Bipolar4LifeSupport

I experienced an interesting confluence of events the other day. My 11-year-old son has been finding out about the great power of online information. Although we limit his access to assistance with homework, he is already a digital whiz kid who knows where to find a great deal of information for writing tomes like Norse history reports. In my day, it would have taken an entire afternoon of digging through texts at a university library to obtain items he found in a few seconds.
The confluence came about because of what I was doing while sitting next to him. While he was Headshotbusy downloading information about Vikings, I was reading an update on a story that I have been following for a few years: the attempt to create a simple, objective blood test that could properly identify mood disorders. That would be a truly handy gadget for mental health professionals to have in their diagnostic tool kits! Going through the literature, which relies heavily on gene expression data, it hit me how profoundly the judicious use of online databases has contributed to the scientific rigor of the research. The Internet was not only seminal to my son’s work but also to this blood test research.

In this column, I will discuss new progress on this Internet-boosted line of inquiry. I will begin with a few basics about differential gene expression and microarrays and will then move on to something that researchers are calling “convergent functional genomics.” As you shall see, the clever use of online databases both confirmed and extended the work done at the bench. As a result, it may very well be possible in the next few years to have a clinic-ready blood test that is capable of diagnosing unipolar and bipolar depression. There may even be a diagnostic test for schizophrenia.

Background

In order to understand this promising research, we first need to review a few facts about differential gene expression, microarrays, and their use in the laboratory. As you may remember, only about 2% of the genome encodes for messenger RNA (mRNA)—sequences usually referred to as class II genes (the rest of the genes encode either ribosomal RNA, called class I genes, or transfer RNA, called class III genes).

You can subdivide class II genes into 2 categories based on the transcriptional activity. Some class II genes are turned on all the time; we often refer to them as “housekeeping” sequences. Some class II genes are expressed quite cell-specifically (a neuron has a very different job description from, say, a gut fibroblast, after all), and they are either completely silent or are called on infrequently, depending on the needs of the cell.

Researchers can capture these “need-specific” class II mRNAs quite easily because of the binding properties of their nucleotides. Consider this example: Suppose you are interested in finding out which neural genes, if any, become activated in the presence of a test medication. You take 2 groups of cells; 1 group will not be exposed to the drug (serving as the unstimulated control), while the other will be exposed to the drug for a set period.

How do you get the medication-specific genes? You simply isolate both sets of mRNA, convert them to helical DNA, and then mix them together. The genes that are commonly expressed in both populations (like those housekeeping genes) will find each other and, with some coaxing, bind together. This makes them double-stranded. The genes that are unique to the medication stimulation have no “partners” and will not bind to anything. This makes them single-stranded.

Since it is easy in the laboratory to separate double-stranded from single-stranded snippets of DNA, we can quickly isolate our “medication-specific” gene population. (This technique can also be used in the opposite direction for some medications, or “turn off” genes.) Simply looking for unpaired populations in the controls can give the researcher valuable information about active and suppressive events related to medication exposure.

Today, populations of nucleotides can be embedded in something we call a “microarray,” which is essentially a plastic tray to which DNA samples have been previously and irreversibly bound. Any DNA can be attached to the microarray, including any (or all) products from the 40,000-plus genes that make up the human genome. Once embedded, you simply wash the plastic with the nucleotide sample that you are testing and see what does and does not bind to the nucleotides on the dish. This hybridization principle was used extensively in the data I am about to describe.

Experimental beginnings

The blood test experiment was an attempt to measure whole genome expression differences in populations with mood disorders (and schizophrenia) using only their blood as the sample substrate. If any unique gene sequences were discovered, would these sequences predict mood disorders in unknown populations? The researchers had their biological work cut out for them. It is quite an experimental leap to ask about events going on in the brain by interrogating only the blood. As you shall see, using a database that looked at human brain–specific gene expression (on the Internet) turned out to be critical for this work.

Le-Niculescu and colleagues1 enrolled 3 cohorts in this study: 2 for depression and 1 for psychotic disor-ders. Twenty-nine patients in the first cohort had been given a diagnosis of bipolar I disorder. The second group, a replicant cohort, consisted of 19 patients with bipolar I disorder. The third group comprised the psychoses-related cohort and included 30 persons with schizoaffective disorders, substanceinduced psychoses, and schizophrenia.

