The role of ADHD in late-life somatic conditions

The role of ADHD in late-life somatic conditions
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Evidence is growing that ADHD, a neurodevelopmental disorder with an early onset, is linked to poor health in adulthood. The mechanisms behind these associations, however, are not well understood. We tested whether ADHD polygenic scores (PRS), a measure of risk, are associated with somatic health at mid-to late life in a sample of broader population. We also explored if potential associations were moderated or mediated by risk factors over the course of life. In 10,645 Swedish Twins born between 1912 and 1958, we calculated ADHD-PRS. Sixteen cardiometabolic, autoimmune/inflammatory, and neurological health conditions were evaluated using self-report (age range at measure 42-88 years) and clinical diagnoses defined by International Classification of Diseases codes in national registers. We used generalized estimating formulas to estimate associations between ADHD-PRS and somatic outcomes. We also tested the moderation and mediating effects of four life-course factors (educational level, BMI, tobacco use and alcohol misuse). The results showed that higher ADHD PRS was associated with an increased risk for seven somatic outcomes, including heart failure, cerebrovascular and peripheral vascular diseases, obesity, type I diabetes, rheumatoid and migraine, with odds ratios between 1.07 and 1.20. We found significant mediation effects of education, BMI and tobacco use for the associations between ADHD-PRS and cardiometabolic outcomes. Multiple testing correction did not produce any moderating effects. Our results suggest that higher ADHD genetic liabilities confer a modest increase in risk for several somatic problems in late life, especially in the cardiometabolic realm. These associations were observed in the general population even without medical treatment for ADHD and they appear to be partly mediated by life course risk factors.

The following is a brief introduction to the topic:

Attention-deficit/hyperactivity disorder (ADHD) is an early onset neurodevelopmental disorder with an estimated prevalence of 5-7% in childhood . ADHD is a persistent disorder, as evidenced by the fact that around 15% of children with ADHD continue to meet diagnostic criteria in early adulthood. An additional 50%-70% still experience symptoms. The prevalence of ADHD in adults and children has increased over the past decade. This is likely because more people are aware and have access to treatment. ADHD is estimated to affect 2-5% of adults, but it’s still underdiagnosed and little data exists on its impact across the lifespan.

Recent reviews found that ADHD was strongly associated with obesity, insomnia, migraines, epilepsy and asthma. Further, increasing evidence supports associations of ADHD with cardiovascular disease, neurodegenerative disorders, and certain autoimmune/inflammatory conditions. These associations may be genetically mediated. Small-to-moderate correlations based upon genome-wide association study (GWAS) between ADHD and obesity, rheumatoid, psoriasis and migraine have been reported. These genetic correlations could be due to shared biology. The same genetic variants which increase risk of ADHD may also increase risk of somatic conditions. The effect of ADHD genetic vulnerability on somatic health can also be moderated or mediated by behavioral risk factors (e.g. tobacco and alcohol abuse), cardiometabolic risk factors (e.g. high BMI) and social-economic (e.g. lower educational attainment). These hypotheses have been tested by few studies, probably due to a lack of samples from mid-to late-life populations that assessed both ADHD and somatic health, as well as genomic data. A second challenge is to separate the effects of ADHD and the treatment of ADHD. It is important to consider this issue, as the prevalence of ADHD medication in clinical populations is high. There has also been a suggestion that ADHD medications could increase risk for various somatic outcomes. ADHD stimulant medication can cause minor blood pressure increases, which may lead to concern that the stimulant could increase cardiovascular risk. However, there is little evidence supporting this. ADHD medication may reduce negative health behaviors such as substance abuse, and thereby lower the risk for certain somatic health outcomes.

To circumvent some of these limitations, one way is to use polygenic risk scores to measure the genetic liability of ADHD in populations that have not been treated for the disorder. These PRS are based on independent genetic discoveries and capture an individual’s autosomal common genetic variant liability. This approach is important because family, twin and molecular studies have shown that ADHD is not only heritable but also exists in the population as dimensional traits. These traits are also underpinned by the same genetic architecture that supports the clinical manifestation. A recent meta-analysis found that ADHD-PRS was associated with ADHD in general populations as well as with ADHD symptoms. It is possible to use ADHD-PRS in order to determine the impact of ADHD genetics on health outcomes even when ADHD symptoms or diagnosis has not been evaluated. This approach was used in two previous studies of the UK Biobank. The sample is a large “healthy” and wealthy population, with a majority of middle-aged people, but only 100 clinically treated ADHD patients. Both studies found a significant correlation between ADHD-PRS and higher BMI as well as other sociodemographic factors and life-course risks, such as lower education attainment, alcohol and smoking consumption. It can be hypothesized, therefore, that higher genetic risk for ADHD is associated to worse somatic health later in life. This association may be partly mediated/moderated through life-course factors. Prior studies have not assessed several somatic conditions, and the mediation or moderating effects of life-course factors related to ADHD and ADHD gene liability are still to be explored. The current study aimed to: (1) determine whether ADHD genetic liability, as measured by ADHD-PRS is associated with late-stage somatic conditions in a sample of people who are not treated for ADHD; (2) examine whether such potential associations were moderated or mediated through life-course factors previously linked with ADHD.


