I decided to take a little break from my typical serious and lengthy addiction/neuroscience blog posts and talk about something a little lighter (and cuter)…dogs and cats! Or to be more specific, the biology of pet aging.
Did you know the oldest cat ever, Crème Puff, was reported as 38 years old and the oldest dog, Bluey, was 29?! Both lived twice as long as average. These types of impressive feats of longevity have never been achieved in humans, not even close (the oldes human was 122 but every heard of a 140 year old?). But what can pet aging tell us about animal aging or aging in general?
In fact, this is the only correlation that’s predictive of animal longevity. Numerous theories have been generated as why this is. One theory says that the higher metabolic rate in small animals leads to increased amounts of damage-causing and age-inducing free radicals. But not much evidence exists to support this idea or others like it.
Dr. Steven Austad of the University of Alabama, an expert on animal aging, thinks it probably has to do with millions of years of evolutionary pressure that favored a slower lifespan for larger animals. From the Science article:
“Whales and elephants can afford to take their time growing because no one is going to attack them, he explains. And that means they can invest resources in robust bodies that will allow them to sire many rounds of offspring. Mice and other heavily preyed-on small animals, on the other hand, live life in fast-forward: They need to put their energy into growing and reproducing quickly, not into developing hardy immune systems, Austad says.”
One interesting turn is that when you compare land mammals to birds, smaller birds tend to outlive their land-locked counterparts. But the same argument can apply: flight is a great way to avoid predators so a similar kind of slowed-down aging may have also evolved in birds for the same reason as in whales and elephants.
Similarly, the naked mole rat and the bat also defy their predicted lifespan given their small size but the mole rat lives most of its life underground and the bat can fly away from danger of course. No need to live hard and fast for these guys (or at least to evolve that type of lifestyle).
However, pets sort of flip the size trend upside down. Cats (both domestic and in the wild) tend to live longer than dogs (or their ancestors wolves). Austad argues this may be due to the incredible resilience of cats whereas dogs are more social and therefore may be more susceptible to communicable disease.
Equally strange is that small dogs live much longer than large dogs, which likely has nothing to do with evolutionary pressure (most dog breeds are only a few hundred years old). One argument involves hyper-secretion of hormones such as insulin-like growth factor 1 (IGF1), which may act a double-edged sword. Big dogs may get a greater boost in growth from IGF1 but accelerated aging too.
A trend that many pet owners should be happy about is that pets live longer today than ever before. And like humans, health care and diet have improved drastically for pets. Plenty of TLC for pets has surely increased their life spans!
However, much is still unknown about pet aging (and aging in general).
Perhaps pet aging can even unlock secrets to human aging. Or as Dr. Daniel Promislow of the Dog Aging Project at the University of Washington says, “If we can understand how to improve the quality and length of life, it’s good for our pets and it’s good for us. It’s a win-win.”
See these other sites for some more tidbits on animal longevity:
Of course, we all know the prevalence and extent of underage drinking, and the damage alcohol has on the developing brain has been heavily researched, not to mention all the significant secondary problems associated with alcohol abuse (car crashes, sexual assault on college campuses, falling off of balconies… ).
But here’s some numbers anyways: as of 2013, 8.7 million youths aged 12-20 reported past month alcohol use, a shockingly high number for an age group this is not legally allowed to drink alcohol…
Similarly, marijuana, which is still illegal in the vast majority of the US, is nearly as ubiquitous. According to the NSDUH 2013 survey, 19.8 million adults aged 18 or older reported past month marijuana use.
But what if the risk of use of alcohol and marijuana by youths could be reduced? What if a teacher could be given the tools to not only identify certain risky personality traits in their students but also use that knowledge to help those at-risk students from trying and using drugs such as alcohol and marijuana? A series of studies coming out of the laboratory of Dr. Patricia A Conrod of King’s College London report having done exactly that.
I had the pleasure of seeing Dr. Conrod speak at the recent Society for Neuroscience Conference as part of a satellite meeting jointly organized by the National Institute on Drug Abuse (NIDA) and National Institute on Alcohol Abuse and Alcoholism (NIAAA). Dr. Conrod presented a compelling story spanning over a decade of her and her colleague’s work, in which certain personality traits amongst high risk youths, can actually be used to predict drug abuse amongst those kids. Dr. Conrod argues that by identifying different risk factors in different adolescents, a specific behavioral intervention can be designed to help reduce alcohol drinking and marijuana use in these youths. And who is best to administer such an intervention? Teachers and counselors, of course: educators that spend a great deal of time interacting with students and are in the best position to help them.
