Genetic risk scores give new insights into the overlap between ADHD and insomnia

Psychiatric disorders, such as ADHD, are defined by categorical diagnostic borders: you either have it or you don’t. Research has shown that these borders do not accurately reflect what is happening on a biological level. In fact, these are complex traits that can be defined as quantitative characteristics that are present in people in different degrees. When you have or experience these traits in a very high degree, you may classify as having a psychiatric disorder. We also know that both genetic and environmental factors contribute to how much an individual is liable to ‘develop’ a psychiatric disorder, and that for each person, it is a different combination of such factors. This large variability between individuals is called heterogeneity.

The fact that ADHD is very often accompanied by other disorders (called comorbidities) also contributes to the notion that these conditions cannot be defined as a simple “yes/no” categorization. This refers to the notion of pleiotropy, meaning that one gene or biological mechanism can result in different outcomes. During my master’s thesis project, we investigated the genetic relationships between ADHD and insomnia, which is one of the most common conditions to co-occur with ADHD. We also looked into the role of depression, another common comorbidity, in the overlap between insomnia and ADHD.

Nowadays, there are very large datasets that we can use to explore such questions. In order to try to disentangle the genetic relationship between ADHD and insomnia, we calculated a genetic risk score for each individual. This method determines the estimated risk that an individual has to develop a certain trait based on their genetic make-up.  We found that the genetic risk score for insomnia was linked to ADHD symptoms, and vice-versa: the genetic risk score for ADHD was linked to insomnia. We also observed a possible distinct genetic relationship between hyperactivity and inattention symptoms and insomnia: while we found that there was a shared genetic risk for insomnia and hyperactivity symptoms, we did not find this link with inattention symptoms.

Next, we tested the effect of depression in these relationships by the inclusion of depression-related variables as covariates in our analyses. We found that the association between genetic risk score for insomnia and ADHD symptoms was no longer considered significant, while the association between the genetic score for ADHD with insomnia was weaker. At last, we analysed the association of cumulative genetic risk for ADHD with insomnia while separating the individuals in two different groups by broad depression. The results suggest that genetic risk for ADHD is similarly associated with insomnia in individuals with and without depression. This indicates that the genetic relationship observed between ADHD and insomnia is not solely a consequence of the comorbidity between depression and the other two conditions.

The take-home message is that with these results we show that there are shared genetic influences between conditions that are traditionally defined as distinct or separate, so all three conditions might be all entangled in their underlying genetic factors. By advancing our understanding of how ADHD and its comorbidities are related, we can better refine the definition of ADHD.  Also, from this research we learn more about the underlying mechanisms of ADHD (and associated conditions) from a biological (genetic) perspective. As the next step, we plan to include genetic data for separate ADHD symptom dimensions (hyperactivity and inattention), as well as depression in our analyses.

Victória Trindade Pons

I have recently concluded my Master’s in Biomedical Sciences at the Radboud University. This work was part of my final internship and was developed under the supervision of Dr. Nina Roth Mota in the Department of Human Genetics of the Radboudumc. This study is part of the CoCa project (Comorbid Conditions of ADHD), which has the aim to gain insight into the mechanisms underlying ADHD comorbidity and calculate the burden associated with such comorbidity for healthcare, economy, and society.

Picture from pixabay.

Are people with ADHD more creative?

It is often said that people with ADHD are more creative than others. But is that true? What does science tell us?

To find an answer to this question, I collaborated with two experts in the field of creativity, Dr. Baas and Prof. Kroesbergen, and PhD-student Marije Stolte. Together, we reviewed scientifically published research on the topic of ADHD and creativity. We summarized behavioral studies looking at creativity performance data in groups of people with and without ADHD. We also reviewed studies looking at the effect of psychostimulant medication on creative performance as people with ADHD often report that their ADHD medication suppresses their creativity1,2.

How to measure creativity
Creativity is a broad concept, but it’s often defined as “the generation of ideas or products that are original as well as useful”3 . The dual pathway to creativity model is a leading theory in the field and describes that there are many different processes involved in creativity. These can be divided into two types: cognitive flexibility and cognitive persistence4,5,6 . Cognitive flexibility is the ease with which people can switch to a different approach or consider a different perspective6 . The most prominent example of cognitive flexibility is divergent thinking. Divergent thinking is frequently assessed with the Alternative Uses Task where one has to name as many uses of a certain item as one can think of (see the below figure for an example).

