By Stephanie Swift, University of Ottawa

My mum and dad are troopers. Every morning, in an effort to stave off old age and dry rot, they down a tablespoon of oily, stinky fish oil. This is done without any obvious signs of distress – clearly, they are from a more stoic generation.

Fish oils – or more accurately, the omega-3 long chain polyunsaturated fatty acids (n-3 PUFAs) in fish oils – have been linked to cognitive performance. The idea of cognitive lubrication has proved very popular. However, for some of us, the idea of choking down fish oil in its liquid form is repulsive.

That’s where fish oil capsules come in. On the surface, they seem to be the perfect solution. The fish oil stays safely trapped in its hard shell until it passes down through the stomach and into the upper intestine. Once there, the capsule degrades to allow the fishy brain lube to be released.

There is just one problem. New research led by Benjamin Albert at the University of Auckland in New Zealand shows that the quality of over-the-counter fish oil capsules is pretty rubbish.

Albert and his team bought 32 different brands of fish oil capsules and measured them for levels of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), supposedly the “good” n-3 PUFAs responsible for brain gains. They found that 69% (29 out of 32 tested) had lower levels of EPA and DHA than was claimed on the label. As an interesting side note, the more expensive capsules were more accurately labelled for EPA and DHA levels.

To achieve such lower than advertised levels of EPA and DHA, either the freshly isolated fish oil had lower concentrations to begin with, or the oil within the capsule had oxidised and degraded over time. Both EPA and DHA are prone to oxidation, and break down to form a soup of peroxides, aldehydes and ketones. In fact, fish oil supplement manufacturers typically add anti-oxidants into their capsules to slow this process.

Quality control

When the New Zealand team tested oxidation values across fish oil capsules, 92% exceeded one or more international recommendations. But older capsules that had been on the shelves for longer didn’t show any difference in oxidation values compared to newer ones. This suggests that there were lower levels of active EPA and DHA at the very beginning of the manufacturing process, and that many companies may be failing to test their individual batches of fish oil.

What might such oxidation values mean for the consumer? While some studies indicate that oxidation breakdown products may in fact be responsible for the anti-inflammatory benefits of fish oil, at high experimental doses, they can cause organ toxicity, stunted growth and accelerated atherosclerosis. The overall effect on health – if any – of consuming products with high oxidation values is still unclear. Since there is no formal assessment of their health effects, oxidation levels in fish oil capsules are subject to recommendations based on palatability rather than legal requirements based on safety.

If this research is representative of the global market, consumers have a one in ten chance of buying fish oil capsules that contain the levels of EPA and DHA that are promised. These odds might improve a little if they stick to high-end brands. Until better standards and regulations hit the fish oil supplement market, it is probably a good idea to look for your brain boost elsewhere.

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By Ken Sayers, Georgia State University

Reconstructions of human evolution are prone to simple, overly-tidy scenarios. Our ancestors, for example, stood on two legs to look over tall grass, or began to speak because, well, they finally had something to say. Like much of our understanding of early hominid behavior, the imagined diet of our ancestors has also been over-simplified.

Take the trendy Paleo Diet which draws inspiration from how people lived during the Paleolithic or Stone Age that ran from roughly 2.6 million to 10,000 years ago. It encourages practitioners to give up the fruits of modern culinary progress – such as dairy, agricultural products and processed foods – and start living a pseudo-hunter-gatherer lifestyle, something like Lon Chaney Jr. in the film One Million BC. Adherents recommend a very specific “ancestral” menu, replete with certain percentages of energy from carbohydrates, proteins and fats, and suggested levels of physical activity. These prescriptions are drawn mainly from observations of modern humans who live at least a partial hunter-gatherer existence.

