Why are people who stay in school longer less likely to get heart disease?

Alice Carter, PhD researcher at the IEU, outlines the key findings from a paper published in BMJ today.

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Heart disease remains the leading cause of death globally, causing over 17.5 million deaths annually. Whilst death rates from heart disease are decreasing in high income countries, the most socioeconomically deprived individuals remain at the greatest risk of developing heart disease. Socioeconomic causes and the wider determinants of health (including living and working conditions, health care services, housing and a number of other wider factors) and are suggested to be the most important driver of health. Behavioural and lifestyle factors, such as smoking, alcohol consumption, diet and exercise, are the second most important contributor to health and disease.

Why does education matter?

Staying in school for longer has been shown to lead to better lifelong health, including reducing the risk of heart disease (cardiovascular disease) and dementia. We also know that those who stay in school are more likely to adopt healthy behaviours. For example, they are less likely to smoke, but more likely to eat a healthy diet and take part in physical activity. These factors, can in turn, reduce the risk of heart disease, such as by reducing body mass index (BMI) or blood pressure. We wanted to understand if these risk factors (BMI, systolic blood pressure and lifetime smoking behaviour) could explain why those who stay in school for longer are less likely to get heart disease, and how much of this effect they explained.

What did we find?

We found that individually, BMI, systolic blood pressure and smoking behaviour explained up to 18%, 27% and 34% of the effect of education on heart disease respectively. When we looked at all three risk factors together, they explain around 40% of the effect. This means that up to 40% of the effect of staying in school reducing the risk of heart disease can be explained by the fact that those who stay in school tend to lead healthier lives. In this work we looked at four outcomes, coronary heart disease (gradual build-up of fatty deposits in arteries), stroke, myocardial infarction (heart attack) and all subtypes of heart disease combined. For all the outcomes we looked at, we found similar results. Notably, the 40% combined effect is smaller than the amount estimated simply from summing the individual effects together. This suggests there is overlap between the three risk factors in how they cause heart disease.

How did we do this?

In our work, we used a few different methods and data sources to answer our questions.

  • We started by looking at observational data (that is the data self-reported by the participants of the study) in UK Biobank – a large population cohort study of around 500 000 individuals. Of these, almost 220 000 people were eligible to be in our analysis.
  • We looked at how their education affected their risk of four types of heart disease. We then looked at how the intermediate factors, BMI, blood pressure and smoking, could help explain these results.
  • Secondly, we replicated these analyses using two types of  Mendelian randomisation analyses (a form of genetic instrumental variable analysis, see below), firstly in the UK Biobank group and secondly by using summary data from other studies in the area.

Why use genetic data?

Typically, epidemiologists collect data by asking people to report their behaviours, lifestyle characteristics and any diagnoses from a doctor. Alternatively, people in a study may have been to a clinic where their BMI or blood pressure is measured. However, this type of data can lead to inaccuracies in analyses.  This could be because:

  • measures are not reported (or measured) accurately. For example, it can be difficult to get an accurate measure of blood pressure, where it changes throughout the day, and even just going to a clinic can result in higher blood pressure.
  • there may be other variables associated with both the exposure and outcome (confounding). One example of this is suggesting that grey hair causes cancer. Really, age is responsible for i) leading to grey hair and ii) leading to cancer. Without accounting for age, we might suggest a false association exists (see figure 1). In our study using education, this could be ethnicity for example, where it influences both staying in school and risk of heart disease.
  • or an individual with ill health may have been advised to change their lifestyle (reverse causality). For example, an individual with a high BMI may have had a heart attack and have been advised by their doctor to lose weight to avoid having a second heart attack.
Diagram showing a picture of grey hair with an arrow linking to cancer, and a third variable - age - above, which explains both.
Figure 1: Does grey hair really cause cancer?

 

One way to overcome these limitations is to use Mendelian randomisation. This method uses the genetic variation in an individual’s DNA to help understand causal relationships. Every individual has their own unique genetic make-up, which is determined, and fixed, at the point of conception.

We are interested in single changes to the DNA sequence, called single nucleotide polymorphisms (or SNPs). For all of our risk factors of interest (education, BMI, blood pressure and smoking) there are a number of SNPs that contribute towards the observed measures, that are not influenced by factors later in life. This means, Mendelian randomisation estimates are unlikely to be affected by bias such as confounding, reverse causality or measurement error, as we might expect when we rely on observational data. By using these genetic variants, we can improve our understanding of if, or how, a risk factor truly causes an outcome, or whether it might be spurious.

