Friday 6 February 2015

Addiction is a brain disease...but does it matter?

This post was originally written by Matt Field for the Mental Elf. You can see the original here.

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The notion of addiction as a ‘brain disease’ has been the dominant paradigm in psychiatry and neuroscience for the past few decades. The consensus is that long-term use of addictive drugs (including alcohol and tobacco) causes long-lasting changes to the structure and function of the brain. Once this metaphorical switch is flipped, people find it very difficult to control their drug use.
In a provocative article, Wayne Hall and colleagues take a critical look at the brain disease model. They conclude that the brain disease model is not supported by evidence, and it has not led to the novel treatments that it promised.

What is the brain disease model of addiction?

The ‘brain disease model’ cannot be traced back to a group of scientists or a collection of published articles. The term is a reference to a broad range of neuroscience theories of addiction that have been highly influential over the previous 30 years or so. These theories make the common claim that chronic drug use leads to changes in the structure and function of the brain, and these changes result in loss of control over drug use. It is very important to note that Hall and colleagues are not directing their critique at the scientists who have developed these theories. Instead, their focus is on the
popular simplification of (this work) that has had a major influence on popular discourse on addiction in scientific journals and the mainstream media.
If there is a specific target for their article, it is a 1997 opinion piece by former National Institute of Drug Abuse (NIDA) director Alan Leshner entitled “Addiction is a brain disease, and it matters” (Leshner, 1997). Leshner’s central arguments were that addiction is a chronic condition that should be managed like other chronic conditions (such as diabetes), and that reframing it as a brain disease would prompt neuroscience research that would generate novel treatments, and reduce stigmatisation of addicts.
The brain disease model has been very influential in setting the funding priorities of NIDA, which in 2014 devoted 41% of its funding to basic neuroscience, a further 17% to the development of novel pharmacotherapies based on this neuroscience, yet only 24% to epidemiology, health services and prevention research.
Neuroscience researchers
The popularity of the brain disease model has made basic neuroscience research a funding priority.

Is the brain disease model of addiction supported by evidence?

Hall and colleagues make a number of observations that appear incompatible with the brain disease model. Firstly, they argue that addiction is not really a chronic condition, because many addicts recover without treatment, or if provided with incentives to do so. However, they acknowledge that a minority of drug users are unable to control their drug use, and therefore perhaps the brain disease model only applies to this minority. But even if this were the case, they identify a number of problems with the brain disease model. I explain each point, and provide my own views on each, below.
1. Laboratory animals can develop compulsive patterns of drug use, and this looks like addiction in humans. However, only some strains of animals, when housed in unnatural conditions (e.g. in isolation rather than in social colonies), will become ‘addicted’ in this way.
Well, perhaps addiction is an (almost) uniquely human disease, and it cannot be artificially induced in most other animal species? Also, we know that in humans, stressful life experiences in early life are a risk-factor for the development of addiction is later life. Isn’t the evidence from laboratory animals completely consistent with this?
2. It has been difficult to pinpoint the genetics of addiction, and this is not consistent with the idea that addiction is a brain disease that is partly heritable.
Heritability estimates for addiction hover around 50%, which is comparable with other psychiatric disorders. It is fair to say that we have not yet identified the ‘addiction gene(s)’, but that is also true of other psychiatric disorders (Munafò, 2014). Unravelling the biological mechanisms that underlie heritability of psychiatric disorders is proving to be more difficult than we thought!
3. Human neuroimaging studies, in which the brains of people with addiction are compared with controls, have yielded findings that are, in essence, too good to be true. Another, often overlooked fact is that ‘addicted’ and healthy brains are more similar than they are different.
This is another valid criticism but again, it is one that applies across the board in psychiatry. Many neuroimaging studies have inadequate statistical power (Button, 2013), and a lot of findings cannot be replicated.
4. Addiction neurobiology has become more complex: initially we thought that the problem was in the reward system and related structures, but it now seems that a much broader network of brain networks is involved, including those implicated in emotion regulation and executive function.
Yes, the neurobiology of addiction is probably more complicated than we initially thought, and theories have been modified to accommodate this complexity. But isn’t that how science progresses? Also, this is another point that arguably applies throughout psychiatric neuroscience.
Un
Understanding the genetic basis of mental health conditions takes time (red bow-ties are very much optional).