But from a scientific standpoint, these kinds of simple characterizations of our ancestors’ behavior generally don’t add up. Recently, fellow anthropologist C. Owen Lovejoy and I took a close look at this crucial question in human behavioral evolution: the origins of hominid diet. We focused on the earliest phase of hominid evolution from roughly 6 to 1.6 million years ago, both before and after the first use of modified stone tools. This time frame includes, in order of appearance, the hominids Ardipithecus and Australopithecus, and the earliest members of our own genus, the comparatively brainy Homo. None of these were modern humans, which appeared much later, but rather our distant forerunners.

We examined the fossil, chemical and archaeological evidence, and also closely considered the foraging behavior of living animals. Why is this crucial? Observing animals in nature for even an hour will provide a ready answer: almost all of what an organism does on a daily basis is simply related to staying alive; that includes activities such as feeding, avoiding predators and setting itself up to reproduce. That’s the evolutionary way.

What did our ancestors actually eat? In some cases, researchers can enlist modern technology to examine the question. Researchers study the chemical makeup of fossil dental enamel to figure out relative amounts of foods the hominid ate derived from woody plants (or the animals that ate them) versus open country plants. Other scientists look in ancient tooth tartar for bits of silica from plants that can be identified to type – for example, fruit from a particular plant family. Others examine the small butchering marks made on animal bones by stone tools. Researchers have found, for example, that hominids even 2.6 million years ago were eating the meat and bone marrow of antelopes; whether they were hunted or scavenged is hotly debated.

Such techniques are informative, but ultimately give only a hazy picture of diet. They provide good evidence that plants’ underground storage organs (such as tubers), sedges, fruits, invertebrate and vertebrate animals, leaves and bark were all on the menu for at least some early hominids. But they don’t give us information about the relative importance of various foods. And since these foods are all eaten at least occasionally by living monkeys and apes, these techniques don’t explain what sets hominids apart from other primates.

So how should we proceed? As my colleague Lovejoy says, to reconstruct hominid evolution, you need to take the rules that apply to beavers and use them to make a human. In other words, you must look at the “rules” for foraging. We aren’t the first researchers to have dabbled in this. As long ago as 1953, anthropologists George Bartholomew and Joseph Birdsell attempted to characterize the ecology of early hominids by applying general biological principles.

Happily, ecologists have long been compiling these rules in an area of research dubbed optimal foraging theory (OFT). OFT uses simple mathematical models to predict how certain animals would forage in a given circumstance. For instance, given a set of potential foods of estimated energetic value, abundance and handling time (how long it takes to acquire and consume), one classic OFT model calculates which resources should be eaten and which ones should be passed over. One prediction — sort of a “golden rule” of foraging — is that when profitable foods (those high in energy and low in handling time) are abundant, an animal should specialize on them, but when they are scarce, an animal should broaden its diet.

Data from living organisms as disparate as insects and modern humans generally fall in line with such predictions. In the Nepal Himalaya, for example, high-altitude gray langur monkeys eschew leathery mature evergreen leaves and certain types of roots and bark — all calorie-deficient and high in fibers and handling time — during most of the year. But in the barren winter, when better foodstuffs are rare or unavailable, they’ll greedily devour them.

In another more controlled study, when differing quantities of almonds in or out of the shell are buried in view of chimpanzees, they later recover larger quantities (more energy), those physically closer (less pursuit time), and those without shells (less processing time) before smaller, more distant, or “with-shell” nuts. This suggests that at least some animals can remember optimal foraging variables and utilize them even in cases where foods are distant and outside the range of immediate perception. Both of these studies support key predictions from OFT.

If one could estimate the variables important to foraging, one could potentially predict the diet of particular hominids that lived in the distant past. It’s a daunting proposition, but this human evolution business was never meant to be easy. The OFT approach forces researchers to learn how and why animals exploit particular resources, which leads to more thoughtful considerations of early hominid ecology. A smattering of scientists have utilized OFT with success, most notably in archaeological treatments of comparatively recent hominids, such as Neandertals and anatomically modern humans.