What else might be important?

Although we find BMI, blood pressure and smoking behaviour explain a very large amount of the effect, over 50% of the effect of education on heart disease is still unknown. In some small sensitivity analyses we looked at the role of diet and exercise as intermediate risk factors; however, these risk factors did not contribute anything beyond the three main risk factors we looked at. Other social factors may be involved. For example, education is linked to higher income and lower levels of workplace stress, but these factors may also be related to those we’ve looked at in this analysis.

One further suggestion for what may be responsible is medication prescribing and subsequent adherence (or compliance). For example, individuals with higher education may be more likely to be prescribed statins (cholesterol lowering drugs) compared to someone who left school earlier, but with the same requirement for medication. Subsequently, of those who are prescribed statins for example, perhaps those with higher education are more likely to take them every day, or as prescribed. We have work ongoing to see whether these factors play a role.

What does this mean for policy?

Past policies that increase the duration of compulsory education have improved health and such endeavours must continue. However, intervening directly in education is difficult to achieve without social and political reforms.

Although we did not directly look at the impact of interventions in this area, our work suggests that by intervening on these three risk factors, we could reduce the number of cases of heart disease attributable to lower levels of education. Public health policy typically aims to improve health by preventing disease across the population. However, perhaps a targeted approach is required to reduce the greatest burden of disease.

In order to achieve maximum reductions in heart disease we now need to i) identify what other intermediate factors may be involved and ii) work to understand how effective interventions could be designed to reduce levels of BMI, blood pressure and smoking in those who leave school earlier. Additionally, our work looked at predominantly European populations, therefore replicating analyses on diverse populations will be important to fully understand the population impact.

We hope this work provides a starting point for considering how we could reduce the burden of heart disease in those most at risk, and work to reduce health inequalities.

Read the full paper in the BMJ

 

Depression: where we’re at and where we’re going

To mark Mental Health Awareness Week, IEU PhD researcher Alex Kwong takes us on a tour of the research on depression in young people.

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What is depression and why should we care?

Depression is one of the biggest public health challenges we’re currently facing and is expected to be the highest global burden of disease by 2030. The world health organisation (WHO) estimates that around 300 million people worldwide currently experience depression and that at least one in five people will experience depression at some stage of their life. Treatment is not always successful with only around 40-60% of individuals responding positively to antidepressant medication, and other forms of treatment such as cognitive behavioural therapy (CBT) or other talking based therapies requiring long waiting times of up to two years. It’s no surprise to see that depression and other mental health treatments are considered to be in a ‘crisis’ as we continually look for new and effective ways to combat this disease.

Research suggests that depression may first begin to manifest early in adolescence and young adulthood. This may have serious downstream consequences as depression during adolescence is related to both concurrent and later self-harm and suicide, corresponding mental health problems (like anxiety, addiction and psychosis) and impaired social functioning (reduced cognitive functioning and reclusiveness), to name a few. It also appears that depression during adolescence and young adulthood may actually be getting worse. Now whether or not this is because young people are talking more about their mental health than before remains to be seen, but that has not stopped researchers identifying potential causes for depression in adolescence in the hope of developing new and effective treatments and interventions. The message seems to be clear: by stopping/reducing depression in young people, we can potentially improve the quality of life later on.

What is responsible for depression in young people?

The lived experience of depression between young people differs from one person to the next, meaning there is no ‘one-size-fits-all’ approach. But with the help of research, we have begun to identify things that individuals experiencing depression have in common, that could be useful for treating and even preventing depression in young people. What follows is a whistle stop tour of some of the findings of potential causes of depression in young people.

Bullying

It may seem obvious, but childhood and adolescent bullying is one of the strongest predictors of current and later depression. One recent study found that individuals who had been bullied during adolescence were almost 3 times more likely to be depressed at age of 18. Bullying is particular prevalent during school years but can also occur well into the workplace or later education, which can have lasting effects on an individual’s mental health. Stopping bullying from occurring will be difficult, but that does not mean we cannot support individuals who have been bullied in order to help prevent depression from occurring or getting more severe.

