written informed consent from participants was not required. 3.2 PAPER II-IV These three studies were part of a larger ongoing population-based birth cohort in the Halland region of Sweden. From October 1, 2007, to December 31, 2008, all parents of newborns were intended to be invited to participate in the study. Originally planned to last for five years, the study has since continued with follow-up assessments through ongoing participant recruitment. 3.2.1 STUDY PARTICIPANTS During the recruitment period, 3,860 children were born in the region. The recruitment of study participants and the study population was originally described in Almqvist et al. (2012). In summary, 2,666 children participated, having been recruited during their first visit to the CHS. Aside from the consent form being available only in Swedish, there were no restrictions on recruitment. The study population is considered representative of children born in Sweden during the same period in terms of sex, birth weight, and gestational age. 3.2.2 VARIABLES 3.2.2.1 PREVALENCE ADHD AND ASD The primary outcomes of these studies were the prevalence of ADHD and/or ASD diagnoses at the age of twelve. Diagnoses were determined using diagnostic codes and prescribed medications recorded in the National Patient Register and the National Prescribed Drug Register. ADHD cases were identified through ICD-10 codes under the F90 category, including various subtypes based on predominant symptoms such as inattention, hyperactivity, or a combined presentation. Diagnostic codes under F84 included F84.0 (autism spectrum disorder), F84.1 (atypical autism), and F84.9 (pervasive developmental disorder, unspecified). Data were collected from both inpatient 24 Karin Fast and outpatient records upheld by the National Board of Health and Welfare. As the F90 codes are not mutually exclusive, individuals could receive multiple or evolving ADHD diagnoses over time. As a result, it was not possible to separate different types of ADHD, such as predominantly inattentive or hyperactive-impulsive forms. Central stimulant prescriptions were used to support ADHD identification, classified using relevant ATC codes (N06BA02, N06BA04, N06BA09, and N06BA12). Five individuals were diagnosed with ADHD based solely on repeated methylphenidate prescriptions, as this medication is exclusively used for ADHD treatment in this study population. However, one individual prescribed Ritalin only once was not classified as receiving ADHD treatment. 3.2.2.2 PERINATAL FACTORS Perinatal data, including birth size, Apgar scores, and diagnoses for both mother and child at discharge, were obtained from the National Medical Birth Register. Apgar score is a quick assessment of a newborn's health performed at one, five and ten minutes after birth based on appearance, pulse, grimace, activity and respiratory effort. The total score ranges from 0 to 10, where higher scores indicate better overall condition. Preeclampsia cases were identified using ICD-10 code O14, with 25 classifieds as severe, 52 as mild, and five unspecified. No cases of haemolysis, elevated liver enzymes, and low platelet syndrome were recorded. Infants were classified as SGA if their birth weight was at least two standard deviations below the expected average for their gestational age. Gestational age was primarily determined by ultrasound or, alternatively, the last menstrual period. Birth weight was missing for one individual, and gestational age was unavailable for three. 3.2.2.3 PARENTAL FACTORS Parental and perinatal information was also gathered through a questionnaire at the first CHS visit. Maternal age was calculated from the birth date. Smoking status was determined based on maternity clinic records and self- reported questionnaires. The smoking question was divided into two items, one binary yes/no and one quantitative asking for the number of cigarettes smoked daily. The mothers were classified as smokers if either question was affirmative. Paternal smoking was only self-reported and was categorised in the same way as maternal smoking. Complete parental smoking data were available, with 214 mothers reporting smoking during pregnancy or the perinatal period, while 2,444 did not. Compared to national data on children 25 Child predisposition to ADHD, ASD, and obesity born in Sweden in 2007, the only significant difference observed in the birth cohort was a lower prevalence of paternal smoking (145). 3.2.2.4 SOCIOECONOMIC FACTORS Socioeconomic status was assessed using parental education level as a key variable. Education was categorised as either low (≤12 years, equivalent to completing upper secondary school in Sweden) or high (>12 years). Parental responses collected at the child’s first appointment at the CHS formed the basis for this classification. Data on educational level were missing for 35 mothers and 186 fathers. 3.2.2.5 BREASTFEEDING Breastfeeding data were collected during each visit to the CHS through questionnaires. Breastfeeding was treated as a binary variable (yes or no), based on whether breastfeeding and partial breastfeeding or exclusive formula feeding had occurred. 3.2.2.6 OVERWEIGHT AND OBESITY In paper IV, alongside ADHD, the primary outcome variable was the classification of children’s weight status at age ten, based on the guidelines from the International Obesity Task Force. Categories included underweight (BMI < 18.5 kg/m2), normal weight (18.5 ≤ BMI < 25 kg/m2), overweight (25 ≤ BMI < 30 kg/m2), and obesity (BMI ≥ 30 kg/m2) for both boys and girls where BMI is calculated in terms of adult equivalence according to Cole et al. 2000 (146). BMI SDS was calculated following Karlberg et al.'s reference values (147). Age in years was determined by dividing the child's age in days by the average number of days in a year. BMI was primarily assessed at ten years of age; if data were missing, data from the closest proximity measurement point between ages eight and twelve years were used for classification. 3.2.2.7 GROWTH PATTERNS In paper IV, an extension of the infancy-childhood-puberty (ICP) model was developed utilising one function to describe the entirety of the growth curve from newborn to adulthood, accounting for both the infancy peak and adiposity rebound. To start with, the data were fitted to a third-degree polynomial, !(#) = & + b  ⋅ x  +  c  ⋅ x!  +  d  ⋅ x", decently capturing of the general shape of a growth curve and yielding a good fit from around 18 months to adulthood. However, the pure polynomial function did not capture the rapid variations 26 Karin Fast during the first 18 months well. An exponential function, . ⋅ .#$/&, increasing quickly towards a constant value, was therefore added to the third-degree polynomial to capture the growth curve in its entirety. SciPy’s curve fitting algorithm (148), however, encountered numerical instability with the complete function and the exponential term had to be fitted by hand to a single common value for all individuals. This value was determined to ! = 2.4 by maximising 3! = 1 − ∑'(7' − !')!⁄∑'(7' − 78)! – where 7', !' and 78 are the observed value, the predicted value and the mean observed value, respectively – across the sample. The final function used for the growth curve approximation was: #!.) !(#)  =  &  +  :  ⋅ #  +  ;  ⋅ #!  +  <  ⋅ #"  +  .  ⋅ . & Eq. 1 3.2.3 ETHICS The studies received ethical approval from the regional ethics review board in Lund, Sweden (299/2007 and 141/2018). Written informed consent was obtained from the parents at the time of their child's inclusion in the study, and renewed consent was secured when the study was extended beyond the initially planned five years. The ethical approvals ensure that the studies follow the ethical guidelines of the relevant ethics committee and the principles of the Helsinki Declaration (1975, revised in 1983). 