But a few brave souls have delved into more remote human dietary history. One team, for example, utilized OFT, modern analogue habitats, and evidence from the fossil record, to estimate the predicted optimal diet of Australopithecus boisei. That’s the famed “Nutcracker Man” that lived in East Africa close to 2 million years ago. The research suggests a wide range of potential foods, greatly varying movement patterns – based on characteristics such as habitat or use of digging sticks — and the seasonal importance of certain resources, such as roots and tubers, for meeting estimated caloric requirements.

Researchers Tom Hatley and John Kappelman noted in 1980 that hominids have bunodont – low, with rounded cusps – back teeth that show much in common with bears and pigs. If you’ve watched these animals forage, you know they’ll eat just about anything: tubers, fruits, leafy materials and twigs, invertebrates, honey and vertebrate animals, whether scavenged or hunted. The percentage contribution of each food type to the diet will depend (you guessed it) on the energetic value of specific foods in specific habitats, at specific times of year. Evidence from the entirety of human evolution suggests that our ancestors, and even we as modern humans, are just as omnivorous.

And the idea that our more ancient ancestors were great hunters is likely off the mark, as bipedality — at least before the advance of sophisticated cognition and technology — is a mighty poor way to chase game. Even more so than bears and pigs, our mobility is limited. The anthropologist Bruce Latimer has pointed out that the fastest human being on the planet can’t catch up to your average rabbit. Another reason to be opportunistic about food.

Simple characterizations of hominid ecology are divorced from the actual, and wonderful, complexity of our shared history. The recent addition of pastoral and agricultural products to many modern human diets — for which we have rapidly evolved physiological adaptations — is but one extension of an ancient imperative. Hominids didn’t spread first across Africa, and then the entire globe, by utilizing just one foraging strategy or sticking to a precise mix of carbohydrates, proteins and fats. We did it by being ever so flexible, both socially and ecologically, and always searching for the greener grass (metaphorically), or riper fruit (literally).

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By Paul Drevnick, University of Michigan

Whether man-made sources of mercury are contributing to the mercury levels in open-ocean fish has been the subject of hot debate for many years.

My colleagues Carl Lamborg, Marty Horgan and I analyzed data from over the past 50 years and found that mercury levels in Pacific yellowfin tuna, often marketed as ahi tuna, is increasing at 3.8% per year. The results were reported earlier this month in the journal Environmental Toxicology and Chemistry.

This finding, when considered with other recent studies, suggests that mercury levels in open-ocean fish are keeping pace with current increases in human-related, or anthropogenic, inputs of mercury to the ocean.

These levels of mercury – a neurotoxin – are now approaching what the EPA considers unsafe for human consumption, underscoring the importance of accurate data. With this article, I’ll explain the evolution of the science to this point and our findings. I expect our analysis will either quiet the debate or add more fuel to the fire.

Ocean sensitivity

Motivated by the seminal environmental book Silent Spring, environmental chemists have long found widespread mercury pollution in wastewater from industrial activities.

Surprisingly, mercury also appeared far from point sources – in “pristine” lakes of Scandinavia and northeastern North America. It took many years and careers to understand why mercury wound up in these “pristine” lakes. Once emitted from natural or man-made sources, such as coal-burning power plants, mercury can travel as a gas many times around the globe before falling with rain, snow, or dust. Once out of the air and in the water, it can then be taken up by fish.

There has been a false perception, however, that the open ocean – far removed from point sources of pollution – is too voluminous to be polluted with mercury from atmospheric fallout.

The shorthand for saying oceans can’t be significant sinks for air-borne pollutants is “dilution is the solution to pollution.” The argument is that lakes are concentrated environments because they are in direct contact with their watersheds that collect rain and snow, but the deep open ocean is an extremely dilute environment.

Two manuscripts published in Science in the early 1970s supported this argument. The first stated that mercury pollution could only result in a negligible increase in mercury levels in open ocean water.