This ambitious study recruited 2,643 students (between 13 and 14 years old) from 21 secondary schools in London (20 of the 21 schools were state-funded schools). Importantly, this study was a cluster-randomized control trial, which means the schools were randomly assigned to two groups: one group received the intervention while the other did not. The researchers identified four personality traits in high-risk (HR) youths that increase the risk of engaging in substance abuse. The four traits are:
A specific intervention based on cognitive behavioral therapy (CBT) and motivational enhancement therapy (MET) was developed to target each of these personality traits. Teacher, mentors, counselors, and educational specialists in each school that were recruited for the study were trained in the specific interventions. In general, CBT is an approach used in psychotherapy to change negative or harmful thoughts or the patient’s relationship to these thoughts, which in turn can change the patient’s behavior. CBT has been effective in a treating a number of mental disorders such anxiety, personality disorders, and depression. MET is an approach used to augment a patient’s motivation in achieving a goal and has mostly been employed in treating alcohol abuse.
The CBT and MET interventions in this study were designed to target one of the four personality traits (for example, anxiety reduction) and were administered in two 90-minute group sessions. The specific lesson plans for these interventions were not reported in the studies but included workbooks and such activities as goal-setting exercises and CBT therapies to help students to dissect their own personal experiences through identifying and dealing with negative/harmful thoughts and how those thoughts can result in negative behaviors. Interestingly, alcohol and drug use were only a minor focus of the interventions.
The success of the interventions was determined through self-reporting. The student’s completed the Reckless Behavior Questionnaire (RBQ), which is based on a six-point scale (“never” to “daily or almost daily”) to report substance use. Obviously due to the sensitive nature of these questionnaires and need for honesty by the students, measures were taken to ensure accuracy in the self-reporting, such as strong emphasis on the anonymity and confidentiality of the reports and inclusion of several “sham” items designed to gauge accuracy of reporting over time. Surveys were completed every 6-months for 24-months (two years) which is a sufficient time frame to assess the effect of the interventions.
Most importantly, schools were blinded to which group they were placed in and teachers and students not involved in the study were not aware of the trial occurring at the school. The students involved were unaware of the real purpose and scope of the study. These factors are important to consider because it held eliminate secondary effects and helps support the direct efficacy of the interventions themselves.
The results were impressive: reduced frequency and quantity of drinking occurred in the high-risk students that received the intervention compared to the control students that did not. While HR students were overall more likely to report drinking than low-risk (LR) students, the HR students saw a significant effect of the personality-targeted interventions on drinking behavior.
A study of this size is incredibly complex and the statistics involved are equally complex. The author’s analyzed the data in a number of ways and published the results in several papers. A recent study modeled the data over time (the 24-months in which the surveys were collected) and used these models to predict the odds that the students would engage in risky drinking behavior. The authors reported a 29% reduction in odds of frequency of drinking by HR students receiving the interventions and a 43% reduction in odds of binge drinking when compared to HR students not receiving the interventions.
Interestingly, the authors report a mild herd-effect in the LR students. Meaning that they believe the intervention slowed the onset of drinking in the LR students possibly due to the interactions between the HR student’s receiving the interventions and LR students. However, additional studies will need to be done in order to confirm this result.
Recall that the Reckless Behavior Questionnaire (RBQ) was utilized in this study to quantify drug-taking behavior. While the study was specifically designed to measure effects on alcohol, the RBQ also included questions about marijuana. So the authors reanalyzed their data and specifically looked at effects of the interventions on marijuana use.
The found that the sensation seeking personality sub-type of HR students that received an intervention had a 75% reduction in marijuana use compared to the sensation seeking HR students that did not receive the intervention. However, unlike the findings found on alcohol use, the study was not able to detect any effect on marijuana use for the HR students in general. Nevertheless, the data suggest that the teacher/counselor administered interventions are effective at reduce marijuana use as well.
While you may be unconvinced by the modest reduction in drinking and marijuana frequency reported in these studies and may be skeptical of the long-term effect on drug use in these kids, keep in mind that the teachers and counselors that administered these interventions received only 2 or 3 days of training and the interventions themselves were very brief, only two 90-minute sessions. What I find remarkable is that such a brief, targeted program can have ANY effects at all. And most importantly, the effects well outlasted the course of the interventions for the full two-years of the follow-up interviews.
These targeted interventions have four main advantages:
Administered in a real-world setting by teachers and counselors
Brief (only two 90-minute group sessions)
Cheap (the cost of training and materials for the group sessions)
The scope of this intervention needs to be tested on a much larger cohort of students in a larger variety of neighborhoods but it is extremely promising nonetheless. Also, it would be interesting to breakdown these data by race, socioeconomic status, and gender, all of which may impact the effectiveness of the treatments and was not considered in this analysis. Finally, how would you implement these interventions on a wide scale? I eagerly look forward to additional work on these topics.
Thanks for reading 🙂
See these other articles in Time and on King’s College for less detailed discussions of these studies.
