Disease-modifying or something else?

 

A paper written by S. Nassir Ghaemi, MD was posted this week and in it he discussed the concept of diseases modifying medications and whether any medication used for psychiatric purposes might be included in that category.  Dr. Ghaemi is a distinguished psychiatrist who has written on diverse topics.  He is a prominent psychiatric theorist and also has complied many of his ideas about psychiatry and psychopharmacology in the book Clinical Psychopharmacology (1).   

In the book he presents a brief discussion of disease modifying medications and how few there seem to be in psychiatry as well as what he considers to be obstacles to the discovery of these agents.  He does suggest in the book that lithium, clozapine, and possibly a few anticonvulsants may be considered disease-modifying rather than symptomatically effective or palliating medications. This recent paper presents his latest ideas on the subject.

In his paper he is much more specific.  His premise is that there are disease-modifying drugs and drugs that only treat symptoms and that nearly all psychiatric drugs fall into the latter category. He reviews his rationale for these classifications and emphasizes the lack of understanding of pathophysiology of mental illnesses as a main reason for this deficiency. His talking points are ideal newspaper headlines and will probably are easily assimilated by many who don’t know much about psychiatry or medicine.  This blog post is an elaboration of this story.

In order to build those arguments, let me start with a brief introduction to rheumatology.  My personal introduction to that field occurred in medical school when I had my first acute gout attack and had a medicine attending who was a rheumatologist and two senior medicine residents who aspired to and eventually became rheumatologists. I happened to be at the medical school with one of the top experts in the field Daniel J. McCarty. MD.   Rheumatology in general looks at inflammation in the narrowest sense in joints but more broadly in the body and in multiple organ systems. Rheumatologists are experts in all forms of arthritis but also systemic illnesses with joint manifestations like systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA).  The American College of Rheumatology lists the diagnostic criteria for 20 major groups of illnesses on their web site with additional criteria for subclassification.

Why should psychiatrists have an interest in rheumatology?  My initial interest was in the diseases themselves as well as the classification system. Like psychiatric categorial diagnoses, the rheumatology classification system is criterion based, based on expert consensus and ongoing scientific review, and the sensitivity of the criteria are adjusted according to what is clinically indicated. For example, a category could be adjusted to be more inclusive with more false positives – if it was important to identify early disease stages and prevent progression in the future.  The disease categories are important to psychiatrists because their overlap with psychiatric diagnoses.  For example, neuropsychiatric SLE (NPSLE) is defined as the usual symptoms of SLE with a central nervous system manifestation like seizures, psychosis, or cognitive problems.  It is in the differential diagnosis of patients with psychosis. In addition, there are currently active hypotheses about the role of inflammation in the pathophysiology of depression, psychosis, and neurocognitive disorders at a level that is far below the threshold for overt rheumatological disease.

The similar classification system brings up similar concerns in rheumatology relative to psychiatry. The issue of classification versus diagnosis for example. In a recent review of that issue the problem described in rheumatology by a group of experts is basically the same problem encountered in psychiatry:

“Rheumatologists face unique challenges in discriminating between rheumatologic and non-rheumatologic disorders with similar manifestations, and in discriminating among rheumatologic disorders with shared features.  The majority of rheumatic diseases are multisystem disorders with poorly understood etiology; they tend to be heterogeneous in their presentation, course, and outcome, and do not have a single clinical, laboratory,pathological, or radiological feature that could serve as a “gold standard” in support of diagnosis and/or classification.” (3)

 Psychiatry or the equivalent term could be substituted for rheumatology, rheumatologic, or rheumatic in the above paragraph without skipping a beat. Before the current pandemic many rheumatology clinics were treating patients with symptoms that could not be clearly attributed to rheumatic disease.  In some cases, about 1/3 of patients were in that category (4).  The issue is complicated by the fact that non-rheumatic origins of some of these symptoms need to be recognized and addressed (5).  The difficulties associated with rheumatic diseases have led to “spectrum” descriptions of illness but as far I can tell no push for dimensional rather than categorical diagnoses. There has also been a concern about recognition in primary care settings with delayed referral to rheumatologists (6).

Disease complexity is difficult to address and rheumatologists like psychiatrists see a number of conditions that do not remit, are progressive and can be fatal and/or very disabling, and for which there are few good treatments. It is common in psychiatry to see patients with rheumatoid arthritis who are treated on a chronic basis with low dose prednisone – where the dose is adjusted according to disease activity and degrees of complications from the medication. In other words, the focus of treatment is symptomatic rather than curing or modifying the course of the disease.   

Unlike psychiatry, rheumatology had an early focus on disease modification and using the term “disease modifying” drugs.  The earliest reference to “disease-modifying” in PubMed that I could find was 1976 (7). 

