Genomics And Metabolomics In Psychiatry As A Combined Tool - Predicting SSRI Response

From Reference 1

The last two posts here were an introduction to some recent work that combine aspects of genomics and metabolomics.  The paper that I will briefly discuss here is done by researchers from the same groups focused on studies in genomics and metabolomics.  They present work that is quite exciting because it illustrates the amount of information necessary to analyze the biological complexity present in brain science and they produce results that may prove very useful from a clinical standpoint.  The study involves serotonin metabolism in depression. I have discussed serotonin in many places on this blog as a significant neurotransmitter that refuses to go away despite the various critics.  This work will illustrate why that may be.  The first paper (1) looks at the relationship between baseline serotonin levels and levels after treatment with selective serotonin reuptake inhibitors and what those phenotypes may be associated with at a genetic level.  A second paper (2) that I will discuss in a subsequent post looks at kynurenine metabolism in depression and the association of that metabolite with symptoms severity and the genetic correlates.  Both papers offer a new look at serotonin metabolism in depression and its genetic basis.

In paper 1, the sample included 366 patients in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS). 918 samples from these patients after 4 and 8 weeks of SSRI therapy were used for the metabolomics part of the study.  Baseline serotonin and changes in these levels were measured.  Patients with higher baseline levels and/or greatest drops in serotonin levels were determined to have the best response to treatment with SSRI medications.

From Reference 1

When a GWAS study was performed looking at the baseline serotonin concentrations as the phenotype and at 4 and 8 weeks of treatment a significant single nucleotide polymorphism (SNP) cluster was noted at the Tetraspanin 5 (TSPAN5) gene on chromosome 4 and a number of SNPs were noted at the Glutamate-rich 3 (ERICH3) gene on chromosome 1.  Both are highlighted in the Manhattan plot at the top of this post. Both of these genes were noted to be novel genes involved in serotonin metabolism and plasma serotonin concentrations.

The SNPs 5' of the TSPAN5 gene were cis regulatory elements for that gene (referred to in the paper as cis-expression quantitative trait loci or eQTLs).  In the ERICH3 cluster SNPs, two were variants that were associated with proteosome mediated degradation of ERICH3.  Changes in the expression of this gene were correlated with plasma serotonin concentrations but the serotonin pathway expression was unaltered.  One of the SNPs was also associated with clinical response in the STAR*D study.

The study is interesting because of the plasma serotonin concentration phenotypes, positive treatment response, and identification of associated genes and SNPs. What has not been determined is the specific mechanism of the drop in serotonin levels and the specific genetic mechanisms.  In the experimental section of their paper they show how the serotonin concentrations in the periphery could also affect levels in the CNS by looking at TSPAN5 and ERICH3 expression in neuroblastoma cells.  The authors were able to demonstrate that both mRNA and protein fold changes of enzymes involved in the synthesis, degradation, and transport of serotonin were affected by knockdown (KD) - (a technique of gene silencing by the introduction of doublestranded interfering RNAs (siRNAs)) or overexpression (OE) (a technique leading to enhanced gene transcription by the introduction of regulatory elements for transcription.) genes.     

The paper is an excellent example of molecular psychiatry and a possible application in the field of precision medicine.  The ultimate goal is to determine the treatment in psychiatry that has a high probability of working as soon as possible and eliminate the long trials that many people have to endure before they find a medication that is effect for (in this case) depression.  Along the way it will be evident that just as clinical psychiatrists have know for some time -  the general categories of psychiatric disorders - just like all polygenic illnesses are really a collection of diverse disorders at the omic levels.

The rapid identification of these many subtypes will not only lead to more rapid and efficient treatment - but also prevent unnecessary exposure to medications that can be both intolerable and ineffective.

In closing, some will question the utility of reading papers that contain a lot of terminology from what we used to call molecular biology.  I like to read these papers because it continues to consolidate what I learned in medical school and add to that knowledge. In my biochemistry seminars back then - there was still a lot of emphasis on enzymatic pathways and protein function. We had to know the basics of nucleic acid structure, function, and analysis - but nothing like the details presented in this paper.  As an example, we knew the synthetic pathways and enzymes for serotonin biosynthesis discussed in this paper - but the idea of analyzing human DNA with a chip encompassing 7.5 million SNPs across hundreds of research subjects would have been mind blowing and in many ways it still is.  Reading papers like this one also assures that you are not stuck in serotonin metabolism and receptor theory from the 1980s.  If that is all you know these days - it is not enough!