The first task for the researchers was to isolate the genetic substrates of the patients in various phases of their mood disorder. Blood samples were collected when the patients were in a high-mood state (a visual analog scale score of 60 or higher) and in a low-mood state (visual analog scale score of 40 or lower). The various populations of mRNAs were isolated from theseFigure 1 blood samples, and the hybridization work involving microarrays began.

Unique gene expression profiles were eventually obtained in both low- and high-mood states and were then divided into forward and reverse mRNA subpopulations.

The forward population represented the manic state. Those mRNA populations were classified as absent in the low (ie, the gene was not expressed in the low-mood state) and present in the high (meaning that the gene was expressed only in the high-mood state). The isolated sequences were considered to be candidate biomarkers for the manic phase of the disorder.

The reverse populations were also isolated. They were absent-in-the-high mood but present-in-the-low mood representatives. These sequences were considered to be candidate biomarkers for the depressive phase of the disorder.

The first category was cross-validation using animal models. This vetting procedure used a pharmacogenomic mouse model for bipolar disorder. Both low-specific and high-specific populations were characterized, and gene sequences were isolated using similar microarray procedures previously deployed in the human work just discussed. In these tests, the source of the mRNAs included not only mouse blood but also brain tissue. The mouse sequences isolated in this fashion were compared with the human sequences previously described.

Next, the strongest gene candidates in each category then underwent an extensive series of tests and cross-checking (Figure). These validation exercises can be divided into 3 categories.

The second category was cross-validation using human postmortem brain sample databases. This vetting procedure involved peering into the Internet and specifically assessing a URL that was carrying data from “GeneCards”—an Online Mendelian Inheritance of Man database (http://www.nslij-genetics.org/search_omim.html). This database contains published reports of changes in the expression of specific genes in postmortem brain tissues that were obtained from patients with bipolar disorder. The idea was to compare the sequences that were isolated from living patient blood samples with sequences that were isolated from the brain samples in deceased patients.

This was a key step because the cross-checking not only involved human-to-human comparisons but it was also the first attempt to establish blood-to-brain connections with the data. As was hinted at previously, the body spends a ridiculous amount of time and resources trying to wall these systems off from each other. Any blood test that is designed to assay something in the brain by looking for something in the blood would need the concordance between the tissues down pat. It is also tricky to determine the phase of illness at which the person died: was it at the low end or at the high end? The researchers assumed that the deaths occurred when the patients were experiencing the low symptoms. Most amazingly, perhaps, the researchers found a number of sequences that converged well with the genes they previously obtained from the blood sample.

The third category was cross-validation using human genetic data linked to mood disorders. This work also involved extensive use of an Internet-borne database. An online sequence-based integrated map of the human genome is published by the University of Southampton in the United Kingdom. (There is a similar collection of information called the Marshfield Clinic Research Foundation database in the United States.) These databases on gene sequences include previously published works shown to have a genetic linkage to mood disorders.

Taken together, 3 separate vetting procedures were used to screen the sequences isolated from the original living human cohorts. At each step, a single question was asked: “Do any of the sequences match?” Answering this question was not straightforward, and statistical analyses were then performed to determine convergence. The researchers termed the entire protocol “convergent functional genomics.”

That any genes could still be present after such screening is a testament to both the rigor of the work and the insightful nature of the experimental design. The researchers did indeed find matches. A total of 10 candidate genes survived the screenings. Five came from the selection in the high-mood, or manic population: Atxn1, EdnRb, Edg2, Fzd3, and Mbp. Five came from the selection in the low-mood, or depressive population: Erbb3, FGfr1, Mag, Pmp22, and Ugt8.

What do these gene sequences do? This is probably the most biologically interesting aspect of the work, and it is easily the most opaque. Some of the gene sequences are involved in the normal myelination of neurons. These included the sequences Edg2, Mag, Mbp, Pmp22, and Ugt8. Several of these are involved in growth factor signaling: Erbb3, FGfr1, Fzd3, Igfbp6, and Ptprm.