3.3 STATISTICS 3.3.1.1 PAPER I A power analysis (G*Power) determined a minimum sample size of 30. Statistical analyses were conducted in SPSS Statistics 25, using paired t-tests for normally distributed and Kruskal-Wallis tests for the nonparametric variables. Changes in BMI SDS and height SDS were analysed, with significance set at p ≤ 0.05. A p-value below 0.05 was considered statistically significant in all papers. All statistical analyses were conducted using IBM SPSS Statistics version 25-29 (IBM SPSS Statistics, Chicago, IL), along with the Python libraries Numpy (149) and Pandas (150). 3.3.1.2 PAPER II The primary dependent variables were diagnoses of ADHD or ASD. Independent variables included sex, gestational age, parental smoking, BMI 27 Child predisposition to ADHD, ASD, and obesity categories, and education levels, which all were selected based on previous research and relevant socioeconomic factors. Missing data points were excluded from the respective sub-analysis. The association between independent variables and ADHD or ASD was assessed using Pearson’s χ2 test to examine categorical differences. For comparisons involving BMI as a continuous variable, a one-sample t-test was used. Both crude and multivariable binary logistic regression models were used to explore the relationships between independent variables and the likelihood of developing ADHD. 3.3.1.3 PAPER III The primary independent variables included breastfeeding, sex, gestational age, maternal age, BMI, and smoking during pregnancy or at delivery. Binary logistic regression was used to analyse the data at four time points, ranging from newborn to twelve months. The dependent variable was presence of an ADHD diagnosis. All independent variables were dichotomised, cases with missing values were excluded from the analysis, and only individuals with a complete dataset at each time point were included in the corresponding regression model. 3.3.1.4 PAPER IV In this study, we examined differences in the timing of the infancy BMI peak and adiposity rebound among children categorised by weight status (normal weight, overweight, and obesity). The aim was to explore how excess weight is correlated to these important stages of growth. Descriptive statistics were presented as both mean with confidence intervals (CI) and median values. To compare groups, the Kruskal-Wallis test and the independent-samples median test were employed. Post-hoc pairwise comparisons were performed for further analyses. The Kruskal-Wallis test was used to evaluate the differences in infancy BMI peak and adiposity rebound across different BMI categories at the 10-year follow-up. The independent-samples median test was utilised to assess the relationship between infancy BMI peak, adiposity rebound, and the presence of ADHD at the age of twelve. 28 Karin Fast 4 RESULTS This chapter presents findings from two separate study cohorts. Paper I is based on a clinical cohort of children diagnosed with ADHD and treated with stimulants. In contrast, papers II–IV encompasses a population-based birth cohort which provided data on a broad range of early-life factors. 4.1 PAPER I Of 701 children with ADHD in medical records, 118 met the inclusion criteria with growth data before/at baseline/one year on psychostimulant treatment and were analysed. One child with Down syndrome was excluded due to atypical growth. See Figure 3 for flowchart. Figure 3. Flowchart for Paper I. 4.1.1 BMI SHIFT AND ADHD TREATMENT The median age at treatment initiation was 9.1 years (range: 5.3-16.6). All the children were diagnosed with ADHD and had been on stimulants for at least a year. At the start of treatment 63% had normal weight while 21% were overweight and 16% were classified with obesity. The most prevalent ADHD subtype was the combined presentation (80%). Twenty-four percent of the children were diagnosed with ASD. 29 Child predisposition to ADHD, ASD, and obesity Evaluating the changes in growth parameter SDS over time, ∆growth parameter SDS⁄∆>, stimulant treatment was found to significantly reduce height SDS across all BMI groups (p < 0.01). Weight SDS also declined, with a greater decrease in children with overweight or obesity (p = 0.025) than in children with normal weight. BMI SDS significantly decreased during treatment (∆BMI SDS⁄∆> = −0.72 in the year after treatment onset versus +0.17 in the year before treatment, p < 0.01) across all weight categories. The variations in BMI SDS led to some children shifting between BMI categories throughout the study, see Figure 4. Thus, 43% of the children with overweight/obesity transitioned to normal weight, increasing the proportion of normal weight children from 63% to 79%. In the year leading up to treatment, the children experienced weight gain, with 5% transitioning from normal weight to overweight or obesity before treatment began. Figure 4. Body mass index (BMI) subgroups for the children one year before, at start of treatment and after one year with treatment. 30 Karin Fast 4.2 PAPER II-IV Papers II-IV are population-based birth cohort studies. They use the same cohort comprising 2,666 children, data being successfully collected from 2,658 individuals, see flowchart in Figure 5. Figure 5. Flowchart for Paper II-IV. 4.2.1 PREVALENCE OF ADHD AND ASD The prevalence of ADHD and ASD was 7.6% and 1.1%, respectively. A total of 203 out of 2,658 children in the cohort had been diagnosed with ADHD by the age of twelve years. A significant proportion of children with ASD, 22 out of 28, also met criteria for ADHD. Both disorders showed a male predominance, with male-to-female ratios of 2.3:1 for ADHD and 2.5:1 for ASD. Due to the relatively low number of ASD cases, further statistical analyses of risk factors for ASD were limited. 31 Child predisposition to ADHD, ASD, and obesity 4.2.2 PERINATAL FACTORS Preterm birth, defined as birth before gestational week 37, was associated with an increased likelihood of ADHD. Specifically, 13.7% of preterm children were diagnosed with ADHD, compared to 7.3% of those born at term. Logistic regression analysis revealed that preterm birth was associated with a 2.13-fold increased risk (95% CI: 1.21–3.74) of ADHD. Due to the small number of ASD cases born preterm (n = 2), no analyses were made regarding correlations between ASD and gestational age. 4.2.3 PARENTAL FACTORS Parental BMI, especially maternal BMI, was a significant predictor of ADHD in the child, see Figure 6. Children born to mothers with overweight (25 ≤ BMI < 30 kg/m²) or obesity (BMI ≥ 30 kg/m²) at the start of pregnancy had a higher risk of developing ADHD. The prevalence of ADHD among children to mothers with normal BMI was 5.7% compared to 11.4% in offspring to mothers with overweight or obesity. This association was found to be dose- dependent, with a stronger correlation observed in cases of maternal obesity (χ² = 42.58, p < 0.001) than in maternal overweight (χ² = 8.44, p = 0.004). Paternal obesity was also associated with an elevated risk of ADHD in the child, with a two-fold increase observed; however, the association was less robust than for maternal BMI and not statistically significant for fathers with overweight. Among children of fathers with obesity, 12.8% had ADHD, compared to 6.4% of children with normal weight fathers. Importantly, maternal and paternal BMI were moderately correlated (r = 0.21), and in 23.1% of cases where the father had obesity, this also applied to the mother. Additional parental factors such as maternal smoking during pregnancy and lower maternal educational attainment were also independently associated with increased ADHD risk. In multivariable regression analyses, maternal overweight or obesity, preterm birth, and maternal smoking remained statistically significant predictors of ADHD, even after adjusting for confounding variables including sex and educational level. Maternal age was excluded due to its strong correlation with educational level and minimal impact on the model when included. 