Also see these related studies from Conrod’s group:
The third and final part of my three part guest blog series on Optogenetics has been published on the Addgene blog. Addgene is a nonprofit organization dedicated to making it easier for scientists to share plasmids and I’m thrilled to be able to contribute to their blog! This post covers the running behavioral experiments utilizing optogenetics.
The second part of my three part guest blog series on Optogenetics has been published on the Addgene blog. Addgene is a nonprofit organization dedicated to making it easier for scientists to share plasmids and I’m thrilled to be able to contribute to their blog! This post covers the material science aspects of running optogenetic experiments.
The first part of my three part guest blog series on Optogenetics has been published on the Addgene blog. Addgene is a nonprofit organization dedicated to making it easier for scientists to share plasmids and I’m thrilled to be able to contribute to their blog!
The biological sciences are in a golden era: the number of advanced technological tools available coupled with innovations in experimental design has led to an unprecedented and accelerating surge in knowledge (at least as far as the number of papers published is concerned). For the first time in history, we are beginning to ask questions in biology that were previously unanswerable.
No field demonstrates this better than genetics, the study of DNA and our genes. With the advent of high-throughput DNA sequencing, genetic information can be acquired literally from thousands of individuals and even more remarkably, can be analyzed in a meaningful way. Genomics, or the study of the complete set of an organism’s DNA or its genome, directly applies these advances to probe answers to questions that are literally thousands of years old.
A recent study, a collaborative effort from scientists in Iceland, the Netherlands, Sweden, the UK, and the US, is an example of power of genomics and to answer these elusive questions.
The scientists posed an intriguing question: if you are at risk for a psychiatric disorder, are you more likely to be creative? Is there a link between madness and creativity?
Aristotle himself once said, “no great genius was without a mixture of insanity” and indeed, the “mad genius” archetype has long pervaded our collective consciousness. But Vincent Van Gogh cutting off his own ear or Beethoven’s erratic fits of rage are compelling stories but can hardly be considered empirical, scientific evidence.
But numerous studies have provided some evidence that suggests a correlation between psychiatric disorders and creativity but never before has an analysis of this magnitude been performed.
Genome-wide association studies (GWAS) take advantage of not only the plethora of human DNA sequencing data but also the computational power to compare it all. Quite literally, the DNA of thousands of individuals is lined up and, using advance computer algorithms, is compared. This comparison helps to reveal if specific changes in DNA, or genetic variants, are more common in individuals with a certain trait. This analysis is especially useful in identifying genetic variants that may be responsible for highly complex diseases that may not be caused by only a single gene or single genetic variant, but are polygenic, or caused by many different genetic variants. Psychiatric diseases are polygenic, thus GWAS is useful in revealing important genetic information about them.
This video features Francis Collins, the former head of the Human Genome Project and current director of the National Institutes of Health (NIH), explaining GWAS studies. The video is 5 years old but the concept is still the same (there’s not many GWAS videos meant for a lay audience).
The authors used data from two huge analyses that previously performed GWAS on individuals with either bipolar disorder or schizophrenia compared to normal controls. Using these prior studies, the author’s generated a polygenic risk score for bipolar disorder and for schizophrenia. This means that based on these enormous data sets, they were able to identify genetic variants that would predict if a normal individual is more likely to develop bipolar disorder or schizophrenia. The author’s then tested their polygenic risk scores on 86,292 individuals from the general population of Iceland and success! The polygenic risk scores did associate with the occurrence of bipolar disorder or schizophrenia.
Next, the scientists tested for an association between the polygenic risk scores and creativity. Of course, creativity is a difficult thing to define scientifically. The authors explain, “a creative person is most often considered one who take novel approaches requiring cognitive processes that are different from prevailing modes of thought.” Translation: they define creativity as someone who often thinks outside the box.
In order to measure creativity, the authors defined creative individuals as “belonging to the national artistic societies of actors, dancers, musicians, and visual artists, and writers.”
The scientists found that the polygenic risk scores for bipolar disorder and schizophrenia each separately associated with creativity while five other types of professions were not associated with the risk scores. An individual at risk for bipolar disorder or schizophrenia is more likely to be in creative profession than someone in a non-creative profession.
The authors then compared a number of other analyses to see if this effect was due to other factors such as number of years in school or having a university degree but this did not alter the associations with being in a creative field.
Finally, the same type of analysis was done with two other data sets: 18,452 individuals from the Netherlands and 8,893 individuals from Sweden. Creativity was assessed slightly differently. Once again creative profession was used but also data from a Creative Achievement Questionnaire (CAQ), which reported achievements in the creative fields described above, was available for a subset of the individuals.
Once again, the polygenic risk scores associated with being in a creative profession to a similar degree as the Icelandic data set; a similar association was found with the CAQ score.