 

 

But the connections to subsequent papers from that original paper seemed to stop in the 1980s.  That suggests to me that there was an evolution of the terms and the medications used as DMARDS.  Searching through modern medicine texts like UpToDate shows that most of the references to disease-modifying medications is focused on rheumatology diseases, multiple sclerosis and some other neurological illnesses, and a few rare conditions.  In some cases, the focus is on a complication is a single organ system or an intermediate phenotype of the main disease.

In a paper specifically written about the term in rheumatology, Buer (8) describes the concept of disease modifying anti-rheumatic drugs or DMARDS beginning in the 1970s with the goal of preventing bone erosion from rheumatoid arthritis. Use of the term increased over the next two decades outlasting several competing terms.  The early purpose was to distinguish between medications that could slow or modify the progression of disease and those that provided symptomatic relief. 

Another potential reason that the disease-modifying was developed in areas of medicine where inflammation and immunological mechanisms where thought to play a part in disease pathology was the longstanding and widespread use of glucocorticoids (GC).  GC drugs like prednisone have been used for 60 years, are used by a substantial portion of the population and that use is growing (15).  The purported mechanisms of action have been clarified over time and are currently characterized as genomic and non-genomic (cytosolic GC receptor mediated and specific/nonspecific effects).  The effect occurs at the level of cytokines, cell membranes, and immune cells. The disease modifying effects of GC were first described in 1995 and are thought to be limited to bone loss in the early stages of rheumatoid arthritis.

Considering the characteristics of an ideal medicine that is curative or preventive and the definitions of a disease modifying drug there is a lot of room for interpretation.  Endocrinopathies come to mind – specifically deficiency states where replacement therapy of thyroxine, corticosteroids, growth hormone, or gonadal hormones corrects the deficiency state that is some cases is life-threatening.  Diabetes mellitus is another example.  Correcting insulin deficiency culminating in human insulins designed to provide more even coverage of glucose levels has resulted in a significantly altered life span for juvenile onset diabetes and for adults. There are also examples in cardiology both from the standpoint of longevity and secondary prevention of heart attacks, strokes, and renal failure. But most of the literature on disease modifying medications is focused on rheumatology and multiple sclerosis (US).

Using MS as an example, I compiled a table of all current FDA approved MS treatments, the year of approval, and what is known about the mechanism of action (MOA).  The MOA in each case is taken directly from the FDA approved package insert.  In the case of natalizumab, there were several paragraphs describing the purported mechanism of action so I included a link to the package insert. The important observation from this table is that in the case of all 18 FDA approved medications – the mechanism of action is unknown. That statement is made in various ways. For example, there may be a suggested hypothetical MOA but it is just that. In the case of MS disease-modifying drugs are based on an unproven hypothesis, rather than a known mechanism of action or theory. I have not constructed a table for rheumatology disease modifying drugs but I expect the same results based on the quotation from reference 3 above. Disease-modifying drugs do not appear to be specifically designed to address and underlying MOA – but are empirically determined based on hypotheses like every other drug.

 