32 Karin Fast Figure 6. Risk of attention-deficit/hyperactivity disorder (ADHD) in children in relation to maternal (pink) and paternal (blue) body mass index (BMI). 4.2.3.1 SOCIOECONOMIC FACTORS Findings from a study by Roswall et al. 2016 conducted on the present population cohort has shown that socioeconomic context influence health outcomes in early childhood (151). In Paper II, socioeconomic indicators were incorporated in the analyses of the prevalence of ADHD and ASD at twelve years of age. We found that lower maternal educational level was associated with an increased risk of ADHD in the child. This association remained statistically significant in multivariable models adjusting for sex, preterm birth, maternal smoking, and parental BMI. 4.2.3.2 BREASTFEEDING AND ADHD In paper III, breastfeeding data were analysed at four time points during the first year of life. At approximately one week of age, 93% (n = 2,419) of the children were breastfed. This proportion declined to 79% (n = 1,889) at three months, 58% (n = 1,317) at six months, and 9% (n = 214) at twelve months of age. The number of children included in the analyses varied depending on 33 Child predisposition to ADHD, ASD, and obesity response rates to the breastfeeding question at each check-up. Loss to follow- up occurred at each check-up because of incomplete data for breastfeeding, as illustrated in the flowchart for Paper III, see Figure 7. Figure 7. Flowchart for Paper III. The study population is based on Figure 5. Statistically significant associations were found between lack of breastfeeding at three and six months and the likelihood of an ADHD diagnosis. No such associations were observed for breastfeeding at one week or twelve months. Logistic regression analyses, adjusted for potential confounders including sex, gestational age, maternal age, maternal BMI, and smoking, confirmed these findings. When stratified by sex, the overall pattern remained consistent for both boys and girls. However, girls who were not breastfed at six months showed a particularly elevated risk of ADHD (OR = 2.16, p = 0.01). Other biological variables such as sex, gestational age, maternal BMI, age, and smoking were also significantly associated with ADHD at all time points, except for preterm birth at three months, which failed to reach statistical significance (OR = 1.63; p = 0.12). 34 Karin Fast 4.2.3.3 BMI AND ADHD The flowchart for paper IV is shown in Figure 8. In this part of the study, 1,124 children had available data on weight and height at ten years of age and could be included in the analysis of BMI and ADHD. Fifty-six children were diagnosed with ADHD at the age of twelve. The majority of children with ADHD were in the normal weight group (n = 42), while a small number were in the underweight (n = 6), overweight (n = 4), or obesity (n = 4) groups. Figure 8. Flowchart for Paper IV. The study population is based on Figure 5. There was a statistically significant association between BMI category at ten years of age and ADHD diagnosis (p=0.007), i.e. the higher the BMI the more likely to have an ADHD diagnosis. However, the number of children with ADHD in the higher BMI categories was very small, and the association was weak. There was no significant difference between children with and without ADHD with respect to BMI peak during infancy or the age of the adiposity rebound. 35 Child predisposition to ADHD, ASD, and obesity 4.2.3.4 GROWTH TRAJECTORIES In the other part of Paper IV 1,032 children had enough measurements from birth to the age of twelve years to allow analyses of growth patterns, specifically the timing of the BMI peak during infancy and the timing of adiposity rebound. The average infancy peak for the cohort when fitting the data to Eq. 1 was approximately 10 months, which is consistent with the literature value for the average occurrence (152). Children were grouped based on their BMI at the age of ten years (underweight, normal weight, overweight, or obesity). The study found that children with higher BMI at ten years of age tended to have an earlier adiposity rebound than their peers with normal or low BMI (p < 0.001). The timing of the BMI peak during infancy also varied slightly between the BMI groups. Children with obesity appeared to have an earlier and somewhat higher BMI peak, but these differences were not statistically significant after adjusting for multiple comparisons. These findings suggest that patterns of early growth and weight gain may play a role in later health outcomes, particularly the timing of the adiposity rebound. 36 Karin Fast 5 DISCUSSION This thesis is based on four papers focusing on identifying early predictors associated with ADHD, childhood growth patterns and obesity in children. In addition to discussing implications for clinical practice and opportunities for preventive strategies, this chapter also presents suggestions for future research. 5.1 CLINICAL IMPACT OF STIMULANT TREATMENT ON WEIGHT STATUS Paper I, showed that stimulant treatment for children with ADHD can contribute to clinically significant weight normalisation. Thus, nearly half of the children with ADHD and concurrent overweight or obesity reached normal weight during stimulant treatment. This clinical observation provides evidence of the metabolic effects of stimulants beyond symptom control, in a subgroup of children at elevated risk for long-term health complications related to excess weight. The observed weight normalisation is especially notable given the rising prevalence of childhood obesity (81), and the potential for ADHD symptoms – such as impulsivity or emotional dysregulation – to negatively influence eating behaviours and physical activity (31). The study also documented weight gain in the year preceding treatment, highlighting a possible window of vulnerability that might be addressed through early diagnosis and intervention. The paper showed the importance of monitoring growth in children with ADHD, which suggests that stimulant treatment may have dual benefits, improving core ADHD symptoms while also supporting healthier weight trajectories in children with comorbid overweight or obesity. 5.1.1 GROWTH MONITORING IN CLINICAL PRACTICE Childhood obesity is a complex but preventable condition requiring a multi- faceted approach (153). Early intervention through lifestyle changes, education, and medical support can improve long-term health outcomes (154). The findings in Paper I highlight not only the potential for weight normalisation during stimulant treatment in children with ADHD and comorbid obesity, but also the value of systematic growth monitoring. 37 Child predisposition to ADHD, ASD, and obesity Especially, the tracking of height, weight and BMI is essential for identifying subtle or gradual changes in growth patterns that may signal emerging health concerns (155). In children with ADHD, growth monitoring is especially relevant for two key reasons. First, stimulant medications are known to affect appetite and height growth velocity in some individuals (13). Regular use of growth charts allows early detection of deviations from the expected growth trajectories, enabling timely adjustments to treatment, dietary support, or further medical evaluation. Second, as demonstrated in the study, a significant proportion of children with ADHD with comorbid overweight or obesity, and stimulant treatment may contribute to weight stabilisation or normalisation in these cases (156). Growth charts help distinguish between healthy changes and potentially problematic patterns – such as rapid weight loss, suppressed growth, or rebound weight gain after treatment adjustments or discontinuation. Furthermore, the observation of weight gain in the year preceding treatment initiation underscores the importance of capturing and reviewing historical growth data. Earlier recognition of rising BMI may support more proactive interventions, including earlier ADHD diagnosis, nutritional counselling, and tailored behavioural strategies. 