The authors conclude that the risk for a psychiatric disorder is associated with creativity, which provides concrete scientific evidence for Aristotle’s observation all those years ago.
However, future analyses will have to broaden the definition of creativity beyond just narrowly defined “creative” professions. For example, the design of scientific experiments involves a great deal of creativity but is not considered a creative profession and is therefore not included in these analyses, and a similar argument could be made with other professions. Also, no information about which genetic variants are involved or what their function is was discussed.
Nevertheless, this exciting data is an example of the power that huge genomic data sets can have in answering fascinating questions about the genetic basis of human behavior and complex traits.
For further discussion, read the News and Views article, a scientific discussion of the paper, which talks about potential evolutionary mechanisms to explain these associations.
This is a fascinating question in neuroscience and at the very core of what makes us human. After all, our entire concept of ourselves is defined by our memories and without them, are we even ourselves? This is a pretty lofty philosophical discussion… but today we’re only interested in the neuroscience of memory.
In specific, what happens to individual neurons in the human brain when a new memory is created and recalled?
Researchers at the University of California-Los Angeles performed a study in humans that has shed some light on this important question. Published recently in the journal Neuron, the novelty of the study involved recording how many times a neuron would fire during a specially designed memory test. In other words, the scientists were able to monitor what happened to individual neurons in a human being as a new memory was being created!
This article is open access (able to downloaded and distributed for free). The article can be found here or download the pdf.
Before I go into what the researchers found, let’s see how it was done.
The subjects in the study were patients being treated for epilepsy. As part of their clinical diagnosis, they had been implanted with an electrode, a tool used to measure neuronal activity or in other words, the electrode measures how often a neuron fires. The fact these patients already had an electrode inserted into the brain for clinical reasons made it convenient for the researchers to conduct this study.
The brain region in which the electrode was implanted is called the medial temporal lobe (MTL). The image to the right is of the left human temporal lobe. The medial region of the temporal lobe is located more towards the center of the brain.
One specific region of the MTL, the hippocampus, is believed to be the primary brain region where memories are “stored”. Specifically, previous studies in animals and humans have suggested that the MTL and hippocampus are very important to encoding episodic memory. Episodic memory involves memories about specific events or places. In this study, the example of episodic memory being used is remembering seeing a person at a particular place. Another example: the game Simon™ can be considered a test of your brain’s ability to rapidly create and recall short-term episodic memories!
*Note: Episodic memory is considered one of the main bifurcations of declarative memory, or memories that can be consciously recalled. The other type of declarative memory is semantic memory, which are memories of non-physical/tangible things, like facts.
To test the episodic memory of remembering a person at a particular place, images were presented to the patients while the neurons were being recorded. There were 5 different tasks (all completed within 25-30min). See Figure 1 below from the paper.
First, a pre-screening was done in which the patients was shown many random images of people and places. The activity of multiple neurons was recorded and the data was quickly analyzed then 3-8 pairs of images were compiled. In each pair, 1 image was “preferred” or “P” image, meaning the neurons being recorded fired when the “P” image was shown. The second image was “non-preferred” or “NP” image, meaning the neurons did not respond to it when it was shown.
The first task is the “Screening” test. Each “P” and “NP” image was shown individually and the neurons response to each was recorded. As you would expect, the neuron would fire heavily to the “P” image and not very much to the “NP” image.
The second task was the “learning task” in which a composite image of each of the “P” and “NP” image pairs was made. The person in the “P” image was digitally extracted and placed in front of the landmark in the “NP” image. After the composite images were shown, the individual images were shown again.
For example, in one image pair for one patient, the “P” image was a member of the patient’s family while the “NP” image was the Eiffel Tower (for this example, see Figure 2). The composite image in the “learning” task was the family member in front of the Eiffel Tower. Another example of a “P” image was Clint Eastwood and the “NP” image was the Hollywood sign. The composite image would therefore be Clint Eastwood in front of the Hollywood sign. (However, in some image pairs the “P” image was a place and “NP” image was a person).
The third task was “assessing learning”. The image of just the person in the composite image was shown and the patient had to pick out the correct landmark he/she was paired with. For example, the picture of the family member was shown and the patient would have to pick out the Eiffel Tower image.
The fourth task was the “recall” task. The landmark image was shown and the patient had to remember and say the person it was paired with. For example, the Eiffel Tower was shown and the patient had to say the family member’s name.
Finally, the fifth task was a “re-screening” in which each individual image was shown again so the neuron’s activity could be compared to the Task 1, pre-learning.
The activity of multiple neurons were recorded for each image for each of the tasks. The data was then analyzed in number of different ways and the activity of different neurons was reported.
And what was found?