FDA approved drugs for MS and Mechanism of Action

Drug	Type	MOA
Glatiramer (Copaxone) Approved 1996	SC Injection	“The mechanism(s) by which glatiramer acetate exerts its effects in patients with MS are not fully understood. However, glatiramer acetate is thought to act by modifying immune processes that are believed to be responsible for the pathogenesis of MS.”
Interferon beta 1a (Avonex) Approved 1996	IM injection	“The mechanism of action by which AVONEX exerts its effects in patients with multiple sclerosis is unknown.”
Interferon beta 1b (Betaseron) Approved 1993	SC injection	“The mechanism of action of BETASERON (interferon beta-1b) in patients with multiple sclerosis is unknown”
Peginterferon beta 1a (Plegridy) Approved 2014	SC injection	“The mechanism by which PLEGRIDY exerts its effects in patients with multiple sclerosis is unknown”
Dimethyl fumarate (Tecfidera) Approved 2013	Oral tab	“The mechanism by which dimethyl fumarate (DMF) exerts its therapeutic effect in multiple sclerosis is unknown. DMF and the metabolite, monomethyl fumarate (MMF), have been shown to activate the Nuclear factor (erythroid-derived 2)-like 2 (Nrf2) pathway in vitro and in vivo in animals and humans. The Nrf2 pathway is involved in the cellular response to oxidative stress. MMF has been identified as a nicotinic acid receptor agonist in vitro.”
Fingolimod (Gilenya) Approved 2010	Oral cap	“Fingolimod is metabolized by sphingosine kinase to the active metabolite, fingolimod-phosphate. Fingolimod-phosphate is a sphingosine 1-phosphate receptor modulator, and binds with high affinity to sphingosine 1-phosphate receptors 1, 3, 4, and 5. Fingolimod-phosphate blocks the capacity of lymphocytes to egress from lymph nodes, reducing the number of lymphocytes in peripheral blood. The mechanism by which fingolimod exerts therapeutic effects in multiple sclerosis is unknown, but may involve reduction of lymphocyte migration into the central nervous system.”
Teriflunomide (Aubagio) Approved 2012	Oral tab	“Teriflunomide, an immunomodulatory agent with anti-inflammatory properties, inhibits dihydroorotate dehydrogenase, a mitochondrial enzyme involved in de novo pyrimidine synthesis. The exact mechanism by which teriflunomide exerts its therapeutic effect in multiple sclerosis is unknown but may involve a reduction in the number of activated lymphocytes in CNS.”
Alemtuzumab (Lemtrada) Approved 2001	IV infusion	“The precise mechanism by which alemtuzumab exerts its therapeutic effects in multiple sclerosis is unknown but is presumed to involve binding to CD52, a cell surface antigen present on T and B lymphocytes, and on natural killer cells, monocytes, and macrophages. Following cell surface binding to T and B lymphocytes, alemtuzumab results in antibody-dependent cellular cytolysis and complement-mediated lysis.”
Mitoxantrone (Novantrone)   Approved 2000	IV Infusion	“Mitoxantrone, a DNA-reactive agent that intercalates into deoxyribonucleic acid (DNA) through hydrogen bonding, causes crosslinks and strand breaks. Mitoxantrone also interferes with ribonucleic acid (RNA) and is a potent inhibitor of topoisomerase II, an enzyme responsible for uncoiling and repairing damaged DNA. It has a cytocidal effect Reference ID: 3105100 on both proliferating and nonproliferating cultured human cells, suggesting lack of cell cycle phase specificity. NOVANTRONEâ has been shown in vitro to inhibit B cell, T cell, and macrophage proliferation and impair antigen presentation, as well as the secretion of interferon gamma, TNFα, and IL-2”
Natalizumab (Tysabri) Approved 2004	IV infusion	“The specific mechanism(s) by which TYSABRI exerts its effects in multiple sclerosis and Crohn’s disease have not been fully defined”  additional
Dalfampridine (Ampyra) Approved 2010	Extended-release tab	“The mechanism by which dalfampridine exerts its therapeutic effect has not been fully elucidated. Dalfampridine is a broad spectrum potassium channel blocker. In animal studies, dalfampridine has been shown to increase conduction of action potentials in demyelinated axons through inhibition of potassium channels.”
Ofatumubab (Kesimpta) Approved 2009	SC injection	“The precise mechanism by which ofatumumab exerts its therapeutic effects in multiple sclerosis is unknown, but is presumed to involve binding to CD20, a cell surface antigen present on pre-B and mature B lymphocytes. Following cell surface binding to B lymphocytes, ofatumumab results in antibody-dependent cellular cytolysis and complement-mediated lysis.”
Cladribine (Mavenclad) Approved 1993	Oral tab	“The mechanism by which cladribine exerts its therapeutic effects in patients with multiple sclerosis has not been fully elucidated but is thought to involve cytotoxic effects on B and T lymphocytes through impairment of DNA synthesis, resulting in depletion of lymphocytes.”
Siponimob (Mayzent) Approved 2019	Oral tab	“Siponimod is a sphingosine-1-phosphate (S1P) receptor modulator. Siponimod binds with high affinity to S1P receptors 1 and 5. Siponimod blocks the capacity of lymphocytes to egress from lymph nodes, reducing the number of lymphocytes in Reference ID: 4409346 12 peripheral blood. The mechanism by which siponimod exerts therapeutic effects in multiple sclerosis is unknown, but may involve reduction of lymphocyte migration into the central nervous system.”
Ocrelizumab (Ocrevus) Approved 2017	IV infusion	“The precise mechanism by which ocrelizumab exerts its therapeutic effects in multiple sclerosis is unknown, but is presumed to involve binding to CD20, a cell surface antigen present on pre-B and mature B lymphocytes. Following cell surface binding to B lymphocytes, ocrelizumab results in antibody-dependent cellular cytolysis and complement-mediated lysis.”
Ponesimod (Ponvory) Approved 2021	Oral tab	“Ponesimod is a sphingosine 1-phosphate (S1P) receptor 1 modulator that binds with high affinity to S1P receptor 1. Ponesimod blocks the capacity of lymphocytes to egress from lymph nodes, reducing the number of lymphocytes in peripheral blood. The mechanism by which ponesimod exerts therapeutic effects in multiple sclerosis is unknown, but may involve reduction of lymphocyte migration into the central nervous system.”
Diroximel fumarate (Vumerity) Approved 2013	Oral delayed release capsule	“The mechanism by which diroximel fumarate exerts its therapeutic effect in multiple sclerosis is unknown. MMF, the active metabolite of diroximel fumarate, has been shown to activate the nuclear factor (erythroid-derived 2)-like 2 (Nrf2) pathway in vitro and in vivo in animals and humans. The Nrf2 pathway is involved in the cellular response to oxidative stress. MMF has been identified as a nicotinic acid receptor agonist in vitro.”
Ozanimod (Zeposia) Approved 2020	Oral capsules	“Ozanimod is a sphingosine 1-phosphate (S1P) receptor modulator that binds with high affinity to S1P receptors 1 and 5. Ozanimod blocks the capacity of lymphocytes to egress from lymph nodes, reducing the number of lymphocytes in peripheral blood. The mechanism by which ozanimod exerts therapeutic effects in multiple sclerosis is unknown but may involve the reduction of lymphocyte migration into the central nervous system.”

