After 5.5 years, the CoCA project has come to an end. In this large-scale European research project, an interdisciplinary group of researchers investigated comorbid conditions of ADHD. They particularly focussed on depression, anxiety, substance use disorder and obesity, as these conditions frequently co-occur with ADHD in adulthood.
What has this extensive study brought us? Experts dr. Catharina Hartman (University Medical Center Groningen, The Netherlands) and prof. dr. Andreas Reif (University Hospital Frankfurt, Germany) were invited by Jonathan Marx for an interview on the online radio program Go To Health Media. In this program they talk about several aspects of the CoCA project: How often do comorbid conditions co-occur with ADHD? What do the genetics of ADHD comorbidities tell us? What should clinicians do to prevent or reduce these comorbidities in ADHD?
As professor Andreas Reif summarizes at the end of the interview, the main things that we learned from the CoCA project are:
Comorbidity in ADHD is a very big problem. Adults with ADHD frequently have co-occuring conditions such as depression, anxiety, obesity and to a bit lesser extent substance use disorder.
The type and prevalence of comorbidities differ between men and women.
There is considerable genetic overlap between ADHD and comorbid conditions. We think that at least part of the overlap between comorbidities is caused by genetic effects (next to environmental effects that also play a role).
The dopamine system plays an important role in comorbidity, through influencing brain processes.
Disturbances in the circadian system (i.e. sleep cycle) are unlikely to play a causal role in these comorbidities, but they might be a consequence.
Clinicans should look out for comorbidities when they treat ADHD patients, and inform their patients about their increased risk to develop comorbidities so that they can take preventive measures (i.e. be careful with alcohol to avoid substance use disorder). Secondly, clinicians should actively look out for ADHD symptoms when treating conditions such as depression, anxiety, substance use disorder or obesity.
Watch the full interview with both experts by clicking on the image below:
Aid for ADHD individuals personal needs, right when it is needed
You might know the tenet of “just in time” from economics. It means bringing goods to a recipient at the right time, exactly when it is needed. But what if we could apply this also to treatments or interventions for mental health problems? Can we provide small interventions at exactly the time when a person needs it? And can this provide us with more insights into what triggers ADHD symptoms?
Just in time economics is possible and required because of dynamic processes in economical markets. Dynamic processes are also present in mental disorders. Attention-deficit/hyperactivity disorder (ADHD) is a condition that is dynamic by nature. Core symptoms of ADHD are hyperactivity, inattention and impulsivity, and many individuals also experience emotion dysregulation. In the past, research focused mainly on how patients with ADHD differ from healthy individuals or other disorders. But what about ADHD individuals’ context or other dynamics, that may trigger symptoms? For this we need to look much more closely at the dynamics of an individual’s life.
Ambulatory Assessment: collecting data in real time and in real life
The Ambulatory Assessment method makes use of smartphones, accelerometers, GPS-tracking and geolocation approaches to track how you feel, what you do, where you go, who you meet, what you eat, and how you’re body is doing (i.e. your heartrate) (1). This method has improved a lot over years and technical progress makes it more and more feasible to investigate associations between variables over time and how these variables interact in everyday life. This provides researchers with new insights into many different factors that can influence a person’s symptoms and mental health.
The importance of context
The Ambulatory Assessment method also enables to better differentiate between real and deceptive associations. Imagine, a person is asked for hyperactivity in the morning at 9:00 am, noon and evening and it turns out that the person is very hyperactive in the morning. Your conclusion may be that this individual is more hyperactive in the morning, but you don’t know why. If you know more about this person’s context, it may turn out that every day at 08:30 am the person drinks two cups of coffee which causes the measured hyperactivity at 9:00 am. This gives you much more insight into what triggers his or her symptoms.
Another example: imagine that a symptom always occurs in a special situation, at a special place or with a special person (e.g., after trying to catch the connecting train every morning at the same time). If you always ask for symptoms at the same time of day, you may miss this special occasion because it always occurs at another time. This way, you may miss out on important associations between symptoms and situations, places or persons. It is therefore very important to measure symptoms at random time points, or when they are triggered by certain events. This gives you much more informative data.
Cause or consequence?
However, the Ambulatory Assessment method is not yet perfect. The main limitation is that it’s difficult to determine what causes what (2). For example, do fluctuations in mood in patients with ADHD lead to impulsivity or hyperactivity? Or does mood change as a consequence of impulsivity? Another example: Do I feel better after exercising or do I move more because I feel good? Researchers recently found evidence for both directions (3,4).
Towards developing just in time treatment
Let’s think about the next step. A better understanding of causes and consequences and associations between symptoms and environmental triggers in an individual’s real world, creates the basis for just-in-time interventions (6). The idea is to react on dynamics in how symptoms are experienced or triggered, by timing the interventions exactly when it is needed. This could be realized by smartphones or wearables, which are already implemented in Ambulatory Assessment research. These devices are then not only used to collect data in real-time, but also to give feedback and provide interventions to reduce or prevent symptoms.
