Probing the overarching continuum theory: data-driven phenotypic clustering of children with ASD or ADHD

The challenge and promise of disentangling neurodevelopmental conditions a commentary on Davis et al. (2024)

This commentary evaluates the study by Davis et al. on the early behavioural manifestations of autism spectrum condition (ASC) and attention deficit hyperactivity disorder (ADHD) in preschool children. Davis et al. show how children who later receive dual diagnoses exhibit significantly more severe symptoms and greater behavioural challenges compared to their peers. The study's methodological strengths, including its prospective longitudinal design and well-validated measures, are highlighted. However, the commentary also critiques the reliance on the traditional diagnostic paradigm, advocating for a shift towards data-driven or dimensional approaches to better capture the complexities of neurodevelopmental conditions. By adopting such methodologies, the commentary suggests improvements in clinical practices through more personalised interventions, thereby advancing our understanding and treatment of ASC and ADHD.

Comparing eating and mealtime experiences in families of children with autism, attention deficit hyperactivity disorder and dual diagnosis

LAY ABSTRACT

Children with neurodevelopmental conditions like autism and attention deficit hyperactivity disorder may experience eating difficulties and related health issues later in life. Sharing family meals can help prevent these issues developing, but most studies have looked at families with neurotypical children. Our goal was to learn more about how families of children with autism, attention deficit hyperactivity disorder and both conditions (autism + attention deficit hyperactivity disorder) experience mealtimes. We developed an online survey asking caregivers about their child's eating, mealtime experience and if they experienced stress. We tested it with nine caregivers and made improvements based on their feedback before recruiting 351 caregivers to complete the main survey. We found that families of children with neurodevelopmental conditions experienced greater food fussiness, emotional undereating, 'problematic' child mealtime behaviours, dietary concerns, higher stress for caregivers and spouses and less frequent conventionally structured mealtimes compared to those without these conditions. Families of children with attention deficit hyperactivity disorder and autism + attention deficit hyperactivity disorder reported greater appetite, 'problematic' mealtime behaviours and increased stress for caregivers and spouses compared to families of children with autism. Meanwhile, families of children with autism and autism + attention deficit hyperactivity disorder reported less enjoyment of food and less structured mealtimes compared to those with attention deficit hyperactivity disorder. Our findings highlight that families of children with neurodevelopmental conditions, particularly those with autism + attention deficit hyperactivity disorder, have different mealtime experiences and eating behaviours compared to those with neurotypical children. These families may benefit from support at mealtimes. Learning why people do or do not participate in shared family meals will be crucial to developing improved mealtime support in the future.

Individualized prediction models in ADHD: a systematic review and meta-regression

There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (β = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research.

Prevalence of comorbidity of autism and ADHD and associated characteristics in school population: EPINED study

Autism and attention deficit hyperactivity disorder (ADHD) comorbidity in the school population have been understudied. This study estimates its prevalence considering both parents' and teachers' reports and clinical diagnosis. Sociodemographic, clinical, and cognitive data were compared by diagnostic groups: autism, ADHD, autism and ADHD, subthreshold autism spectrum disorder (ASD), subthreshold ADHD, and children without neurodevelopmental conditions. Following a two-phase design, 3727 parents and teachers (1802 preschoolers, 1925 school-age children) participated in the first phase. Subsequently, 781 participants underwent individual assessment for DSM-5 diagnoses. The estimated prevalence of the comorbid diagnosis was 0.51% (0.28%-0.74%), with significant sex differences (0.16% girls, 0.89% boys). The cooccurrence of symptoms of autism and ADHD reported by parents or teachers was 3.2% and 2.6%, respectively. ADHD comorbidity was observed in 32.8% of autistic children and 31.4% of those with subthreshold ASD. ASD comorbidity was observed in 9.8% of children with ADHD and 5.7% of those with subthreshold ADHD. Comorbidity was reported by at least one informant in 95% of children. Only 15.8% of children with autism and ADHD had been previously diagnosed with both conditions. Early detection and accurate comorbidity diagnosis are crucial to address the clinical and socio-educational needs of these children.

Cluster analysis of teachers report for identifying symptoms of autism spectrum and/or attention deficit hyperactivity in school population: EPINED study

An early detection of Neurodevelopmental Disorders (NDDs) is crucial for their prognosis; however, the clinical heterogeneity of some disorders, such as autism spectrum disorder (ASD) or attention-deficit hyperactivity disorder (ADHD) is an obstacle to accurate diagnoses in children. In order to facilitate the screening process, the current study aimed to identify symptom-based clusters among a community-based sample of preschool and school-aged children, using behavioral characteristics reported by teachers. A total of 6894 children were assessed on four key variables: social communication differences, restricted behavior patterns, restless-impulsiveness, and emotional lability (pre-schoolers) or inattention and hyperactivity-impulsivity (school-aged). From these behavioral profiles, four clusters were identified for each age group. A cluster of ASD + ADHD and others including children with no pathology was clearly identified, whereas two other clusters were characterized by subthreshold ASD and/or ADHD symptoms. In the school-age children, the presence of ADHD was consistently observed with ASD patterns. In pre-schoolers, teachers were more proficient at identifying children who received a diagnosis for either ASD and/or ADHD from an early stage. Considering the significance of early detection and intervention of NDDs, teachers' insights are important. Therefore, promptly identifying subthreshold symptoms in children can help to minimize consequences in social and academic functioning.