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Recent GWAS allow us to calculate polygenic risk scores for ADHD. At the imaging level, resting-state fMRI analyses have given us valuable insights into changes in connectivity patterns in ADHD patients. However, no study has yet attempted to combine these two different levels of investigation. For this endeavor, we used a dopaminergic challenge fMRI study (L-DOPA) in healthy participants who were genotyped for their ADHD, MDD, schizophrenia, and body height polygenic risk score (PRS) and compared results with a study comparing ADHD patients and healthy controls. Our objective was to evaluate how L-DOPA-induced changes of reward-system-related FC are dependent on the individual polygenic risk score. FMRI imaging was used to evaluate resting-state functional connectivity (FC) of targeted subcortical structures in 27 ADHD patients and matched controls. In a second study, we evaluated the effect of ADHD and non-ADHD PRS in a L-DOPA-based pharmaco-fMRI-challenge in 34 healthy volunteers. The functional connectivity between the putamen and parietal lobe was decreased in ADHD patients. In healthy volunteers, the FC between putamen and parietal lobe was lower in ADHD high genetic risk participants. This direction of connectivity was reversed during L-DOPA challenge. Further findings are described for other dopaminergic subcortical structures. The FC between the putamen and the attention network showed the most consistent change in patients as well as in high-risk participants. Our results suggest that FC of the dorsal attention network is altered in adult ADHD as well as in healthy controls with higher genetic risk.
Changes in glutamatergic neuroplasticity has been proposed as one of the core mechanisms underlying the pathophysiology of depression. In consequence components of the glutamatergic synapse have been explored as potential targets for antidepressant treatment. The rapid antidepressant effect of the NMDA receptor antagonist ketamine and subsequent approval of its S-enantiomer (i.e. esketamine), have set the precedent for investigation into other glutamatergic rapid acting antidepressants (RAADs). In this review, we discuss the potential of the different glutamatergic targets for antidepressant treatment. We describe important clinical outcomes of several key molecules targeting components of the glutamatergic synapse and their applicability as RAADs. Specifically, here we focus on substances beyond (es)ketamine, for which meaningful data from clinical trials are available, including arketamine, esmethadone, nitrous oxide and other glutamate receptor modulators. Molecules only successful in preclinical settings and case reports/series are only marginally discussed. With this review, we aim underscore the critical role of glutamatergic modulation in advancing antidepressant therapy, thereby possibly enhancing clinical outcomes but also to reducing the burden of depression through faster therapeutic effects.
Rationale: Attention deficit/hyperactivity disorder (ADHD) is common in alcohol use disorder (AUD). Continuous performance tests (CPTs) allow to measure ADHD related deficits in a laboratory setting. Most studies on this topic focused on CPTs measuring inattention or impulsivity, disregarding hyperactivity as one of the core symptoms of ADHD.
Methods: We examined N = 47 in three groups (ADHD N = 19; AUD N = 16; ADHD + AUD N = 12) with questionnaires on ADHD core symptoms, executive functioning (EF), mind wandering, and quality of life (QoL). N = 46 (ADHD N = 16; AUD N = 16; ADHD + AUD N = 14) were examined with a CPT (QbTest®) that also measures motor activity objectively.
Results: Inattention and impulsivity were significantly increased in AUD vs. ADHD and in AUD vs. ADHD + AUD. Hyperactivity was significantly higher in ADHD + AUD vs. ADHD and ADHD + AUD vs. AUD, but not in ADHD vs. AUD. EF was lower in both ADHD groups vs. AUD. Mind wandering was increased in both ADHD groups vs. AUD. QoL was significantly lower in ADHD + AUD compared to AUD. In contrast, results of the QbTest were not significantly different between groups.
Conclusion: Questionnaires are more useful in assessing ADHD core symptoms than the QbTest®. Hyperactivity appears to be a relevant symptom in ADHD + AUD, suggesting a possible pathway from ADHD to AUD. The lower QoL in ADHD + AUD emphasizes the need for routine screening, diagnostic procedures and treatment strategies for this patient group.
Structural brain morphometry as classifier and predictor of ADHD and reward-related comorbidities
(2022)
Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, and around two-thirds of affected children report persisting problems in adulthood. This negative trajectory is associated with high comorbidity with disorders like obesity, depression, or substance use disorder (SUD). Decreases in cortical volume and thickness have also been reported in depression, SUD, and obesity, but it is unclear whether structural brain alterations represent unique disorder-specific profiles. A transdiagnostic exploration of ADHD and typical comorbid disorders could help to understand whether specific morphometric brain changes are due to ADHD or, alternatively, to the comorbid disorders. In the current study, we studied the brain morphometry of 136 subjects with ADHD with and without comorbid depression, SUD, and obesity to test whether there are unique or common brain alterations. We employed a machine-learning-algorithm trained to classify subjects with ADHD in the large ENIGMA-ADHD dataset and used it to predict the diagnostic status of subjects with ADHD and/or comorbidities. The parcellation analysis demonstrated decreased cortical thickness in medial prefrontal areas that was associated with presence of any comorbidity. However, these results did not survive correction for multiple comparisons. Similarly, the machine learning analysis indicated that the predictive algorithm grouped most of our ADHD participants as belonging to the ADHD-group, but no systematic differences between comorbidity status came up. In sum, neither a classical comparison of segmented structural brain metrics nor an ML model based on the ADHD ENIGMA data differentiate between ADHD with and without comorbidities. As the ML model is based in part on adolescent brains, this might indicate that comorbid disorders and their brain changes are not captured by the ML model because it represents a different developmental brain trajectory.
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=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.
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.