 

Effect sizes for the above medications can be calculated from the package inserts.  The typical active drug/placebo comparisons include relapse frequency (per time interval), percentage of relapse-free patients, reduction in relapse rates, time to first or second relapse, progression free days, and numbers of new Gadolinium enhancing lesions on MRI scan. This data is also plotted on survival curves. The calculations will be made at some point and compared to similar data for lithium and selected DMARDs.

With that backdrop consider the main points in Dr. Ghaemi’s paper – that do go beyond the disease-modifying concept:                                                                                                                                                

     1.  Symptomatic versus disease modification:

As I hoped to capture in the preceding paragraphs – the issue of disease modification is a laudable goal but a complex one. Even chemotherapy treatments that are curative vary in effectiveness and can leave patients with complications from treatment that are disabling or even fatal. There can also be at higher risk for future cancers unrelated to the original treated cancer. Many symptomatic medications used on a maintenance basis decrease mortality risk and disability (hard outcomes) even though they are not disease-modifying. Anticonvulsant medications are a good example.  Where seizure risk in generalized tonic-clonic seizures can be decreased it significantly reduces the risk of sudden unexpected death in epilepsy (SUDEP) (9).

      2.     Effect size:

The paper cites effect size as being problematic at two levels.  The first is the actual calculated effect size and the second in the end point – clinical metrics versus hard outcomes measures. The first issue has been explored in the literature at an exhaustive level. The unfortunate approach by many including a prominent epidemiologist who suggested antidepressants had no effect and then later was a coauthor of a paper showing an effect is a dichotomous one rather than an exploration of reality. The issue is the same with all polygenic heterogenous diseases.  There will be a group of responders, a group of partial responders, and a group of non-responders.  There is an associated overlay of placebo and nocebo responders. And depending on the trial there are varying levels of severity, heterogenous recruitment levels, and varying levels of support for research subjects confounding the trials.

The classification of effect sizes has also been problematic. Benchmarking of mild, moderate, and robust effects sizes have been suggested but are generally considered a weak approach.  The actual effects sizes can be calculated and discussed along with moderating factors. It is possible to include different effect size calculations in the same table by specifying the method used and the relevant parameters of the trial.   Effect sizes that are considered low can become significant over large populations.

      3.     Disease modification specific to psychiatry:

Lithium, clozapine, and some anticonvulsants are known to be disease-modifying drugs in psychiatry largely measured with the hard outcomes of time to relapse or number of relapses in a set period of time. These medications address some purported mechanisms at the hypothetical level since there is no widely accepted theory about how they work and there are many hypothetical mechanisms.  Considering the entire course of illnesses in psychiatry medications that are not technically disease-modifying can make a significant difference in hard outcomes. The best example that I can think of and Luther Bell (10) described a mortality rate of 75% in a cohort of 40 patients admitted to McClean Hospital in 1849. Today with the advents of advances in both medical treatment and electroconvulsive therapy the mortality in this group of patients is essentially zero. Does preventing death qualify a medication approach as disease-modifying?  If so, the modern medical treatment of catatonia (benzodiazepines, antipsychotics, mood stabilizers) qualify. Another example is the use of long acting injectable (LAI) antipsychotic medications.  These medications clearly reduce the rate of relapse in both schizophrenia and bipolar disorder.  Does that qualify them for disease modifying status even though the specific mechanism of action is unknown?  Clinical psychiatry has clearly made progress in terms of hard outcomes irrespective of where you draw the line on disease modification.