What does the isolation of these sequences mean to our biological understanding of mood disorders? Not much, unfortunately. Growth factor and signal transduction sequences seem to hold the greatest promise for obtaining early leads. The presence of so many myelination-specific genes in an affective disorder, however, is less intuitive and certainly more surprising, and their roles are nearly a complete mystery.

Discovering biological roles was not the point of this work, however. There was a more practical issue: Given the blood data, how well did these sequences actually predict a mood disorder?

The answer makes these data especially compelling for the future clinic. Using the original populations, these 10 biomarkers were tasked to predict which patients had what disorder and which phase they were experiencing at the time of the test.

Such prediction is relatively easy. The researchers calculated a score on the basis of the ratio of high-mood to low-mood genes, using both sensitivity scores and specificity inventories. Their results were a stunner. In the first cohort (high mood only), sensitivity was 84.6% and specificity was 68.8%. In the second cohort (high mood only), sensitivity was 70.0% and specificity was 66.7%.

Similar results were obtained when predicting low mood. In the first cohort (low mood only), sensitivity was 76.9% and specificity was 81.3%. In the second cohort (low mood only), sensitivity was 66.7% and specificity was 61.5%.

These are extraordinary figures. As the researchers themselves pointed out, these scores are comparable to results obtained in prenatal tests that can predict Down syndrome. They do indeed seem to have uncovered a working blood test for an affective disorder.

Conclusion

These data were obtained with adults who were experiencing a specific disorder in an even more specific phase. The test was conducted with an assay that could be administered in any clinic capable of drawing someone’s blood. Although not mentioned in this space, similar results were obtained in predicting disease states in the psychoses cohort. Some believe
that blood test kits that are capable of such diagnostic discrimination could be available in as few as 5 years.

Of course, the robustness of these findings immediately suggests the commissioning of larger, more prospective studies. It also suggests something equally extraordinary: the critical role of the creation of specific databases and how their unfettered, online access took part in uncovering such big science.

Jan/Admin @ http://www.bipolarandsupport.com

Bipolar disorder causes havoc in patients’ lives. Even in the best of circumstances, successful treatment is challenging. Treatment targets constantly shift; patients are frequently nonadherent; and comorbidity is the rule, not the exception. Diagnosis of bipolar disorder is often difficult. Comorbidities need to be identified and addressed if treatment is to be effective.
The importance of an accurate diagnosis

With apologies to Charles Dickens, bipolar disorder is often experienced as the “best of times and the worst of times.” This polarity often causes bipolar disorder to be undiagnosed, overdiagnosed, or misdiagnosed. Bipolar disorder is associated with a significantly elevated risk of suicide. Moreover, bipolar patients often use highly lethal means for suicide.1 Contributing factors include early age at disease onset, the high number of depressive episodes, comorbid alcohol abuse, a history of antidepressant-induced mania, and traits of hostility and impulsivity.

Bipolar I disorder, with episodes of full-blown mania, is usually easier to diagnose than bipolar II disorder, with episodes of subtler hypomania. Recognizing that the primary mood state may be irritability rather than euphoria increases the likelihood of diagnosis as does the recognition that symptoms often last fewer than the 4 days required for diagnosis by DSM-IV.2 Focusing more on overactivity than mood change further improves diagnostic accuracy, and the use of structured questionnaires is helpful.

Given the greater frequency of depression than manic episodes in bipolar disorder, what clues indicate bipolar disorder rather than unipolar depression? The Table lists factors that may help identify unipolar depression.

A moving target needs moving treatment

Effective personalized treatment recognizes bipolar disorder as a biopsychosocial disorder, but mood-stabilizing medications are the backbone of treatment. These medications fall into 3 categories: lithium, antikindling/antiepileptic agents, and second-generation antipsychotics. The mechanisms of actions by which these medications work are numer-ous and include increasing levels of serotonin, γ-aminobutyric acid, and brain-derived neurotrophic factor (BDNF) and decreasing glutamate levels; modifying dopamine pathways; stabilizing neuronal membranes; decreasing sodium channels; decreasing depolarization; decreasing apoptosis; and increasing neural cell growth/arborization.