Parental Depression

A lot of research has focused on the role of parental mood and later depression in young people. The role of parenting cannot be understated as numerous studies have shown that children of depressed parents are more likely to go on to have depression themselves, see research by Pearson et al, Stein et al and Gutierrez-Galve et al. However, it’s not clear if this is passed on genetically from the parent to child, or if there is something in the “environment” that transmits depression from parent to child. Whilst we don’t know for sure, the answer looks like it could be a bit of both. Parents may pass on depression genetically to their children, but depressed parents may also create an environment that makes the child more liable to depression. It is even possible that the parent passes on their genetics and the child then creates an environment for themselves that makes them more liable to depression. This is a form of gene-environment correlation that I won’t discuss in detail, but research is beginning to tease this apart with regard to parent and childhood depression.

Genetics

Interest in the genetics of depression has been heightened in the last few years. We always knew from twin studies that depression was likely to be heritable (i.e., that depression can be passed on from generation to generation), but convincing some that depression could have a strong genetic basis was tough (for a really good debate on this involving Professor Marcus Munafò, you can listen to this episode of BBC Start the Week). Most recently it has been shown that common genetic variants associated with depression in adulthood seem to predict greater levels of depression in children and adolescents, as well as varying patterns of depressive mood across adolescence. Importantly, it’s clear that there is no ‘one gene’ for depression. Instead, there are multiple genes which can be referred to as ‘polygenicity’ or ‘polygenic risk scores’; “poly” meaning multiple and “risk” indicating that individuals carrying multiple risk genes are more liable or ‘at risk’ to depression. By using polygenic risk scores we can begin to identify individuals experiencing depression early by using knowledge of their genetic make-up. However, it is really important to state here that genetic liability to depression does not equal genetic determinism. Just because someone is more genetically liable to depression, does not mean they will get depressed. There are multiple other factors at play, and we do not know how genetic liability to depression impacts on other pathways (i.e., does having genetic liability make you more likely to seek out an environment that could leave you more depressed?); but many researchers are beginning to ask these questions.

Taken together, these findings highlight how diverse depression is and how many factors could underlie depression in adolescence. There are a ton of other factors that have been related to adolescent depression that I have not had time/space to talk about. That is not to say they are not important, because most likely some are. As research develops and we are able to utilise different methods, we will get a better picture of what underpins depression in adolescence and what can be done to prevent and treat it.

What can we do?

Well for one, we have to keep up the research. We don’t know nearly enough about the underlying mechanisms and pathways that truly underlie adolescent depression. Researchers are beginning to examine this further with novel and promising techniques, but we also have to streamline the time it takes for research to be put into practise. The prolific mental health blog “The Mental Elf” states that it takes 17 years for research to reach clinical practise. That’s a long time and means a lot of people could miss out on the treatment they deserve.

Secondly, we have to be more forthright in how we talk about depression. You may have heard the expression ‘it is ok to be not be ok”. Avoiding telling people to “man-up” when they’re feeling depressed, speaking out and campaigns will only drive this forward. We have to normalise the fact that depression is a disease and like any other disease, it is good to talk about it. Only by talking about depression can we really move forward to end the stigma that being depressed is some kind of weakness. In fact one of my favourite instances of this recently was well explained by the England international Danny Rose.

Where do we go from here?

We appear to be reaching a turning point where more and more people are discussing mental health issues. This may be celebrities, royals or just your average Jo from down the street. But what is important is that we recognise the problem. That depression is a global burden that may be getting worse and requires our utmost attention and action. We are beginning to understand the causes of depression and how we might tackle it through research and reducing the social stigma that surrounds depression. However, the question is whether or not we can take advantage of these changes to really make a difference. Can we build on the progress we have made to finally one day beat depression? Yes. I really believe we can.

Resources for if you’re feeling down

If you’re ever feeling low, then I cannot speak highly enough for these guys: https://www.samaritans.org/

There are a lot of charities who specialise in mental health and depression who provide some excellent resources and information:
https://www.mqmentalhealth.org/
https://www.mind.org.uk/

There are some awesome twitter feeds out there who I have always found to be really helpful and supportive of mental health issues. These people really get depression and are leading the charge in one way or another so do please give them a follow:

MENtalHealth
Paul McGregor
Gareth Griffith
Miguel Cordero Vega
Louise Arseneault
Dr Erin C Dunn

 

 

It’s the mother! Is there a strong scientific rationale for studying pregnant mothers so intensively?