The second broad type of creative thinking is cognitive persistence, defined as the degree of sustained and focused task-directed cognitive effort. A prime example of a persistent process is convergent thinking. This is often measured with the Remote Associations Task where participants have to generate a word that connects three stimulus words (e.g., black, bean, break; answer: coffee).

The Alternative Uses Task and the Remote Associations Task are both performance tasks, but in creativity research there are also measurements of creative achievements and abilities in daily life using questionnaires. These for instance ask about if you have ever won a price in a drawing competition or published a poem.

Here, you see one example of the Alternative Uses Task that measures divergent thinking. A participant is asked to name as many different possible uses for a shoe. For instance, it can be home to a small pet, you can keep money in it, use it as a weight, or as a pot for a plant.
Can you think of more uses? Use your creativity!

Outcomes of our review of the published articles
Results from population-based studies found, in general, that people with a high number of ADHD symptoms, score high on divergent thinking. However, when comparing individuals with a clinical ADHD diagnosis to those without, they found no difference on divergent thinking. Studies that measured convergent thinking found no differences related to ADHD symptoms or diagnoses. But studies that assessed creative abilities or achievements in daily life, did find a positive association with ADHD. Lastly, we did not see an overall negative effect of psychostimulants on creativity in the studies that have been published so far.
In addition to reviewing the outcomes of the studies, we also looked at the quality of the studies and found that almost all studies were underpowered to detect effects. Also a lot of different creativity instruments were used and a number of important potential confounding factors, such as presence of comorbid disorders, were left out of the research. Therefore, in the future we propose to collaborate to better take care of these quality issues.
In conclusion, our review shows that certain aspects of creativity are indeed linked to ADHD. However, we have also identified gaps in the knowledge on this subject. For example, in future studies, we should try to find out what it is that causes the differences in divergent thinking between those with an ADHD diagnosis (low performance) compared with those without an ADHD diagnosis but with many ADHD symptoms (high performance).

Relevance
For years we have been studying the deficits that are linked to ADHD, but there might also be advantages, such as increased creativity. With this review we indeed show a link between creativity and ADHD, but we also identified many more questions that we want to address in future studies. With a focused research agenda we will address the identified gaps in the literature to try to improve our understanding of the link between creativity and ADHD, generating a more complete picture of ADHD. The increase of knowledge about the positive aspects of ADHD may aid in treatment and coping with ADHD, reduce stigmatization, and increase the quality of life of patients. We hope that in the future we can translate the science output to more practical implications such as educational programs in the classroom.

This blog is based on the following article:

Hoogman M, Stolte M, Baas M, Kroesbergen E. Creativity and ADHD: A review of behavioral studies, the effect of psychostimulants and neural underpinnings. Neuroscience Biobehavioral Reviews. 2020 Oct 6;119:66-85. doi: 10.1016/j.neubiorev.2020.09.029.

Martine Hoogman is funded by a personal Veni grant from NWO (Netherlands Scientific Organization) on the topic of creativity in ADHD.

References

  1. Brinkman, W.B., Sherman, S.N., Zmitrovich, A.R., Visscher, M.O., Crosby, L.E., Phelan, K. J., Donovan, E.F., 2012. In their own words: adolescent views on ADHD and their evolving role managing medication. Acad. Pediatr. 12 (1), 53–61. https://doi.org/ 10.1016/j.acap.2011.10.003.
  2. Kovshoff, H., Banaschewski, T., Buitelaar, J.K., Carucci, S., Coghill, D., Danckaerts, M., Sonuga-Barke, E.J.S., 2016. Reports of Perceived Adverse Events of Stimulant Medication on Cognition, Motivation, and Mood: Qualitative Investigation and the Generation of Items for the Medication and Cognition Rating Scale. J. Child Adolesc. Psychopharmacol. 26 (6), 537–5547. https://doi.org/10.1089/cap.2015.0218.
  3. Amabile, T., Conti, R., Coon, H., Lazenby, J., Herron, M., 1996. Assessing the work environment for creativity. Acad. Manag. J. 39-5, 1154–1184.
  4. Boot, N., Baas, M., van Gaal, S., Cools, R., De Dreu, C.K.W., 2017d. Creative cognition and dopaminergic modulation of fronto-striatal networks: integrative review and research agenda. Neurosci. Biobehav. Rev. 78, 13–23. https://doi.org/10.1016/j. neubiorev.2017.04.007.
  5. Mekern, V., Hommel, B., Sjoerds, Z., 2019. Computational models of creativity: a review of single-process and multi-process recent approaches to demystify creative cognition. Curr. Opin. Behav. Sci. 27, 47–54. https://doi.org/10.1016/j. cobeha.2018.09.008.
  6. Nijstad, B.A., De Dreu, C.K.W., Rietzschel, E.F., Baas, M., 2010. The dual pathway to creativity model: creative ideation as a function of flexibility and persistence. Eur. Rev. Soc. Psychol. 21, 34–77. https://doi.org/10.1080/10463281003765323

We want to acknowledge the noun project and Maxicons, Mikicon, Gregor Cresnar, Dannister, Arle for the pictures.