Has the brain disease model of addiction delivered?

Hall and colleagues go on to ask if any meaningful developments in treatment or drug policy have occurred as a result of the brain disease model. Specifically, they argue that:
5. Few new drugs or vaccines for addiction have been developed in the past 20 years, and those that have are arguably no more effective than older (and cheaper) medications such as methadone and nicotine replacement therapy.
They may have a point, and again this is a recurring theme throughout psychiatry. However, we can point to some novel pharmacotherapies, such as acamprosate for alcohol dependence (Walsh, 2014), that do seem to be effective.
6. Direct brain interventions, such as surgery and brain stimulation, might ultimately prove to be effective, but they are very expensive and therefore not available to the majority of addicts.
This is true, and indeed it is difficult to anticipate these types of interventions ever being sufficiently non-invasive or cheap to be widely used.
7. The brain disease model has prompted over investment in a reductionist search for a biological ‘cure’, at the expense of public health and population based interventions that have the potential to reduce or prevent harmful drug use in a larger number of people.
Here I do agree, and indeed it is clear that neuroscience receives the majority of addiction research funding (at least in the USA, and I suspect also in the UK). The authors cite high taxes and bans on tobacco advertising and smoking in public places as having a huge impact on tobacco smoking in countries in which they have been introduced. Other policy changes such as plain cigarette packs (Badenoch, 2013) and a minimum unit price for alcohol (Maynard, 2014) are likely to have a large impact on tobacco smoking and alcohol consumption in the general population.
But, is this a fair comparison? Why shouldn’t we take a public health approach to reduce or prevent drug use in a large chunk of the population, whilst continuing to fund neuroscience research that may ultimately help the minority of people who become addicted? In fairness, Hall and colleagues acknowledge the need for both approaches, but they question whether we have got the balance right. Also, to repeat an earlier point, their critique is directed at the ‘popular simplification of this work’, rather than at the people who actually do the research.
8. Finally, they link the brain disease model with prohibitionist policies on illicit drugs, the aim of which are to disrupt the manufacture and smuggling of illicit drugs in order to reduce their availability. The argument goes that, as addiction is a chronic relapsing brain disease, the best way to manage it is to reduce the availability of drugs.
I admit this did not make much sense to me, but reading the supporting reference (Courtwright, 2010) did (and it’s a good history lesson, so you might enjoy it too)!
Has government funding focussed too much on basic neuroscience research, at the expense of population wide
Has government funding focused too much on basic neuroscience research, at the expense of population-based interventions?

Summary

The paper by Hall et al should be seen in the context of broader dissatisfaction with reductionist approaches in psychiatry, because they neglect the social and cultural context in which psychiatric disorders exist (I recommend this recent article and particularly, the comments on it). I am sympathetic to this anti-reductionist view, so I was also sympathetic with the key arguments made in this paper.
However, I felt that the attack on the brain disease model was a bit of a straw man argument. You won’t find any neuroscience theories of addiction making the extravagant claims that are dismantled by Hall and colleagues. Even the 1997 Leshner paper went to great lengths to emphasise that the addiction brain disease had to be seen in the social and cultural context in which it occurred.
Perhaps it is premature to judge the treatment implications of the brain disease model. Ongoing crises about the replicability of brain imaging and genetic findings and the equivalence of novel versus older pharmacotherapies have had a big influence in the addiction field, as they have in psychiatry more broadly. It is also apparent that the brain mechanisms involved in addiction, and the genetic influence on these, is likely to be much more complicated than originally thought. So, it is important to continue to invest in neuroscience research so that we improve our understanding of how the brain works, and what goes wrong in the addicted brain. We may need to wait a little longer for this knowledge to translate into novel and cost-effective treatments.
Baby, bathwater, nuff said...
Baby, bathwater, nuff said…

Links

Hall, W., Carter, C., & Forlini, C. (2015). The brain disease model of addiction: is it supported by the evidence and has it delivered on its promises? Lancet Psychiatry, 2, 105-110.
Leshner A. (1997). Addiction is a brain disease, and it matters (PDF – requires free registration). Science 3 October 1997: Vol. 278 no. 5335 pp. 45-47DOI:10.1126/science.278.5335.45
Munafò MR. (2014). Schizophrenia and genetics: a new landmark study. The Mental Elf, 1 Aug 2014.
Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, Munafò MR. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013 May;14(5):365-76. doi: 10.1038/nrn3475. Epub 2013 Apr 10. [PubMed abstract]
Maynard O. (2014). Alcohol minimum unit pricing: time to take action? The Mental Elf, 3 Oct 2014.
Courtwright DT (2010). The NIDA Brain Disease Paradigm: History, Resistance and Spinoffs. History Faculty Publications. Paper 2.

