August 2015 – TARG Blog

Are changes in routine health behaviours the missing link between bereavement and poor physical and mental health?

Posted on August 6, 2015July 14, 2015 by meryemgrabski

This blog originally appeared on the Mental Elf site on 6th July 2015.

While bereavement can occur at any point during the lifespan, it is much more common later in life and is a risk factor for both poor physical and mental health.

While the Mental Elf has blogged previously about the impact of childhood bereavement on mental health, the impact of bereavement on the health of older people can be even more severe, given the ongoing declines in health as a result of their age.

Due to the high prevalence of bereavement in this age group, understanding how bereavement leads to declines in health among older adults is important. Behavioural changes may partially account for these negative health outcomes.

To examine this, Stahl and Schulz (2014) conducted the first systematic review to examine the relationship between bereavement and five routine health behaviours:

Physical activity
Nutrition
Sleep
Alcohol use
Tobacco use

As well as one modifiable risk factor associated with health:

Body weight

This review

Methods

The authors searched databases to find 34 studies which met the following criteria:

Quantitative and qualitative studies with either observational or intervention-based designs;
Older adults (aged over 50 years) who had experienced the death of a spouse;
Health behaviours were assessed.

Results

Physical activity

18 studies: 4 cross-sectional, 8 prospective longitudinal, 5 post-bereavement longitudinal

Physical activity was assessed using self-report in all studies and physical activity ranged from social activities such as visiting friends to sports activities.
As a result, the evidence was mixed, with bereavement increasing the prevalence of social activities, but decreasing the prevalence of sports. Furthermore, while this pattern applied to bereaved women, bereavement decreased all forms of physical activity among men.

Nutrition

12 studies: 5 cross-sectional, 5 prospective longitudinal, 3 post bereavement longitudinal

Nutrition was assessed using a range of self-report questionnaires.
There was consistent evidence for a strong relationship between bereavement and increased nutritional risk, including worse nutrient intake and poor dietary behaviours, particularly within the first year of bereavement.

Sleep quality

9 studies: 1 cross-sectional, 0 prospective longitudinal, 8 post-bereavement longitudinal

Sleep quality was assessed using both self-report and objective measures such as electroencephalography and actigraphy (measurement of movement using small body sensors).
While the self-report studies consistently showed strong support for a link between bereavement and poorer sleep quality, no relationship was observed when sleep disturbance was measured objectively.

Alcohol consumption

7 studies: 2 cross-sectional, 3 prospective longitudinal, 2 post-bereavement longitudinal

There was moderate evidence (from longitudinal studies only) that bereavement was associated with increased self-reported alcohol consumption, for both men and women.

Tobacco use

7 studies: 2 cross-sectional, 4 prospective longitudinal, 1 post-bereavement longitudinal

Smoking status and frequency of tobacco use was assessed using self-report.
There was inconsistent evidence for the impact of bereavement on smoking behaviour, with bereavement reducing smoking frequency among current smokers (particularly men) but increasing the likelihood of smoking initiation among female non-smokers.

Weight status

6 studies: 1 cross-sectional, 5 prospective longitudinal, 0 post-bereavement longitudinal

There was consistent evidence across the studies that bereavement led to unintentional weight loss among both men and women.

nutrition, sleep quality and weight status

Limitations and directions for future research

The studies were heterogeneous and many did not report effect sizes, meaning that quantitatively assessing them (i.e. using meta-analysis) was not possible.
The majority of studies used self-report which may be affected by recall bias. For studies exploring sleep quality, only those which used self-report, rather than objective measures observed a negative effect of bereavement.
Few of the longitudinal studies reported the length of the bereavement period or when assessments were taken. Precise information on measurement intervals is important in determining when behavioural changes are most likely to occur and would be important for treatment.