AI Technologies May Help to Improve Early Identification of Youth with ADHD At Risk of Substance Use

Substance use disorder (SUD) – also known as drug addiction – is one of the most problematic co-occurring conditions with ADHD. Children with ADHD are twice as likely to develop SUD later in life, compared to those without ADHD [1, 2].  Identifying children and adolescents who are at risk, even before they ever use substances, can be an important step in preventing SUD. But how do you know who is at risk? Artificial Intelligence (AI) technologies are promising tools to identify risk factors and predict the likelihood of an individual developing SUD later in life.

Image by rebcenter-moscow
SUD and ADHD

SUD is a mental illness that involves impulsive use of substances such as alcohol, marijuana, nicotine and opioids. Substance use often starts with experimentation in adolescence, such as cigarette smoking and drinking. Studies have found that adolescents with ADHD often start these experiments at younger ages, more quickly become heavier users and develop more severe functional impairments [3].  Indeed, younger ages of first use of a substance is associated with a higher risk of abusing the substance later in life. 

While SUD causes significant harm, and personal and societal burdens, it is actually a preventable and treatable condition. Early identification of at-risk youth is especially important because it would allow for more targeted early interventions.  Studies have shown that school- and community-based prevention programs can be highly effective at reducing substance use. Treating and managing ADHD, both with medication and behavioral therapy, has also been found beneficial in terms of reducing substance use [4, 5].  Being able to screen and identify the high-risk adolescents before their first attempt and being able to further “discourage” and “prevent” such onsets of substance use would probably be the most cost-effective prevention program for SUD.

But how do you know who is high-risk and who is not, even before they start using drugs? One method is to use Artificial Intelligence (AI) technologies in combination with very large databases.

Machine Learning Prediction Models for SUD

Machine learning is a special subset of AI technologies, that can learn hidden patterns and attributes in a completely data-driven fashion. Machine learning algorithms learn from the iterative exposures to a large amount of data, and subsequently make predictions based on what the model has learned from these training examples.

In this recently published EU-funded study in the Journal of Child Psychiatry and Psychology [6],  my colleagues and I applied machine learning models to data collected from the Swedish national registers. These registries contain family and health data of millions of people, including information on clinical diagnoses such as ADHD and SUD. For our research, we focused on children with ADHD and trained various models to predict those who would eventually have a diagnosis of SUD, and those who would not. More than 19,000 children with ADHD were used in the study. The collected information that we used to train the models included psychiatric and somatic diagnoses, family history of these disorders, socioeconomic status, and birth complications.

The machine learning algorithm produced two useful models to predict SUD in children or adolescents with ADHD. The first one makes a prediction at age 17 for future SUD diagnosis between age 18-20. This is an important period when young adults, often leaving home for the first time, are more subjective to peer influence and start their first use of substance. The second model, a longitudinal model, makes a yearly prediction at every age from age 2 to 17 for SUD diagnosis in up to 10 years in the future.  We found that both models were able to make significant predictions. This means that when we tested the models on part of the dataset (that was not used to train the model), the model was more often correct to predict SUD outcome than could be expected from chance. You can interpret this as that the model had learned certain parameters in the data that predict whether a person with ADHD will develop SUD or not.

Early Detection of SUD Risk

One important discovery was that the longitudinal model was able to make significant prediction at as early as age 2 for up to 10 years into the future. The earliest age of a valid SUD diagnosis in our dataset was at age 12. Using a method called “supervised learning”, we “taught” the model to identify those children at age 2 who would be diagnosed with SUD at age 12.  Such supervised training was carried out for each age for any given outlook of 1, 2, 5 or 10 years.  Being able successfully predict at-risk children years before their first attempt of substance use is a really promising result.  Furthermore, the yearly prediction at every age provides a monitoring system tracking the risk for SUD, either increasing or decreasing risk over years.

What have we learned from our prediction models?