I realize this is not for everybody - but for some of us it is very exciting stuff.

 
George Dawson, MD, DFAPA

Graphics Credit:

All of the above figures are from reference 1, per a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License.  The graphics are unaltered and are taken from the paper as they are printed in reference 1 below.  This is a non-commercial and not-for-profit blog.




References:

1:  Gupta M, Neavin D, Liu D, Biernacka J, Hall-Flavin D, Bobo WV, Frye MA, Skime M, Jenkins GD, Batzler A, Kalari K, Matson W, Bhasin SS, Zhu H, Mushiroda T, Nakamura Y, Kubo M, Wang L, Kaddurah-Daouk R, Weinshilboum RM. TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics. Mol Psychiatry. 2016 Dec;21(12):1717-1725. doi: 10.1038/mp.2016.6. Epub 2016 Feb 23. PubMed PMID: 26903268

2: Liu D, Ray B, Neavin DR, Zhang J, Athreya AP, Biernacka JM, Bobo WV,Hall-Flavin DK, Skime MK, Zhu H, Jenkins GD, Batzler A, Kalari KR, Boakye-Agyeman F, Matson WR, Bhasin SS, Mushiroda T, Nakamura Y, Kubo M, Iyer RK, Wang L, Frye MA, Kaddurah-Daouk R, Weinshilboum RM. Beta-defensin 1, aryl hydrocarbon receptor and plasma kynurenine in major depressive disorder: metabolomics-informed genomics. Transl Psychiatry. 2018 Jan 10;8(1):10. doi: 10.1038/s41398-017-0056-8. PubMed PMID: 29317604.


3: Ji Y, Biernacka JM, Hebbring S, Chai Y, Jenkins GD, Batzler A, Snyder KA, Drews MS, Desta Z, Flockhart D, Mushiroda T, Kubo M, Nakamura Y, Kamatani N, Schaid D, Weinshilboum RM, Mrazek DA. Pharmacogenomics of selective serotonin reuptake inhibitor treatment for major depressive disorder: genome-wide associations and functional genomics. Pharmacogenomics J. 2013 Oct;13(5):456-63. doi: 10.1038/tpj.2012.32. Epub 2012 Aug 21. PubMed PMID: 22907730.

4: Mrazek DA, Biernacka JM, McAlpine DE, Benitez J, Karpyak VM, Williams MD, Hall-Flavin DK, Netzel PJ, Passov V, Rohland BM, Shinozaki G, Hoberg AA, Snyder KA, Drews MS, Skime MK, Sagen JA, Schaid DJ, Weinshilboum R, Katzelnick DJ. Treatment outcomes of depression: the pharmacogenomic research network antidepressant medication pharmacogenomic study. J Clin Psychopharmacol. 2014 Jun;34(3):313-7. doi: 10.1097/JCP.0000000000000099. Erratum in: J Clin Psychopharmacol. 2014 Oct;34(5):558. PubMed PMID: 24743713.

5: Ji Y, Schaid DJ, Desta Z, Kubo M, Batzler AJ, Snyder K, Mushiroda T, Kamatani N, Ogburn E, Hall-Flavin D, Flockhart D, Nakamura Y, Mrazek DA, Weinshilboum RM. Citalopram and escitalopram plasma drug and metabolite concentrations: genome-wide associations. Br J Clin Pharmacol. 2014 Aug;78(2):373-83. doi: 10.1111/bcp.12348. PubMed PMID: 24528284; PubMed Central PMCID: PMC4137829.

6: Athreya A, Iyer R, Neavin D, Wang L, Weinshilboum R, Kaddurah-Daouk R, Rush J, Frye M, Bobo W. Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder. IEEE Comput Intell Mag. 2018 Aug;13(3):20-31. doi: 10.1109/MCI.2018.2840660. Epub 2018 Jul 20. PubMed PMID: 30467458; PubMed Central PMCID: PMC6241311.