Let’s go back to the family member/Eiffel tower example. The researchers were able to show that a neuron in the hippocampus responded heavily to the picture of the family member (“P” image) but not to the Eiffel Tower (“NP” image). After showing the composite image, the neuron now responded to the Eiffel Tower too in addition to the family member! (The neuron also fired a comparable amount to the individual family member image as the composite image).
As you can see in Figure 2, each little red or blue line indicates when a neuron fired. For example, in Task 1 you can clearly see more firing (more lines) to the “P” image than the “NP” image. You can see that after Task 2, the neuron responds to either the “P” or “NP” image (especially obvious in the Task 5). The middle graph indicates the firing rate of the neurons to the “NP” image and it clearly shows increased firing rate of the neuron after learning (AL) compared to before learning (BL). It may look small, but the scientists calculated a 230% increase in firing rate of the neuron to “NP” image after the learning/memory task took place!
What does this mean? It means that a new episodic memory has been created and a single neuron is now firing in a new pattern in order to help encode the new memory!
This was confirmed the other way around too. In another patient, the “P” neuron was the White House and the “NP” image was beach volleyball player Kerry Walsh. The neuron that was being recorded fired a lot when the image of the White House was shown but not so much for the Kerri Walsh image. Then the composite image was shown and the learning/recall tasks were performed. The neuron was shown to fire to both the White House image AND the Kerry Walsh image! The neuron was responding to the new association memory that was created!
Keep in mind these are just two examples. The scientists actually recorded from ~600 neurons in several different brain regions besides the hippocampus but they only used about 50 of them that responded to visual presentation of the “P” image, either a person or a landmark (the identification of visually responsive neurons was crucial part of the experiment). Remarkably, when the firing rates of all these neurons was averaged before and after the memory/learning tasks, a similar finding to the above examples was found: the neuron now responded to the “NP” image after the composite was shown!
Many other statistical analyses of the data was done to prove this was not just a fluke of one or two neurons but was consistent observation amongst all the neurons studied but I won’t go into those details now.
But what’s going on here? Are the neurons that respond to the “P” stimulus now directly responding to the “NP” image or is more indirect, some other neuron is responding to the “NP” which in turn signals to the “P” neuron to increase in firing? The authors performed some interesting analyses that both of these mechanisms may apply but for different neurons.
Finally, were all the recorded neurons that were engaged in encoding the new episodic memory located in the hippocampus? The answer is no. Responsive neurons were identified in several brain regions besides the hippocampus including the entorhinal cortex and the amygdala. But most of the responsive cells were located within the parahippocampal cortex, a region of the cortex that surrounds the hippocampus, thus not surprising it is very involved in encoding a new memory.
In conclusion, the scientists were able to observe for the first time the creation of a new memory in the human brain at the level of a single neuron. This is an important development but such a detailed analysis has never before been done in humans and, most importantly, in real time. Meaning, the experiment was able to observe the actual inception of a new memory at the neuronal level.
However, one major limitation is that the activity of these neurons were not studied in the long term so it’s unknown if the rapid change in activity is a short-term response to the association of the two images or if it really represents a long-term memory. The authors acknowledge this limitation but the problem is really in the difficulty of doing such studies in humans. It’s not really ethical to leave an electrode in someone’s brain just so that you can test them every week!
But what does all of this mean? The authors do suggest that the work may help to resolve a debate that has been going in on the psychology field since the 40s. Do associations form gradually or rapidly? These results strongly suggest new neurons rapidly respond to encode the new memory formation.
But how will these results shape the neuroscience of memory? The answer is I don’t know and no one does. Thus is the rich tapestry of neuroscience, another thread weaved by the continuing work of scientists all over the world in order to understand what it is that makes us human: our brains.
I hate to be condescending but how the scientific community perceives a phenomena and how the public at large perceive the exact same thing can be starkly different.
For example, there is still a debate over the scientific legitimacy of global warming and climate change. Of course, this flies in the face of reality. In the scientific community, there is no more doubt over climate change than there is over heliocentricity (the theory that states the Earth revolves around the Sun). Study after study comes to the came conclusion, the scientific evidence is overwhelmingly in favor. But I’m not writing to debate climate change.
The same type of dichotomy exists for replacement/maintenance therapies for addiction. Methadone and the related compound buprenorphine (Suboxone, one of its formulations) are still considered controversial or ineffective or “replacing one drug for another.”
In brief, methadone is a compound that acts on the same target as heroin (the mu opioid receptor) but unlike heroin, it acts for a very long time (24hrs). Dr. Vincent Dole, a doctor at the Rockefeller University in New York, and his colleague, Dr. Marie Nyswander, had the brilliant idea of using this very long-acting opioid compound as a way of treating heroin addiction. Indeed, methadone has the advantage of not producing the intense, pleasurable high that heroin produces but is still effective at curbing cravings for heroin and eliminating withdrawal symptoms. Dole and Nyswander published their first study in 1967 and methadone has been an approved—and effective—treatment for heroin addiction worldwide ever since.