Wednesday 4 February 2015

Current studies

Here is a full list of the addiction group's current studies. If you would like to participate in any of these and live in the Merseyside area please email the relevant researcher for more information.




AlSAP 2.1.: A smart-phone app based assessment of alcohol use and cognition

We are seeking healthy volunteers to take part in a study that evaluates a smartphone ‘app’ that assess cognitive processes and alcohol use. The study lasts one week and requires you to attend the department of psychological sciences to complete some questionnaires about alcohol use and a computerised task. You will then be given a smartphone containing an app that contains a simple 5 min task that you must complete three times per day for one week. You will also be required to state how many units of alcohol you consumed on each day. Participants who complete the study will receive financial compensation upon return of the smartphone.

We are seeking participants who are aged between 18 and 50, fluent English speakers, regular social drinkers (consume alcohol on at least one occasion in an average week) and beer is their favourite choice of an alcoholic drink. Participants who have ever been diagnosed with, or treated for, alcohol use disorders, are pregnant or currently breastfeeding, or have colour-blindness are not eligible to take part.

For further information about the study, please contact Mr. Panos Spanakis at panagiotis.spanakis@liverpool.ac.uk

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  • The effects of alcohol on comedy perception
We are seeking healthy volunteers to take part in a psychology experiment which investigates the effects of alcohol on comedy perception.

Volunteers are required to attend laboratories in the School of Psychology, on the University of Liverpool campus, for one experimental session which will last approximately 1 hour.

During the experiment, alcoholic drinks will be provided.  Participants will also be asked to complete questionnaires and tasks.

In order to take part, you should be a healthy, social drinker, aged above 18 years, and a fluent English speaker.

Unfortunately you cannot take part if there is any chance that you are pregnant, or if you are unhappy with consuming alcohol as part of the study.

Participants will be compensated for their time and effort with a high street voucher (love2shop)

If interested, please contact Natasha Clarke, email: n.clarke2@liv.ac.uk
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Study title: VALUATION OF CHOCOLATE STIMULI


We are seeking healthy volunteers to take part in a psychology study which investigates the relationship between evaluations of pictures of chocolate, attention, and categorisation speed. Participants are required to attend a laboratory in the School of Psychology, on the University of Liverpool campus, for an experimental session that takes no more than 75 minutes. During the session participants will provide ratings for different pictures, make choices between different pictures whilst their eye movements are recorded, and categorise pictures as quickly as possible by pressing keys on a keyboard
In order to take part, you should be:
  • A fluent English speaker,
  • Have normal or corrected to normal vision. Unfortunately, participants who wear glasses cannot take part
  • Consume chocolate regularly (at least once per week)

If interested, please contact Lisa: dilemma@liverpool.ac.uk . You will be then sent a Participant Information Sheet.

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Attention & Reward

We are recruiting 30 healthy volunteers who fulfil the following criteria:

1)   Are aged 18-35 years
2)   Fluent English speaker
3)   Are regular alcohol drinkers. You should only participate if you drink alcohol at least once per week. You should only participate if you drink alcohol at least once per week and drink at least 10 alcohol units per week. For example, there are 10 units of alcohol in three and a half pints of lager OR five 175ml glasses of wine.

If you meet these criteria, then you are eligible to take part. However, you CANNOT take part if you meet any of the following criteria:

1)   Have been diagnosed as colour blind.
2)   Wear glasses (contacts are fine).
3)   Have an aversion/allergy to vodka or lemonade.
4)   This study also contains one questionnaire of a sensitive nature (consisting of questions relating to one’s home/family life) – individuals who may be particularly concerned or upset by such questions should NOT take part.

The study takes place during a single lab session during which you will be asked to complete 3 computer tasks while your eye-movements are recorded by an eye-tracker.

Overall the experiment lasts approximately 1hr 15m.