Exercise intervention through a smartphone app
The antecedent of just-in-time-adaptive-interventions are ecological momentary interventions (EMIs). One example of such an EMI or electronic diary intervention with a smartphone and an accelerometer for individuals with ADHD is the PROUD trial of the European funded project CoCA (5). In this trial, individuals with ADHD received a smartphone and a kind of sports watch (that measures your movement) that together measured their behavior, activity, daylight exposure, mood and symptoms during the day. The smartphone also provided an intervention, either in the form of sports exercises or in the form of bright light therapy. During the exercise intervention, participants are given instructions to perform exercises via a smartphone app by which they are guided through their training by weekly goals, motivational reminders, and training videos. Every evening, they get feedback on performed intervention parameters from that day in real time. This system was not yet so developed that it also changed the type or timing of the intervention to the data that was collected during the day, but that would be the next step to create a just-in-time intervention.
In conclusion, it is important to investigate the associations between ADHD individuals’ symptoms and their personal everyday lives. This helps researchers to understand the dynamic processes behind ADHD and to create tailor-made interventions that can easily be integrated in the everyday life of these individuals. A physician cannot support a patient throughout every step he/she takes, but there are already devices that can be supportive around the clock and technical innovations will surely pave the way to improve personal just-in-time interventions in the near future.
This blog was written by Elena Koch. She is a PhD student at Karlsruhe Institute for Technology in Germany.
A group of researchers from Spain, The Netherlands, Germany, Estonia, Denmark and USA have joined efforts to gain insight into the genetics of ADHD and its comorbidities. This ambitious objective was addressed by the Work Package 2 of a big project called CoCA: “Comorbid Conditions of Attention deficit/hyperactivity disorder (ADHD)”, funded by the European Union for the period 2016-2021.
In psychiatry, the co-occurrence of different conditions in the same individual (or comorbidity) is the rule rather than the exception. This is particularly true for ADHD, where conditions like major depressive disorder or substance use disorders frequently add to the primary diagnosis and lead to a worse trajectory across the lifespan.
There are different reasons that may explain the advent of the comorbidities: Sometimes the two conditions have independent origins but coincide in a single patient. Comorbidity can also appear as a consequence of a feature of a primary disorder that leads to a secondary disorder. For example, impulsivity, a trait that is common in ADHD, can be an entry point to substance use. Comorbidity can also be the result of shared genetic causes. The latter has been the focus of our investigations and it involves certain risk genes that act on different pathologies, a phenomenon called pleiotropy.
Our project started with an approach based on the exploration of candidate genes, particularly those involved in neurotransmission (i.e. the connectivity between neurons) and also in the regulation of the circadian rhythm. We used genetic data of more than 160,000 patients with any of eight psychiatric disorders, including ADHD, and identified a set of neurotransmission genes that are involved at the same time in ADHD and in autism spectrum disorder . In another study we identified the same gene set as involved in obesity measures .
Then we opened our analyses to genome-wide approaches, i.e. to the interrogation of every single gene in the genome. To do that we used different statistical methods, including the estimation of the overall shared genetics between pairs of disorders (genetic correlation, rg), the prediction of a condition based on the genetic risk factors for another condition (polygenic risk score analysis, PRS) and the establishment of the causal relationships between disorders (mendelian randomization). As a result, we encountered genetic connections between ADHD and several psychiatric disorders, like cannabis or cocaine use disorders [3, 4, 5], alcohol or smoking-related phenotypes [6, 7, 8], bipolar disorder , depression , disruptive behavior disorder , but also with personality or cognition traits, like neuroticism, risk taking, emotional lability, aggressive behavior or educational attainment [6 , 11, 12, 13], or with somatic conditions, such as obesity [11, 12].
All these results and others, reported in more than 40 (!) scientific publications, support our initial hypothesis that certain genetic factors cut across psychiatric disorders and explain, at least in part, the comorbidity that we observe between ADHD and many other conditions. This information can be very useful to anticipate possible clinical trajectories in ADHD patients, and hence prevent potential negative outcomes.
Dr. Bru Cormand is full professor of genetics and head of the department of Genetics, Microbiology & Statistics at the University of Barcelona. He leads workpackage 2 of the CoCA project (www.coca-project.eu) on the genetics of ADHD comorbidity.
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.
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 . 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 , 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 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 .
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 .
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 .
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 . 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 . 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.
Schultz, W. (2015). Neuronal reward and decision signals: From theories to data. Physiological Reviews, 95(3), 853-951. doi:10.1152/physrev.00023.2014
Wise, R. A. (2002). Brain reward circuitry: Insights from unsensed incentives. United States: Elsevier Inc. doi:10.1016/S0896-6273(02)00965-0
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
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
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
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.
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.
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.
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.
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.
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.
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  . 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 . 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 . 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.
