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Attention-Deficit/Hyperactivity Disorder (ADHD) is frequently comorbid with other psychiatric disorders and also with somatic conditions, such as obesity. In addition to the clinical overlap, significant genetic correlations have been found between ADHD and obesity as well as body mass index (BMI). The biological mechanisms driving this association are largely unknown, but some candidate systems, like dopaminergic neurotransmission and circadian rhythm, have been suggested. Our aim was to identify the biological mechanisms underpinning the link between ADHD and obesity measures. Using the largest GWAS summary statistics currently available for ADHD (N=53,293), BMI (N=681,275), and obesity (N=98,697), we first tested the association of dopaminergic and circadian rhythm gene sets with each phenotype. This hypothesis-driven approach showed that the dopaminergic gene set was associated with both ADHD (P=5.81×10−3) and BMI (P=1.63×10−5), while the circadian rhythm gene set was associated with BMI only (P=1.28×10−3). We then took a data-driven approach by conducting genome-wide ADHD-BMI and ADHD-obesity gene-based meta-analyses, followed by pathway enrichment analyses. This approach further supported the implication of dopaminergic signaling in the link between ADHD and obesity measures, as the Dopamine-DARPP32 Feedback in cAMP Signaling pathway was significantly enriched in both the ADHD-BMI and ADHD-obesity gene-based meta-analysis results. Our findings suggest that dopaminergic neurotransmission, partially through DARPP-32-dependent signaling, is a key player underlying the genetic overlap between ADHD and obesity measures. Uncovering the shared etiological factors underlying the frequently observed ADHD-obesity comorbidity may have important implications in terms of preventive interventions and/or efficient treatment of these conditions.
Attention-Deficit/Hyperactivity Disorder (ADHD) and obesity are frequently comorbid, genetically correlated, and share brain substrates. The biological mechanisms driving this association are unclear, but candidate systems, like dopaminergic neurotransmission and circadian rhythm, have been suggested. Our aim was to identify the biological mechanisms underpinning the genetic link between ADHD and obesity measures and investigate associations of overlapping genes with brain volumes. We tested the association of dopaminergic and circadian rhythm gene sets with ADHD, body mass index (BMI), and obesity (using GWAS data of N = 53,293, N = 681,275, and N = 98,697, respectively). We then conducted genome-wide ADHD–BMI and ADHD–obesity gene-based meta-analyses, followed by pathway enrichment analyses. Finally, we tested the association of ADHD–BMI overlapping genes with brain volumes (primary GWAS data N = 10,720–10,928; replication data N = 9428). The dopaminergic gene set was associated with both ADHD (P = 5.81 × 10−3) and BMI (P = 1.63 × 10−5); the circadian rhythm was associated with BMI (P = 1.28 × 10−3). The genome-wide approach also implicated the dopaminergic system, as the Dopamine-DARPP32 Feedback in cAMP Signaling pathway was enriched in both ADHD–BMI and ADHD–obesity results. The ADHD–BMI overlapping genes were associated with putamen volume (P = 7.7 × 10−3; replication data P = 3.9 × 10−2)—a brain region with volumetric reductions in ADHD and BMI and linked to inhibitory control. Our findings suggest that dopaminergic neurotransmission, partially through DARPP-32-dependent signaling and involving the putamen, is a key player underlying the genetic overlap between ADHD and obesity measures. Uncovering shared etiological factors underlying the frequently observed ADHD–obesity comorbidity may have important implications in terms of prevention and/or efficient treatment of these conditions.
Attention-Deficit/Hyperactivity Disorder (ADHD) and obesity are frequently comorbid, genetically correlated, and share brain substrates. The biological mechanisms driving this association are unclear, but candidate systems, like dopaminergic neurotransmission and circadian rhythm, have been suggested. Our aim was to identify the biological mechanisms underpinning the genetic link between ADHD and obesity measures and investigate associations of overlapping genes with brain volumes. We tested the association of dopaminergic and circadian rhythm gene sets with ADHD, body mass index (BMI), and obesity (using GWAS data of N=53,293, N=681,275, and N=98,697, respectively). We then conducted genome-wide ADHD-BMI and ADHD-obesity gene-based meta-analyses, followed by pathway enrichment analyses. Finally, we tested the association of ADHD-BMI overlapping genes with brain volumes (primary GWAS data N=10,720–10,928; replication data N=9,428). The dopaminergic gene set was associated with both ADHD (P=5.81×10−3) and BMI (P=1.63×10−5), the circadian rhythm was associated with BMI (P=1.28×10−3). The genome-wide approach also implicated the dopaminergic system, as the Dopamine-DARPP32 Feedback in cAMP Signaling pathway was enriched in both ADHD-BMI and ADHD-obesity results. The ADHD-BMI overlapping genes were associated with putamen volume (P=7.7×10−3; replication data P=3.9×10−2) – a brain region with volumetric reductions in ADHD and BMI and linked to inhibitory control. Our findings suggest that dopaminergic neurotransmission, partially through DARPP-32-dependent signaling and involving the putamen, is a key player underlying the genetic overlap between ADHD and obesity measures. Uncovering shared etiological factors underlying the frequently observed ADHD-obesity comorbidity may have important implications in terms of prevention and/or efficient treatment of these conditions.
Abstract
The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer’s disease, bipolar disorder, and Tourette’s syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.
Highlights
Local genetic correlations found even in the absence of global correlations.
Both positive and negative local correlations found for IR-neuropsychiatric pairs.
Enrichment for immune, and insulin signalling pathways, among others.
Pinpointed shared likely causal variants within 10 genomic regions.
Identified therapeutic targets, e.g., SLC39A8 and HLA-DRB1, for drug repurposing.