Contact: hljduckw@liverpool.ac.uk

All participants will be compensated for their time.






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Volunteers required for online survey

We are seeking healthy volunteers to fill out a short online questionnaire about the effect of prize information on willingness to pay for alcohol. This will take approximately 10 minutes. 

Volunteers must:
- be 18 years or older
- be a UK resident
- drink more than 14 UK units per week (roughly 6 glasses of wine, 6 pints of beer/cider or 14 shots)
- like beer and / or cider, and drink them regularly

Volunteers will be entered into a prize draw for a chance of winning 1 of 4 £10 Amazon vouchers. 

Click here to access the survey, or contact the researcher for more information: i.kersbergen@liv.ac.uk



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Volunteers required for online study


We are looking for volunteers to complete a short online questionnaire about alcohol, personality and other people’s drinking. This will take approximately 10-15 minutes.

Volunteers must be aged 18-25 and speak fluent English.

Volunteers can choose to be entered into a prize draw for £100 worth of love2shop vouchers.

Just click the link below to take part.



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Drink Less: A free app to help people reduce their consumption of alcohol

Are you interested in drinking less but not sure how to go about it?

Drink Less is an app created by a team of psychologists at University College London to help people reduce their consumption of alcohol. The app is easy to use and feature-rich, with content derived from theory-based behaviour change techniques and the best available evidence.

The app allows you to:
·         Keep track of your drinking and see how it changes over time
·         Set goals for the targets that are important to you and get feedback on your progress
·         Complete a daily mood diary in order to better understand the effects of your hangover
·         Play games designed to strengthen your resolve to drink less alcohol
·         Create plans for dealing with situations when you may be tempted to drink excessively
·         Take part in exercises designed to change your relationship with alcohol

This is also part of an experiment designed to allow researchers to understand what helps people to drink less. So downloading and using the app will not only allow you to improve your health, it will also help many more people do the same.

You can use the app fully without taking part in our study and can opt-out at any time.

Please note, the app is currently only available for iPhones.

The app can be downloaded here: http://apple.co/1U8UDI5 and our web site is drinklessalcohol.com

The development of the app has been funded by the National Institute for Health Research School for Public Health Research (NIHR SPHR), the UK Centre for Tobacco and Alcohol Studies (UKCTAS) and the Society for the Study of Addiction.




For more online psychology studies visit: www.onlinepsychresearch.co.uk











Childhood adversity linked to psychotropic drug use in later life

This post was originally written by Andrew Jones for the Mental Elf. You can see the original here.
 

shutterstock_121428757Mental health problems such as depression and anxiety are significant contributors to burden of disease, the loss of quality of life and major societal and economic costs. The prevalence of mental health disorders in the European Union is 27% of the adult population, which is estimated at 83 million people (WHO, 2015).
 
Psychotropic drugs (i.e. medicines that alter chemical levels in the brain which impact mood and behavior) are often the first line of defence for mental health problems in developed countries. In Finland, psychotropic drugs are the largest drug group in terms of sales, with most recent figures demonstrating 8% of the total population receiving antidepressants at some point.
 
Relationships between childhood adversities and adult mental disorders are widely reported throughout the literature (see Chapman et al, 2004; Chen et al 2010; and also our own elf blogs Shepherd, 2014). However, these studies tend to only focus on single circumstances of adversity. Furthermore, population based research examining the use of psychotropic drugs as an indicator of poor mental health in adulthood is potentially important, but this research is lacking.
 
 
Therefore, a recent study published in the Journal of Epidemiology and Community Health (Koskenvuo et al, 2014) set out to examine whether multiple adverse experiences in childhood, such as familial conflicts or poor parent-child relationships, predict the use of psychotropic drugs in adulthood.
 

Methods

The data was derived from the Health and Social Support follow up study on a randomly selected sample of the Finish population. The initial survey was carried out via a postal questionnaire in 1992, and a follow up in 2003/4 to all those who responded to the initial questionnaire. Individual data was linked to national registers (N = 24,284).
 
Childhood adversities were coded by asking whether the respondents had experienced any of the following during childhood;


The study used a randomly selected sample (24,284) of the Finnish population between the ages of 20 and 54.
 