Discussion

This systematic review observed:

Strong support for changes in nutrition, sleep quality and weight status after bereavement
Moderate evidence for an impact on alcohol consumption
Mixed evidence for effects on physical activity and tobacco use

Although this review did not explore why bereavement led to these changes in health behaviours, the authors provide a number of explanations, which should be examined in future studies:

Loss of social support and the onset of depression and grief. This may reduce motivation to engage in health-promoting behaviours such as physical activity and also exacerbate or trigger physical symptoms such as poor sleep and headaches.
Changes in daily routines. Previously shared activities, such as exercise, food preparation or sleeping, may be difficult to maintain following spousal loss.

Crucially, however, this review is only one part of the puzzle. While it shows us that bereavement is associated with changes in health behaviours, we don’t know whether these changes mediate the relationship between bereavement and physical and mental health, the key outcome we’re interested in.

Given the known health burden associated with bereavement, it is critical that we further investigate this link and if this link were observed, interventions could target health behaviours to reduce the impact of bereavement on physical and mental health.

Future studies should explore whether specific health behaviours can reduce the negative impact that bereavement has on our physical and mental health.

Can we use the inhalation of 7.5% CO2 as a model to probe cognition and behaviour in anxiety?

Posted on August 4, 2015August 3, 2015 by meryemgrabski

by Alex Kwong @tskwong

A lot of the work conducted in the Tobacco and Alcohol research group (TARG) mainly focuses around tobacco and alcohol research (funny that…). However, when we’re not getting people intoxicated in the name of science (yes we do that), we’re also carrying research ranging from body perception, to emotion recognition and anxiety research. The latter is something that I’ve focused on, and to cut a long story short, we make people anxious by making them breathe in air enriched with carbon-dioxide (CO₂), about 7.1% more than what you would normally breathe. Once people are anxious, we assess them on a number of outcomes, some clinically relevant, some more practical and applied.

Needless to say, breathing in about 7% more CO₂ for a period of up to 20 minutes should make you anxious for a number of reasons (to be explained later on). But can breathing in a gas that is enriched with CO₂ act as a viable model for anxiety, capable of assessing cognition and behaviours that are susceptible to anxiety? In this post I’ll explore some of the previous research utilising this model, and look at some of the future directions of the model and how it could be used as a training tool to help improve performance under anxiety. By then, hopefully you’ll agree with me that the model is good at experimentally inducing anxiety, and you’ll sign up for all our studies.

Possibly the most influential research on the inhalation of CO₂ has been by Bailey et al. (2005) and work from David Nutt’s former lab in Bristol. They found that breathing in CO₂ enriched gas for a period of 20 minutes decreased positive mood (feelings of happiness and relaxation) and increased negative mood (worry and fear). Since then, a plethora of research has supported this, and also found that the model induces symptoms such as sweating, increased heart rate and blood pressure and hypoxia, all common in generalised anxiety disorder (GAD). Interestingly, other research has found that we can actually reduce these responses to the CO₂ model by giving people anxiolytic drugs. As such, the model of 7.5% CO₂ has been considered a validated model of human anxiety induction that is generalisable to anxiety disorders such as GAD.

But why does breathing in a gas that is enriched with CO₂ cause these sort of feelings? One explanation is that breathing in CO₂ causes chemoreceptors to mislead the body into thinking that it is starved of oxygen. This leads to fear like responses, as well as increased breathing rates and higher blood pressure and heart rate. If you’ve ever had the pleasure of taking part in one of these CO₂ experiments, you’ll likely agree that these things happen. I’ll just stress at this point that effects of the gas are transient and usually disappear quickly after the inhalation. Some people even enjoy the experience, so I hope I’m still selling this to you.

CO2 set-up — A typical experimental set up with the CO2.

So if it makes you feel like you’re experiencing physiological anxiety, then it’s obviously a model of human anxiety right? Well what about the psychological components? People with GAD often have a hypervigilance to threat, even when there is nothing threatening around. Additionally, their attention to negative stimuli is increased, even in the presence of other emotional content. Anxious sufferers also interpret ambiguous information as potentially dangerous or threatening. Can the CO₂ model can tap into some of these psychological components that are common in GAD?