It is important to stress that the predictions of these models were not perfect. The algorithms are not always correct at identifying who will develop SUD and who does not. However, they do give insight into some important predictors that are more informative than others. For example, the early detection and diagnosis of ADHD and socioeconomic status.  At the population level, the early diagnosis of ADHD was associated with a lower risk of developing SUD later in life, while adverse socioeconomic status was associated with a higher risk.  However, we could not identify any predictors at the individual level that contributed their increased or decreased risks.  The ability to identify such specific risk for each individual would be extremely useful when targeted interventions can be most effectively applied.

Despite that the prediction accuracies were modest and not ready for real-life deployment yet, these findings clearly have many, broad implications for policy-makers, parents, teachers and clinicians. Given that more and more data will become available to train these AI models, more accurate and generalizable predictions can be made at early ages regarding the disease risk, it is imperative to develop more effective and personalized measures for prevention and risk-reduction. We will therefore continue our work to expand the prediction accuracy of our models.

Overall, we provided a proof-of-concept that machine learning AI technologies – by leveraging the large volume of data, such as those of national registers and other electronic health records – can be used to predict disease risk, such as SUD, long before the disease onset.

Dr. Yanli Zhang-James is an associate professor at SUNY Upstate Medical University Department of Psychiatry and Behavioral Sciences. She is involved in the CoCA Project.

1.         Ercan, E.S., et al., Childhood attention deficit/hyperactivity disorder and alcohol dependence: a 1-year follow-up. Alcohol Alcohol, 2003. 38(4): p. 352-6.

2.         Wilens, T.E., et al., Does ADHD predict substance-use disorders? A 10-year follow-up study of young adults with ADHD. J Am Acad Child Adolesc Psychiatry, 2011. 50(6): p. 543-53.

3.         Kousha, M., Z. Shahrivar, and J. Alaghband-Rad, Substance use disorder and ADHD: is ADHD a particularly “specific” risk factor? J Atten Disord, 2012. 16(4): p. 325-32.

4.         Molina, B.S., et al., Delinquent behavior and emerging substance use in the MTA at 36 months: prevalence, course, and treatment effects. J Am Acad Child Adolesc Psychiatry, 2007. 46(8): p. 1028-40.

5.         Schoenfelder, E.N., S.V. Faraone, and S.H. Kollins, Stimulant treatment of ADHD and cigarette smoking: a meta-analysis. Pediatrics, 2014. 133(6): p. 1070-80.

6.         Zhang-James, Y., et al., Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data. J Child Psychol Psychiatry, 2020.

What do rewards have to do with mental health problems?

Photo by Jacqueline Munguía

What do you think of when I say “rewards”? Perhaps you think of the points you collect every time you shop or the badges you get when playing a videogame. Well, then you’re right!  A reward can be anything. A good grade, going on a trip with friends, a smile, and even that dessert you crave in the middle of the night. Rewards are any stimuli with the potential to make us seek and consume them, and if we like, we will probably want to get them again [1].

Actually, you crave that dessert because you ate it once, and you liked it so much that your brain learned that eating that dessert again will make you feel good. This happens because of a neurotransmitter called “dopamine” released when you eat the dessert, giving you that little rush of pleasure. Now your brain knows what you like and will want more of that.

By now, you probably have realized that rewards are present in virtually everything we do in our daily lives. That is why seeking and consuming rewards are considered to be a fundamental characteristic of human behavior. These rewards that we keep consuming guarantee that we stay alive by eating and drinking water, for example. Rewards also have a huge influence on how we experience positive emotions, motivate ourselves to perform tasks, and learn new things [2].

What about the relationship between rewards and mental health problems?

Although rewards are natural stimuli that make us keep doing healthy and nurturing things, it can also become a problem. Rewards are not the problem itself, but some people can have an unhealthy behavior towards rewards. That’s where mental health problems come in. Did you know that most mental health conditions have alterations in how rewards are processed in the brain? It’s so common that these so-called reward processing alterations are now considered a “transdiagnostic feature,” meaning we can find them across different mental health conditions [3].

Reward processing is a term to refer to all aspects related to how we approach and consume rewards. For instance, how you respond after getting a reward (responsiveness), how motivated you are to go after a reward (drive/motivation), how impulsive you are when trying to get new and intense rewards (fun-seeking/impulsivity). So, as you can see, it’s not only about getting the rewards, but many different things play a role in a simple action we do.

Let’s think of an example: You are going to a party with your best friends. You are motivated to go out with your friends because you’re always happy when you are around them [this is the drive/motivation]. Once you are at the party, you meet your friends, talk, laugh and are happy you decided to join because you’re feeling that rush of pleasure [this is the responsiveness aspect]. At some parties, things can get a bit out of control, and some people might do risky things on the spur of the moment, like binge drinking. You refuse to binge drink because you thought of the risks, and you don’t want to be in trouble later [that’s the third aspect, the fun-seeking/impulsivity].