Double-blind placebo-controlled studies of the medications—lithium, divalproex, carbamazepine, and atypical antipsychotics—used to treat symptoms of acute mania have demonstrated a response rate of approximately 50% to these drugs. Response was defined as a 50% decrease in symptoms using the Young Mania Rating Scale (YMRS) with onset of response within a few days.

An increasingly intriguing aspect of treatment with lithium and atypical antipsychotics involves their effect on BDNF. In a study of 10 manic patients treated with lithium for 28 days, most (87%) showed an increase in BDNF level (ie, from 406 pg/mL to 511 pg/mL).3

TABLE

Factors that suggest bipolar depression rather than unipolar depression
In a typical 3-week study of acute mania, approximately half of the benefit was seen by day 4. A 3-week, double-blind, inpatient study of olanzapine and risperidone in 274 patients with acute mania found that of 117 patients who had a less than 50% decrease in the YMRS score at 1 week, only 39% responded and 19% had symptom remission at end point. Of 40 patients with a less than 25% decrease in the YMRS score at 1 week, only 25% responded and only 5% had symptom remission at 3 weeks. Of 157 patients who had at least a 50% decrease in the YMRS score at week 1, 84% responded and 64% had symptom remission at 3 weeks.4 Clinically, a medication change should be considered for patients who do not demonstrate substantial benefit by week 1.

A meta-analysis comprising 16,000 patients who had acute mania found that the most effective agents were haloperidol, risperidone, and olanzapine. The least effective were gabapentin, lamotrigine, and topiramate.5

A combination of medications—typically lithium or an antiepileptic with an atypical antipsychotic—is often necessary to successfully treat acute mania. A meta-analysis found the response rate increased from 42% to 62% when an antipsychotic was added.6

Bipolar depression has proved to be more resistant to medication treatment than mania. The same medications are used, with lamotrigine for maintenance treatment. The FDA has approved Seroquel, Seroquel XR, and Symbyax (the combination of olanzapine and fluoxetine), for the acute treatment of bipolar depression. Studies of acute bipolar depression have typically lasted 8 weeks. Approximately half of the benefit oc-curs by week 2, with statistical separation from placebo between weeks 1 and 3.7-9

The best treatment is prevention

Patients who have bipolar disorder almost always require lifelong maintenance treatment, frequently with 2 medications: one to prevent the upside (ie, hypomania/mania), and another to prevent the downside (ie, depression).

Findings from a registration trial showed that lamotrigine more effectively prevented depressions than lithium but lithium prevented mania/hypomania more effectively than lamotrigine.10

Another study added placebo or lamotrigine to lithium treatment for 124 patients. The median time to relapse/recurrence was 3.5 months for those taking lithium monotherapy but 10 months for those who received combination treatment.11

The effectiveness of a combination maintenance regimen was also seen in a study of 628 patients with bipolar I disorder treated for 2 years: 65% of those taking lithium or divalproex alone experienced a recurrence compared with 21% who received quetiapine added to lithium or divalproex.12 However, combination treatment may result in more adverse effects and increased risk of drug-drug interactions.

The best mood stabilizer

The best mood stabilizer for a patient is the one he or she will take. No matter how effective a medication is, it will not relieve symptoms if it is not being taken. The key to effective personalized treatment of bipolar disorder is a good patient-physician connection in which the patient is part of the treatment decision-making process.

Psychotherapy is an integral part of the effective treatment of bipolar disorder, not just an augmentation strategy. Psychotherapies that are helpful include cognitive-behavioral therapy and social rhythm therapy.13 Psychotherapy can focus on several areas, such as education, comorbidities, medication adherence, and interpersonal relationships. In addition, therapy can challenge the automatic, distorted, and dysfunctional thoughts and help the patient maintain social rhythms (eg, consistent sleep). The involvement of family members in treatment enhances success.