Does Intimate Knowledge of Your Personal Genome - Really Help?

As an offshoot of my previous post - I think this is an obvious question.  I am speaking from a medical context and not from the standpoint of genealogy.  DNA studies of human origins that I posted here in the past are also a valuable use of genomic material.  The latter are common reasons why people send their DNA off for analysis.  Another reason is to learn if they have certain disease susceptibilities and that is where the real problems come in.

The genetics of many major diseases causing significant mortality and morbidity were worked out before the genomic era. They are the typical heritable disorders and inborn errors of metabolism that are flagged in popular sites like 23 & Me.  I can't imagine that there are many surprises when subscribers find out that they do not have a fatal error of metabolism as an adult. Most of the concern is about complex polygenic disorders that may or may not have a significant environmental factor and that also lead to significant morbidity and mortality.  From a philosophical standpoint this is also an interesting group of illnesses because there are clear parallels between psychiatric disorders and what are typically considered usual medical problems like hypertension, coronary artery disease, asthma, and diabetes mellitus.  For the past decade, the standard genomic approach to study these disorders has been to scan large groups of genomes looking for mutations associated with these disorders.  Those analyses are complex.

I thought I would continue with some clear cut examples form my own genome to illustrate the polygenes behind both heritability of complex disorders as well as the polygenes behind my current chronic conditions.

The at risk condition is bipolar disorder.  My mother had severe bipolar 1 disorder.  That is why nobody in my family ever doubts that bipolar disorder or "diagnoses" exist in psychiatry.  Bipolar disorder is not a subtle condition and it is currently fairly easy to diagnosis.  I credit DSM technology with making this an easy to recognize diagnosis. That was not the case 2 generations ago.  The treatment in those days was less clear and there were very few resources to treat people in their communities.  Despite my mother's diagnosis none of my siblings or their offspring has been diagnosed with bipolar disorder or any mood disorder.  One of the inquiries for my genome is whether or not there are any polygenes associated with a bipolar disorder diagnosis.

The methodology I used for this post was to export all of my 23&Me data to Promethease, a search and cataloging software that arranges specific SNPs by disease, medication, genes, and several additional classification parameters.  Before this software was available - I was stuck looking up every rsID in PubMed. The processing time of my entire genome in Promethease was 133 seconds.  All of the correlates posted below are from that data.  The first graphic is for bipolar disorder risk (click to enlarge).

Ten SNPs associated with bipolar disorder were identified.  The risk is modest 1.39-2x. A more interesting feature is the facts that some of the identified SNPs were protective against bipolar disorder.  In some cases, the SNP identified had to be in association with another gene in order to create the risk. Ethnic groups are also noted in association with some of these SNPs to increase and decrease risk.  Standard approaches to this in the literature are to construct equations with risk terms from identified SNPs to determine which of those equations is the best predictor of risk.  A second approach that I will discuss in a subsequent post is to use neural networks to determine associations between the SNPs and quantitative estimates to determine risk.  At a macro level,  the lesson is that a person with a non-bipolar disorder phenotype can carry multiple SNPs that may confer risk for bipolar disorder.

What about actual disease phenotypes?  I am fortunate enough to have several for analysis.  The first and in many cases the most illustrative is asthma.  I have had asthma since childhood with various diagnoses along the way.  The first diagnosis was a misdiagnosis and that it was a psychosomatic condition and not really asthma.  Then it was diagnosed as allergic asthma.  Then it was exercise induced asthma.  I have received just about every conceivable treatment for asthma including some that have been determined to not be effective.  There was also the famous disproven mechanism of action (increased intracellular cAMP) that was used to explain the mechanism of action for theophylline. I had a long quiescent period of about 20 years where I did not require any medical treatment at all.  That ended about 6 years ago when I developed an upper respiratory infection and I have had to take medications ever since.  My experience with available treatments has generally been disappointing.  The variable course of the illness seems to have a more significant effect.  That is probably why most treated asthmatics are symptomatic and the clinical markers of illness are mostly subjective.  I have several posts on asthma on this blog discussing why it is an ideal comparison disease to polygenic psychiatry disorders.  What does my genome say? (click to enlarge)

I have one sibling and one offspring of a sibling with asthma.  In this case the situation is more complicated - 47 SNPs with varying risk and qualifiers based on numerous contexts such as ethnicity, smoking status of the parents, exposure to allergens, medication responsiveness  and others. I highlighted a couple of SNPs that show a very high risk compared to what was seen in the bipolar disorder - specifically an odds ratio of 7.84 and a 3-fold to 39-fold increase in risk.  Like the bipolar disorder case - some of the polygenes decrease risk as well. There is no available level of data integration beyond that and no clear guidance in terms of therapy.