Now, let’s think of how that party would be for people with reward processing alterations. In the case of a very high drive, they would be super motivated to hang out with friends. On the other hand, if they have low responsiveness, they wouldn’t be able to have fun at the party even though all of their friends are there and the party is super fun. Lastly, in the case of high fun-seeking/impulsivity, they wouldn’t think of the risks and consequences and engage in binge drinking anyways.

As I mentioned before, these alterations play a role in different mental health conditions. They can affect one or more aspects of reward processing, and they can be either lower or higher than average. For example, people with ADHD can show higher risk-taking, meaning that they are more susceptible to take big risks without thinking about the consequences [4]. This impulsive behavior might be a reflection of the altered fun-seeking aspect of reward processing. Another example is the lack of interest in social interactions in people with autism spectrum disorders [5]. This lack of interest might reflect a reduced drive/motivation to go after social rewards.

These are just some examples of what reward processing alterations might look like in the context of mental health problems. There are still a lot of open questions. As part of my PhD research, I am trying to answer some of them. For example, which came first? Are reward processing alterations causing mental health problems, or are they just mere symptoms of these conditions? If you want to learn more about this topic, stay tuned as more blog posts will come!

Dener Cardoso Melo is a PhD candidate at the University Medical Center Groningen (UMCG). He is using data from the CoCA project together with other datasets to investigate the potential causal role of reward processing alterations in different mental health conditions.

References

  1. Schultz, W. (2015). Neuronal reward and decision signals: From theories to data. Physiological Reviews, 95(3), 853-951. doi:10.1152/physrev.00023.2014
  2. Wise, R. A. (2002). Brain reward circuitry: Insights from unsensed incentives. United States: Elsevier Inc. doi:10.1016/S0896-6273(02)00965-0
  3. Zald, D. H., & Treadway, M. T. (2017). Reward processing, neuroeconomics, and psychopathology. Annual Review of Clinical Psychology, 13(1), 471-495. doi:10.1146/annurev-clinpsy-032816-044957
  4. Luman, M., Tripp, G., & Scheres, A. (2010). Identifying the neurobiology of altered reinforcement sensitivity in ADHD: A review and research agenda. Neuroscience and Biobehavioral Reviews, 34(5), 744-754. doi:10.1016/j.neubiorev.2009.11.021
  5. Stavropoulos, K. K., & Carver, L. J. (2018). Oscillatory rhythm of reward: Anticipation and processing of rewards in children with and without autism. Molecular Autism, 9(1), 4. doi:10.1186/s13229-018-0189-5

Common mental health symptoms in ADHD

Image by Anastasia Gepp from Pixabay
Excessive, uncontrolled mind-wandering is common to ADHD, but also to other mental health conditions. Mobile apps that prompt questions during the day can give more insight into the nature of these symptoms and how they differ between (often comorbid) conditions.

The majority of individuals with ADHD have one or more comorbid disorders. Comorbidity is a technical (and admittedly, not very cheerful) word for ‘co-occuring’, meaning that multiple disorders or conditions are present at the same time. Anxiety and depression are the most prevalent conditions that co-occur with ADHD.

Researchers and clinicians want to better understand this comorbidity in ADHD. Does having ADHD increase your risk of developing other conditions? Is there a biolgical mechanism that underlies both ADHD and other conditions? Or are symptoms of ADHD actually broader than the attentional, hyperactivity and impulsivity problems defined by the DSM/ICD, and therefore also linked to other conditions? Or all of the above?

Going with the third option (which by no means excludes the alternatives), clinicians have noticed that many individuals with ADHD experience symptoms that are not specific to ADHD, but are also often seen in other psychiatric conditions. You could call these symptoms ‘mainstream’, or ‘common’ mental health problems. Some examples that are often experienced by those with ADHD are emotional instability, sleep problems, low self-esteem, distractibility and concentration problems, and mental restlesnesss or excessive mind wandering.

Understanding these comorbidities better is important, because often one condition can hide the ‘true’ underlying condition. For instance, a person with ADHD who experiences many symptoms that are also characteristic of anxiety (i.e. low self-esteem, excessive mind-wandering, sleep problems, avoiding difficult situations). In such a case, the person could receive treatment for anxiety problems, while he or she is actually needing treatment for ADHD.

To distinguish between these conditions better, we need to find out more about these common symptoms. Being distracted can have many different causes and can happen in many different situations. For instance: are you distracted due to pervasive negative thoughts, because the task you’re doing is boring, or because you’re thinking of many related things and drift off to new ideas?