Patients may stop taking their medications because the adverse effects become intolerable; they may miss what they perceive as their more satisfying and productive hypomania; and they might believe that a period without symptoms means that they are cured and no longer need medications. One study of 3640 patients with bipolar disorder who made 48,000 physician visits found that 24% of patients were nonadherent (defined as missing at least 25% of doses) 20% of the time. Factors associated with nonadherence included rapid cycling, suicide attempts, earlier onset of illness, anxiety, and alcohol abuse.14
Patients who have bipolar II disorder spend far more time depressed than hypomanic. Lithium appears to be less effective than antikindling agents for rapid cycling as well as for mixed bipolar disorder states.15

Maintenance treatment is necessary for patients with acute mania or acute depression; therefore, choose medications that are more tolerable to the patient to facilitate long-term adherence. Recognize that medications may need to be adjusted or changed—in the acute phase of illness, rapid efficacy is often the priority, while medication adherence is the priority during the maintenance phase.

Other factors to consider when choosing the best medication for a particular patient include:

• A history of treatment response

• A family history of response

• Adverse effects of a particular drug

• Drug interactions

• Pregnancy

• Breast-feeding

Antidepressants

The use of antidepressants in bipolar disorder is controversial because they may induce rapid cycling, especially in patients with episodes of rapid cycling.16 In a study by Altshuler and colleagues,17 patients who had breakthrough depression despite treatment with a mood stabilizer were treated with antidepressants for at least 60 days. Patients who had symptom remission for 6 weeks were followed up for 1 year: 36% of patients who continued antidepressants for longer than 6 months relapsed versus 70% who discontinued antidepressants before 6 months.

A randomized discontinuation study with antidepressants found no statistically significant symptomatic benefit in the long-term treatment of bipolar disorder.18 Trends toward mild benefits, however, were found in patients who continued antidepressants. This study also found, similar to studies of tricyclic antidepressants, that rapid-cycling patients had worsened outcomes with continuation of modern antidepressants, including SSRIs and SNRIs.

An NIMH study of 159 patients who had breakthrough depression despite receiving a mood stabilizer were treated with sertraline (mean dosage, 192 mg/d), bupropion (mean dosage, 286 mg/d), or venlafaxine (mean dosage, 195 mg/d) for 10 weeks with a 1-year follow-up.19 At the end of 1 year, only 16% of the patients had continued remission while more than 55% had switched to mania/hypomania. The worst results were seen with venlafaxine and the best with bupropion.

In a study by Sachs and colleagues,20 patients who had breakthrough depression despite being treated with mood stabilizers were randomized to paroxetine (mean dosage, 30 mg/d), bupropion (mean dosage, 300 mg/d), or placebo. No significant differences on any effectiveness or safety outcome, including remission rates or affective switch frequency, were found.

Overall, these studies indicate that the role of antidepressants is limited and that, in fact, a trial of a mood stabilizer cannot be considered to have failed unless the failure occurs in the absence of an antidepressant. A meta-analysis of 18 studies with 4105 patients found that combination treatment including a mood stabilizer and an antidepressant was not statistically superior to monotherapy.21

When symptoms persist

Establish the context of each appointment by focusing on changes in occupational, social, family, and health status. Evaluate medication regimens, with a focus on effectiveness for carefully chosen target symptoms and adherence to treatment, as well as medication tolerability and patient attitudes. Be alert to the emergence of early symptoms of mood change, and adjust medications if necessary. Remember that treatment modalities often need to change over time.

Mood stabilizers should be optimized with combination therapy for sustained remission. Antidepressants may worsen the disease course, and a true trial of a mood stabilizer can-not occur within the setting of antidepressants. If symptoms persist, ask: Is the patient taking anything that is making symptoms worse, eg, drugs, alcohol, or antidepressants? Is the patient taking the medications? Is treatment adequate? Is another condition (including subclinical hypothyroidism) interfering with treatment? Is psychotherapy being ignored?

Jan/Admin @ http://www.bipolarandsupport

Astute readers of obituaries know that “died unexpectedly” is a common euphemism for “died by suicide”. The family and friends of suicide victims are often reluctant to openly discuss the cause of death because of profound sadness, sense of privacy, embarrassment, or cultural taboos. Public discourse on suicide is also limited, perhaps because of unease with the topic of self-destruction or cultural bias against suicide. The news media generally pay scant attention to suicide other than celebrity suicides¹ and suicide clusters.² Yet, we are all affected by suicide. There are few among us who have not been touched by the loss of a loved one, friend, colleague, or patient who has chosen to end their life by suicide. It occurs in all countries and all cultures. It can happen in any family, including your own. Suicide has been reported in children as young as 6 years old,³ the very old,⁴ and all ages between. For the victim of suicide, it is a life needlessly lost. For the survivors of suicide victims, the family and friends, there is an enormous toll in terms of grief, guilt, and a lifetime of unanswered questions.⁵ Suicide is an act that is contrary to what is perhaps the strongest of human instincts—survival. To voluntarily end one’s own life is incomprehensible for most of us.