To address this, one study found that the inhalation of 7.5% CO₂ caused quicker eye-movements to be made towards threatening stimuli. Another study found that CO₂ caused attention to reflect a hyper vigilance to threatening information. Otherresearch in preparation has found that people were worse at correctly identify emotional faces during CO₂. Lastly, Cooper et al. (2013) found that CO₂ caused people to interpret ambiguous information in CCTV footage as threatening. These findings support the 7.5% CO₂ model affecting psychological processes similar to those in GAD.

Great! So the model seems to be similar to the experience of GAD, what next? Well, what’s also quite fascinating is that if we have a model for anxiety, we could predict how people will behave in situations like sport, musical performances, decision-making, medical and security services etc – behaviours that can induce feelings of anxiety or be affected by anxiety, even in those without a disorder. Understanding how people will behave in stressful situations might help improve performances in the future.

The CO₂ model has been used to investigate this. Attwood et al. (2013) found that 7.5% CO₂ impaired the ability to match pairs of faces, a finding which has tremendous implications for military and forensic settings (e.g., border crossings and proof of sale purchases like alcohol and tobacco). More recently, we also found that the inhalation of 7.5% CO₂ impairs the ability to remember faces that have previously been seen. Importantly, ‘witnesses’ did not report lower confidence of their choices despite this impaired ability, which has implications for the judicial system (e.g., courtrooms and line-ups).

Upcoming research has suggested that CO₂ impairs decision-making on a gambling task, by making people choose more exploratory decisions which in turn causes less money earned. Other research has suggested that the CO₂causes excessive force production which could affect military, surgical and sporting behaviours. The same research also suggested that people speed up when asked to tap in time with a metronome, which could detriment musical performances and any task that requires accurate bodily timing. Together, this research shows that the inhalation of 7.5% CO₂may be a useful tool for examine how anxiety may affect behaviours.

Mask — The amount of Bane and Darth Vader impressions I got from participants was staggering – “It would be extremely anxious…, for you”

By now you should be getting the picture that a) the CO₂ model is good for inducing anxiety and b) that I am incredibly biased in favouring this model. But I think there are good reasons to endorse this stance. Many previous studies that induce anxiety are time limited, meaning that ‘anxiety’ may only affect certain stages of the task. Other studies only produce one single ‘hit’ to cause anxiety (e.g., one phobic stimuli, one bodily stressor), which may not be characteristic of anxiety as a whole. However, one anxiety inducer that I think is quite neat is the threat of electric shock. Threatening people with electric shock is a great way to induce anxiety but in some experiments, the shock doesn’t actually come, so people quickly learn that there is no threat and thereby no longer remain anxious, which is a problematic for anxiety research.

The CO₂ model is not without its flaws. Tasks can only be conducted within the 20 minute inhalation window. That said, there is no limit to how many times someone can be CO₂’d. Practically, people may decide they no longer want to feel anxious during the inhalation and so drop out, but this is likely to be a problem in anxiety research generally. Perhaps most importantly, whilst we have conducted numerous CO₂ experiments, we are still unsure exactly how the model works on all attentional and behavioural mechanisms. Future research is looking at how the CO₂ model affects the brain, and our eye-movements. There is also research that has explored psychological interventions, such as mindfulness training, and whether this can reduce some of the symptoms brought on by the CO₂ inhalation. It’ll also be really interesting to see whether the model can be utilised as a training tool for people who need to perform under anxious conditions. Research has shown that practising under conditions of anxiety can help improve performance at a later stage and so the next step would be to see if people can perform better in real life anxious situations, if they’ve practised on the CO₂ model first.

In summary, the CO₂ model seems to be a reliable way to induce anxiety that can impact on both cognition and behaviour. The model is validated by a wealth of research showing its similarity to GAD. Although the model is not perfect for inducing anxiety, it is one of the more promising tools we currently have, and subsequent research should continue to use the model as a viable probe for exploring cognition and behaviour under anxiety.