UpToDate  has a brief chapter (1 ) on the genetics of asthma.  The authors point out that it is a complex polygenic illness that in some cases depends on environmental interactions.  Like pre-genomic twin and family studies in psychiatric disorders there is a range of heritability.  The authors recommend genetic testing in patients with asthma only to exclude monogenic obstructive lung diseases that can be misdiagnosed as asthma, such as cystic fibrosis, primary ciliary dyskinesia, and alpha-1 antitrypsin deficiency.  They see other genetic testing as useful at the heuristic but not at a clinical level.   They point out that the study of asthma genetics is complicated by the lack of a gold standard test and the uneven application of clinical diagnostic criteria.  That has led to a study of a number of asthma traits.

From a pharmacogenomics standpoint up to half of asthma patients do not respond well to initial pharmacotherapy and even then the response is quite variable.  They review the strategies used for genetic analyses, including SNPs as mentioned here but do not comment on any specific SNPs.  I had a previous post on genes and GWAS studies of asthma. They do name several genes.  After reading this chapter it is clear that the parallels between asthma and major psychiatric disorders is clearer than ever.  All of the features of complex psychiatric illness including polygenic inheritance, complex heritability, lack of a gold standard medical test, and a lack of or incomplete response to medication that also occurs with severe psychiatric disorders.

 The final chronic condition is atrial fibrillation.  I had an onset about 8 years ago and as long as I take flecainide - I have no atrial fibrillation.  I had one grandparent with atrial fibrillation.  The SNPs identified follow and it is similar to asthma except fewer identified SNPs.  Multiple SNPs with associated conditions (embolic or ischemic stroke) and qualifiers.

Fifteen SNPs noted in my genome for atrial fibrillation.  Some of these genes have bare bones information (GWAS = genome wide association study, OMIM = online Mendelian Inheritance in Man).  There are complementary approaches that involve using other databases like the GWAS database.  Searching atrial fibrillation in that database. identifies a number of genotypes that were not found in my genome (rs247617, rs2129977, rs2220427, and rs6843082) and one that was - rs6843082.

Clearly, the approach I have outlined about is an improvement over searching Medline and my genome for SNP correlates but below any threshold for being able to use this information for precision medicine.  That means it is below any standard required to look at diagnosis, prevention, or treatment.

There is probably a lot of information even at this level that is sitting there under analyzed.  From my own genome, the involvement of the cytokine system (interleukins are cytokines) with multiple SNPs affecting those genes is a case in point.  Many asthmatics have multiple allergic conditions including atopy, eczema, urticaria, and episodic anaphylaxis.  These same individuals will see allergists, get tested and learn that they are allergic to everything. Those associated conditions are currently treated as medical mysteries or symptomatically as they flare up occasionally.  Are there deeper patterns in the immune system that have not been realized at this time?  Give the complexity of this system, I think that there are.

One of the key questions is whether the identified genes are producing identifiable products.  At that level the short answer is that current detailed genomic information is interesting from an academic perspective - but like genomic testing we are years away from clinical applications.  I could see the shadows of some serious family illnesses in my DNA like systemic lupus erythematosus and diabetes mellitus.  The reasons why my relatives developed these diseases and I did not is not clear at this time. I think most people might come to that same conclusion if they compare their personal genome with SNP markers of diseases.

With a few exceptions, it takes more than correlating mutations in your own DNA to what is known about those mutations across a much larger population and coming up with a diagnosis.  It probably takes more than knowing the mutations exist.  Multiple omics approaches might provide better information and I hope to post one one of those experiments soon and the result of that experiment in the case of selective serotonin reuptake inhibitor (SSRI) antidepressants will be shocking.