However, controversy over the use of methadone exists. Even the opening of a methadone clinic can incite protests. The persistence of negative attitudes towards methadone and the stigma against treating addiction as a medical disease has prevented addicts from receiving proven medical treatments that are effective at curbing cravings and actually keeping them off of heroin and in treatment programs.
So just for a moment, let’s suspend our preconceived notions about what methadone is or how it works and let’s just ask our selves two simple questions:
Does methadone work?
Does methadone keep addicts off of heroin and in treatment?
The answer is a resounding YES!
Many controlled, clinical studies have examined the effectiveness of methadone. But a comprehensive comparison of methadone versus control, non-medication based treatments has not been considered amongst the various studies.
Researchers at the Cochrane Library performed this type of comprehensive analysis. Data was considered from 14 unique, previous clinical studies conducted over the past 40 years. Researchers compared methadone treatment versus control, non-medication based treatment approaches (placebo medication, withdrawal or detoxification, drug-free rehabilitation clinics, no treatment, or waitlist).
11 studies and 1,969 subjects were included in their final analysis.
The results were clear. Methadone was found to keep people off of heroin and in treatment more effectively than control treatments. Urine analysis confirmed methadone-treated addicts were more likely to be heroin-free and regularly seeking treatment.
Of course, as I stated above, this is nothing new. But it’s important to note that abstinence therapies or treatments that encourage addicts to go “cold turkey” don’t really work; inevitably, relapse will occur. A medical treatment exists to help addicts fight their cravings so their brains are not fixated on obtaining heroin and these people are able to regain normal daily functions. And in time, methadone doses can be tapered down as intensity and frequency of cravings decrease.
The debate now should not be on whether methadone works, but on how to use it effectively and how to expand its use so that as many people as possible can benefit from it.
Most importantly, methadone helps an addict to return to normal life. End of story.
It’s been a few weeks since my last post. Apologies! Just finished up a big experiment and grant proposal. My goal is to release a few small posts over the next few days and here’s the first:
Numerous reported the dramatic increase in opioid addiction and death’s due to overdose over the past decade. Abuse of prescription opioid pain medication, such as oxycodone and hydrocodone, has skyrocketed. Even more disturbing is the surge in addiction to heroin, which was in decline during the 80s and 90s. I already reviewed an article that cites some of the statistics. Read it here.
Some key facts cited in today’s paper:
Abuse of prescription opioid drugs has been increasing dramatically over the past decade, especially amongst young people (18-26)
Opioids, such as hydrocodone and oxycodone, are the second most abused drug amongst young adults, after cannabis.
Very little data exists on initiation of drug abuse (i.e. first drugs abused) among injection drug users.
This study is a epidemiology/public health study that recruited 50 young (under 30), active injection drug users (e.g. heroin users) from New York and Los Angeles and interviewed them about their drug use. Note that this is a small study as far as epidemiology studies go, and the authors admit this and describe it as an exploratory study, but the trends they find are consistent with other studies (see the National Survey on Drug Use and Health).
The conclusions are simple: the majority of injection drug users began by abusing prescription opioids.
The average age for first use of prescription opioids was 12.6 years old and 41/50 reported swallowing (compared to 8 that snorted or 1 injecting). And 30/50 reported getting the prescription opioids from the homes of either immediate or extended family members that had a prescription.
Even more disturbing is that 36/50 injection drug users reported having a prescription for opioid pain medications during their lifetime, which occurred on average at 14.6 years of age. 8 of these 36 reported their opioid abuse began from their own prescriptions.
Several other interesting trends can be found in this study but the conclusions are pretty stark: injection of heroin began with abuse of pain pills.
Clearly tighter control of available prescriptions and careful monitoring of prescription opioids is required to help control their abuse among adolescents. However, the specific policy recommendations and medical attitude changes necessary are complex. Hopefully the more knowledge about the topic will provide an impetus for this important and necessary discussion.
Why does one person become an addict and another person does not?
The vulnerability/susceptibility to addiction is one of the most important questions in the addiction field and also one of most difficult to answer. Is it genetics, the environment, or the addictive power of the drug itself? Spoiler alert: the answer is all three! But rather than trying to explain the answer in mere blog post (which is impossible), I think it’s better to tackle different aspects of the question in multiple posts (well, I probably could do it in one but I’m scientist: I would be a doing a disservice to you and to myself if I didn’t do a thorough job). This is the first post in this series.
Over the years, a lot of research has been done that has been able to show that stress can contribute to why one person becomes an addict and why another person does not. But how do we know that stress is important? And what is “stress” anyways? Let’s get our information straight from the horse’s mouth so to speak: a review of a few research papers that look at this question.
What is Stress?