5.1.2 COLLABORATIVE CARE The findings from Paper I highlight the importance of coordinated care between somatic and psychiatric services, particularly for children with ADHD and comorbid conditions such as obesity, asthma or other somatic complaints. Despite frequent overlap in clinical settings, care is often fragmented, risking missed opportunities for early intervention and inconsistent monitoring of physical health during psychiatric treatment (28). To improve outcomes, close collaboration between paediatricians, school health nurses, child psychiatrists, dietitians, and psychologists is essential (39). The sharing of growth charts, treatment plans, together with common communication platform can help addressing both physical and mental health needs in an integrated, child-centred way. This approach aligns with the ESSENCE framework, which emphasises the importance of early, multidisciplinary attention to children presenting with developmental concerns before a specific diagnosis is established (157). Early signs of growth deviation may surface in primary or school health care, while behavioural symptoms emerge in mental health or educational settings. Without active collaboration, these patterns may go unrecognised, delaying 38 Karin Fast diagnosis and support (158). Structured models – such as joint case conferences or shared medical records – can help bring together perspectives across disciplines, especially for children whose needs span multiple domains. 5.2 PREVALENCE AND EARLY-LIFE PREDICTORS OF ADHD AND ASD The updated prevalences offer a rare combination of longitudinal design and detailed early-life data. Several perinatal and parental factors were identified as significant predictors of ADHD, including preterm birth, maternal smoking, and elevated maternal BMI at the start of pregnancy. The findings underscore the multifactorial nature of ADHD and highlight the importance of early-life influences. Since the same early-life risk factors are also known to increase the risk for childhood obesity, the results suggest that the mechanisms contributing to neurodevelopmental and metabolic outcomes in children overlap (30, 159). 5.2.1 PREVALENCE RATES IN THE CONTEXT OF PREVIOUS RESEARCH The findings in Paper II align closely with those of previous population-based studies conducted in Sweden and across Europe. For example, Simonoff et al. 2008 (29) reported similar prevalence figures in a UK-based cohort, while Scandinavian studies, such as Ghirardi et al. 2018 (160) and Lichtenstein et al. 2010 (161), also demonstrated comparable rates and similar co-occurrence of ADHD and ASD. Importantly, the identification of preterm birth, maternal smoking during pregnancy, and elevated maternal BMI as significant predictors of ADHD aligns with a growing body of evidence supporting a multifactorial model for the development of neurodevelopmental conditions (109, 162, 163). These early-life risk factors are also known contributors to childhood obesity, as previously shown in longitudinal studies by Anderson et al. 2016 (164) and Larqué et al. 2019 (165). Together, these findings point to potential shared developmental pathways affecting both cognitive regulation and metabolic regulation in early childhood. The suggestion of overlapping mechanisms is particularly relevant in light of the co-occurrence of ADHD and obesity, reported by Cortese and Tessari in 2017. They proposed that factors such as impaired impulse control, poor sleep, and dysregulated stress responses may bridge the gap between neurobehavioral dysregulation and altered energy balance (31). The results from Paper II therefore contribute to a more nuanced understanding of how prenatal and 39 Child predisposition to ADHD, ASD, and obesity perinatal exposures may simultaneously shape both mental and physical health trajectories. 5.2.2 SEX DIFFERENCES IN ADHD AND ASD The male-to-female ratios for ADHD and ASD found in Paper II are consistent with prior reports showing that neurodevelopmental disorders are more frequently diagnosed in boys than in girls (29, 166). However, the reasons behind these sex differences are likely multi-factorial and remain a subject of ongoing debate and research (167). Girls with ADHD are often underdiagnosed or diagnosed later than boys, possibly due to more subtle symptom presentation, such as internalising behaviours, inattentiveness without hyperactivity, or better coping in structured environments (168). Boys, on the other hand, are more likely to present externalising behaviours that are easily noticed in school and clinical settings, which may partly explain the observed sex ratio (19, 169). Similarly, in ASD, sex bias in diagnostic criteria or clinical awareness may contribute to under-recognition in girls (166). Research suggests that girls with autism may exhibit fewer repetitive behaviours, more advanced social imitation skills, or better camouflaging abilities – making their symptoms less likely to meet traditional diagnostic thresholds. Girls may also be more likely to present with co-occurring anxiety or mood disorders, which can mask the core features of ASD and delay appropriate diagnosis and intervention (170). The findings from this study therefore underscore the importance of developing sex-sensitive screening tools and diagnostic approaches, as well as promoting greater clinical awareness of how ADHD and ASD may manifest differently in girls. Early identification is crucial not only for timely access to support and interventions but also for reducing the risk of secondary complications, including academic underachievement, low self-esteem, bullying, and mental health problems (19). In future research and clinical practice, sex and gender should be considered not only as a demographic variable but also as a biologically and socially relevant factor influencing symptom expression, diagnostic pathways, and treatment outcomes in neurodevelopmental conditions (171). 40 Karin Fast 5.3 NEURODEVELOPMENTAL BENEFITS OF BREASTFEEDING Breastfeeding exemplifies how early-life caregiving and nutritional exposures may influence neurodevelopmental trajectories. Paper III investigated the association between breastfeeding during the first year of life and later diagnosis of ADHD. The findings show that a lack of breastfeeding at three and six months was associated with an increased risk of ADHD by age twelve, even after adjusting for confounders, including sex, gestational age, maternal age, BMI, and smoking. Notably, lack of breastfeeding has also been associated with an increased risk of overweight and obesity in children, suggesting that shared developmental pathways influence both metabolic and neurodevelopmental outcomes. This aligns with several earlier studies reporting similar associations. For instance, a meta-analysis by Yan et al. 2014 found that breastfeeding was significantly associated with a reduced risk of childhood obesity (172), while Britton et al. 2006 and Victora et al. 2016 highlighted potential long-term cognitive and emotional benefits associated with breastfeeding, including improved self- regulation and parent-child bonding (173, 174). More specifically related to ADHD, studies have observed lower prevalence of ADHD symptoms in children who were breastfed longer, though the mechanisms remain debated (175-177). The results presented here contribute to previous studies the idea that early nutrition and caregiving experiences may shape long-term health (173). However, as the study is observational, it cannot establish