Classifications and ‘declassifications’ are not new to schizophrenia. DSm 4 had 5 subtypes, DSm5 removed them all. Icd 10 still has 8 subtypes. Symptom dimensions ( positive, negative , affective and cognitive ) are accepted as the more useful way to subtype this disorder in the new classifications.These classifications do no reflect the clinically valid concept of treatment responsive and treatment refractory schizophrenia. Oliver Howes and Shijit Kapur argue that the biology behind treatment response should be one guiding factor in classification.
Evidence for pre synaptic dopamine dysfunction in schizophrenia is robust. Over 50 in vivo molecular imaging studies support this. Demonstration of substantial D2 blocking by effective antipsychotics provide converging evidence in support of dopamine theory.
Why do not all patients on Dopamine blockers improve? PET imaging show that some patients show little or no response even with high levels of dopamine receptor blockade.
Are there any difference in dopamine system between responders and non responders?
Dopamine metabolite level studies show a bimodal distribution i.e. there is a group with high dopamine and another one with no alteration. Patients who responded show higher dopamine levels in striata in postmortem studies compared with those who did not respond. Amphetamine do not increase psychosis in a subgroup of schizophrenia. Dopamine synthesis capacity was increased in treatment responsive group compared to resistant group.
Based on the above observations, authors argue that may be there are two biological types of schizophrenia: the A- hyper dopaminergic, type B- norm dopaminergic. There are potential advantages to such attempts, as focusing on mechanisms will help open some doors to new treatments. Early identification of Type B patients would perhaps help them to choose clozapine early on. PET imaging of dopamine may help make such decisions.
What underlies type B? probably glutamatergic mechanisms. we don’t know now. Authors are of the view that it is time to move on from descriptive typing.
Summary of the article:
Howes OD, Kapur S. Br J Psychiatry. 2014 Jul;205(1):1-3.
Happiness is a difficult concept to define and measure. One foundation on which concept of happiness is built is rewards. Momentary measures of happiness ( =hedonic well-being) can be studied by sampling the experiences of individuals and analysing how these are related to antecedent life events /rewards. However, we do not know much about how the cumulative influence of such daily life events are aggregated into subjective feelings.
Is it the outcome of tasks ( reward) or the expectation that determine happiness?
What are the brain correlates of such momentary happiness?
Can the happiness be predicted using computational models / formula?
Robb B. Rutledgea, Nikolina Skandalia, Peter Dayanc, and Raymond J. Dolan from University College London reports the results of an interesting study that attempts to answer these questions?
Participants performed a probabilistic reward task (gambling) and were asked about their level of happiness after every few trials. ( i.e. overall emotions state ). Participants were scanned (fmRI) while they made choices. The team created a computational model of momentary well-being that includes expected values of chosen gambles and the difference between experienced and predicted rewards ( both from dopamine activity) and looked at its ability to predict momentary happiness. Additional behavioural experiments were carried out to validate these models. 18420 people participated in a smart phone experiment ( The great brain experiment/ app) which essentially repeated the lab experiment in a large cohort.
Momentary happiness reflects NOT how well things are going, BUT instead whether things are going BETTER than Expected. This includes positive and negative expectations, even in the absence of outcomes.
Rewards received many trials ago have no influence on current happiness in the task. Recent rewards affected ongoing reactive happiness rating.
Overall effect of expectations on happiness is negative. i.e. positive expectations effectively reduce the overall emotional impact of trials with positive outcomes.Negative expectations, reduce the overall emotional impact of trials with negative outcomes. ( = if expecting negative outcome, then prepare with more negative expectations to reduce distress later)
Lowering expectations can increase the probability of positive outcome . ( used in sports= ‘underdog’ approach).However lowering expectations reduce well-being before an outcome arrives.
A sufficiently negative expectation can help create an overall positive emotional impact from a negative event. ( =predict a longer delay ( flight/ parcels/appointments) and anything less than that reduce the distress.
Remembered feelings depend most on how experiences were at the END. (= painful medical procedures are remembered as less unpleasant when they end with a less painful period).
Phasic dopamine release in striatum is linked to momentary happiness.
Momentary happiness is a result of the combined influence of recent reward expectations and prediction errors arising from those expectations.
Comments: It is not what actually happens that determine how you feel, it is what happened in relation to your expectation that matters.
Summary of the article:
Rutledge RB, Skandali N, Dayan P, Dolan RJ. Proc Natl Acad Sci U S A. 2014 Aug 4
Course of schizophrenia is highly heterogeneous. Predicting outcomes in schizophrenia is an extremely difficult task. The popular assumption of rule of thirds” ( in a group of schizophrenic patients one-third improves, one third deteriorates and one third has an intermediate course) do not have an empirical basis.
