Fast food 'linked to depression'

“Eating junk food has a negative effect on mental health, making those who consume it regularly feel depressed,” said The Daily Telegraph.

The news is based on a Spanish study that looked at how 9,000 people’s consumption of fast food and baked goods, such as pies and pastries, related to their risk of depression. In a week when tax on pasties and pies has been the source of great distress for some, researchers found that people who consumed the most fast food and baked good were 37% more likely to become depressed over a six-year period than people with the lowest consumption.

This study had some strengths. For example, it established people’s diets before they were followed to see if they developed depression, which means their diets preceded their depression. However, it cannot conclusively show that fast food directly causes depression. For example, it is just as plausible that diet and depression are both the result of a common factor. Therefore, it's too early to rebrand the burger and fries as an "unhappy meal".

Where did the story come from?

This Spanish study was carried out by researchers from the University of Las Palmas in Gran Canaria and the University of Navarra. It was funded by the Spanish Government’s Carlos III Institute of Health.

The study was published in the peer-reviewed journal Public Health Nutrition.

The study's methods were covered appropriately by the media. However, the 51% increase in risk of depression that was quoted by the Telegraph and Daily Mail did not appear in the research paper. The paper reported an increased risk of 37%.

What kind of research was this?

This prospective cohort study assessed the relationship between eating fast food or processed pastries and developing clinical depression. The research project, called Seguimiento Universidad de Navarra (SUN), is a long-running cohort study that involves university graduates in Spain. The study continuously recruits new participants, and collects data on a variety of factors using mailed questionnaires.

Prospective cohort studies assess participants and then look at the development of various factors over time. They have the advantage of initially measuring the exposure of interest (in this case, consumption of fast foods or processed pastries) in a group of people who do not already have the outcome of interest (in this case, clinical depression). This allows the researchers to be certain that the exposure came before the outcome, which is important for determining a cause-and-effect relationship.

Cohort studies can collect data on a number of other factors that may also account for the relationship between the exposure and outcome. These factors are known as confounders. Adjusting their results to account for the influence of confounders allows researchers to be fairly certain that these confounding factors do not influence the results. However, they cannot take into account factors that weren’t measured during the study. Therefore, it is possible that, during a cohort study, unknown factors may account for the relationship seen, rather than the exposure of interest.

What did the research involve?

The researchers used data from the SUN study to identify participants for their research. They included people who did not have a clinical diagnosis of depression and who were not taking antidepressant medication (to ensure that the participants were free of depression at the beginning of the study). All participants were also free of cardiovascular disease, diabetes and hypertension.

The participants completed the food frequency questionnaire at the beginning of the study. They assessed two exposure variables: fast food consumption (which included hamburgers, sausages and pizza) and consumption of commercial baked goods (which included muffins, doughnuts, croissants and other baked goods). The researchers then divided the cohort into five groups (quintiles), based on the amount of each food group that they usually consumed.

The participants were then followed up for a median of 6.2 years. The researchers used a mailed questionnaire to determine whether the person had been diagnosed with clinical depression or had been prescribed antidepressant medication during this time. 

The researchers collected data on other variables they thought might influence the relationship between eating habits and depression. These included age, sex, body mass index, smoking status, physical activity level, total energy intake and healthy food consumption. They then adjusted for the influence of these variables during the statistical analysis.

What were the basic results?

In total, 8,964 participants were included in the study. Participants with the highest consumption (quintile 5) of fast food and baked goods were more likely to be single, younger, less active and have worse dietary habits than participants with the lowest consumption (quintile 1).

After a median follow-up of 6.2 years, 493 cases of clinical depression were reported.

When assessing the relationship between fast food consumption and the development of depression, the researchers found:

• There were 97 cases of depression in the group with the lowest consumption (quintile 1) compared with 118 cases in the group with the highest consumption (quintile 5). When the sizes of the quintiles were taken into account, this equated to people with the highest levels of consumption having a 37% greater risk of developing depression than those with the lowest levels of consumption (hazard ratio [HR] 1.37, 95% confidence interval [CI] 1.01 to 1.85).
• Intermediate levels of consumption (quintiles 2, 3 or 4) were not associated with significantly increased risk of developing depression compared to the lowest consumption level.