In The Lancet, three reviews help us to better understand the incomprehensible, each with the aim of contributing to strategies to reduce the risk of self-destructive behaviour. Keith Hawton and colleagues⁶ review the current state of knowledge for self-harm and suicide in adolescents; Alexandra Pitman and colleagues⁷ do the same for suicide in young men. The third article by Paul S F Yip and colleagues⁸ is devoted to prevention of suicide by means restriction—ie, the limiting of access to highly lethal methods of suicide. The articles are informative for health-care providers and provide a context for the development and modification of suicide prevention strategies. Although each article addresses different aspects of suicide, there are some common themes, either implied or explicitly stated, that are worthy of further elaboration. These include the complexity of factors that lead to suicidal behaviour, pain as a unifying feature in the framework of suicide, and means restriction to prevent suicides.

Among the many risk factors for suicide are mental illness, physical illness, previous suicide attempt, substance abuse, family history of suicide, impulsiveness, hopelessness, isolation, and loss (relationship, social, work, financial).⁹ Most of us who encounter such challenges learn to cope with them or find ways to overcome them, going on to survive and sometimes flourish. However, an individual with limited psychological reserves who faces the same challenges might come to feel that suicide, however undesirable, is preferable to living. Although there is no simple explanation for such counter-intuitive human behaviour, social and cultural factors, media exposure, and availability of lethal means are woven in a complex web with other risk factors that can lead to suicide.^{6, 7, 8} The complexity of risk factors for suicide suggests that many approaches to suicide prevention should be considered and customised to accommodate local circumstances.

Memory wall dedicated to those lost to suicide, Hazleton, PA, USA

Jamie Pesotine/AP/Press Association Images

View Large Image

Suicide and pain are closely linked.¹⁰ Suicide might be chosen as the ultimate solution to end psychological pain (eg, from depression or bullying) or chronic physical pain when there is a perception that no other option for relieving the pain is available. Pain is also a consequence of suicide. The family and friends of suicide victims suffer from psychological pain in ways that are mostly silent but nevertheless profound, and they themselves are at high risk for suicide due to the loss they have experienced.¹¹ Pain management is good medical care for these people and might reduce the risk of suicide.¹²

There is strong empirical evidence that restriction of access to lethal means reduces suicides.¹³ The benefit of this approach is predicated on the impulsivity of suicide. It is commonly a very short time, often minutes to hours, between the decision to attempt suicide and the act of suicide, with the urge to die by suicide rapidly dissipating if not completed. Many who die by suicide do not provide advance warning and do not seek help from others.¹⁴ When a common and highly lethal means of suicide (eg, handguns in the USA, pesticides in Asian countries) is easily available, a suicide attempt is likely to result in death. When access to highly lethal means is thwarted (eg, waiting period for purchasing a handgun in the USA, restricted access to pesticides in Asian countries), another chosen means (eg, drug overdose) might be less likely to result in death. Of those who survive a suicidal impulse or a suicide attempt, many go on to live long and productive lives. Means restriction is an effective population-based approach that should be considered for inclusion in all comprehensive suicide prevention strategies.

EML and SAM are the father and fiancée, respectively, of a victim of impulsive suicide. They both declare that they have no conflicts of interest.

Jan/Admin @ http://www.facebook.com/groups/bpandsupport   &

http://www.bipolarandsupport.com   (CLOSED GROUPS)

Mental disorders are common in all countries, affect every community and age group, contribute substantially to the overall burden of disease, and have major economic and social consequences and effects on human rights. However, the greatest inequities are cross-national: 80% of people affected by mental disorders live in low-income and middle-income countries, which benefit from scarcely 10% of global mental health resources. Global mental health initiatives attempt to improve the availability of, access to, and quality of services for people with mental disorders worldwide. Diagnostic categories and a classification of mental disorders, which are essential to achieve objectives of global mental health, are needed for a range of stakeholders: for health-care practitioners to make treatment decisions and implement clinical guidelines; for policy makers to make decisions about allocation of resources; and for patients and their families to gain an understanding of their disorders. But can contemporary psychiatric classifications meet these needs?