      4.     The DSM is biologically invalid:

Somewhat of a straw man – I don’t think there was ever a claim that it was. That said there has been rumored validity markers of psychiatric disorders that have apparently never been released by the DSM study groups and the most obvious marker of robust medication effect has never been used.  Further study of the RDoC and other proposed alternate systems of classification do not seem any more biologically valid at this point. At the minimum biological phenotyping may be useful and it currently exists to a limited degree (catatonia).  A lot of mileage has been made out of the fact that a focus on the biological aspects of psychiatric illness has not yielded any pertinent clinical information and that this somehow justifies increased psychosocial research. That minimizes the issue of complex heterogenous diseases and what it takes to understand them. Psychiatry compared with rheumatology is a good example – but on the other hand psychiatric disorders are more intimately linked to conscious states and those states can affect every level of interpretation of a drug response.  

      5.     Clinical trial design deficiencies

 There are many and I have already listed a few.  An additional deficiency is the general regulatory scheme that seems to focus on getting a minimal efficacy signal.  Pharmaceutical companies are incentivized to complete these trials as soon as possible.  Anyone who has worked as an investigator in a clinical trial knows that this is a frustrating process largely due to the inclusion and exclusion criteria. There are pressures to recruit the necessary patients as soon as possible.  Randomization is a hurdle.  What does it say when the number of people declining participation in the study greatly outnumbers the people who have been recruited?  Many of them decline because of randomization to possible placebo or an inability to be notified after the study about whether they received placebo or not. At the design level, the recruiting problem also can affect choices of comparator drugs and the doses of those drugs. More long-term studies require more funding and retaining patients in the study becomes an important task for researchers.  Intent-to-treat analysis based on considering all of the patients entering the protocol as the denominator in the study is another limitation in that it does not resemble clinical practice where getting to responder status as soon as possible independent of any particular drug is a priority.  

 The discontinuation design in maintenance studies of antidepressants were described as a problem in terms of falsifiability.  Most of them show an active drug effect and apparently psychiatric medications are the only class of drugs that the FDA allows to use this discontinuation paradigm. The practical issue in terms of clinical treatment is what happens when antidepressants are stopped.  Some early work in the pattern analysis of antidepressant response suggested that the placebo effect faded over time but the active drug effect did not.  Psychiatrists need to know what the treatment scenarios are with drug discontinuation.

 There has not be enough discussion of registry and observational studies. The advantages are that they use large data bases and can look at hard outcomes like relapse, hospitalization, suicide, and other types of mortality.  It fits the current FDA regulatory category of Real World Evidence (RWE) and Real World Data (RWD).  The main advantage is the population studied in the registry is not screened by inclusion and exclusion criteria or by a participatory agreement and therefore more accurately approximates a true clinical population (14).  The time interval for RCTs is typically limited by funding for a duration of years. Registry studies based on a database can be much longer in duration and the data is a standard administrative feature. Safeguards have been developed to reduce bias in registry studies and some groups consider them to be a good indication of how a medication works in real clinical settings. Although I have not seen it done, registry studies could potentially confirm some of the effect sizes when applied to much larger populations.

      6   Academics versus Industry versus Clinical Practice:

Closer collaboration between the pharmaceutical industry and may be useful, but there will always be significant conflict of interest issues.  The pharmaceutical industry is clearly looking for an efficacy signal they can use to get FDA approval and market a drug. The trade-off is that these are typically small studies with stringent inclusion criteria that can result in later drug withdrawal due to complications noted only with greater exposure in post-marketing surveillance. It is not clear to how this system will ever produce medications that are disease-modifying versus those that are used to treat symptoms.

 An even larger problem is that clinicians are typically an afterthought by the academics and pharmaceutical industry.  The job of every psychiatrist is to see people who are acutely symptomatic and diagnose and treat those people. Psychiatrists are currently under more constraints than they have ever been.  Managed care companies demand that people are discharged from hospitals barely treated while psychiatrists are concerned about adequate treatment of the symptoms that led to hospitalization.  There are very few – if any clinical trials that apply to this scenario.  In 22 years of acute inpatient care – my estimate would be that about 5% of the people I treated would not be excluded from a standard clinical trial.  That experience was reinforced by my experience as an investigator in clinical trials of antidepressants, anxiolytics, antipsychotics, and Alzheimer’s disease. From a clinician’s perspective, the main failure of drug development is continuing to ignore real-world patients for an idealized clinical trials process.