But my colleagues and I found these conclusions were based on faulty data. Before the advent of clean sampling techniques that prevent contamination before, during, or after collection, it was accepted that natural mercury levels of open ocean waters ranged in the low parts-per-billion. We now know that a typical mercury level is about 200 parts-per-quadrillion. That means the natural mercury level of open ocean water is about 5,000 times lower than previously thought and that it takes a lot less mercury from other sources to pollute the open ocean.

The second manuscript reported no difference in mercury levels in tuna between museum specimens dating from 1878-1909 and samples caught during 1970-1971. This finding may be true, but also has a critical error in that mercury levels in the museum specimens were not “corrected” for lipid (fat) loss. Mercury is primarily in fish muscle and preservation with ethanol causes significant loss of fats. The net effect is that this preservation technique “inflates” the mercury concentration in the tissue that remains.

As a result, we question how valid these findings are. In other words, this second study doesn’t conclusively demonstrate whether mercury levels in fish have gone up, down, or stayed steady.

Sources of mercury

More recently, the focus of debate has been on the source of mercury in open-ocean fish. The mercury absorbed by fish is a compound called methylmercury, a form readily taken up by plant and animal cells but not easily eliminated. Because of this, mercury is concentrated with each step of the food chain. As a result, methylmercury levels in predatory fish are about a million times greater than in the water in which they swim.

In lakes, there is overwhelming evidence that methylmercury is formed in sediments and bottom waters that are devoid of oxygen. But where is methylmercury in oceans formed?

In 2003, Princeton scientists published a hypothesis to answer the question of where methylmercury comes from in open ocean fish. The hypothesis was based on the observation, mentioned above, that there was no increase in mercury levels in yellowfin tuna near Hawaii between 1971 and 1998.

With no increase in mercury levels in tuna during a period of greatly increasing anthropogenic mercury emissions, the scientists presented the idea that methylmercury in the open ocean forms from mercury naturally present in deep waters, sediments, or hydrothermal vents.

Subsequently, however, independent studies have shown that there is not enough methylmercury in deep waters of the ocean to account for mercury in open ocean fish.

One of these studies also found that methylmercury is formed on sinking particles in the water that provide a micro-environment devoid of oxygen. That research showed that the methylmercury is formed from mercury coming from above – that is, the atmosphere – which we know is polluted from human activities. Finally and most importantly, we know mercury levels in ocean water are increasing globally.

What the numbers say

Given the ongoing debate, our study set out to test a simple question: have mercury levels in fish stayed the same over time?

We assembled data from published sources for mercury in yellowfin tuna from Hawaii to compare three different time periods: 1971, 1998, and 2008. The comparison had to factor in the size of each tuna for each time period, because mercury level increases with size.

The statistical comparison indicated mercury levels were higher in 2008 than in either 1971 or 1998. As a result, we concluded that mercury levels are increasing in yellowfin tuna near Hawaii. The rate of increase between 1998 and 2008 of 3.8% per year is equivalent to a modeled increase in mercury in ocean waters in the same location.

What’s the source of the mercury? The overwhelming scientific evidence points to anthropogenic sources of mercury polluting open ocean waters and methylmercury being produced in the water column and then accumulating in fish.

The average mercury level in a Pacific yellowfin tuna is approaching a level the US EPA considers unsafe for human consumption (0.3 parts-per-million).

Fish are an important source of food for billions of people worldwide and a solution to the problem is not to eat less fish, but to choose fish lower in mercury, as the EPA and FDA jointly recommend.

The ultimate solution to the problem is to control mercury emissions to the atmosphere at their source, which is the aim of the new United Nations Environment Programme’s Minamata Convention on Mercury.

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By Hazhir Rahmandad, Massachusetts Institute of Technology

The prevailing notion about obesity is that if we just work out harder and eat a little bit better, then perhaps the obesity trend will subside in a few years. However, the key to really making a difference is food – the number of calories we eat is the most important factor in obesity. But changing the way people eat will take a very long time.