Some studies show that 45% to 70% could be defined as remitters at some point during the course of the illness. Shorter duration of untreated psychosis (DUP), a better premorbid adjustment, lower illness severity at baseline, early symptomatic improvement, medication adherence and remitted substance abuse are thought to increase the likelihood for remission.
There are conflicting findings regarding predictability of outcome based on symptoms. Italian researchers Carlo Marchesi and colleagues investigated whether the severity of positive, disorganized and negative symptoms assessed at onset in first-episode patients with schizophrenia predicted remission (using RSWG criteria) after several years of illness.
56 patients admitted with first episode psychosis between 95-99 were followed up for average of 16 years. These patients accessed ‘reasonably comprehensive’ public mental health services. They were re-assessed in 2010 (blind to the baseline evaluation). Of the 56 patents enrolled, 48 were reassessed at follow up. Diagnosis remained same in all at follow up.
38% achieved remission criteria. Among remitted patents (18/48), 3 were in complete symptomatic recovery.
Remission and symptoms at onset: Remitters had milder positive and negative symptoms and a lower overall symptom severity at baseline.
Severity of positive symptoms at onset do not impact on later symptomatic remission. But recurrence of positive symptoms ( = number of episodes and admissions) during the course of illness could prevent remission.
Negative symptoms remained stable throughout the course of the illness, and exerted a negative effect on the achievement of remission.
Negative symptoms present at the onset of schizophrenia appear stable and is associated with a poor long-term outcome (i.e. remission. Initial positive symptoms seem to have less prognostic value, but their recurrence during the course of the disorder can impede long-term remission.
Marchesi C, Affaticati A, Monici A, De Panfilis C, Ossola P, Tonna M. Compr Psychiatry. 2014 May;55(4):778-84.
Life time prevalence of suicidal ideas and attempts among teens is 12% and 4% respectively. Suicide is a leading cause of death among teens. Majority of teens (in many countries) have access to internet and vast majority use social network sites. Electronic bullying/ cyber bullying is now coming up as a risk factor for teen suicide. Early research from US suggest that compared to traditional bullying, there is a stronger association between cyberbullying victimization with depression and suicidality.
Bullying is the “aggressive behavior intended to cause harm or distress. The behavior may be physical or verbal”. intentionality, repetitiveness, and power imbalance are the key features.
How common is cyber bullying? What is its relationship to sadness, suicidal ideas and attempts?
Erick Messias, Kristi Kindrick and Juan Castro report the results of their study addressing the relationship between school bullying, cyberbullying, and both forms of bullying victimization, to depression and suicide in a nationally representative sample of high school students. The data is collected as part of US Youth Risk Behaviour Survey. Students were asked about bullying ( school or cyber ) in the last 12 months. Participants also provided information on sadness, suicidal ideas and attempts. 17,672 students participated in this.
Girls are more likely to be report being bullied (31 % vs. 23%), in particularly to be cyberbullied (22 % vs. 11%). Overall bullying decreases from age 14 (33%) to age 18 and older (21%), this decrease is due mostly to a decrease in exclusive school bullying (from 17 % to 7 %) while exclusive cyberbullying increased from 6 % in 14 year-olds to 7 % among 18 year-olds.
Girls were significantly more likely to reports 2-week sadness (36% vs. 21%), suicidal ideation (19% vs. 12%), suicide plan (15% vs. 11%) and attempts ( 10% vs 6%).
Data show that cyberbullying is of higher risk than school bullying. Those who faced both had highest risk. the dose–response pattern is like this— no bullying < school bullying < cyberbullying
Limitations: cross- sectional analysis cannot show the sequence of events leading to the association noticed.Prospective studies are required to answer the question about which came first.
Conclusions: Bullying, both school bullying and cyberbullying, is prevalent (27 %). Those reporting either form of bullying are at higher risk for suicidal ideas and attempts. Cyberbullying do not decrease with age. Cyberbullying can be of higher risk.
Summary of the article:
School bullying, cyberbullying, or both: correlates of teen suicidality in the 2011 CDC Youth Risk Behavior Survey. Messias E, Kindrick K, Castro J. Compr Psychiatry. 2014 Jul;55(5):1063-8.