causality. It remains uncertain whether the observed association reflects a direct protective effect of breastfeeding, the biological composition of breastmilk, or whether lack of breastfeeding may serve as an early marker for other underlying vulnerabilities – such as maternal stress, infant regulatory difficulties, or broader environmental or hereditary factors. Mothers with ADHD may experience difficulties with breastfeeding, possibly due to symptoms such as reduced persistence or hyperactivity. As ADHD is highly heritable, early parent–child dynamics may be influenced by both genetic and behavioural factors. When interpreting findings, it is important to consider the potential for guilt or stigma related to breastfeeding difficulties. Despite these limitations, the findings have important clinical and public health relevance. Promoting and supporting breastfeeding in infancy may offer not only nutritional benefits, but also relational and regulatory advantages that support healthy neurodevelopment. Furthermore, difficulties with 41 Child predisposition to ADHD, ASD, and obesity breastfeeding could be seen as a potential early indicator of families who may benefit from additional support or early intervention (178). 5.3.1 BREASTFEEDING AS AN EARLY INDICATOR Beyond its potential protective effects, the act of breastfeeding and its duration may also serve as a valuable early indicator of child and family risk. The findings in Paper III suggest that difficulties with breastfeeding – whether due to maternal, infant, or social factors – may reflect early neurodevelopmental or regulatory challenges that are not yet fully visible but may later manifest as ADHD or related conditions. This perspective is supported by prior research indicating that infants who experience poor self-regulation, feeding difficulties, or early irritability may have a higher likelihood of later behavioural or attentional difficulties (179, 180). Similarly, maternal factors such as stress, mental health challenges, or insecure attachment patterns may interfere with sustained breastfeeding and may also co-occur with elevated risk for neurodevelopmental disorders in offspring (180, 181). Therefore, while breastfeeding may offer biological and relational support for early brain development, its absence or interruption could also act as a signal for clinicians – prompting closer observation of early developmental milestones, parent-infant interaction, and family needs. In this way, infant feeding trajectories may serve a dual role: not only as a potential modifiable exposure but also as a window into early vulnerability (174, 180). Integrating questions about breastfeeding and feeding difficulties into routine child health assessments could thus offer practical, low-cost opportunities for early detection and the initiation of supportive services (178). This may be particularly valuable for families not yet engaged with mental health or developmental services but who may benefit from closer follow-up or anticipatory guidance. Future studies are needed to disentangle the complex pathways linking early feeding, caregiving, and later neurodevelopment, ideally through longitudinal designs with integrated biological, behavioural, and environmental data (182). Until then, breastfeeding status may be considered an informative, albeit indirect, marker of early developmental context, rather than a stand-alone protective factor. 42 Karin Fast 5.4 GROWTH PATTERNS IN EARLY CHILDHOOD Paper IV elucidates the complex interplay between neurodevelopmental conditions and early growth trajectories by examining the relationship between BMI and ADHD at ten years of age, and how this relates to growth patterns in early childhood. Although a statistically significant association was found between BMI category and ADHD, the correlation was weak, and the number of children with ADHD in the higher BMI categories was limited. These results suggest that while ADHD and BMI may intersect, the relationship is likely influenced by additional biological, behavioural, and environmental factors. A more robust finding from the study was the observation that children with higher BMI at the age of ten years had experienced an earlier adiposity rebound, a growth milestone known to be associated with increased risk of later overweight and obesity (69, 72). These findings align with previous research indicating that the timing of adiposity rebound can serve as an early marker for identifying children at risk of developing obesity (71, 183). The fact that children with obesity also tend to have ADHD or related behavioural difficulties, indicates that developmental and behavioural observations should be integrated with growth monitoring in primary care and child health settings (31). From a clinical perspective, this reinforces the potential value of monitoring early growth trajectories, particularly BMI patterns, as part of routine child health surveillance. Detecting an earlier-than-expected rebound may provide a critical window for preventive intervention, especially in children with additional susceptibility, including neurodevelopmental conditions such as ADHD (31, 33). In summary, the study indicates that integrating growth monitoring with behavioural and neurodevelopmental assessments may improve early identification of children at risk for adverse health outcomes. This may therefore enable early tailored interventions. 43 Child predisposition to ADHD, ASD, and obesity 5.4.1 EARLY IDENTIFICATION The findings highlight the value of early identification of children who may be at increased risk for both neurodevelopmental and weight-related challenges, particularly through monitoring of early growth patterns. The timing of the adiposity rebound is known to be a useful marker for predicting later overweight and obesity (69, 72). Thus, Rolland-Cachera et al. (69) and Geserick et al. (72), demonstrated that an adiposity rebound before five to six years of age is associated not only with an increased risk of adolescent and adult obesity but also with metabolic and cardiovascular complications later in life. To further complicate the connection with neurodevelopmental disorders, maternal and parental weight and childhood obesity might share common environmental and behavioural risk factors but also reflect similar early-life influences – such as prenatal nutrition, stress, and caregiving – that shape both brain development and metabolism, pointing to overlapping developmental pathways (184). Recent findings from a Swedish register-based cohort study suggest that while gestational weight gain primarily influences growth in infancy, maternal BMI has a stronger impact on growth patterns beyond the first 18 months. This further highlights the importance of monitoring growth trajectories over time, particularly when early deviations coincide with behavioural signs such as inattention or emotional dysregulation (164, 185). 5.4.2 TARGETED INTERVENTIONS Rather than framing early identification of risk factors solely in terms of intervention, the results from Paper III and IV invite reflection on the potential value of supportive approaches that consider the child’s needs in a holistic and family-centred context. While this was not a primary focus of the study, the observation that earlier adiposity rebound was common among children with higher BMI at the age of ten years, including those with ADHD, raises important questions about how such early markers might be used in practice to offer timely guidance and support. 