Antidepressants during pregnancy and risk of persistent pulmonary hypertension of the newborn

Posted on August 2, 2015July 14, 2015 by meryemgrabski

by Meg Fluharty @MegEliz_

This blog originally appeared on the Mental Elf site on 2nd July 2015.

Persistent pulmonary hypertension of the newborn (PPHN) is associated with increased morbidity and mortality of infants and occurs in 10-20 per 10,000 births.

Those who survive face chronic lung disease, seizures, and neurodevelopmental problems as a result of hypoxemia and aggressive treatment (Walsh-Sukys et al 2000; Farrow et al 2005; Clark et al 2003; Glass et al 1995).

Based on a single study in 2006, the FDA issued a public health advisory that late pregnancy exposure to SSRIs may be associated with an increased risk of PPHN (FDA 2015; Chambers 2006). However, a review yielding conflicting findings led the FDA to conclude that they were premature in their conclusion.

This is the background to a new study by Huybrechts et al (2015), which sets out to investigate SRRI and non-SSRI antidepressants and the associated risk of PPHN in late stage pregnancy.

PPHN is a potentially fatal condition affecting mainly full-term babies, in which the blood flow to the lungs shuts down because the main arteries to the lungs constrict.

Methods

Cohort and data

Participants were drawn from the Medicaid Analytic eXtract (MAX) cohort, which holds the health records of medicate beneficiaries in the United States.

Antidepressants

If women filled 1 antidepressant prescription 90 days before delivery, they were considered ‘exposed.’ Antidepressant medications were classified as either SSRIs (Selective Serotonin Re-uptake Inhibitors) or non-SSRIs. Women exposed to both types of antidepressant were excluded from the analysis. A reference group of women was created, whom had not been exposed to either SSRI or non-SSRIs at any time during pregnancy.

Persistent Pulmonary Hypertension of the Newborn (PPHN)

PPHN was defined by the ICD-9 diagnostic criteria for persistent foetal circulation or primary pulmonary hypertension in the first 30 days following delivery.

Analysis

A sensitivity analysis was conducted to control for possible misclassification, with exposure status defined as filling 2 prescriptions during 90 days before delivery, and outcome redefined as only severe cases of PPHN (respiratory assistance, extracorporeal membrane oxygenation, or inhaled nitric oxide therapy).

This very large (3.8 million pregnant women) population-based study included mothers in the US on low income and with limited resources.

Results

Within 3,789,330 pregnancies, 3.4% of women used antidepressants in the 90 days before delivery, of which 2.7% were SSRIs and 0.7% were non-SSRI antidepressants.

Antidepressant versus non-use

31.0 (95% CI, 28.1 to 34.2) per 10,000 infants exposed to antidepressant use had PPHN
20.8 (95% CI, 20.4 to 21.3) per 10,000 infants not exposed to antidepressant use had PPHN

SSRI versus non-SSRI antidepressant use

31.5 (95% CI 28.3 to 35.2) per 10,000 infants exposed to SSRIs had PPHN
29.1 (95% CI 23.3 to 36.4) per 10,000 infants exposed to non-SSRIs had PPHN

Depression diagnosis

After restricting to a diagnosis of depression:

33.8 (95% CI, 29.7 to 38.6) per 10,000 infants exposed to SSRIs had PPHN
34.4 (95% CI, 26.5 to 44.7) per 10,000 infants exposed to non-SSRIs had PPHN
14.9 (95% CI 23.7 to 26.1) per 10,000 infants not exposed to antidepressant use had PPHN

Sensitivity analysis

Women who filled 2 prescriptions in the 90 days before delivery did not have stronger associations
Changing the definition for PPHN did not alter associations in either SSRIs or non-SSRIs

The chances of a baby getting PPHN when its mother was not taking an SSRI are around 2 in 1,000, compared to around 3 in 1,000 when the mother had taken an SSRI in the last 90 days of pregnancy.

Discussion

Overall, the authors found evidence that SSRI exposure in the last 90 days of pregnancy may be associated with an increased risk of PPHN. However, the magnitude of risk observed is less than has previously been reported. Furthermore, sensitivity analyses did not amplify these risks.