Things like individual routines, menus, food access and affordability, and cultural practices all influence how we live and eat. All of these things can influence the energy imbalance gap (EIG). The EIG is essentially how many calories you consume versus how many calories you burn in a day. It controls the speed of change in body mass and is at the core of understanding obesity.

Think of the EIG like a gas pedal in a car. If you push the pedal, the gap is positive and obesity trends speed up. If you push the brake, then the gap becomes negative and we would have fewer obese people. A zero gap is like cruise control with a steady obesity prevalence. For example, an EIG of about 10 calories a day leads to weight gain of approximately one pound per year.

Measuring the energy imbalance gap

In a recent study, my colleagues and I applied system dynamics, a simulation method for understanding complex socio-technical systems, to estimate EIG trends in the US.

Measuring EIG directly is complex – even a 1% error in measuring daily energy intake would render the EIG values unreliable. And in typical, self reported calorie intake logs, the main EIG direct measure applicable to large groups, have errors in excess of 10%. In fact few previous studies provided reliable EIG estimates for large populations. So we developed a method to reverse engineer the EIG trends based on weight data, just as you can estimate acceleration rates from data on the speed of a car at different times. This method separates the contribution of EIG to population weight profile from other factors such as differential mortality rates due to obesity.

Based on weight data from National Health and Nutrition Examination Survey (NHANES) our research looked at changes in the EIG over the past four decades in representative samples of three different population groups: Non-Hispanic Whites, African-Americans, and Mexican-Americans. We found significant differences among these three population groups as well as between genders within each group.

For Non-Hispanic Whites, the largest group in the survey (and the largest population group in the US), we found that the average EIG has been positive over the last four decades. That means that this group has been gaining weight consistently, a trend reflected in the current obesity epidemic. But our model shows that the gap is actually shrinking. Once the gap reaches zero, the obesity rate will have stabilized (meaning it is not growing nor shrinking) – and for this population we may be already at that point. This doesn’t mean that the problem of obesity is solved for this group, but it does mean that the problem is no longer getting worse.

We see a different story for African-Americans and Mexican-Americans. For African-Americans, the rate of obesity is growing and the EIG is still not close to zero. The average gap is positive, around 15 extra calories per day, which is a powerful engine behind continued obesity trends. The good news for African-Americans is that the energy gap has started to shrink. Based on current trends we may expect the gap to stay positive for at least another decade before it begins to close. This means that in the future we’ll see more challenges with obesity in the African-American community, which may peak in a decade or so.

The situation for Mexican-Americans is more critical. Not only is the gap positive, at approximately 20 calories a day, it is above the estimates for any other population group. And the EIG is growing at an alarming rate. Not only this group faces a obesity epidemic today, but also the situation is getting worse at an accelerating rate. This population group needs much more attention to turn the tide of the obesity epidemic.

As for gender, our most notable finding pertains to African-American females, who generally have had higher EIGs than African-American males. This means that obesity has been getting worse for females faster than for males. More recently Mexican-Americans show a similar gender gap, with higher EIG for females in the last few years. For Non-Hispanic Whites, the energy gaps are bigger for males than females, thus obesity trends are growing faster for men than women. From food and activity environment to social norms, various factors may explain these differences across population groups, and more research is needed to pin down the exact contribution of each factor.

Where do we go from here?

We know obesity is an epidemic so these findings aren’t that surprising. However what is remarkable is the differences among ethnicities. There are numerous programs and policies that target obesity with varying success. Focusing on the ones that research finds cost-effective and targeting population groups most at risk would best leverage the limited available resources in controlling future obesity trend and its costs.

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By Jackson Fyfe, Victoria University and Jon Bartlett, Victoria University

Carbohydrate-rich diets are often recommended as part of exercise regimes to promote recovery and maximise performance. But recent research suggesting such foods may not help exercise recovery and their potential link with metabolic diseases are raising questions about whether this advice is still appropriate.