In the Screening Across the Lifespan Twins (SALT) Study, all twins born in Sweden before 1958 (N = 52 080) were contacted between 1998 and2002 for a phone interview. SALT interviewed 44,919 people (85% response rate). The study design and data collection have been described in more detail elsewhere. In the TwinGene Study, SALT participants who had previously donated blood were asked to do so again between 2004 and 2008. 12,614 people responded (response percentage 56%). All dizygotic and monozygotic pairs of twins (n = 9896), as well as one twin for each pair (n=9896), were genotyped with the Illumina OmniExpress (700K) BeadChip. The genotypes of non-genotyped, monozygotic pairs were derived from the genotyped twin of their co-twin. Details can be found elsewhere. The 1000 Genomes Project version 3 panel was used to impute genetic data. The supplement contains information on quality control and the imputation of genetic data. The analytic final sample size was 10,645 with genotype and/or phenotype information.

Polygenic risk scores

The ADHD-PRS was calculated in the SALT sample by adding the SNP doses, weighted according to the allelic effects from the discovery set for all single nucleotide (SNP), under eight different thresholds of p-value (0.001 =PT =1). The SNP weights came from the summary statistics for the largest ADHD GWAS metaanalysis available (19,099 controls, 34,194 cases), which was restricted to European descent. The target sample and discovery data were independent. Indels, symmetric/ambiguous, symmetric/multi-allelic, and multi-allelic SNPs have been excluded. SNPs are further filtered based on the imputation quality of SNPs (INFO > 0.8). We used the 1000 Genomes European samples to determine a set of SNPs that were relatively independent for the PRS calculation. The PRS was calculated for each individual using PLINK version 1.9 (commands score -q-score range) as the summation of SNP dosages, weighted according to the effect on the discovery data across all SNPs. All ss values were recoded as positive. Then, we performed a principal components analysis (standardized with a mean=0 a SD =1) across all p-value thresholds. We retained the first PC in order to create a single PC-PRS that was used for subsequent analyses. This method maximizes the variance across PRS thresholds while avoiding overfitting.

We calculated population covariates using the principal component analysis in PLINK.v.1.9 to account for possible stratification. The allele frequencies of a group of LD-pruned snps were determined in unrelated individuals. The PCs were calculated by calculating the variant weights of unrelated individuals, and then projecting the remaining samples onto these PC scales. The first six PCs were used in the subsequent analyses as covariates.

Somatic health outcomes

Participants in SALT answered a questionnaire asking if they have ever received medical treatment or been diagnosed with a variety of somatic conditions. The details of the checklist and its use as a disease screening tool, along with specificity and sensitivity, for each condition are described elsewhere. We selected 16 cardiometabolic, autoimmune/inflammatory, and neurological conditions previously associated with ADHD (Table . We chose conditions that peak in prevalence between mid-life and late life. Type 1 diabetes, epilepsy (which can start in childhood, but may be chronic or develop later in life), and other conditions such as obesity were also included. In addition to ADHD, we included obesity because of the strong correlation between them. Our obesity indicators are clinical outpatient diagnosis from 2001 and/or self reported largest weight. This is likely to reflect adult obesity. The Swedish National Patient Register provided us with clinical diagnoses for the same conditions. We also included information from the register on sleep disorders and Alzheimer’s disease, which was not available through self-report. The NPR contains inpatient and specialist care codes according to the International Classification of Diseases. (ICD-8 1968-1986, ICD-9 1997-onwards) We identified additional cases by using the Swedish Cause of Death Register with nearly complete coverage since 1952. We also identified cases for migraine, sleep disorders and Parkinson’s Disease using the Prescribed Drug Register. This register records all prescriptions in Sweden coded according to Anatomical Therapeutic Chemicals (ATC) classification.

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