The authors conclude by suggesting clinicians should take the increase of risk of PPHN into consideration when prescribing these drugs during pregnancy.

Limitations

There are a few limitations in this study to be noted:

Possible misclassification of the exposure or outcome, (e.g. filling a prescription does not guarantee it was taken as prescribed) which may bias the results. However, the authors did conduct a sensitivity analysis in order to control for this.
The baseline characteristics varied between women taking antidepressants and those who did not, with women prescribed antidepressants more likely to be older, white, taking other psychotropic medicines, be chronically ill, be obese, smoke, and have health care issues. While the SSRI and non-SSRI groups were more comparable, non-SSRI women had higher overall illness, more comorbidities, and co-medication use. Additionally, the participant population was drawn from a relatively low-income group, in which comorbid illness is likely to be higher than general populations, which may account for the difference in risk of previous studies.

This evidence would suggest that the benefits of antidepressants taken during pregnancy outweigh the risks of rare events such as PPHN.

Professor Andrew Whitelaw, Professor of Neonatal Medicine at the University of Bristol, said of the study:

Taking this study with the previous evidence, I conclude that there is a slightly increased risk of PPHN if a pregnant woman takes an SSRI but this only brings the risk up to 3 per 1000 births. I do not suggest that seriously depressed pregnant women should be denied SSRI treatment, but it would be wise for them to deliver in a hospital with a neonatal intensive care unit in case PPHN does occur.

Links

Primary paper

Huybrechts K, Bateman B, Palmsten K, Desai R, Patorno E, Gopalakrishnan C, Levin R, Mogun H, Hernandez-Diaz S. (2015) Antidepressant Use Late in Pregnancy and Risk of Persistent Pulmonary Hypertension of the Newborn. 2015: 313(21). [Abstract]

Other references

Walsh-Sukys MC, Tyson JE, Wright LL et al. (2000) Persistent pulmonary hypertension of the newborn in the era before nitric oxide: practice variation and outcomes. Pediatrics. 2000;105(1 pt 1):14-20. [PubMed abstract]

Farrow KN, Fliman P, Steinhorn RH. (2005) The diseases treated with ECMO: focus on PPHN. Semin Perinatol. 2005;29(1):8-14. [PubMed abstract]

Clark RH, Huckaby JL, Kueser TJ et al. (2003) Clinical Inhaled Nitric Oxide Research Group. Low-dose nitric oxide therapy for persistent pulmonary hypertension: 1-year follow-up. J Perinatol. 2003;23(4):300-303. [PubMed abstract]

Glass P, Wagner AE, Papero PH et al. (1995) Neurodevelopmental status at age five years of neonates treated with extracorporeal membrane oxygenation. J Pediatr. 1995;127(3):447-457. [PubMed abstract]

US Food and Drug Administration. (2006) Public health advisory: treatment challenges of depression in pregnancy and the possibility of persistent pulmonary hypertension in newborns.

Chambers CD, Hernández-Diaz S, Van Marter LJ, et al. Selective serotonin-reuptake inhibitors and risk of persistent pulmonary hypertension of the newborn. N Engl J Med. 2006;354(6):579-587. [PubMed abstract]

– See more at: http://www.nationalelfservice.net/treatment/antidepressants/antidepressants-during-pregnancy-and-risk-of-persistent-pulmonary-hypertension-of-the-newborn/#sthash.kEFM7Ik8.dpuf

Smoking and risk of schizophrenia: new study finds a dose-response relationship

Posted on August 1, 2015July 14, 2015 by meryemgrabski

by Marcus Munafo @MarcusMunafo

This blog originally appeared on the Mental Elf site on 1st July 2015.

Almost exactly a year ago, a landmark study identified 108 genetic loci associated with schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). In a Mental Elf post on that study I wrote: “Genetic studies also don’t rule out an important role for the environment – [genome-wide association studies] might even help identify other causes of disease, by identifying loci associated with, for example, tobacco use.”