 Concluding this post – I hope that I have communicated alternate viewpoints that capture the broader clinical landscape. It is not intended as a refutation of Dr. Ghaemi’s viewpoint and I don't consider anything in his paper to be controversial.  What I am suggesting is that psychiatrists need to know all of the viewpoints on these topics and why they exist in order succeed in clinical settings. For example, they need to know how to use both symptom-modifying and disease-modifying medications and the limitations of that distinction. They need to know the limitations of any medication prescribed and how to rapidly determine when a medication needs to be discontinued and a new medication or mode of therapy initiated. They need to know about placebo and nocebo effects as well as the entire range of side effects, effects on comorbid medical illnesses, and drug interactions. And they need to know the relative merits of randomized clinical trials using intent-to-treat analysis and real-world observational and registry studies.  All of those knowledge is necessary to treat complex polygenic illnesses that probably have many underlying biological processes and that consideration is not limited to psychiatry.

That is the true state-of-the art in the field.  There is no royal road to the truth and the current road is never easy.  Many people go into psychiatry for that reason, they get to know this body of knowledge and the associated decision-making and they are very good at helping people with significant problems.

George Dawson, MD, DFAPA

 

 References:

1:  Ghaemi SN.  Clinical Psychopharmacology: Principles and Practice.  New York, Oxford University Press 2019.

2:  Ghaemi SN. Symptomatic versus disease-modifying effects of psychiatric drugs. Acta Psychiatr Scand. 2022 Jun 2. doi: 10.1111/acps.13459. Epub ahead of print. PMID: 35653111.

3:  Aggarwal R, Ringold S, Khanna D, Neogi T, Johnson SR, Miller A, Brunner HI, Ogawa R, Felson D, Ogdie A, Aletaha D, Feldman BM. Distinctions between diagnostic and classification criteria? Arthritis Care Res (Hoboken). 2015 Jul;67(7):891-7. doi: 10.1002/acr.22583. PMID: 25776731; PMCID: PMC4482786.

4:  N. L. Maiden, N. P. Hurst, A. Lochhead, A. J. Carson, M. Sharpe, Medically unexplained symptoms in patients referred to a specialist rheumatology service: prevalence and associations, Rheumatology, Volume 42, Issue 1, January 2003, Pages 108–112, https://doi.org/10.1093/rheumatology/keg043

5:  Smythe HA. Explaining medically unexplained symptoms: widespread pain. The Journal of Rheumatology. 2009 Apr 1;36(4):679-83.

6:  Gran JT, Nordvåg BY. Referrals from general practice to an outpatient rheumatology clinic: disease spectrum and analysis of referral letters. Clinical rheumatology. 2000 Nov;19(6):450-4.

7:  Gumpel JM. Cyclophosphamide, gold and penicillamine--disease-modifying drugs in rheumatoid arthritis--tailored dosage and ultimate success. Rheumatol Rehabil. 1976 Aug;15(3):217-20. doi: 10.1093/rheumatology/15.3.217. PMID: 968355

8:  Buer JK. A history of the term "DMARD". Inflammopharmacology. 2015 Aug;23(4):163-71. doi: 10.1007/s10787-015-0232-5. Epub 2015 May 23. PMID: 26002695; PMCID: PMC4508364

9:  Pensel MC, Nass RD, Taubøll E, Aurlien D, Surges R. Prevention of sudden unexpected death in epilepsy: current status and future perspectives. Expert Rev Neurother. 2020 May;20(5):497-508. doi: 10.1080/14737175.2020.1754195. Epub 2020 Apr 26. PMID: 32270723.

10:  Leucht S, Helfer B, Gartlehner G, Davis JM. How effective are common medications: a perspective based on meta-analyses of major drugs. BMC Med. 2015 Oct 2;13:253. doi: 10.1186/s12916-015-0494-1. PMID: 26431961; PMCID: PMC4592565.

11:  Bell, L. 1849. On a form of disease resembling some advanced stage of mania and fever. Am. J. Insanity 6, 97–127.  

12:  Fava M.  Rational use of antidepressants. Psychother Psychosom 2014;83:197–204. doi: 10.1159/000362803

13:  Cosci F, Fava GA. Prescribing Pharmacotherapy for Major Depressive Disorder: How Does a Clinician Decide?. Biomedicine hub. 2021;6(3):118-21.

14:  Taipale H, Tiihonen J. Registry-based studies: What they can tell us, and what they cannot. Eur Neuropsychopharmacol. 2021 Apr;45:35-37. doi: 10.1016/j.euroneuro.2021.03.005. Epub 2021 Mar 25. PMID: 33774390.

15: Frew AJ.  Glucocorticoids. In:  Clinical immunology: principles and practice, 5^th edition. Rich RR, Shearer WT, Schroeder HW, Frew AJ, Weyland CM, editors. Amsterdam: Elsevier; 2019. p 1165-1175

Supplementary:  This post is another work in progress. I hope to calculate effects sizes of the above medications for MS, another table for rheumatic conditions (RA or SLE) and compare them to effect sizes for lithium, clozapine, valproate, and carbamazepine.  I am interested in the longest RCTS and registry studies that examine these problems.  If you have favorite studies please post the references here or email them to me. 