To learn more about the nature of these symptoms, researchers have given mobile apps or smartwatches to participants with ADHD. Several times a day, the watch buzzes and the app prompts a question that the person has to give answer to immediately. Questions can for instance be: How are you feeling right now? Have good/bad things happend to you in the last hour? How much has this affectd you? Were you concentrating on a task or where you distracted? Where you tinking about something (un)pleasant? etc. This method called ‘experience sampling’ can give very valuable information about someone’s symptoms. When combining the information from a lot of individuals, this can also identify differences between different disorders, that were not really known before.

If you want to learn more about this topic, you can watch this webinar by professor Philip Asherson from King’s College London. He explains the common mental health symptoms of ADHD in more detail, and gives examples from his research, also using experience sampling.

This blog is based on the webinar by Philip Asherson “ADHD in the mainstream” that was created as part of the CoCA project. The CoCA project investigates comorbid conditons of ADHD: http://www.coca-project.eu.

Webinar: Does physical activity improve ADHD symptoms?

There is a lot of anecdotal evidence that physical activity reduces ADHD symptoms. Some athletes, like Michael Phelps and Louis Smith, have said that their intenstive training helped them loose excessive energy and gain structure in their lives. But what is the scientific evidence for this?

Researcher dr. Jonna Kuntsi and her team from King’s College London have done a lot of reserach on this topic. They have reviewed the available literature on physical activitiy and ADHD, conducted analyses on twin-data and are conducting several experiments to test this. In this webinar she explains what’s known and what’s not yet known about whether physcial activity can improve ADHD symptoms

We previously wrote blogs about this topic as well:

Beneficial effects of high-intensity exercise on the attentive brain

Living day-to-day with ADHD and experience of the CoCA clinical trial

CoCA-PROUD trial ready to roll

How psychiatric genetics can help to guide diagnostic practice and therapy

Recently, professor Stephen Faraone from SUNY Upstate University in the USA gave a webinar about genetic research in psychiatry (especially ADHD) and how this can help to better understand diagnosis and provide better treatment. In this blog I will share with you some highlights from this webinar.

  1. ADHD is a continuous trait in the population

ADHD is not something that you either have or don’t have. Rather, symptoms or characteristics of ADHD are present in the entire population, in varying severity. The system for psychiatric diagnoses is however based on categorical definitions that determine when a certain combination of symptoms and severity can be classified as a particular disorder. Although these categories can be of great help to provide public health data or determine insurance coverage, they often don’t really match individual cases. Hence there arise problems with heterogeneity, subtypes, subthreshold cases and comorbidity.

Genetic research has shown that psychiatric conditions such as ADHD are not caused by a few single genes, but rather by thousands or tens of thousands genetic variants that each contribute slightly to the ADHD risk. These so-called polygenic risk scores form a normal distribution across the entire population, with the majority of people having low polygenic risk scores (so a low to average risk of ADHD), while a small portion of individuals have a very low or very high risk. This adds to our understanding that ADHD is a continuous trait in the population.

Image from the webinar by prof. Stephen Faraone. The higher the number on the x-axis, the higher the genetic risk of having ADHD. Negative numbers mean reduced genetic risk of ADHD.

2. Comorbidity in psychiatry is the norm, rather than the exception

In the webinar, Stephen Faraone explains that in 90’s it was thought impossible that an individual can have both ADHD and depression. Now, we know better than that. There are substantial genetic correlations between different psychiatric disorders, meaning that the genes that increase the risk of for instance ADHD, also increase the risk of schizophrenia, depression, bipolar disorder, autism and tic disorder. This is further evidence that psychiatric conditions are not separate, categorial entities but rather arise from similar biological mechanisms.

3. Personalised medicine and pharmacogenetics are not yet sufficiently established to adopt widely and replace current medication on a broad scale

The second part of the webinar was about pharmacogenetic testing. This means that an individual’s genetic profile is used to determine whether a drug will be effective, and in what dose. Although this sounds promising, there is still a lot of discussion about the validity of such tests. This is due to varying results, differing protocols and large heterogeneity between studies. In some cases, pharmacogenetic testing can help to find the right treatment for an individual, for instance when this person is not responding well to regular treatment, but it is definitely not a fool-proof method yet. Better randomized controlled clinical trials are needed to improve reliability of these tests.