I mentioned this because one of the loci identified is strongly associated with heaviness of smoking. There are two possible explanations for this: either this locus influences both smoking and schizophrenia, or smoking causes schizophrenia.

Smoking and schizophrenia are highly co-morbid; the prevalence of smoking among people with a diagnosis of schizophrenia is much higher than in the general population. It is widely believed that this is because smoking helps to alleviate some of the symptoms of schizophrenia, or the side-effects of antipsychotic medication.

The possibility that smoking itself may be a risk factor for schizophrenia has generally not been widely considered. Now, however, intriguing evidence has emerged that it may be, from a large study of data from Swedish birth and conscript registries (Kendler et al, 2015).

The leading causes of premature mortality in people with schizophrenia are ischaemic heart disease and cancer, both heavily related to smoking.

Methods

The authors linked nationwide Swedish registers via the unique 10-digit identification number assigned at birth or immigration to all Swedish residents. Data on smoking habits were collected from the Swedish Birth Register (for women) and the Military Conscription Register (for men). The date of onset of illness was defined as the first hospital discharge diagnosis for schizophrenia or non-affective psychosis.

Cox proportional hazard regressions were used to investigate the associations between smoking and time to schizophrenia diagnosis. To evaluate the possibility that smoking began during a prodromal period (where symptoms of schizophrenia may emerge prior to a full diagnosis), buffer periods of 1, 3 and 5 years were included in the models. In the female sample, data from relatives (siblings and cousins) were also used to control for familial confounding (genetic and environmental).

Results

Smoking status information was available for 1,413,849 women, and 233,879 men.

There was an association between smoking at baseline and a subsequent diagnosis of schizophrenia for:

Women
- Light smoking: hazard ratio 2.21, (95% CI 1.90 to 2.56)
- Heavy smoking: hazard ratio 3.45 (95% CI 2.95 to 4.03)
Men
- Light smoking: hazard ratio 2.15 (95% CI 1.25 to 3.44)
- Heavy smoking: hazard ratio 3.80 (95% CI 1.19 to 6.60)

Adjustment for socioeconomic status and prior drug abuse (i.e., confounding) weakened these associations slightly.

Taking into account the possibility of smoking onset during a prodromal period also did not weaken these associations substantially, irrespective of whether the buffer period (from smoking assessment to the date at which a first schizophrenia diagnosis would be counted) was 1-, 3- or 5-years. Theoretically, if prodromal symptoms of schizophrenia lead to smoking onset (i.e., reverse causality) the smoking-schizophrenia association should weaken with longer buffer periods.

Finally, the co-relative analyses compared the association between smoking and schizophrenia in the female sample, within pairs of relatives of increasing genetic relatedness who had been selected on the basis of discordance for smoking (i.e., one smoked and one did not). If the smoking-schizophrenia association arises from shared familiar risk factors (genetic or environmental) the association should weaken with increasing familial relatedness. Instead, only modest decreases were observed.

As a validation check on the accuracy of their measure of smoking behaviour, the authors confirmed that heavy smoking was more strongly associated with both lung cancer and chronic obstructive pulmonary disease, two diseases known to be caused by smoking.

These results show a dose-response relationship between smoking and risk of schizophrenia, i.e. the more you smoke, the stronger the association.

Conclusion

This study provides clear evidence of a prospective association between cigarette smoking and a subsequent diagnosis of schizophrenia. However, observational associations are notoriously problematic, because these associations may arise because of confounding (measured and unmeasured), or reverse causality. Since these analyses were conducted on observational data, these limitations should be borne in mind and we cannot say with certainty that smoking is a causal risk factor for schizophrenia.