Non-specialist health professionals working in routine health-care settings deliver more than 90% of mental health care worldwide. Psychiatric disorders are frequently diagnosed in epidemiological surveys in community and primary care populations, in particular the common mental disorders of depression, anxiety, somatoform, and stress-related disorders (all of which are distinct categories in contemporary classifications). However, a large gap often exists between the numbers reported in surveys and those recorded by primary care workers. The response of psychiatry has traditionally been to assume that these workers are not well informed about contemporary psychiatric concepts. Many strategies, such as the use of simple diagnostic algorithms, straightforward management guidelines, training programmes to improve skills, and advocacy campaigns, were developed and implemented to address this gap.¹ However, these approaches did not seem to substantially change primary care worker behaviour, or improve detection rates or outcomes in developed nations or low and middle-income countries.^{2, 3, 4} Indeed, one of us (VP) had proposed that even the term “mental” needed to be dropped altogether from such training.⁴

The American Psychiatric Association, who produced the Diagnostic and Statistical Manual of Mental Disorders fourth edition (DSM-IV), and WHO, who produced the International Classification of Diseases tenth revision (ICD-10), also published concise versions of these classifications for use in primary care (DSM-IV PC and the ICD-10 PHC).^{5, 6} They reduced the number of categories (eg, from hundreds of ICD-10 categories to 26 in ICD-10 PHC), the many categories of depressive and anxiety disorders were reduced into single categories, and the more obscure diagnostic categories (eg, dissociative stupor) were dropped altogether. However, these simplified schemes remained virtually unknown and unused in primary care worldwide. Nevertheless, there are plans to release similar primary care versions (DSM-5 PC and ICD-11 PHC) to correspond to the latest revisions of these classifications. Given the allegiance that these classifications owe to their parent classifications, we doubt that they will be suitable for the global mental health cause.

The vast differences in settings, patient populations, and perspectives between psychiatrists and primary health-care professionals demand caution in the translation of specialist concepts and classifications for use in primary care. Primary care workers typically see patients with mild and non-specific symptoms, subsyndromal and mixed presentations, often clustered around the case threshold and frequently associated with psychosocial adversity and with physical health problems.^{7, 8, 9} They recognise the importance of psychosocial circumstances (eg, stress, personal resources, coping, social supports, and culture) and their effect on mental health. They prefer not to use mental disorder labels because of the high rates of spontaneous remission and placebo response and the absence of improvement with antidepressant drugs in those with mild disorders.¹⁰A related serious concern for primary care workers is the medicalisation of human distress.^{11, 12} They contend that the use of symptoms to diagnose mental disorders, without consideration of context, in particular psychosocial adversity, essentially flags non-clinically significant distress, especially at lower degrees of severity.

Although psychiatrists prefer to use disorder labels, primary care workers often favour dimensions of distress for presentations of common mental disorders. Primary care workers seem to be uncomfortable with the use of the notion of mental disorder, with its disease halo, which sidesteps the disease–illness dichotomy while attempting to encompass both disease and distress. Consequently, these practitioners mostly do not use psychiatric categories at all, preferring to avoid potentially stigmatising and meaningless labels, or use categories like “mixed anxiety–depression” and “adjustment disorders”. However, mixed anxiety and depression is not included in DSM-5,¹³ ICD-11-PHC,¹⁴ or in the recent WHO guidelines, which are designed explicitly for primary care.¹⁵Adjustment disorders are also excluded from ICD-11-PHC and the WHO Mental Health Gap Action Programme and have a subordinate status in the DSM-5. Dimensional approaches to distress and categories that are most common and useful in primary care are poorly addressed in contemporary classifications. Psychiatry has also reified common symptoms of disorders frequently seen in specialist settings, while primary care workers, who understand local contexts, acknowledge several variants of distress. Perhaps the most outstanding example of this discrepancy is the diagnosis of depression: not surprisingly, people with depression who consult a mental health specialist proffer their emotional and cognitive symptoms, but those in primary care almost never do so, and instead prefer to discuss their somatic and behavioural symptoms.