When assessing the relationship between commercial pastry consumption and the development of depression, the researchers found:

• People with the highest level of consumption (quintile 5) had a 37% increased risk of developing depression compared to the lowest consumption group (quintile 1) (HR 1.37, 95% CI 1.01 to 1.85).

How did the researchers interpret the results?

The researchers concluded that their results demonstrate “a positive dose-response relationship between the consumption of fast food and the risk of depression”. In other words, as consumption of fast food increases, so does the risk of depression. They also said that “consumption of commercial baked goods was also positively associated to depressive disorders.”

Conclusion

This study has found an association between consuming high levels of fast food and baked goods and the risk of developing depression. Even though this was a prospective study, it cannot conclusively show that eating lots of hamburgers, sausages and pizza causes depression. The tendency to consume fast food and develop depression may both have stemmed from some common factor, rather than fast food directly causing depression. For example, participants with the highest fast food consumption were generally all single, younger and less active, which may have influenced both their diet and their risk of depression.

Several important factors should be noted:

• This study used a questionnaire to determine whether a person had clinical depression. This method may be less reliable than either a clinical interview or a diagnosis confirmed by medical records. Some people with depression may not have reported that they had been given a diagnosis. Alternatively, other people may have considered themselves to have depression without having a clinical diagnosis from a doctor. Equally, some people who would have met diagnostic criteria for depression had they seen a doctor may not have realised that they had the condition.
• Though the researchers adjusted their results for lifestyle and socioeconomic factors that may have influenced diet and depression risk (potentially confounding the relationship between the two), depression may be triggered by many factors. It is difficult to ensure that all possible confounders were taken into account.
• If there is a direct association between these dietary items and risk of depression, the underlying mechanism by which eating these foods could lead to depression is not known.
• The cohort excluded people with multiple underlying illnesses and conditions, such as cardiovascular disease and high blood pressure. While this allowed the researcher to ensure these conditions did not influence their results, it makes it difficult to generalise the results to the wider population. Also, these types of illnesses may influence both diet and risk of depression, so it is arguable that including people with them could have been a valid option.
• The cohort was divided into groups based on their relative consumption of fast foods and commercial baked goods, and not on an absolute level of consumption. Therefore, the results of this study would only apply to a population that had a similar pattern of consumption.

Overall, this study suggests that there may be an association between eating a lot of fast food or baked goods and developing depression. It is, however, difficult to apply the findings to other groups of people, and it is unclear if the relationship would remain under different conditions.

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Almond milk smoothie

Prep time: 10 min, plus overnight soaking

Serves: 1

Merrilees' quick to make smoothie has all the natural goodness of whole fruit and is packed full of healthy protein and fibre

• 1 handfuls almonds, peeled and soaked overnight in water
• 1 tbsp golden linseeds
• ½ ripe mango, or 1 ripe banana
• 2 handfuls mixed berries

Method

1. Drain and rinse the almonds. Put them in the blender and add a little fresh water. Blend to make a smooth paste.

2. Add more water bit by bit and blend until you have a rich almond milk. If it's too thick, just add more water – you're aiming for something with the consistency of its dairy counterpart.

3. Add the linseeds and fruit and blend until smooth. Serve immediately. 

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Breast cancer blood test' needs more work

A genetic test could help predict breast cancer many years before it develops,” the Daily Mail has today reported. The newspaper says the test is based around identifying a type of DNA change called methylation, which is reportedly caused by “exposure to environmental factors such as hormones, radiation, alcohol, smoking and pollution”.

The research behind this news was a large study looking at how breast cancer risk might be linked to the levels of chemicals attached to certain sections of our DNA. Researchers analysed historic blood samples from over 1,300 women, some of whom had breast cancer and some who did not. They were interested in looking at a particular gene in white blood cells, comparing patterns of methylation between the two groups of women. They found that women with the highest levels of methylation had an 89% increase in the odds of developing breast cancer compared with women with the least modification. The researchers therefore concluded that methylation on the surface of the gene could potentially serve as a marker for breast cancer risk. They added that further research may identify similar markers.

Methylation has been in the news several times in recent months, with some studies linking it to disease risk and others looking at whether factors such as exercise could reverse the process. However, despite some news stories suggesting that blood tests looking at methylation may predict or detect early cancer, it is not yet known how this modification might influence risk, or how it interacts with other breast cancer risk factors. Importantly, a simple blood test based on this research is not available and is unlikely to be available for some time.