You can watch the full webinar here: https://www.youtube.com/watch?v=DLgqdJWZKIo

The genetics of having multiple mental health conditions

We know that psychiatric conditions have a strong genetic component. This means that genes play an important role in determining an individual’s risk or vulnerability to develop a psychiatric condition. However, there is evidence that there are genetic variants that increase the risk for multiple psychiatric disorders. This is called pleiotropy. Researchers of the “Cross-Disorder Group of the Psychiatric Genomics Consortium” have searched the entire genome of 727,000 individuals (of whom 233,000 were diagnosed with a psychiatric disorder) to identify genetic variants with such pleiotropy.

The researchers found one particular gene – called DCC – that increases vulnerability for all eight disorders that were investigated: ADHD, autism spectrum disorder, anorexia nervosa, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia and Tourette syndrome.

They also found more than 100 genetic variants that predispose to at least two psychiatric disorders, and around 20 variants that are associated with four or more. This means that the genes that contain these variants can be interesting to further understand why certain individuals are more vulnerable to develop psychiatric illnesses than others.

One of the researchers, professor Bru Cormand, explains more about this research in this blog.

Further reading: Cross-Disorder Group of the Psychiatric Genomics Consortium (2019): Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders. Cell, 179(7): 1469-1482.e11. https://www.sciencedirect.com/science/article/pii/S0092867419312760

Professor Cormand is involved in the CoCA research consortium where he investigates the genetic overlap between ADHD, major depression, anxiety disorder, substance use disorder and obesity. To read more about this, see for instance this other blog by him and dr. Judit Cabana Dominguez.

Why following instructions is essential for treatment success (and why this is really difficult)

 

Clara Hausmann, Mental mHealth Lab / Chair of Applied Psychology, Karlsruhe Institute of Technology



When visiting your doctor due to a simple cold you’ve caught, you will probably get the following advice: Get a rest from work, stay in bed for a week, drink a lot of herbal tea and go for a slow walk once a day. Well, you might follow the advice as you’ve been told. But possibly, you can’t stand tea or you are currently under pressure to finish some urgent work and anyway, you don’t feel that bad anymore after one day in bed. The degree to which a patient correctly follows medical advice is called compliance.

            Compliance is also an important term in the psychological and medical research, we are conducting – especially in our ambulatory settings where patients are treated outside of the hospital. In contrast to doing research in very well controlled laboratory settings, embedding research into everyday life  avoids  a lot of methodological disadvantages. For example, participants’ behavior won’t be biased by the presence of a researcher or the artificial situation in the lab. Another great feature of ambulatory assessment lays within the opportunity to gather real time or near real time data. Participants will be regularly asked about their current state of mind, so researchers don’t have to take into account the inaccuracy of patients’ retrospective reports [1] .  Still, we are facing some difficulties when using ambulatory settings – reaching a good compliance is part of it.

            In the CoCA PROUD study, for instance, we are ambulatorily monitoring our ADHD-diagnosed participants’ mental and physical state. Therefore, they are equipped with a smartphone and a small activity sensor. Participants keep an eDiary, by fulfilling repeated questionnaires on the smartphone while the activity sensor on their wrists measures physical activity. Meanwhile, they will take part in some non-pharmalogical interventions (daily physical exercise training or bright light therapy), which promise to alleviate some core symptoms of ADHD and it’s comorbidities such as depression.

            In this study, „compliance“ is what we call the percentage of prompts, that were answered, in order to fulfill the eDiary. All in all, participants receive four prompts per day, including questions about their current mood, social context and ADHD symptomatology. Furthermore, we can analyze how often the sensor was worn. Additionally, checking for the compliance during the interventions allows us to calculate how much time was spend on actively carrying out the instructions (e.g. doing strengthening and aerobic exercises).

In general, we aim to reach a good compliance. The more our participants contribute, the better the quality of data and the understanding of ADHD can be. However, one can imagine that general facts of life such as situational distraction or simple forgetting can be a hindrance for participants, to answer prompts [2].  Apart from this, researchers must be aware, that ambulatory assessment is inherently disruptive to participants’ daily lives. For instance, the activity trackers that participants wear are quite big, and getting daily prompts from the eDiary can be a real nuisance. The art lies in the design of the research: It is unquestionably essential to find a good balance between participants’ expenditure in time and energy and the amount and quality of data collected [3]. In order to find this balance, we’re always first testing the research study on ourselves to check for the feasibility, comfort, and ease of participation.