44 Karin Fast In cases where early deviations in BMI coincide with behavioural traits such as impulsivity, inattention, or emotional dysregulation, it may be reasonable – within a broader clinical or public health framework – to view these patterns as possible markers for families that may benefit from additional resources or follow-up. Although these approaches were not examined within this study, examples of supportive strategies could include: • Parental guidance related to daily routines, emotion regulation, and healthy lifestyle habits (186). • Nutritional support adapted to the child’s behavioural and sensory profile (187). • Encouraging physical activity through play-based or interest-driven movement, aligned with the child’s self-regulation needs (188). • Open, non-stigmatising conversations with parents about observed growth or behavioural changes, emphasising shared understanding rather than clinical labelling (189, 190). These thoughts are intended to stimulate further research and clinical discussion, regarding how growth monitoring can be integrated into early supportive care for children showing signs of neurodevelopmental vulnerability, see Figure 9. 45 Child predisposition to ADHD, ASD, and obesity Figure 9. Illustration of a child deep in thought, symbolising the inner world of children with ADHD or ASD as they process information, navigate challenges, and seek their own understanding. Illustration: Cecilia Kullberg, created for this thesis. 46 Karin Fast 5.5 METHODOLOGICAL CONSIDERATIONS AND LIMITATIONS This thesis draws strength from its mixed-methods approach, combining both clinical and population-based data across four studies. Paper I, contributes real- world clinical insights from a retrospective review of children with ADHD treated in a specialist setting, offering valuable information about treatment outcomes and growth monitoring in routine care. The strength of papers II–IV lie in their use of a large sample set and a population-based design, combined with detailed and prospectively collected early-life data. Together, these complementary methods provide a broader understanding of how biological, behavioural, and environmental factors overlap in child development. This combination also supports a life-course approach to child health, highlighting early windows of opportunity for identification, support, and prevention. However, several important limitations must be acknowledged. 5.5.1 GROWTH DATA LIMITATIONS One major limitation concerns the lack of more complete growth data, which affected the analyses in both Paper I and Paper IV. In Paper I, only a minority of the children met the inclusion criteria due to insufficient clinical routines for recording height and weight before and during stimulant treatment. As a result, the study may not be representative of all children treated with stimulants, and selection bias cannot be excluded. Earlier work has reported on growth outcomes in similar populations. Landgren et al. 2017 reported reduced BMI among children with ADHD as a side effect of stimulant treatment, especially among those with pre-existing overweight or obesity (191). In Paper I, a substantial proportion of the children reached normal weight within a year, suggesting that the effect of stimulants on weight reduction was faster compared to the observations by Landgren et al. (191). While stimulant-related weight loss has often been viewed as a negative side effect, particularly due to concerns about growth suppression, the clinical response has typically been to recommend additional caloric intake – even for children with pre-existing overweight or obesity (192, 193). This approach overlooks the potential benefits of weight normalisation in children where excess weight by itself is a health risk. Few studies have explored whether stimulant treatment may contribute, not only to reduced BMI, but also to improved eating behaviour. This highlights the need for a more nuanced 47 Child predisposition to ADHD, ASD, and obesity understanding of growth and nutrition in children with ADHD, especially in the context of long-term outcomes. Missing growth data also affected the statistical analyses in Paper IV. Thus, it was necessary to create two distinct subgroups – one for analysis of BMI and ADHD (n = 1124) and one for growth trajectory modelling (n = 1032). This limitation of the dataset may have reduced the statistical power in subgroup analyses and may also introduce bias if those lost to follow-up differed systematically from those included. 5.5.2 DIAGNOSTIC DATA AND REPORTING BIAS In Papers II–IV, ADHD and ASD diagnoses were retrieved from national and regional health records, which is a strength in terms of diagnostic reliability and validity. While a bias may be introduced by regional differences in diagnostic routines, this is likely a minor concern, given the standardised healthcare structure in region Halland. However, the likelihood of underdiagnosis – particularly among girls – is a more pressing concern. Research has shown that girls with ADHD are often diagnosed later than boys, or not at all, due to differences in symptom presentation and clinical recognition. As a result, children with clinically significant symptoms may have been missed, leading to an underestimation of true prevalence and potentially affecting the observed associations with early-life risk factors. At the same time, concerns about potential overdiagnosis have also been raised, particularly among boys; for instance, a recent report indicated that 10.5% of boys in the Stockholm area had received an ADHD diagnosis (194). Socioeconomic factors may also influence when and whether children receive a diagnosis or treatment, further contributing to possible under- or overrepresentation in certain subgroups. Additionally, some variables – such as breastfeeding (Paper III) – are based on self-reported data, which may be subject to social desirability bias. While this is common in large-scale epidemiological studies, it remains a limitation in studies linking parental behaviours to child outcomes. 5.5.3 SOCIOECONOMIC MEASURES AND GENERALISABILITY Another limitation concerns the measurement of socioeconomic status. In this cohort, maternal education was used as a proxy for socioeconomic status, which is standard in many studies. However, this measure may not fully capture a family’s broader economic or social context. In this particular 48 Karin Fast cohort, mothers had higher education levels than fathers (151), which reflects changing gender dynamics but also raises questions about how socioeconomic status should be defined and interpreted in a contemporary setting. 5.6 SOCIOECONOMIC STATUS Socioeconomic status is a well-known health factor, affecting outcomes across the life course. Traditionally, socioeconomic status has been assessed using measures such as parental education, occupation, or household income. Among these, maternal education has often been used in perinatal and child health research due to its perceived stability and association with health behaviours, access to care, and parenting practices. However, today, the relevance and sufficiency of this measure may be limited. In the present cohort, maternal educational levels were generally high and, notably, higher than those for the fathers (151). Educational attainment alone may no longer fully capture the complexities of social positioning or access to health-related resources. Roswall et al. found that children living in neighbourhoods with low purchasing power had a significantly increased risk of being overweight at four years of age. The association remained after adjusting for maternal education, parental BMI, and smoking, suggesting that area-level socioeconomic disadvantage plays an important and independent role in shaping early growth trajectories. Mothers with twelve years of education or less were more likely to have children who were overweight compared to those with higher education. While parental age was included in the analyses, it did not emerge as a strong independent predictor once other factors were considered (151). In the 1980’s sociologist James