Where did the story come from?

The study was carried out by researchers from Imperial College, the Institute for Cancer Research and other institutions throughout the UK, Europe, the US and Australia. The research was funded by the Breast Cancer Campaign and Cancer Research UK.

The study was published in the peer-reviewed medical journal Cancer Research.

This study was covered appropriately in the media, with The Guardian pointing out that this research has only identified an association between DNA changes and breast cancer risk. It hasn’t identified a definitive link between the two nor the underlying mechanism that may be involved.

What kind of research was this?

Human DNA contains sections of code that perform a specific function, and these are known as genes. These genes contain instructions for making proteins, which then go on to perform a host of important functions in the body. This case-control study examined the association between a type of genetic modification called “methylation” within DNA and the development of breast cancer. DNA methylation occurs when a molecule binds to a gene. The addition of this molecule can “silence” (turn off) the gene and prevent it from producing the protein it normally would.

Case-control studies compare people with a particular disease or condition (the cases) with a group of comparable people without that condition. Case-control studies are a useful way to investigate risk factors for a relatively rare disease, as cases are identified on the basis that they already have a particular disease, This allows researchers to recruit a large enough number of subjects with a condition to produce a statistically meaningful analysis. This would be much harder to do if they followed a large group of volunteers and simply waited for a sufficient number to develop a particular disease.

In standard case-control studies, both cases and controls are asked about their previous exposure to risk factors, allowing researchers to analyse how their past exposure related to the risk of developing the condition being studied. This, however, does not always accurately measure risk factors, as the participants may not correctly recall their exposure, or information on the exposure may not be readily available. It is also difficult to guarantee that the exposure occurred before the development of the disease.

To get around these limitations, researchers may conduct what are called “nested case control studies", in which the participants are drawn from existing “cohort studies” – where a large population is followed over time to see who went on to develop a particular disease. Sourcing participants from a cohort study means researchers can evaluate participants’ circumstances and exposures before they developed the disease, providing a better appraisal of participants’ past exposure than simply asking about their histories, as would happen in a normal case-control study.

In this research, participants were drawn from three cohort studies that had collected blood samples from a large group of women who were judged to be free of breast cancer at the time they entered these studies. These women were then followed up over time. The researchers identified women from these cohorts who had gone on to develop breast cancer, and matched them to other cohort participants who had not developed the disease. Nesting the study in this way ensured that the analysed blood samples were drawn before the cancer was diagnosed, allowing researchers to compare pre-diagnoses methylation levels between the two groups of women.

What did the research involve?

The researchers used three prospective cohort studies to identify breast cancer cases and matched control participants. The first study involved women with a family history of breast cancer who were considered to be at high risk for developing the disease. The second and third studies were cohort studies conducted among the general population. All of the cohort participants had had a blood sample taken as part of the original study, before any cancer diagnosis.

All of the cohort studies collected blood samples from the participants. Samples were taken an average of 45 months before breast cancer was diagnosed in the first study, 18 months in the second and 55 months before diagnosis in the third study. In addition to blood samples, information was collected on other breast cancer risk factors, such as hormonal and reproductive factors, smoking status and alcohol drinking status.

The researchers analysed white blood cells in the blood samples to determine the degree of methylation they had within a specific gene called the ATM gene. The ATM gene is involved in many functions, including cellular division and the repair of damaged DNA. The researchers then compared the average level of methylation between cases and controls in each cohort study to determine whether there was a significant difference in the degree of modification to the ATM gene.

The researchers then divided the study participants into five groups based on their level of methylation. For each methylation group, the researchers assessed the odds of having breast cancer. They then compared the odds of developing the disease in the groups with the higher levels of methylation with the group with the lowest level. This analysis combined the data from the three cohort studies and controlled for a variety of confounders that could potentially account for the association between gene methylation and breast cancer diagnosis. This analysis was also stratified by participant age, family history of breast cancer and the length of time from blood test to diagnosis in order to assess whether or not these factors modified the relationship.

What were the basic results?