Nevertheless, the authors conducted a number of analyses to attempt to rule out different possibilities. First, the associations were weakened only slightly when adjusted for socioeconomic status and prior drug abuse, so the impact of measured confounders appears to be modest (although other confounding could still be occurring). Second, the inclusion of a buffer period to account for smoking onset during a prodromal period also weakened the associations only slightly, which is not consistent with a reverse causality interpretation. Finally, the co-relative analysis did not indicate that the association differed strongly across levels of familial relatedness, suggesting that the impact of unmeasured familial confounders (both genetic and environmental) is relatively modest.

This study provides clear evidence of a prospective association between cigarette smoking and a subsequent diagnosis of schizophrenia.

Limitations

There are some limitations to the study that are worth bearing in mind:

First, there were no data on lifetime smoking, although the authors validated their measure of smoking against outcomes known to be caused by smoking.
Second, the authors used clinical diagnoses, and included both schizophrenia and non-affective psychosis, so the specificity of the findings to these outcomes is uncertain.
Third, because of the small number of schizophrenia diagnoses the co-relative analyses used non-affective psychosis only.

This study is not enough to say with certainty that smoking is a causal risk factor for schizophrenia.

Summary

There are three main ways in which the association between smoking and schizophrenia might arise:

Schizophrenia causes smoking,
Smoking causes schizophrenia, and
The association arises from risk factors common to both.

This study suggests that the first mechanism cannot fully account for the association; if anything there was more support for the third mechanism, including stronger evidence for a role for familial factors than for socioeconomic status and drug abuse. However, critically, this study also finds support for the second mechanism, including a dose-response relationship between smoking and risk of schizophrenia.

Despite this study’s strengths, and the care taken by the authors to explore the three possible mechanisms that could account for the association between smoking and schizophrenia, no single study is definitive. However, evidence is emerging from other studies that support the possibility that smoking may be a causal risk factor for schizophrenia.

Recently, McGrath and colleagues have reported that earlier age of onset of smoking is prospectively associated with increased risk of non-affective psychosis (McGrath et al, 2015).

In addition, Wium-Andersen and colleagues report that tobacco smoking is causally associated with antipsychotic medication use (but not antidepressant use), in a Mendelian randomisation analysis that uses genetic variants as unconfounded proxies for heaviness of smoking (Wium-Andersen et al, 2015).

Identifying potentially modifiable causes of diseases such as schizophrenia is a crucial part of public health efforts. There is also often reluctance among health care professionals to encourage patients with mental health problems (including schizophrenia) to attempt to stop smoking. If smoking is shown to play a causal role in the development of schizophrenia, there may be more willingness to encourage cessation. Since the majority of the mortality associated with schizophrenia is due to tobacco use (Brown et al, 2000), helping people with schizophrenia to stop is vital to their long-term health.

There is now mounting evidence that supports the possibility that smoking may be a causal risk factor for schizophrenia.

Links

Primary paper

Kendler, K.S., Lonn, S.L., Sundquist, J & Sundquist, K. (2015). Smoking and schizophrenia in population cohorts of Swedish women and men: a prospective co-relative control study. American Journal of Psychiatry. doi: 10.1176/appi.ajp.2015.15010126 [Abstract]

Other references

Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421-427. doi: 10.1038/nature13595

McGrath, J.J., Alati, R., Clavarino, A., Williams, G.M., Bor, W., Najman, J.M., Connell, M. & Scott, J.G. (2015). Age at first tobacco use and risk of subsequent psychosis-related outcomes: a birth cohort study. Australian and New Zealand Journal of Psychiatry. [PubMed abstract]

Wium-Andersen, M.K., Orsted, D.D. & Nordestgaard, B.G. (2015). Tobacco smoking is causally associated with antipsychotic medication use and schizophrenia, but not with antidepressant medication use or depression. International Journal of Epidemiology, 44, 566-577. [Abstract]

Brown S, Inskip H, Barraclough B. (2000) Causes of the excess mortality of schizophrenia. Br J Psychiatry. 2000 Sep;177:212-7.

– See more at: http://www.nationalelfservice.net/mental-health/schizophrenia/smoking-and-risk-of-schizophrenia-new-study-finds-a-dose-response-relationship/#sthash.u3UiDOlG.dpuf