The energy status of exercising muscles has been thought to be an important element in exercise performance since the late 1960s. As carbohydrate is the preferred energy source for muscle contraction during moderate–to-high intensity exercise, typical sports nutrition guidelines advocate eating carbohydrate-rich food before, during and after exercise to maximise performance.

Such guidelines, which are mainly for professional athletes, suggest consuming just over one gram of carbohydrate for every kilogram of your body mass, each hour for four hours, to maximise replenishment. But is a high carbohydrate intake really required to maximise exercise recovery? And is it appropriate for people who aren’t overly concerned with competitive performance?

Performance vs recovery

Before exploring these questions, it’s important to distinguish between exercise recovery and performance.

Recovery describes the processes within muscles that are stimulated by the stress of exercise sessions. These processes accumulate and eventually result in increased endurance and muscle growth. Adaptations like these improve the body’s ability to cope with future exercise stress.

Exercise performance, on the other hand, relates to the ability to perform exercise at a desired intensity and duration.

Nutrition plays a role in both, and the quality of recovery can affect future exercise performance. But nutritional recommendations for performance may not be ideal for promoting recovery in all instances.

Carbohydrates and endurance training

Although the beneficial role of carbohydrates for improving exercise performance is widely accepted, researchers have recently observed that restricting carbohydrate intake close to endurance training sessions might actually help muscle recovery. They found reducing carbohydrate availability (by either an overnight fast or restricting carbohydrate intake close to exercise sessions) may help promote early recovery, possibly leading to long-term improvements in endurance.

Several studies show that high carbohydrate intakes can suppress the activation of several genes linked to exercise adaptations. Our recent research shows it’s possible to complete two sessions of high-intensity interval exercise separated by up to 12 hours of carbohydrate restriction. We also found early recovery is more likely when exercise is performed with low carbohydrate availability.

Eating large amounts of carbohydrate during early recovery may also be counterproductive for achieving fat loss. We found restricting carbohydrates during recovery from exercise increased fat metabolism and decreased carbohydrate metabolism. In fact, approximately three times more fat was used when carbohydrate intake was restricted during exercise recovery.

Given that many of us exercise to lose weight, consuming carbohydrates before and after exercise may be doing more harm than good!

Carbohydrates and resistance exercise

But what about the role of carbohydrates for recovery from resistance exercise, which includes lifting weights or performing bodyweight-type exercises with the goal of increasing muscle mass and strength?

Consuming protein when doing this kind of exercise is known to benefit muscle growth. High carbohydrate intakes have traditionally been recommended to support resistance exercise performance and recovery.

But several studies now show that carbohydrates don’t further benefit recovery processes after resistance exercise compared to protein alone.

What’s more, performing resistance exercise when muscle carbohydrate stores are low also doesn’t compromise early recovery. Taken together, this suggests dietary carbohydrate plays little to no role in recovery from resistance exercise.

Another common belief is that people doing resistance training need extra energy intake (in other words, to eat more) to increase muscle mass. And one way of increasing energy intake is to increase the carbohydrate consumption. There’s no evidence for this belief but research does show muscle recovery after resistance exercise is promoted by protein, even when the person exercising is in energy deficit.

Potential health risks

Not only do the dietary recommendations for increasing carbohydrate consumption for better exercise recovery not apply for the non-athlete exerciser, they are actually a cause for concern. Carbohydrates have a potential role in the development of metabolic diseases, including type 2 diabetes and obesity.

Consuming a lot of carbohydrate-rich food is thought to over-stimulate the hormone insulin by causing chronically high blood sugar levels. One of the many roles of insulin is blocking the use of fats as a fuel source. At the same time, insulin promotes the storage of excess carbohydrate as fat and reduces the body’s ability to control blood sugar levels.

For recreationally active people whose exercise goals are often to improve general health and body composition – reduce fat mass and increase muscle mass – eating a high-carbohydrate diet may actually have the opposite result.

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