The impact and causal directions for the associations between diagnosis of ADHD, socioeconomic status, and intelligence by use of a bi-directional two-sample Mendelian randomization design

Michaëlsson, Madeleine; Yuan, Shuai; Melhus, Håkan; Baron, John A.; Byberg, Liisa; Larsson, Susanna C.; Michaëlsson, Karl

doi:10.1186/s12916-022-02314-3

Cited by 19 publications

(12 citation statements)

References 61 publications

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“…Other two-sample MR studies also reported the bidirectional effect of EA and ADHD. Additionally, they suggest this effect is independent of IQ 27,39 . Interestingly, our MR estimate of the effect of EA on ADHD is lower when using SNP-effects based on within-sibship EA GWAS, suggesting that assortative mating, population stratification, and/or gene-environment correlation might bias MR estimates.…”

Section: Adhdmentioning

confidence: 99%

Evaluating the causal relationship between educational attainment and mental health

Demange

Boomsma

Bergen

et al. 2023

Preprint

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We investigate the causal relationship between educational attainment (EA) and mental health using two research designs. First, we compare the relationship between EA and seventeen psychiatric diagnoses within sibship in Dutch national registry data (N = 1.7 million), controlling for unmeasured familial factors. Second, we use two-sample Mendelian Randomization, which uses genetic variants related to EA or psychiatric diagnosis as instrumental variables, to test whether there is a causal relation in either direction. Our results suggest that lower levels of EA causally increase the risk of MDD, ADHD, alcohol dependence, GAD and PTSD diagnoses. We also find evidence of a causal effect in the opposite direction for ADHD. Additionally, we find inconsistent results for schizophrenia, anorexia nervosa, OCD, and bipolar disorder, highlighting the importance of using multiple research designs to understand the complex relationship between EA and mental health.

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Section: Adhdmentioning

confidence: 99%

Evaluating the causal relationship between educational attainment and mental health

Demange

Boomsma

Bergen

et al. 2023

Preprint

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“…In a previous study, it was shown that family education, household income, and social deprivation index may intelligence-independently increase the risk of an ADHD diagnosis. These factors were not available in our study and may need to be explored in the future ( Michaëlsson et al, 2022 ). Moreover, this study was based on hospital data from 2011 to 2020, and the situations of other hospitals and communities are still unclear.…”

Section: Discussionmentioning

confidence: 99%

Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test

et al. 2023

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BackgroundAttention deficit hyperactivity disorder (ADHD) is a well-studied topic in child and adolescent psychiatry. ADHD diagnosis relies on information from an assessment scale used by teachers and parents and psychological assessment by physicians; however, the assessment results can be inconsistent.PurposeTo construct models that automatically distinguish between children with predominantly inattentive-type ADHD (ADHD-I), with combined-type ADHD (ADHD-C), and without ADHD.MethodsClinical records with age 6–17 years-old, for January 2011–September 2020 were collected from local general hospitals in northern Taiwan; the data were based on the SNAP-IV scale, the second and third editions of Conners’ Continuous Performance Test (CPT), and various intelligence tests. This study used an artificial neural network to construct the models. In addition, k-fold cross-validation was applied to ensure the consistency of the machine learning results.ResultsWe collected 328 records using CPT-3 and 239 records using CPT-2. With regard to distinguishing between ADHD-I and ADHD-C, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 88.75 and 85.56% in the training and testing sets, respectively. The replacement of CPT-2 with CPT-3 results in this model yielded an overall accuracy of 90.46% in the training set and 89.44% in the testing set. With regard to distinguishing between ADHD-I, ADHD-C, and the absence of ADHD, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 86.74 and 77.43% in the training and testing sets, respectively.ConclusionThis proposed model distinguished between the ADHD-I and ADHD-C groups with 85–90% accuracy, and it distinguished between the ADHD-I, ADHD-C, and control groups with 77–86% accuracy. The machine learning model helps clinicians identify patients with ADHD in a timely manner.

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“…Socio-demographic variables. We investigated sex, recorded at birth and family income in each trajectory group since previous evidence has shown links between socioeconomic status and ADHD (Micha€ elsson et al, 2022;Russell, Ford, Williams, & Russell, 2016), and conflicting evidence for associations with autism depending on country (Kelly et al, 2017). Family income was reported by mothers when children were aged 11 as the average household income including social benefits each week on a 10-point scale from <£120 to ≥£800.…”

Section: Characterising Trajectory Classesmentioning

confidence: 99%

Co‐development of attention deficit hyperactivity disorder and autistic trait trajectories from childhood to early adulthood

Shakeshaft

Heron

Blakey

et al. 2023

Child Psychology Psychiatry

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BackgroundAttention deficit hyperactivity disorder (ADHD) and autism, defined as traits or disorders, commonly co‐occur. Developmental trajectories of ADHD and autistic traits both show heterogeneity in onset and course, but little is known about how symptom trajectories co‐develop into adulthood.MethodsUsing data from a population cohort, the Avon Longitudinal Study of Parents and Children, we examined correlations between ADHD and autistic traits across development, using the Social Communication Disorders Checklist and ADHD subscale of the Strengths and Difficulties Questionnaire. We modelled joint developmental trajectories of parent‐reported ADHD and autistic traits between 4 and 25 years, then characterised trajectory classes based on sociodemographic, perinatal, psychopathology, cognition and social functioning variables and tested for associations with neurodevelopmental/psychiatric polygenic scores (PGS).ResultsThree classes of trajectories were identified; a typically developing majority with low‐stable ADHD‐autistic traits (87%), a male‐predominant subgroup with child/adolescent‐declining traits (6%) and a subgroup with late‐emerging traits (6%). ADHD‐autistic trait correlations were greatest in young adulthood for the two nontypically developing classes. There were higher rates of emotional and conduct problems, low IQ, childhood seizures and poor social functioning in the declining and late‐emerging classes compared to the low‐stable class. Emotional, conduct and peer problems were more prevalent during childhood in the childhood/adolescent‐declining class compared to other classes, but were more prevalent in young adulthood in the late‐emerging class. Neurodevelopmental/psychiatric PGS also differed: both nontypically developing classes showed elevated ADHD PGS compared to the low‐stable group, and the late‐emerging group additionally showed elevated schizophrenia PGS and decreased executive function PGS, whereas the declining group showed elevated broad depression PGS.ConclusionsDistinct patterns of ADHD‐autism co‐development are present across development in the general population, each with different characterising factors and genetic signatures as indexed by PGS.

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The impact and causal directions for the associations between diagnosis of ADHD, socioeconomic status, and intelligence by use of a bi-directional two-sample Mendelian randomization design

Cited by 19 publications

References 61 publications

Evaluating the causal relationship between educational attainment and mental health

Evaluating the causal relationship between educational attainment and mental health

Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test

Co‐development of attention deficit hyperactivity disorder and autistic trait trajectories from childhood to early adulthood

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