The exact number of women involved in the three studies is not featured in the study paper but the details mentioned suggest it was around 640,000 in total. Among these women, the researchers identified 640 breast cancer cases and 780 healthy control subjects. They found that, in two of the three studies, cases had significantly higher average levels of methylation at a specific point on the ATM gene than controls did.

When comparing the odds of developing breast cancer between the highest and lowest levels of methylation, the researchers found that:

• Participants in the fifth quintile (with the highest degree of methylation) had significantly higher odds of having breast cancer compared with the lowest methylation group (OR 1.89, 95% CI 1.36 to 2.64).
• Participants in second, third and fourth quintiles (intermediate degrees of gene methylation) showed no significant difference in the odds of having breast cancer compared with the lowest methylation group.

When the results were stratified by participant age, the researchers found that this pattern was strongest among women under the age of 59, and not significant among women between the ages of 59 and 91.

How did the researchers interpret the results?

The researchers concluded that high levels of methylation (modification of the ATM gene) might be a marker of breast cancer risk.

Conclusion

This case-control study provides evidence that a type of molecular modification (methylation) at a particular genetic site may be associated with an increased risk of developing breast cancer.

The researchers said that the identification of a white blood cell DNA methylation marker for breast cancer is quite useful because it can be detected through assessing a simple blood sample, as opposed to the extraction of tissue samples that is often needed to identify cancer markers.

This study had several strengths, including:

• The case-control study was “nested” from three large, independent cohort studies. Nesting is a process were participants are taken from existing studies so that researchers can examine details of their histories that have been formally recorded at the time, rather than being simply recalled.
• Using blood samples taken before a cancer diagnosis allowed the researchers to be confident that the study results were not due to “reverse causality” (that is, the possibility that active cancer or treatment might cause DNA methylation).

There are some limitations to the study that should be considered:

• The selection of appropriate controls is important for case-control studies, as ideally subjects should be from the same study base. For the first study, cases consisted of women with a strong family history of breast cancer, while their friends with no family history were selected as controls. This is not an ideal method of identifying controls, as controls lacked the key risk factor of a family history of the disease.
• Across the three cohort studies, there were varying strengths in the association between white blood cell DNA methylation and breast cancer risk. The strongest association was seen in the cohort study that included women with a strong family history of the disease. Whether this strong association was due to genetic predisposition to the disease or weaknesses in the case-control design for this cohort is difficult to say at this point.

The researchers said that additional research is needed in order to investigate the effect of age on the association between methylation and risk of breast cancer. They also said that their results supported the further investigation of common variations in DNA methylation as risk factors for breast cancer as well as other cancers.

It’s important to note that a simple blood test based on this research is not available yet, and is unlikely to be available for some time. There are various known genetic, medical and lifestyle risk factors for breast cancer, and the extent to which any modification of this white blood cell gene influences risk, or interacts with other breast cancer risk factors, has not been established.

Although media reports suggest that these findings could lead to a simple blood test to screen women, or to detect the earliest stages of cancer, it is far too early to be sure of this. Before any screening test is introduced, extensive research and consideration is needed to determine in which groups of people the benefits of screening (such as reduced incidence of breast cancer and improved survival) would outweigh the risks (such as false positive or false negative results, further diagnostic tests and treatments or associated anxiety).

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Breakfast smoothie

Prep time: 5 min

Serves: 2

Jo Pratt's satisfying meal in a glass simply oozes with goodness – and it tastes great to boot!

Ingredients

• 2 bananas
• 300 ml fruity bio yogurt
• 150 g blueberries
• 400 ml cranberry juice
• 1 tbsp clear honey
• 2-3 tbsp wheat germ
• chopped toasted hazelnuts, to serve   

Method

1. Put everything apart from the nuts in a blender and blitz until smooth.

2. Sweeten to taste with extra honey if desired.

3. Pour into glasses and scatter over the nuts. Serve immediately, as the blueberries will cause the smoothie to thicken (if this happens, simply stir in some extra cranberry juice). 

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Diagnosed By A Supercomputer?

It is one of the great dreams of science fiction -- a computer that can speak, a computer that can simulate human thinking. Sometimes in our imaginings, it is friendly like the talking computer on Star Trek, and sometimes it is not like the Terminators from the movies. In any case, for good or evil, they now seem to be here, in the real world. We have Siri on the iPhone and, more notably, an IBM supercomputer that not only speaks, but also, performs advanced medical diagnosis and suggests treatment plans.