Dr Gemma Sharp, University of Bristol 

For many years, researchers have been studying how our early life experiences, including those that happen before we are born, can affect our lifelong health. In an article we wrote last year, Debbie Lawlor (University of Bristol), Sarah Richardson (Harvard University) and I show that most of these studies have focused on the characteristics and behaviours of mothers around the time of pregnancy. In a recent paper published in the Journal of Developmental Origins of Health and Disease, Debbie Lawlor, Sarah Richardson, Laura Schellhas and I show that there have been more studies of maternal prenatal influences on offspring health than any other factors (read more here).

We argue this is because people assume that mothers, through their connection to the developing fetus in the womb, are the single most important factor in shaping a child’s health. This assumption runs deep and is reinforced at every level, from researchers, to research funders, to journalists, to policy makers, to health care professionals and the general public  (see figure 1).

In our article, we question the truth behind this assumption.

Is there a strong scientific rationale for studying pregnant mothers so intensively?

Well, no actually. Although a lot of studies have found correlations between maternal characteristics and offspring health, the evidence that these characteristics actually have a causal effect is pretty weak. And since there haven’t been many studies of the effects of fathers and other factors, it’s difficult to say how important any maternal effect might be compared to any other early life experience.

Focusing so intensively on pregnant mothers, and interpreting all evidence as causal (if a mother does X, their unborn child will have Y), can have very damaging effects. Complex, nuanced scientific findings are being rushed into simplified advice that, although well-meaning, places the burden of blame on individual pregnant women. For example, there has been very little research on the effects of low-level drinking during pregnancy, but the current advice in the United States is for all sexually active women of reproductive age to avoid alcohol completely if they are not using birth control, for fear of fetal harm.

Fig. 1 Assumptions that the health, lifestyle and behaviours of mothers around the time of pregnancy have the largest causal influence on their children’s health and risk of disease drives research at all stages, from study design to research translation, and is also reinforced by research itself.

A culture of blame

The culture of blame is more overt in the media, where articles are often guilty of scaremongering. This feeds into public beliefs about how pregnant women should and shouldn’t behave, which can limit pregnant women’s freedom and even lead to questions around whether their behaviour is criminal. For example, pregnant women have reportedly been refused alcoholic drinks in bars, and taking drugs during pregnancy is legally classed as child abuse in many US states.

In our article, we make a number of recommendations that we hope will create more of a balance. In particular, we call for more research on how child health might be influenced by fathers and other factors, including the social conditions and inequalities that influence health behaviours. We also call for greater attention to be paid to how health advice to pregnant women is constructed and conveyed, with clear communication of the supporting scientific evidence to allow individuals to form their own opinions.

The EPoCH study

In June, I’ll begin work on a new project to investigate how both mothers and fathers’ lifestyles might causally affect the health of their children. Funded by the Medical Research Council, the EPoCH (Exploring Prenatal influences on Childhood Health) study will highlight whether attempts to improve child health are best targeted at mothers, fathers or both parents. I’m excited to work closely with the people behind WRISK to help ensure that findings from this project are communicated effectively and responsibly.

I hope that, along with the rest of the research community, we can produce high quality evidence to support health care and advice that maximises the health of all family members and stops blaming women for the ill health of the next generation.

The original article can be accessed (open access) here, and the authors’ full list of recommendations can be found below.

Full recommendations

Our full recommendations, which apply variously to researchers, journalists, policy makers and clinicians:

  • Collect more and better quality data on partners of pregnant women.
  • In addition to studying the effects of mothers, study and compare the effects of partners/fathers, social and other factors on child health.
  • Look for causal relationships between these factors and child health, not just (potentially spurious) correlations.
  • Publish all results, including negative results, to give a balanced view of the evidence.
  • Be aware and critical of the current imbalance in the scientific literature and how this will bias our overall understanding of the truth.
  • Collaborate with social scientists to consider the social implications of this research and the role of cognitive bias and social assumptions when interpreting findings.
  • When communicating findings, put the risk in context: compare findings to the broader scientific literature and the social environment.
  • Avoid language that suggests individual mothers are responsible for direct harm to their foetuses (most of the evidence will be based on averages in a population and can’t be assumed to apply to all individuals).
  • Where there is evidence of a paternal effect, aim public health advice at both parents.
  • Explain the level of risk in a way that empowers people to assess the evidence and form their own opinions (i.e. avoid over simplification).

This blog post is an edited version of one originally posted on the WRISK project website.