Stress is one of those terms that is used often but may not be well understood. At one point or another we’ve all described our day as “stressful” and we all understand what this means but just take a moment and try to describe what “stressful” means in words that apply to ALL “stressful” situations. It’s tough, right? That’s because stress can mean any number of things in a number of different contexts.
In biology, we have a specific definition of stress: a response (usually immediate and automatic) to an environmental condition or factor, a stimulus, or other type of challenge. The body has several systems in place that mediate the stress response. For example, you probably have heard of “fight-or-flight”, which is one of the body’s stress responses.
Another of the key components of the body’s response to stress is the activation of the hypothalamic-pituitary-adrenal (HPA) axis. See the diagram. The HPA axis is hormonal system that involves chemical communications between several organs.
First, something happens that requires the body to respond to it, this could be sudden change in temperature, or an attack by an aggressor, or some other challenge. This factor is called a stressor.
Second, the stressor causes the hypothalamus, a region of the brain that controls many of the body’s functions, to release a small protein molecule called corticotropin releasing factor or hormone (CRF or CRH)
Third, CRF acts on the anterior pituitary gland, a small organ that secretes many different hormones. CRF stimulates the pituitary to release another small protein called adrenocorticotropic hormone (ACTH). ACTH then enters the blood stream.
Fourth, ACTH travel through the bloodstream until it finds its way to the adrenal glands, small organs that are located on top of the kidneys.
Finally, ACTH causes the adrenal gland to release cortisol (corticosterone in rodents), the “stress hormone.” Cortisol has many effects on many different organs throughout the body. Cortisol can also act on the hypothalamus and the pituitary gland themselves in order to inhibit their release and turn the HPA axis “off” until the next stressor. This is called a negative feedback loop.
Note: This is of course, a simplified model and there is a whole field of research devoted to working out the precise molecular mechanisms that regulate the HPA axis and how it responds to many different kinds of stressors.
Stress plays an important role in addiction. Stressors can make a drug seem more appetizing or even make it even feel better (more pleasurable). Anecdotally, after a stressful day, did you ever feel like you really needed a drink? Or, for the current and/or former smokers, how a cigarette was especially satisfying after a particularly jarring event? There’s a neurobiological reason for that feeling!
How do we know stress is important in addiction?
We are going to examine a few research papers that span over two decades (this discussion will be split over two posts). Each paper will reveal a little piece of the puzzle about why stress makes drugs more addictive. However, a Google Scholar search for “stress and addiction” gives you 527,000 hits! Basically, I chose these ones because they are easy to explain and, more or less, fit together in a sequence. Also, they all use one or more of the techniques that I described in my last post: The Scientist’s Toolbox: Techniques in Addiction Research, Part 1. I encourage you to read it before proceeding.
As we go through, try to keep the question we are trying to answer at the back or your mind: Does exposure to stress make it easier to become an addict and, if so, how does it do this? But this is a big question so it’s broken down into little pieces that each paper will try to answer. By the end of the second post, all the little pieces should add up to the bigger story.
Both of the papers we’ll go over today look at what effect stress has on the behaviors of rats exposed to psychostimulants, either amphetamine or cocaine.
As I described in The Scientist’s Toolbox, psychostimulants cause an animal to move around more, and subsequent doses, over a period of a few days, increase that movement. Recall that this phenomenon is called locomotor sensitization.
In the first paper, rats are given 4 injections of amphetamine, one injection of amphetamine every three days and, sure enough, after the fourth injection exhibit greater locomotor activity; these rats are exhibiting locomotor sensitization to amphetamines. These results are shown in the left panel of Figure 1: black circles (4th dose of amphetamine) vs white circles (1st dose). Similarly, rats were given the same regimen of injections and 24hrs after the fourth injection self-administration of amphetamine was tested. As show in the right panel of Figure 1, only animals that were previously exposed to amphetamine (black circles) compared to saline-exposed rats (white triangles) self-administered amphetamine (nose-poked in order to receive drug infusions).
For the next experiment, there are two groups of rats: one group is exposed to stress and other is not. The type of stressor used in these experiments is called tail-pinch and it is exactly what it sounds like: a device is set to deliver a quick squeeze to the rat’s tail. This causes just a mild amount of pain and is very unexpected to the animals, thus it “stresses them out”. This means, as shown in other studies, that tail-pinch activates the HPA axis (increased cortisol secretion). In this experiment, no apparatus is used so instead the rats are placed in a bowl one at a time and then the scientist pinches the tail using forceps (tweezers).
Each animal in the stress group is exposed to 1min of tail pinch, 4times/day for 15days. This represents a chronic stress. The non-stress group rats are also placed in the bowl but no tail-pinch is applied. This is important to make sure that simply being handled or being put in the bowl is not having an effect. This non-stress group is an essential part of the experiment because it allows us to compare the effects of the stress test to animals that did not receive the test. It is called a control group. Controls are necessary for every experiment so that the scientist can make a useful comparison and allows him/her to interpret the experimental results.