George Dawson, MD, DFAPA   



References:

1:  Author: Barnes KC.  Section Editors: Barnes, PJ; Raby BA, Deputy Editor: Hollingsworth H.  Genetics of asthma.  In: UpToDate  Accessed on August 14, 2018.

Patentable Biomarkers of Suicide

From: Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach (reference 1 with permission).

One of the most interesting aspects of biological psychiatry is the attempt to characterize complex biological systems.  It may not have been apparent but complex biological systems factored in a recent post about bronchitis.  Lungs are certainly complex with two different blood supplies and complicated immunology, but the lungs are not thinking organs.  They don't come up with any secondary concepts that need to be analyzed as possible derivatives of the biological substrate.  And even then, basic syndromes that we all learned about in medical school and in clinical rotations, defy more useful classifications.  I have previously posted on endophenotypes and their usefulness in the treatment of asthma and only recently noted that they have proliferated to include an obese endophenotype and how that affects response to therapy.  Diagnostic and treatment approaches to asthma and bronchitis are necessarily crude, largely because the biological complexity in these processes is not fully appreciated and addressed.

The brain is certainly the most complex organ in the body.  Cellular arrays in the brain produce a stream of consciousness, robust unconscious processing, unique conscious states, and all forms of emotional, social and intellectual constructs that can be observed, monitored, and changed.  That brings me to a paper from Molecular Psychiatry on possible biomarkers for suicide.  Not just any paper - at this point it is the most downloaded paper from the top-ranked psychiatry journal (1/140) in the world.  Molecular Psychiatry has an impact factor of 14.496 and that is the highest impact factor of all psychiatry journals.  In part that is probably driven by how absurdly expensive that similar journals like Biological Psychiatry are or other barriers to purchase like needing to be a member of the sponsoring society.  This is a public access journal that uses Creative Commons Licenses for their content.  The authors in this case have provided a 20 pages article and 124 pages in Supplemental Information.

The idea of a biomarker for suicide is very attractive to psychiatrists, because assessing suicide risk is a big part of what we do.  Current clinical guidelines suggest that we need to make that assessment at every patient visit.  The actual prediction of suicide is difficult due to the fact that mental states change over time and people may not be able to communicate their true level of risk.  I have had people tell me in retrospect that they lied about their degree of suicidal thinking and level of control when I asked them about it.  I have had acute care colleagues tell me that they were weary of having to guess about whether a person was going to try to kill themselves or not - many times a day.  The assessment is further complicated by a lack of acceptable acute care options that may further hinder complete self disclosure.  A biomarker would potentially be beneficial.  I qualify that by the fact that the dexamethasone suppression test was once considered a biomarker of suicide (1), but these days it is rarely done and certainly not as part of a suicide assessment.  A study by Coryell, et al (9) notes that the DST was not able to differentiate patients who died from suicide or cardiovascular disease when long term mortality was determined by the National Death Index.  Those authors suggest it may be useful as a predictor of suicide only in patients with depression.

In this article the authors take a look at possible biomarkers in blood that could predict both suicide and some associated markers like risk of hospitalization.  There is a lot going on in this paper.  All the research participants were men.  They studied four different patient cohorts including 217 patients followed longitudinally.  This group was called the Discovery Cohort because markers were discovered based on 37 patients who had a switch from a no suicide state to a high suicide state defined as a score of 2 - 4 on the HAMD question about suicide.  26 deceased patients who committed suicide were used to validate the initial markers.  Two psychiatric cohorts of 108 and 157 to look at prediction of suicidal ideation and hospitalization with the chosen tests.  The flow of these experiments in depicted in the graphic at the top of this post from the original paper.  In the diagram, the designations AP (absent-present) and DE (differential expression) are techniques to capturing genes that are turning of and turning on and off and gradual  changes in gene expression.  The respective genes in this analysis are color-coded based on those properties.  The Convergent Functional Genomic (CFG) Approach is depicted in the box.  Candidate genes are ranked in the triangles according to CFG score.  The CFG score was the sum of various weighted factors including evidence of human brain expression, evidence of human peripheral presence, human genetic evidence and linkage with weighted scores in the CFG box.  Using their discovery and validation sequence the authors were able to pare down the total number of genes down from 412 to 208 to 143 and ultimately to 76 genes.  The supplementary information provides the validation of biomarkers and a table that looks at each gene and prior human genetic evidence, prior evidence of brain expression and prior human evidence of peripheral expression.