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Almost every person, healthy or not, suffers from occasional problems with sleep and circadian rhythm. In the modern days of 24/7 smartphone use and transcontinental flights, our internal body clock is having a hard time adjusting to the external cues. For the persons suffering from mental health issues, their impaired sleep cycle can be one of the cornerstone problems of daily living. Sleep problems have been confirmed to be a first symptom, consequence, or even a cause of such psychiatric conditions as major depression, bipolar disorder, ADHD, autism, substance abuse, and even aggressive behaviour. Their strong relations, however, have not been studied systematically and broadly just yet.
Why study the circadian rhythm?
Circadian rhythm is our inner clock that regulates a lot of important processes in the human body, including the sleep/wake cycle, the release of hormones and even the way we process medicines. This clock is run by the brain region called the hypothalamus, which piles up a protein called CLK (referring to “clock”), during the daytime. CLK, in turn, activates the genes which make us stay awake, but also gradually increases the creation of another protein called PER. When we have a lot PER, it turns off CLK production and makes us ready to sleep. As CLK is getting lower, this causes a decrease in PER, so that the process starts again with elevating CLK waking us up. This cycle happens at around 24-hour intervals and is greatly influenced by so-called zeitgebers, or time-givers, like light, food, noise and temperature. When our retina neurons catch light waves, the suprachiasmatic nucleus in our brain stops the production of the hormone called melatonin that induces sleep and starts producing noradrenaline and vasopressin instead to wake us. This is the exact reason why you cannot fall asleep after watching a movie at night.
Sometimes our body clock fails to function, as in the case of jetlag when we feel bad after changing a time zone or social jetlag when we have to start work early at 8 am. It can go as far as a circadian rhythm disorder meaning you have either a delay or advancement of sleep phases or an irregular or even non-24-hour daily activities preference. However, in the general population, a small variation in the rhythm is quite normal and is usually referred to as a chronotype. It defines your preference of when to go to sleep and do your daily activities and is divided into 3 distinct versions. The radical points of these variations include a morning chronotype, or “larks”, who go somewhat 2-3 hours ahead of the balanced rhythm, and an evening chronotype, or “owls”, who are a little delayed. The larks feel and function better during the first half of the day and go to bed rather early, while the owls prefer to work in the evenings and go to bed and wake up naturally late. The third chronotype is the in-between, balanced version of these two.
What’s my study about?
Previous research has shown that many psychopathologies are linked to an evening circadian preference. For my master thesis research, I am investigating whether we can identify specific profiles in sleep and circadian rhythm problems that are linked to specific mental health problems. There was even a curious study where researchers linked the Dark Triad personalities, which include people with tendencies for manipulation, lack of empathy, and narcissism, to the evening chronotype. Maybe this leaves some evidence for the famous quote that “evil does not rest”. However, there’s a great variation in sleep duration and perceived quality of sleep in patients with various diseases. We hope to divide such persons into more or less accurate groups with a sleep profile that would predict and aid the correct diagnosis of one or the other mental health condition.
The psychopathologies are included in our study as so-called dimensions, which look at each psychiatric syndrome not as with a norm/pathology cut-off but rather as a continuum of symptoms severity. This approach allows us to see if the sleep/circadian profile we identify refers to mental health in general or can be a distinguished part of a certain psychiatric condition. It might be that all dimensions, like depression and autistic spectrum disorders, have an evening chronotype and some non-specific sleep problems. Alternatively, we might find out that a person with symptoms of depression would sleep more or less than average and go to bed later, whereas a person with anxiety would go to sleep later as well but wake up at night very often despite an average summed up sleep duration.
The circadian rhythm changes throughout a lifetime from an early to an evening chronotype towards adolescence and then gradually shift back to the earlier preference with older age. Across the whole lifespan people constantly face varying quality of night sleep. Moreover, each psychiatric condition has a particular age of onset and sometimes changes its character with time. These are the reasons why our study will also look at how the sleep/circadian profiles change within the development phases from children (4-12 years) to adolescents (13-18) to adults (19-64) to the elderly (≥65) and if they affect males and females differently.
Why would it matter?
Should we discover distinct links between the profiles of sleep/circadian problems and certain conditions, other studies can then look into whether these profiles could be the reasons behind developing a mental health condition. It’d be interesting to finally learn what is a chicken and an egg in each profile-disease relation. For instance, should we really treat ADHD patients with melatonin and bright-light lamps instead of stimulants?
Dina Sarsembayeva is a neurologist and a research master’s student at the University of Groningen. She is using the data from the CoCa project to learn if the chronotypes and sleep problems can be turned into profiles to predict specific psychiatric conditions.
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Logan, R. W. & McClung, C. A. Rhythms of life: circadian disruption and brain disorders across the lifespan. Nature Reviews Neuroscience vol. 20 49–65 (2019).
Jones, S. G. & Benca, R. M. Circadian disruption in psychiatric disorders. Sleep Med. Clin. 10, 481–493 (2015).
Taylor, B. J. & Hasler, B. P. Chronotype and Mental Health: Recent Advances. Curr. Psychiatry Rep. 20, (2018).