S. Coleman argued that a child’s success is not solely dependent on family structure or parental education, but also on the availability of supportive social networks and shared community values – what he termed social capital (195). In line with this, Evert Vedung has discussed the role of informal networks, including schools, sports clubs, and healthcare systems, buffering social disadvantage (196). These insights are particularly relevant in contemporary societies, where families may present with diverse constellations of strengths and challenges not easily captured by conventional variables of socioeconomic status. While variables such as income, employment security, housing conditions and maternal age at pregnancy may be important components of a family's socioeconomic context, they are often difficult to measure and compare across studies. For example, income data may fluctuate over time or be sensitive to self-reporting biases, especially in households with dual-income or with part- 49 Child predisposition to ADHD, ASD, and obesity time or informal employment. Similarly, age at first pregnancy, varies widely by cultural and individual circumstances. The challenge in accurately capturing socioeconomic status is not merely academic. It has practical implications for how risk is assessed, how inequalities are identified, and how resources are allocated. Factors such as household income, parental age, employment security, and social support may also influence both child development and the ability to access health- promoting resources. Relying too heavily on a single proxy, risks oversimplifying complex social realities and may obscure vulnerabilities or strengths within certain populations. For instance, highly educated mothers may still face barriers related to income, mental health, or social support – factors not readily accounted for by educational data alone. 5.6.1 THE INTERPLAY WITH HERITABILITY ADHD, ASD, and obesity all show moderate to high heritability in twin and family studies (26, 197-200). However, genetic predisposition does not act in isolation. The environment in which a child grows up – shaped by socioeconomic factors such as parental education, income, and access to health care – influences how the genetic risks are expressed and managed (201, 202). Heritability reflects how much of the variation in a trait, within a population, can be explained by genetic differences, under a particular set of environmental conditions. This means that the same genetic predisposition may have different outcomes depending on the child’s surroundings. For example, a child with a genetic risk for ADHD may do better in a stable home with early support, compared to one in a more stressful or resource-limited environment. Similarly, the risk of obesity may be affected by family routines, food environments, and opportunities for physical activity – factors often tied to socioeconomic status. It is well-known that environmental influences, e.g. prenatal exposure for smoking and Bisphenol A, can interact with biology through epigenetic mechanisms, affecting how genes are turned on or off during critical periods of development (203). These processes provide a biological explanation for how stress or disadvantage in early life may influence long-term outcomes, including neurodevelopment and weight regulation (204). A child’s development is shaped not only by inherited traits but also by the opportunities, stressors, and supports present in their environment. Recognising this complexity is essential for understanding variations in outcome and for designing effective, early, and equitable support systems, 50 Karin Fast particularly for children with neurodevelopmental needs or risk factors for obesity. 5.7 FINDINGS ACROSS PAPERS Taken together, the findings from all four papers support a multi-faceted understanding of how early biological, behavioural, and environmental factors interact in the development of ADHD, ASD, and obesity. Despite differences in methodology, a consistent theme emerges from the four papers, showing that early growth patterns, caregiver behaviours (such as breastfeeding), and parental characteristics (including BMI and smoking) all play critical roles in shaping later health outcomes. In this manner, the results support the hypothesis that early-life indicators, from feeding behaviours and growth curves to parental health and perinatal risks, can serve as early signals of susceptibility. This calls for a more nuanced, multidisciplinary approach in child health surveillance that integrates neurodevelopmental, somatic, and psychosocial perspectives (205). 5.8 CHICKEN OR EGG A study by Eiffener et al. 2019 offers valuable insights into the complex relationship between emotional and behavioural problems and obesity treatment outcomes (206). Their findings highlight the bidirectional nature of this association, often described as a "chicken or egg" scenario, where it is challenging to determine whether emotional and behavioural issues contribute to obesity or vice versa. On the one hand, the Eiffener and colleagues’ report that ADHD was associated with increases in BMI. This suggests that pre-existing emotional and behavioural challenges may hinder the effectiveness of obesity interventions, potentially due to difficulties in adhering to treatment protocols or implementing recommended lifestyle changes. Conversely, the study also found that obesity treatment led to significant improvements in various emotional and behavioural fields. This indicates that addressing obesity can have a positive impact on a child's well-being, supporting the notion that obesity itself may exacerbate or contribute to the emotional and behavioural issues (206). The findings by Eiffener’s group underscore the importance of adopting a wide-ranging approach in treating paediatric obesity – one that simultaneously addresses both weight management and neurodevelopmental 51 Child predisposition to ADHD, ASD, and obesity disorders. Furthermore, the study highlights the need for individualised treatment plans that consider the unique psychological and behavioural profiles of each child (206). 5.9 THE CHILD BEHIND THE DATA Among statistical associations, clinical predictors, and early biomarkers, it is easy to lose sight of the children and families behind the research – the children and families who live with these challenges every day. This thesis explores risks and trajectories, but each data point reflects a child with unique needs, vulnerabilities, and potential. Children with neurodevelopmental conditions and/or obesity often struggle in environments not built with their needs in mind. They may experience emotional dysregulation, difficulties in peer relationships, a sense of not fitting in. Crucially, many of their behaviours are expressions of unmet needs, not of obstinacy. In addition, the parents likely already carry the weight of worry, guilt, or exhaustion Some have struggled for years in order for their child to get the attention of professionals. They may have had a sense that something is amiss, long before a diagnosis is finalised. When parents speak up – with concerns about feeding, sleep, regulation, or growth – these moments are invitations to listen, not to dismiss (39). Early identification does not begin with screening tools or algorithms. It begins with a mindset: remembering what we know about early risk signs, being attentive to patterns, and recognising the power of early support. Sometimes, the most important intervention is to stay – to listen, to confirm, to walk alongside the family and not to rush into correction, trivialising or explanation. This thesis does not offer all the answers. But perhaps one step forward is to pause, see the child in front of us, and consider: What might this child need to feel safe, supported, and understood? 