The authors discussion of the biological relevance of their findings was interesting.  They did pathway analysis looking at Ingenuity, KEGG, and GeneGO databases.  Of these only the Kyoto Encyclopedia of Genes and Genomes (KEGG) is publicly available without a subscription fee.  It is very useful to know about KEGG because of the relevance of pathway analysis in the psychiatric literature.  As an example, I have been teaching about the mTOR pathway discussed in this article in my neurobiology of addiction lectures for the past 4 years.    

This article is very interesting and can be read at  several levels.  It is premature to consider it definitive at this point and based on this paper and the work of the associated lab these authors are working on additional validation strategies.  If they are  correct,  suicidality may be captured in time as a polygenic event based on a combination of genes that are turned off and on and others that gradually change.  I titled this post as "patentable genes" because the only conflict of interest cited is the lead author is listed as an inventor on a patent application being filed by Indiana University.  For trainees and early career psychiatrists a familiarity with this technology and its potential uses and limitations would be one of the reading goals and including Molecular Psychiatry and its sister journal from the same group Translational Psychiatry (8) is probably a good idea.  Both are potentially good sources of neuroscientific information in psychiatry and if popularity is any indication - fill a niche in the field.  Some of the tools that they developed along the way are useful to think about from a clinical perspective (4, 5).  The thought that the CFI-S Scale was particularly interesting because it is a 22 point binary scale that looks at factors (excluding suicidal ideation) that they determined to be important.  The factors are also classified as to whether they represent increased reasons (IR) or decreased barriers (DB) to suicide.  The emphasis on suicide as a discrete syndrome independent of diagnosis is a research strategy that has been called for recently based on the need to come up with better ways to diagnose and treat the problem.  In a clinical setting I think that clinicians are still frequently surprised by suicide attempts and suicides being able to determine if a patient is in a high risk state based on a blood test independent of their clinical presentation and statements would be useful both in terms of the test but also the associated dialogue.

What I really like about this paper is that it is an attempt to deal with a common psychiatric problem at the appropriate level of complexity.  Clinical trials do exactly the opposite.  As an example, clinical trials in psychiatry will look at heterogeneous groups of patients pulled together under a vague diagnostic category.  There may be rating scales or global ratings just because the rating scales don't seem to have much discriminatory power.  In the end, the entire study is generally collapsed for a very simple statistical analysis.  Getting to those final variables and what has been ignored in the process is always the critical question.  I think it is trendy these days to commiserate about the fact that there are inconclusive, weak and non-reproducible results from the standard clinical trials technology.  I don't know why anyone would expect a different result.  If anything this paper illustrates that a lot of biological information can be considered and analyzed.  The popularity of this paper leaves me hopeful that this is a positive trend for the future.            


George Dawson, MD, DFAPA


References:

1:  Niculescu AB, Levey DF, Phalen PL, Le-Niculescu H, Dainton HD, Jain N,Belanger E, James A, George S, Weber H, Graham DL, Schweitzer R, Ladd TB, Learman R, Niculescu EM, Vanipenta NP, Khan FN, Mullen J, Shankar G, Cook S, Humbert C, Ballew A, Yard M, Gelbart T, Shekhar A, Schork NJ, Kurian SM, Sandusky GE, Salomon DR. Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach. Mol Psychiatry. 2015 Aug 18. doi: 10.1038/mp.2015.112. [Epub ahead of print] PubMed PMID: 26283638.

2:   Lee BH, Kim YK. Potential peripheral biological predictors of suicidal behavior in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2011 Jun 1;35(4):842-7. doi: 10.1016/j.pnpbp.2010.08.001. Epub 2010 Aug 11. Review. PubMed PMID: 20708058.

3:   Collection of references for biomarkers in suicide.

4:  Simplified Affective State Scale (SASS).

5:  Convergent Functional Information for Suicide (CFI-S) Scale.

6:  Laboratory of Neurophenomics Web Site.