            Besides that, there are specific challenges for participants diagnosed with ADHD. For instance, the tendency to show irregularities in the day-and-night-rhythm might not always match the time of the smartphone prompts, that are sent in regular intervals. Furthermore, some patients tend to have problems in keeping their belongings organized. Especially for young patients, it might be challenging to keep the phone both charged and on their person. Inattention and lack of concentration as core symptoms of ADHD, are additional burdens to the conscientious and constant work on the questionnaires. Particularly young patients are expected to be quickly bored by the repeated questions, incoming day by day.

            We encounter those difficulties in multiple ways. An important tool is the smartphone’s chat function. Participants can easily reach a contact person and vice versa. Hence, individual or technical problems can be detected and solved quickly. In order to facilitate the start, we send reminding and motivating messages during the first four days of the measurement. To keep participants’ motivation high, they receive daily feedbacks, visualizing how they have performed when exercising.

            Taken as a whole, compliance, whether good or not, provides a lot of important information about the quality of the intervention. A treatment can only be considered as promising and helpful, when patients are able and motivated to include it into their daily lives. Therefore, the combination of ambulatory assessment and compliance monitoring gives us a realistic idea of a treatment’s actual feasibility and – in the consequence – it’s quality.

 

References:

[1] Trull, T. J., & Ebner-Priemer, U. W. (2013). Ambulatory Assessment. Annual review of clinical psychology, 9, 151–176. doi:10.1146/annurev-clinpsy-050212-185510 

[2] Piasecki et al. (2007). Assessing Clients in Their Natural Environments With Electronic Diaries: Rationale, Benefits, Limitations, and Barriers. Psychological Assessment,19(1), 25-43. doi:10.1037/1040-3590.19.1.25


[3] Carpenter, R. W., Wycoff, A. M., & Trull, T. J. (2016). Ambulatory assessment: New adventures in characterizing dynamic processes. Assessment, 23(4), 414–424. https://doi.org/10.1177/1073191116632341


 

Is it safe to use ADHD medications during pregnancy?

“Should I discontinue stimulants when I am pregnant?” “Is it harmful to my developing baby if I take ADHD medications during my pregnancy?” “What are the risks both to me and my baby if my ADHD goes untreated?” “What is the best way to manage my ADHD during pregnancy?” – For women with ADHD who become pregnant, especially those with moderate or severe ADHD symptoms, the next few months are filled with questions. One important decision for the pregnant women and their clinician is whether to remain on or cease their ADHD medication treatment before or during pregnancy, or while breastfeeding. Unfortunately, there is no clear ADHD treatment guidelines for pregnant women, which further complicates these decisions. Therefore, there is a need for high-quality evidence to support guidelines for the use of ADHD medication during pregnancy.

Given that, it is unethical to include pregnant and breastfeeding women in clinical trials, evidence-based guidelines need to rely on findings from naturalistic studies. So, what does the available findings from naturalistic studies tell us?  

In our newly published paper in CNS Drugs (https://doi.org/10.1007/s40263-020-00728-2), we conducted a systematic review to synthesize all available evidence regarding the safety of ADHD medication use while pregnant, with a focus on how these studies have handled the influence of confounding, which may bias the estimates from observational studies.

We identified eight cohort studies that estimated adverse pregnancy-related and offspring outcomes associated with exposure to ADHD medication during pregnancy. These studies varied a lot in data sources, type of medications examined, definitions of studied pregnancy-related and offspring outcomes etc. Overall, there was no convincing evidence for an association between maternal ADHD medication use during pregnancy and adverse pregnancy and offspring outcomes. Some studies suggested a small increased risk of low Apgar scores, preeclampsia, preterm birth, miscarriage, cardiac malformations, admission to a NICU, and central nervous system (CNS)-related disorder, but other available studies failed to detect similar associations. Because of the limited number of studies and inadequate confounding adjustment, it is currently unclear whether these small associations are due to a causal effect of prenatal exposure to ADHD medication or confounding.

In conclusion, the current evidence does not suggest that the use of ADHD medication during pregnancy results in significant adverse consequences for mother or offspring. However, the data are too limited to make an unequivocal recommendation. Therefore, physicians should consider whether the advantages of using ADHD medication outweigh the potential risks for the developing fetus according to each woman’s specific circumstances.

More information here:

Li, L., Sujan, A.C., Butwicka, A. et al. Associations of Prescribed ADHD Medication in Pregnancy with Pregnancy-Related and Offspring Outcomes: A Systematic Review. CNS Drugs (2020). https://doi.org/10.1007/s40263-020-00728-2

Authors:

Lin Li, MSc, PhD student in the School of Medical Science, Örebro University, Sweden.

Henrik Larsson, PhD, professor in the School of Medical Science, Örebro University and Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Sweden.