These discrepancies force primary care workers to favour the recording of reasons for the clinical encounter or the recognition of broader clinical presentations instead of using specific psychiatric labels. This situation is a key reason why primary care workers use the International Classification of Primary Care-2,¹⁶ which focuses on reasons for clinical encounters, patient data, and clinical activity. Although primary care workers do make specific diagnoses for medical disorders when reliable, valid, and laboratory diagnoses are available, they tend to focus on reasons for the clinical consultation, distress, and functioning in other contexts. Primary care workers argue that psychiatric syndrome labels are unsuitable because most of their patients have mild, subsyndromal, and mixed presentations,^{7, 8, 9, 17} which are often associated with psychosocial adversity. They reject the mechanistic application of psychiatric hierarchies, which subordinate adjustment problems to mental disorders by discounting psychosocial contexts. Consequently, specialist perspectives, which dominate the academic discourse and official classifications, are rarely used in primary care practice. Despite buy-in by academic primary care workers who endorse psychiatric approaches in primary care, there seem to be very few takers in front-line primary care, especially in low-income and middle-income countries. The many differences in patient populations and perspectives suggest a so-called category fallacy when specialist cultures are blindly imposed on primary care.

Most evidence argues that contemporary classifications (eg, DSM-IV/5 and ICD-10/11) and their diluted versions for primary care will be of little use in global mental health. Poor agreement between the diagnostic approaches adopted by psychiatry and those that are useful in primary care will also result in a failure of policy and health system strategies to improve mental health care. The DSM is a context-bound classification unique to the health system characteristic of psychiatric care in the USA. Conversely, the ICD for mental disorders is bound to the specialist culture of international psychiatry. In view of the enormous barriers to their application in most mental health consultations worldwide, primary care workers, although cautious about expressing resistance, openly use a different dialogue among themselves, which then dictates their practice. However, any resistance or challenge to specialist perspectives on mental disorders in primary care usually generates incredulity among psychiatrists and a reinforcement of their belief that re-training of primary care workers is the solution. We disagree. Our experience shows the problem is not just about the skills of primary care workers but also the approach adopted in contemporary classifications.

Global mental health needs a pragmatic classification that addresses the prevalent disorders in primary care and community settings, acknowledges both the dimensional and categorical nature of common mental health symptoms, addresses contextual issues, and that can easily be incorporated into routine health-care platforms across diverse settings. This aim does not just mean reduction in the number of labels or in their complexity, but the creation of categories relevant to and frequently seen in primary care. The fact that labels will vary across languages and cultures should be explicitly acknowledged, and diagnostic practices must be adapted to suit these variations. Put bluntly, if very few patients report feeling depressed, then the value of imposing the label “depression” is questionable; unsurprisingly, most global mental health interventions targeting depression in low-income and middle-income countries avoid the use of the label altogether.¹⁸

Many narratives of mental distress and disorder across settings exist that argue against one authentic version of these complex accounts. Their diversity and contradictions are a testimony to the pluralism of mental health-care traditions. Consequently, primary care needs to consider its own reality and devise a classification for mental distress and disorder that is usable in routine clinical practice, rather than work with a handed-down, diluted version of specialist approaches. Mental disorders in primary care need to be understood and theorised independently of specialist perspectives.¹⁹ The process of the identification of common presentations and development of a classification system and management protocols in primary care needs to be led and owned by primary care workers, and this process needs to engage with communities, donors, or policy makers who are major stakeholders for global mental health.

Declaration of interests

The views expressed in this Viewpoint are those of the authors. KSJ is a member of WHO’s Primary Care Consultation Group involved in the revision of the International Classification of Diseases of Mental Disorders tenth revision for primary care. VP is supported by a Wellcome Trust Senior Research Fellowship in Clinical Science, and by grants from the National Institute of Mental Health, the Department of International Development, Grand Challenges Canada, Autism Speaks, and the Sir Jamsetji Tata Trust. He was cochair of the DSM-5 working group on somatoform disorders. The authors declare that they have no other competing interests.

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