Depressed individuals show an increased risk of developing various ageing-related physical diseases like coronary heart disease,1 type 2 diabetes, obesity, dementia and cancer. This might be partly due to unhealthy lifestyle behaviours. Evidence also suggest that this might be via independent effects of depression as well.Accelerated biological ageing is suggested as one such independent route. Shorter telomere lengths (TL) is seen in ageing related disorders . The same is seen in depression as well. ( Simon et al 2006 Lung et al 2007 Hartmann et al 2010 Wolkowitz et al 2011)
JE Verhoeven, D Révész1, ES Epel, J Lin, OM Wolkowitz and BWJH Penninx studied whether TL was associated with MDD status in a large adult sample (N = 2407). Participants were from the ongoing longitudinal cohort ofNetherlands Study of Depression and Anxiety (NESDA). Three groups were created: control subjects, persons with remitted MDD and persons with current MDD. Leukocyte TL was measured by PCR. Severity of depression in the past week was assessed by the 30-item Inventory of Depressive Symptoms—Self Report. Covariates studied included gender, age and years of education,BMI,alcohol consumption,smoking,physical activity and somatic disorders.
Average TL in the entire sample was 5477 bp. TL exhibited a significant negative correlation with age (r = − 0.326, P o 0.001), which corresponded to a shortening rate of 14 bp per year.
Compared with healthy controls (mean bp=5541), TL was significantly shorter among remitted MDD patients (bp = 5459; P = 0.014) and current MDD patients (bp = 5961; P = 0.012), adjusted for age, gender and education.Differences remained significant in analyses fully adjusted for health and lifestyle variables.
Higher current depression severity and longer symptom duration within the past 4 years were associated with shorter TL
The differences observed indicate 4–6 years of accelerated aging for the current MDD sample as compared to controls.
Depression is associated with several years of biological aging, especially among those with the most severe and chronic symptoms.
Summary of the article:
Verhoeven JE, Révész D, Epel ES, Lin J, Wolkowitz OM, Penninx BW. Mol Psychiatry. 2014 Aug;19(8):895-901.
The idea that all antipsychotics are of same efficacy ( or comparable efficacy ) is widely accepted. Chlorpromazine (CPZ) has been the gold standard to compare antipsychotic effect and It would be interesting to see how all other antipsychotics fare against CPZ.
Myrto T. Samara, Haoyin Caob, Bartosz Helferb, John M. Davisd & Stefan Leucht report the results of a meta analysis of all randomised controlled trials that compared oral formulations of chlorpromazine with any other oral antipsychotic for the treatment of schizophrenia or related disorders.
The primary outcome was response to treatment defined as at least 50% reduction of rating scales.
128 randomized trials published over a period of 55 years from 1956 to 2011 were included.Chlorpromazine was compared with 43 other antipsychotics. ( risperiodne only 1 study, quetiapine 4 studies , olanzapine 4 studies, Zotepine 2, clozapine 7). The mean/median doses in the chlorpromazine group ranged between 50 and 2000 mg, with a median of 525 mg.
Chlorpromazine was significantly more efficacious than four antipsychotic drugs : butaperazine, mepazine, oxypertine, and reserpine. After Bonferroni correction ( correction for multiple comparisons) only reserpine remained statistically less efficacious than chlorpromazine.
CPZ was significantly less efficacious than four antipsychotic drugs: clomacran, clozapine, olanzapine, zotepine.
The sensitivity analysis (exclude single blind studies, open label studies, studies with unclear level of blindness): confirmed the differences of chlorpromazine compared to butaperazine, clomacran, clozapine and zotepine.
There were no statistically significant differences between chlorpromazine and the remaining 28 antipsychotics.
Small sample sizes in most of the trials ( median :50) A clinically meaningful responder difference of 10% (which would lead to 100 additional responders among 1000 treated), assuming the average response rate in this meta-analysis (47%), would require a total sample size of 390 which is not met by most comparisons.
The studies varied substantially in design, patient populations ,dosing and other factors. Most of the included studies were carried out more than 30 years ago.
Authors argue that the “equal efficacy statement” regarding antipsychotics is not evidence based. The observation of some antipsychotics being different support their argument. A recent Cochrane review that compared haloperidol with all other first-generation antipsychotics yielded similar results (Dold and Leucht, 2012).
One could argue that the present analysis show that CPZ do not differ in efficacy with majority of antipsychotics. Among antipsychotics in use there is difference in efficacy with clozpaine and zotepine only.
‘Equal efficacy argument ‘ has always made exemptions. (mainly for Clozapine). This analysis also supports that clozapine is superior .
Other recent Meta analysis have shown that some SGAs have small superiority over other FGA s and SGAs.
Comments: This analysis in general support the idea that antipsychotics in general are of comparable efficacy. Clozapine’s superiority is supported by other evidence.
Summary of the article:
Samara MT, Cao H, Helfer B, Davis JM, Leucht S. Eur Neuropsychopharmacol. 2014 Jul;24(7):1046-55.