Image credit:  My wife took this photo of the Bong Bridge between Duluth, MN and Superior, WI. It is an expansive structure and hope I communicated that concept in the above writing.

Cannabis and Causation

Cannabis use is highly politicized in the US at this time largely due to legalization rhetoric that has spilled over into scientific research on the topic.  Despite the broad movement to legalize cannabis across the US, only a minority of the population are regular cannabis users.  More widespread use will undoubtedly lead to increased problems associated with wider exposure, especially wider exposure in populations with vulnerabilities to the toxic effects of cannabis.  The toxic effects of interest include addiction and psychosis.  It is common in clinical practice to encounter daily cannabis smokers who stopped using the substance after several years because they started to get panic attacks, paranoia, or both.  The people I see have all moved on to something else, but there are also a substantial number of chronic smokers who are addicted.  That number is about 9% of users, and that is comparable to the amount of people who have problems from drinking alcohol.  Inpatient psychiatrists commonly see people with florid psychotic episodes from smoking significant quantities of cannabis.  They also see repeat admissions from people who are either detoxified or treated for these psychotic episodes, are discharged and smoke more cannabis to the point of a repeat psychotic episode.  The longstanding controversy among people who are not doing the work and just speculating is whether any good observational studies can be done to show that cannabis does cause psychosis or if this is an artifact of observational methodology.  In other words,  could a reverse causality bias exist that makes people who are prone to schizophrenia or psychotic episodes more likely to smoke cannabis.  In my opinion, there have been excellent observational studies showing the association between cannabis use and psychosis, but as long as that is the technology these studies will always contain the old association is not causation qualifier.

A recent paper (1) in Molecular Psychiatry may have just illustrated the causation that psychiatrists have been experiencing firsthand for decades.  The authors use a novel genetic appraoch to look at the issue of causation.  The main assumption of this study is that using specific genotypes as the independent variable rather than observed individuals gets rid of the confounding demographic and environmental variables that could be casual.  They point out that any actual clinical trial looking at the issue of whether cannabis causes psychosis would be unethical, but that a model that looks at whether causation can be established by looking at single nucleotide polymorphisms (SNPs) from a Genome Wide Association Study (GWAS) looking at the any cannabis use phenotype.  They looked at the top 10 SNPs from that data that were used to calculate gene-exposure (SNP-cannabis) estimates.  SNP-risk of schizophrenia exposure estimates were calculated from available data from the Psychiatric Genomics Consortium.  Instrumental variable estimates were made by dividing the risk of schizophrenia/risk of any cannabis use.  The instrument variable analyses were pooled across SNPs and analyzed with fixed effect meta-analysis.  The authors provide a detailed discussion and rationale for their statistical calculation in the full text of the article and supplementary material.  At this point I am going to post their main graphics.  Click on any graphic to enlarge it.

The first graphic looks at prospective observational studies.  The authors were interested in determining whether their genetically based analysis was in the same direction of this meta-analysis. Ever use cannabis use was associated with a 43% increase in schizophrenia or psychosis.

Figure 1. Meta-analysis of prospective observational studies reporting an association between use of cannabis and risk of schizophrenia or related disorders. Meta-analysis uses a random-effects model. Studies are sorted by type of outcome (schizophrenia only vs schizophrenia and related outcomes). Odds ratios (ORs) and 95% confidence intervals (CIs) express the risk of schizophrenia or psychotic symptoms for ever use of cannabis (compared with never use). For additional information on each study, see Supplementary Table S1. Dunedin, Dunedin Multidisciplinary Health & Development Study; ECA, Epidemiologic Catchment Area; EDSP, Early Developmental Stages of Psychopathology Study; NEMESIS, Netherlands Mental Health Survey and Incidence Study; SC, Swedish Cohort.

Figure 2 looks at the Mendelian Randomization analysis of 34,241 cases of schizophrenia and 45,604 cases of ever use cannabis.  This shows a 37% risk of cannabis users versus non-users for schizophrenia/psychosis risk.  The authors did a sensitivity analysis of this same data by removing each SNP from the analysis to calculate a summary causal effect of 1.33 across all 10 SNPs or 1.88 when restricted to 2 functional SNPs.

Figure 4 is included here to illustrate the authors' sensitivity analysis showing a summary casual effect of about 1.37 (red line).