The computer, named 'Watson,' burst onto the scene in 2011 as a contestant in the game show Jeopardy, where it beat the pants off two of the best human contestants in the history of the show. Watson made a huge splash in part because it processes inquiries spoken to it in normal language and answers in a pleasant voice. The computer takes its name from IBM founder Thomas Watson -- not from Sherlock Holmes' sidekick. Watson looks something like a normal laptop (at least the part you see) but has a larger screen. All of the massive memory and processing power are stored elsewhere -- out of sight. And it's definitely the fast kid on the block, able to process 200 million pages of data in three seconds flat. In other words, it can perform extremely complex analyses and functions way faster than humans can.

Watson can potentially be used in many ways, but the first industry to seize on its capabilities for commercial application has been the medical field. Wellpoint Insurance has paired up with Memorial Sloan-Kettering Hospital in New York to use the computer to help physicians working with cancer patients. The computer is being fed enormous amounts of data regarding cancer diagnosis and treatment, including obscure literature that might otherwise have eluded physicians, practitioner experiences worldwide, individual medical records of millions of patients (sans all identifying information), and anecdotal, plain language information about patient reactions to various courses of treatment. It will take about a year to finish gathering and programming all the data.

Watson is expected to function as a bedside tool, allowing physicians to diagnose and treat cancer more quickly and effectively. Eventually, its database will be stored in the cloud so that physicians everywhere will be able to access it.

Experts say that it typically takes up to 15 years for medical breakthroughs to become known throughout the medical community. According to IBM's chief medical science officer, Dr. Martin Cohn, "What Watson can do is read and understand huge volumes of information. There is so much information being developed in health care in general, and oncology in particular, that the ability to understand all the information out there is becoming progressively more challenging. What Watson does is bring information to the doctor."

Watson will work by allowing physicians to enter all the quirky information relevant to a particular patient, including information about a patient's psychological and social circumstances as well as that patient's individual treatment preferences. For instance, if a patient doesn't want a treatment that would result in hair loss, that would go into the system, as would the fact that a patient has little support from others and so can't undertake risky at-home treatments. Then Watson would go to work, crunching the patient symptoms, personal preferences, and history against its vast storehouse of information, and in a matter of seconds, suggests a diagnosis and a first-choice treatment plan, with several back-up options ranked in order of preference. Ostensibly, the physician and patient can then choose whether to honor or disregard the recommendations. Then again, insurance companies might have something to say about that -- especially if cost factors are programmed in. Remember: Wellpoint Insurance is one of the partners in this project.

According to Lori Beer, WellPoint's executive vice president of Enterprise Business Services, "The implications for healthcare are extraordinary. We believe new solutions built on the IBM Watson technology will be valuable for our provider partners, and more importantly, give us new tools to help ensure our members are receiving the best possible care."

Certainly, that's the hope, but some fear that insurers will use the technology to impose medical solutions on patients and to justify refusing to pay for less conventional treatment. If the medical establishment views a diagnosis and prescription from Watson as inviolable, then very soon the poor patient will have even less voice in determining what type of treatment he or she prefers than now. What Watson says will go. And the scary thing is that Watson can make mistakes, and in fact did make mistakes on Jeopardy. Plus, Watson relies on numbers, facts, and probabilities -- in other words, "evidence-based medicine," discounting the possibility of the rare exception to the rule that some who opt for alternative medicine might hope for. Keep in mind: "unexplained," spontaneous remissions do happen! Plus, it isn't clear just how much information about alternative courses of treatment Watson will be fed. Will the computer consider treatments based on nutraceuticals, acupuncture, energy healing, and so on? Watson can only make recommendations according the data in its information banks. If its programmers decided not to include information about a promising new experimental therapy, Watson can't evaluate it -- or respond to it. Given that insurers don't often reimburse for these sorts of treatment, the outlook is cloudy.

On the other hand, at least we can count on Watson to be sober as it dispenses treatment plans, unlike so many real-life doctors. That, combined with its huge storehouse of data, may actually work to improve the current state of medicine. Watson can never forget or let slip an important treatment option... as long as the option was fed to it in the first place.

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