7.  Niculescu AB Medline Collection on additional convergent functional genomics references.

8.  Translational Psychiatry Web Site.

9.  Coryell W, Young E, Carroll B.  Hyperactivity of thehypothalamic-pituitary-adrenal axis and mortality in major depressive disorder.  Psychiatry Res. 2006 May 30;142(1):99-104. Epub 2006 Apr 21. PubMed PMID: 16631257.

Attribution:

The figure at the top of the post is from the original article listed completely in reference 1 under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License.  To view the condition of that license view it here.

Supplementary:

1.  There is a Mayo Clinic Conference coming up this fall for anyone interested in translational approaches to psychiatric disorders and addictions.  Further information is available at this web site.

2.  There is also the 3rd Annual Update and Advances in Psychiatry conference at the US Madison and one of presentations is by Daniel Weinberger, MD on the neuroscience of schizophrenia and psychotic disorders.   Information on that conference and the conference brochure is available at this web site.

The Lancet's Illogical Digression

The latest editorial in the Lancet has an illogical digression.  The brief note starts out by stating that there will soon be a revolution in psychiatry based on a genomics study published in the Lancet.  It concludes with a digression to a discussion of about the provision of mental health services across the lifespan with a pejorative connotation:

"The child with ADHD at 7 years could be seen by a child psychiatrist, but at the age of 18 often loses access to mental health services altogether, until he presents with a so-called adult mental health problem. Substance misuse and personality disorders may complicate the picture."

It seems to me that practically all adult psychiatrists would not have any difficulty at all in getting a history of an earlier diagnosis of ADHD and deciding how that would be treated.  I wonder if the Lancet's editors would make the same commentary on childhood asthma presenting to an Internal Medicine clinic.  Would that be "so-called adult asthma"?  The asthma example is instructive because it turns out that what physicians have been calling asthma for decades is more complicated than that.  Recent research has adopted the endophenotype/endotype methodology that has been used to study schizophrenia.  The reason why adults are seen by adult psychiatrists rather than child psychiatrists is the same reason why people stop seeing their pediatricians as adults.  Treating cormorbid substance misuse and personality disorders is just a part of that reason.

As far as the idea that the future of psychiatry is set to change any more than the future of the rest of medicine consider the statement:

"The future of psychiatry looks set to change from the current model, in which ADHD, bipolar disorder, or schizophrenia are considered as totally different illnesses, to a model in which the underlying cause of a spectrum of symptoms determines the treatment."

If that were true, psychiatry would have suddenly catapulted into the most scientifically advanced medical specialty because currently there is no other medical specialty that treats illness based on an underlying genetic cause.   The Lancet's attached paragraph on access to services across the lifespan is accurate, but it really has nothing to do with the possible genetic revolution in psychiatric diagnosis.  If the services are anywhere near as bad in the UK as they are in the United States (Is public health rationing as bad as rationing done by corporations?) there is a widespread lack of services and disproportionate rationing relative to the rest of medicine.

Until psychiatrists, psychiatric services, and mental illness are destigmatized there is no reason to think that a genetic revolution will mean more access to services.

George Dawson, MD, DFAPA

The Lancet.  A revolution in psychiatry.  The Lancet - 1 June 2013 ( Vol. 381, Issue 9881, Page 1878 ) DOI: 10.1016/S0140-6736(13)61143-5.

Cross-Disorder Group of the Psychiatric Genomics Consortium.  Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis.  The Lancet - 20 April 2013 ( Vol. 381, Issue 9875, Pages 1371-1379 ) DOI: 10.1016/S0140-6736(12)62129-1

Hamshere ML, Stergiakouli E, Langley K, Martin J, Holmans P, Kent L, Owen MJ, Gill M, Thapar A, O'Donovan M, Craddock N. A shared polygenic contribution between childhood ADHD and adult schizophrenia. Br J Psychiatry. 2013 May 23.  [Epub ahead of print] PubMed PMID: 23703318.

Larsson H, Rydén E, Boman M, Långström N, Lichtenstein P, Landén M. Risk of bipolar disorder and schizophrenia in relatives of people with attention-deficit hyperactivity disorder.  Br J Psychiatry. 2013 May 23. [Epub ahead of print] PubMed PMID: 23703314.