All things considered this may be a compelling story for causation.  I qualify that of course in a couple of domains.  First. there are a lot of statistical models and calculations operating here.  In my experience mapping complex statistical estimates onto the most complex object in the universe has not worked out very well.  My first hand experience was statistical modeling of quantitative EEG and claims that is was predictive of psychiatric diagnosis.  Those compelling calculations published in Science (4) did not pan out at all in the long run.  It will be interesting to see if the authors applications are more widely applied to other SNPs to determine disease causation from other risk factors.  The second potential problem is a slight variation on that theme and that is the overall imprecision of meta-analysis.  The known  approximate prediction/concordance rates of meta-analyses for clinical trials (2.3) suggests that it may not be good predictor of a reproducible result.  The authors themselves suggest that the potential limitations of their study start with the fact that none of the chosen SNPs met conventional genome wide significance thresholds.  The specific dose effect of cannabis could not be investigated in the study.  The age at exposure is may be a developmental variable of interest and that was unknown.  The Mendelian Randomization techniques may have not been powerful enough to detect pleiotropic (one gene affecting more than one trait) effects, but they discuss how an alternate analysis applies in this situation.

The other question I had was about epigenetic effects on this model.  The authors were certainly aware of smoking as a confounding variable.  The known epigenetic effects of nicotine on brain chromatin would seem to cloud SNPs as pure genetic risk factors.  But this is nonetheless one of the more interesting models and concepts I have seen in a while.

They conclude that their study is "the closest approximation to a randomized trial on the effect of ever use of cannabis and risk of schizophrenia" when such a clinical trial is unethical.   That is an interesting take on their method and causation.  Hopefully it will open up the way for other studies of causation using these techniques.  If that is the case, it is a good idea to study this paper and the supplementary material (26 pages) and have a good idea about its difference from observational/association studies.  The supplementary material is also very useful for the calculations used in the study, a Venn diagram of the overlap between the schizophrenia-GWAS group (N=79,845) and the ever-use cannabis GWAS group (N=37,957), and their review methods of the best observational studies of cannabis use and  schizophrenia/psychosis.  



George Dawson, MD, DFAPA



References:

1: Vaucher J, Keating BJ, Lasserre AM, Gan W, Lyall DM, Ward J, Smith DJ, Pell JP, Sattar N, Paré G, Holmes MV. Cannabis use and risk of schizophrenia: a Mendelian randomization study. Mol Psychiatry. 2017 Jan 24. doi: 10.1038/mp.2016.252. [Epub ahead of print] PubMed PMID: 28115737.

2: LeLorier J, Grégoire G, Benhaddad A, Lapierre J, Derderian F. Discrepancies between meta-analyses and subsequent large randomized, controlled trials. N Engl J Med. 1997 Aug 21;337(8):536-42. PubMed PMID: 9262498.

3: Ioannidis JPA, Cappelleri JC, Lau J. Issues in Comparisons Between Meta-analyses and Large Trials. JAMA. 1998;279(14):1089-1093. doi:10.1001/jama.279.14.1089

4:  John ER, Prichep LS, Fridman J, Easton P. Neurometrics: computer-assisted differential diagnosis of brain dysfunctions. Science. 1988 Jan 8;239(4836):162-9. PubMed PMID: 3336779.

"The standard for psychiatric diagnosis and categorization in the United States and Canada is now DSM-III and soon will be DSMIIIR. The categories defined therein have often been criticized as nothing more than a compilation of symptoms. The results obtained with neurometrics have shown that at least the categories studied are much more than arbitrary groupings of symptoms. ............. Validity-the great deficiency of psychiatric nosology - is beginning to emerge and, thus far, to reveal an impressive concordance with biology." p. 169

5: Smith GD, Ebrahim S. Mendelian Randomization: Genetic Variants as Instruments for Strengthening Causal Inference in Observational Studies. In: National Research Council (US) Committee on Advances in Collecting and Utilizing Biological Indicators and Genetic Information in Social Science Surveys; Weinstein M, Vaupel JW, Wachter KW, editors. Biosocial Surveys. Washington (DC): National Academies Press (US); 2008. 16. Available from: https://www.ncbi.nlm.nih.gov/books/NBK62433/

Selected References on Mendelian Randomization

Attributions:  All graphics except my home-made one at the top are from reference 1 per a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.




Supplementary 1:

With today's publicly available genetic technology it is possible for a person to search their own DNA for the SNPs found in this study.  When I do that using a database where my DNA analysis resides I found the following SNPs from this study from chromosomes 15, 4, and 12 respectively.  I have linked them  to the dbSNP database at NLM:

rs4984460

rs7675351

rs2099149

It is interesting to speculate on what it means to have 3/10 genetic markers for schizophrenia/psychosis susceptibility if any cannabis exposure.


Supplementary 2:  Click on my homemade graphic to see how beautiful it is.  Blogger does not do it justice.