The cohort study used a randomly selected sample (24,284) of the Finnish population between the ages of 20 and 54.
  • Divorce or separation of parents
  • Long-term financial difficulties
  • Serious familial conflicts
  • Fear of a family member
  • Severe illness of a family member or an alcohol problem
  • Parent-child relationships were also assessed

The use of psychotropic drugs was taken from Finland’s National Drug Prescription Register, which holds data on outpatient purchases of all psychotropic drugs prescribed by healthcare professionals.


Results

  • Use of any psychotropic drugs was found for 24.3% (N = 5,896) of the participants

  • The most commonly prescribed drugs were:
    • Antidepressants (17.6%)

  • The least commonly prescribed drugs were:
    • Drugs for bipolar disorder (2.4%)
    • Antipsychotics (3.6 %)

  • Frequent fear of a family member demonstrated the strongest association with psychotropic drug use:
    • A 3-fold increase was found for multiple antidepressant exposure (OR = 3.08, 95% CIs 2.72 to 3.94)

    • There was a 2-fold increase in multiple exposure to:
      • Anxiolytics (OR = 2.69, 95% CIs 2.27 to 3.20)
      • Drugs for bipolar disorder (OR = 2.09, 95% CIs 1.49 to 2.95)
      • Antipsychotics (OR = 2.28, 95% CIs 1.72 to 3.02)
      • Hypnotics/sedatives (OR = 2.45, 95% CIs 2.06 to 2.93)

  • Serious familial conflicts were also associated with 2-fold increases in:
    • Drugs for bipolar disorder (OR = 2.07, 95% CIs 1.54 to 2.77)

    • Antidepressants (OR = 2.10, 95% CIs 1.87 to 2.35)

  • Divorce of parents demonstrated the weakest relationship with psychotropic drugs

  • A graded association was found between childhood adversities and psychotropic drug use. As number of childhood adversities increased, number of purchases of each drug also increased:
    • For example, 1-2 childhood adversities was associated with an increase in 1-2 purchases of antipsychotics (OR = 1.97, 95% CIs 1.46 to 2.67)

    • Whereas 5-6 childhood adversities was associated with a larger increase in >16 purchases of antipsychotics (OR = 5.63, 95% CIs 3.52 to 9.00)

    • The authors also examined the effects of multiple childhood adversities and found a significant effect on any psychiatric drug use, with the association remaining after adjustments for alcohol and smoking use, BMI work status and recent life events (OR = 2.82 95% CIs 2.42 to 3.28)
The number of psychotropic drugs used as an adult corresponded to the number of adversities experienced as a child.
The number of psychotropic drugs used as an adult corresponded
 to the number of adversities experienced as a child.

Conclusions

The results of this population-based cohort study demonstrate a significant effect of multiple childhood adversity measures on future mental health problems, as measured by psychotropic drug use. The strongest association was found between frequent fear of a family member and antidepressant use.


Iden
This research underlines the importance of identifying
and supporting families at risk of adversity.
Limitations of the study include no information about medications taken during periods of inpatient hospital care. Furthermore, there was significant overlap between diagnostic drugs, suggesting drugs may not be diagnostic specific.

In conclusion, many symptoms of mental health problems which appear in early adulthood or in later life are traceable to early circumstances. The results here emphasise the importance of early recognition of families at risk.

Links

Koskenvuo, K., Koskenvuo, M., (2014). Childhood adversities predict strongly the use of psychotropic drugs in adulthood: a population-based cohort study of 24 284 Finns. Journal of Epidemiology and Community Health doi:10.1136/jech-2014-204732

WHO (2015). Prevalence of mood disorders – data and statistics. WHO website, last accessed 30 Jan 2015.

Chapman, DP., Whitfield, CL. Felitti, VJ., Dube, SR., Edwards, VJ., Anda, RF. (2004). Adverse childhood experiences and the risk of depressive disorders in adulthood. Journal of Affective Disorders, 82, 217-25.

Chen, LP., Murad, MH., (…) Zirakzadeh, A. (2010). Sexual abuse and lifetime diagnosis of psychiatric disorders: systematic review and meta-analysis. Mayo Clinical Proceedings, 85, 618-29.

Shepherd, A. (2014). Childhood abuse and adverse life events interact synergistically to produce a high risk for psychotic experiences. The Mental Elf, 15 Jul 2014.