Back to the experiment: 24hrs after the last tail pinch, both groups of animals are give an injection of amphetamine and their locomotor activity is measured. As you can see in the left panel of Figure 2, amphetamine caused greater movement in the animals that were stressed (black triangles) compared to the non-stressed control group (white triangles). This means, the ability of amphetamine to affect the animal’s movement was enhanced by stress.
In the second part of this experiment, the same stress exposure procedure is done but then the animals undergo a self-administration experiment (if you’re interested in the details, the catheter surgeries are completed before the stress exposure is started). As shown in the right panel of Figure 2, the stress group (black triangles) successfully acquired self-administration, meaning they gradually self-administered more and more amphetamine every day of the experiment. This behavior is similar to how human addiction begins, escalation in the amount of drug taken each time. Interestingly, the non-stress control group (white triangles) self-administered amphetamine for the first two days but gradually stopped and didn’t really seem interested in receiving the drug by day 5.
In Figure 3 the authors of this study compared the effect of prior exposure (sensitization) to stress for both the locomotor and self-administration experiments. They did this by dividing the experimental data by the control data (this is called normalization). There appears to be no difference between prior exposure and stress on locomotor activity and self-administration.
The authors conclude that stress is as potent as prior exposure to enhance the properties of the drug; stress exposure may be a significant factor why some people become addicted while others do not.
So very cool, it looks like stress can cause rats to want to self-administer more amphetamine and enhance the physical effects of the drug. Many other studies have found similar effects of stress. Let’s take a look at one paper that uses a different stress and a different drug.
In this study, the drug studied is the psychostimulant cocaine and the stressor is social stress. There are many variations of the procedure used for social stress but many are similar. In this paper, the rat to be stressed (the intruder) is placed in the home cage of a different rat (the aggressor). Because rats are territorial, this provokes the aggressor to attack the intruder. The intruder is left in the aggressor’s cage until it is bitten 10 times by the aggressor. The intruder rat is then placed in a mesh cage and put back in the home cage of the aggressor for a period of time. This way the intruder can still see and smell its attacker but can’t be physically attacked. This is repeated for several days. Social stress has been shown to be a very potent stressor, probably more so than tail pinch.
Note: The other study looked only at males but this study is interested in both males and females but for what we are interested in, this is a minor detail.
First, activity of the HPA axis is measured by looking at corticosterone levels (this it the rodent equivalent of cortisol) when exposed to a novel environment (a novel environment is itself a type of mild stress). As you can see in Figure 1, rats that were previously exposed to social stress (black symbols) released higher amounts of coricosterone when placed in the novel environment compared to their unstressed counterparts (white symbols). This means the social stress has resulted in activation of the rat’s stress response, the HPA axis. Interestingly, female rats seemed to have a greater stress response overall.
Next, the effect of social stress on self-administration of cocaine is tested. As we saw with tail pinch stress and amphetamine, social stress caused an enhanced acquisition of cocaine self-administration whereas unstressed animals did not acquire cocaine self-administration. These data are presented in Figure 2, stressed rats (black symbols) and unstressed rats (white symbols).
In this paper, the authors also conclude that social stress—and activation of the HPA axis—makes it easier for a rat to acquire to cocaine self-administration; stress makes the rat want to self-administer cocaine.
To summarize: these studies have found that two different types of stress have a similar effect on two different kinds of drugs. The first study found that tail-pinch stress increases the amount of locomotor activity induced by amphetamine. This stress also increases the amount of drug that the animals will self-administer. The second paper found that a different kind of stress, social stress, caused an activation of the HPA axis and had the same effect on cocaine self-administration: animals exposed to stress acquired self-administration behavior.
Based on the self-administration data, we conclude that stress caused the drugs to have a greater reinforcing effect. This is measure of the amount of pleasure the animals get from the drug. Therefore, we interpret that the stress made the drugs more pleasurable to the animals because they wanted to self-administer more drug.
However, there are some caveats that need to be briefly discussed. Both of these studies only looked at short term self-administration experiments (5 days) and both used relatively low doses. Many studies have found the rats and mice will self-administer cocaine and amphetamine regardless of whether they were exposed to stress or not. Nevertheless, these two papers are examples of how exposure to stress can cause a drug to be more addictive (technically, more reinforcing).
Next, we’ll look at some more stress studies that try to identify the molecular mechanisms—what stress is actually doing to the brain—of stress and addiction.
If you made it this far, thanks so much for sticking with it!
Just as a last thought: both of these are old papers, from the 90s and both are not very extensive (compared to today). This may sound incredible but it’s just an example of how difficult and time consuming science really is!