52 Karin Fast 6 CONCLUSION This thesis highlights the complex interplay between early biological, environmental, and behavioural factors in the development of neurodevelopmental conditions and childhood obesity. Drawing on both clinical and population-based cohorts, the findings suggest that ADHD, ASD, and obesity may share overlapping early risk factors, including maternal health indicators, perinatal complications, and early feeding patterns. Stimulant treatment for ADHD was shown to contribute to weight normalisation in a significant number of children with concurrent overweight or obesity, underlining the importance of integrated care that includes both somatic and psychiatric dimensions. Moreover, early growth markers – such as the timing of adiposity rebound and breastfeeding patterns, emerged as potential early indicators for later developmental trajectories, including neurodevelopmental outcomes such as ADHD or ASD, as well as metabolic trajectories associated with obesity. Identifying and monitoring these early markers may offer valuable opportunities for targeted interventions aimed at promoting healthier developmental pathways across multiple domains. Taken together, these findings underscore the need for early identification strategies that focus both on the neurodevelopmental circumstances and on growth data. Thus, monitoring of BMI patterns and behavioural regulation in infancy may help identify children who would benefit from tailored, family- centred support. An approach should prioritise collaboration across disciplines and systems – linking child health services, mental health care, and family support structures. In future research and clinical practice, embracing a life-course and biopsychosocial perspective is essential. A better understanding of how early exposures shape both neurodevelopment and metabolism can enable earlier, more effective, and less stigmatising support for children at risk. 53 Child predisposition to ADHD, ASD, and obesity 6.1 TOWARD EARLY PREVENTIVE MODELS This thesis underscores the need to develop early preventive models rooted in child-centred and family-based care and to avoid addressing ADHD, ASD or obesity as isolated conditions. My findings also highlight the potential for early warning systems – such as growth monitoring and behavioural observation – in primary care and child health settings. Such models could better identify at- risk children and offer timely, supportive interventions before the challenges become entrenched. Future policy should therefore consider not only diagnostic outcomes but also developmental pathways, modifiable exposures, and the timing of support. 54 Karin Fast 7 FUTURE PERSPECTIVES This thesis highlights the deep interconnectedness between biological predispositions and early environmental exposures. The longstanding dichotomy of "nature versus nurture" (207) is increasingly being replaced by a more dynamic understanding of how genes and environment interact continuously across developmental stages. See Figure 10 for an illustration of the positive well-being and confidence that represent desired outcomes of early and integrated support efforts. Figure 10. Symbolising the sense of confidence and well-being that we want for all children. Illustration: Cecilia Kullberg, created for this thesis. 55 Child predisposition to ADHD, ASD, and obesity 7.1 NATURE AND NURTURE – REFLECTIONS AND RESEARCH IDEAS From the prenatal environment and early caregiving to growth trajectories and ADHD treatment, this thesis highlights that the emergence of ADHD, ASD, or childhood obesity cannot be fully explained by biology alone, nor solely by environmental factors. Instead, it emphasises the need for integrated perspectives that consider both an inherited risk and modifiable environmental factors – particularly in the critical early years. From a clinical perspective, this calls for greater collaboration across medical, psychological, and public health domains. Paediatricians, child psychiatrists, school health services, and family support professionals must work in interdisciplinary teams to identify early signs of vulnerability and provide tailored, family-centred care. Prospective longitudinal studies that integrate biological markers with psychosocial and socioeconomic data are needed to better identify children at risk. Information on parental factors such as employment status, income, housing, and ethnicity could strengthen risk prediction and guide early support. Incorporating multimodal data – including growth charts, genetics, behavioural assessments, and parental mental health – may improve predictive models and enhance prevention efforts. In this context, providing parental education and support may play a crucial role in promoting healthy development and mitigating risks. To sum up, this thesis provides evidence that the origins of neurodevelopmental challenges and obesity are neither fixed nor linear. Instead, they are shaped through a complex interaction between nature and nurture, where early identification and timely support can make a profound difference. 56 Karin Fast ACKNOWLEDGEMENT First and foremost, I want to thank the families – all the children and parents – and healthcare staff, who made these studies possible. Without their participation and commitment, there would have been no thesis to write. To my PhD supervisor, Jovanna Dahlgren: thank you for being both a professional guide and a personal support throughout this journey. Your belief in me, especially during times of doubt, has meant everything. To my co- supervisors Elisabet Wentz, Stefan Bergman, and Josefine Roswall: thank you for your wise input and encouragement whenever I reached out between clinical shifts. And to Gerd Almqvist-Tangen, thank you for the many meaningful conversations about the complexities of breastfeeding. To my colleagues in the research group: thank you for your support, your collaboration, and for sharing life – as parents, physicians, and doctoral students. I am also grateful to my clinical supervisors at the Children’s Hospital, Ralph Bågenholm and Emma Goksör, for your trust and support – and for always leading with both heart and intellect. To my wonderful colleagues at Queen Silvia’s Children’s Hospital: thank you. In a place where we meet children and families when life is at its most difficult, your compassion and support – even after long shifts in the paediatric emergency department – have meant the world. To my closest friends: Louise, who is always near, whether in Öckerö, London, or San Francisco; Sofia and Max, for all the dinners next door; Cecilia, for listening – always – to my deepest thoughts and tangled reflections; Caroline, for being there in both joy and sorrow; and Anna, for the laughter, the long talks, and all the dog walks. To my parents, Birgitta and Peder – thank you for your constant love, presence, and unwavering support. And to my in-laws, Ing-Marie and Gunnar, thank you for your heartfelt involvement – I’m fairly certain few people have read as many theses as you two! Finally, Björn – thank you. For your love, for your wisdom, and for standing by me in life and through this dissertation. Thank you for putting up with my emotionally driven visions and for always believing in me, even when my logic needed some polishing. 57 Child predisposition to ADHD, ASD, and obesity To our brave, clever, and kind girls, Greta and Ebba – you fill my heart and challenge me in the best possible ways. For your hugs and your bubbly laughter, I could climb mountains. Thank you to everyone who has supported me during these past ten years – one medical license, two children, two dogs, and a newly renovated boat later – and now, finally, this thesis in hand. Thank you! 58 Karin Fast REFERENCES 1. 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