What to drink?

There's probably no other thought that pops into our heads more often than "what should I eat?"  The answer should be simple, but the question has become frustratingly complex.  Should we eat for taste, or for health?  What about convenience?  And what about the more nebulous, but vitally important, question of the ecological impact of the food we are eating?

A corollary of the mealtime question, of course, is "what should I drink?"  We can again ask many questions about our liquid refreshments, and fortunately, a couple studies from the recent issue of The American Journal of Clinical Nutrition provide us with some answers.

One of these is not like the others...

The first study, by a group in Denmark, indicates what not to drink.  Designed as an experiment with their subjects freely roaming the world, the researchers gave groups of participants a different liter of fluid to drink each day for six months.  The experimental drink was good ole' Coca-Cola (made with sucrose rather than high fructose corn syrup in Europe), and the control drinks were reduced fat milk, diet-Coke, and water.  Drinks were delivered to the subjects homes at the start of the study, and empty bottles were collected afterwords; as such, it's likely that at least some of the daily allotments made it down the drain instead of the gullet.  But regardless of the precise amount consumed, the results are telling.

The regular cola, or as the Danish-scientists fondly called them, sucrose-sweetend soft drinks (SSSDs), but none of the other drinks, drove fat to accumulate in all of the wrong places.  The regular soda caused a 135% increase in liver fat, a 120% increase in intramuscular fat (think marbling in a corn-fed steak), and a 25% increase in visceral fat, which is the particularly deleterious fat that surrounds the organs within the abdomen.  Together, the liver and muscles comprise the greatest amount of insulin-sensitive tissue in the body.  When fat infiltrates these organs, they have trouble following insulin's orders, so the pancreas has to squeeze out more insulin to compensate.  This is insulin resistance.  But while none of the subjects showed compromised insulin function in the first 6 months, ectopic, or out of place, fat deposits are paramount to insulin resistance and the associated metabolic syndrome.

And the relative non-effects of the other drinks are equally interesting.

Despite a similar caloric content to the SSSBs, daily milk intake decreased the ratio of visceral fat to subcutaneous fat, meaning that milk helped carve people into a "pear" rather than an "apple" shape.  A calorie is a calorie, indeed.

Surprisingly, both diet soda and milk lead to a decrease in blood pressure.  What's more, the diet soda didn't cause any fat gain.  Diet soda is thought, at least by some, to contribute to weight gain because the sweet taste of diet soda is sufficient for the pancreas to secrete some insulin.  This study clearly argues against an "obesogenic" effect of artificial sweeteners, both directly, or indirectly by encouraging a sweet tooth.

But what shall we order at Happy Hour?

Red wine has become famous for being "heart healthy."  After all, the Mediterraneans are famous for drinking wine and having a relative absence of heart disease.  Additionally,  the grape-derived compound resveratrol has been shown to extend life... in metabolically compromised mice, anyway.  But really pinning down the benefit of red wine as been a challenge.

There isn't actually a whole lot of resveratrol in wine, and most animal studies employ pharmacological doses extracted from grapes.  So is it some other compound in wine?  Is it a combination of some?

And epidemiological evidence on red wine must be taken with a grain of salt.  Prospective cohort studies - like the Nurse's Health Study - are the best form of observational epidemiology, but they cannot exclude selection bias, that is, people who tend to drink red wine also tend to be wealthier, healthier, or live more moderate lives.  Selection bias prohibits researchers from truly knowing if it's the red wine or if it's simply the people who choose to drink red wine, that's the determinant of health.  Fortunately, researchers conducted an experiment.

This study examined the impact of red wine on heart disease risk factors, specifically the abundance of inflammatory proteins and immune cells in the blood.  Heart disease is characterized by systemic inflammation and the accumulation of immune cells, like macrophages and lymphocytes, within the blood vessel walls.  There's good reason to think that it's this inflammation, rather than cholesterol per se, that contributes to cardiovascular disease.  Any modality that can reduce inflammation likely protects the heart.

Each subject in the experiment participated in each of three four-week trials where they got to enjoy a couple of daily servings of red wine, gin, or de-alcoholized red wine.  The gin allowed the researchers to examine the impact of the alcohol, while the de-alcoholized wine isolated the multitude of polyphenols unique to fermented grape juice.  The results are intriguing, if not a bit complex.

Does this mean that my birthday celebration was good for my heart?
I'd like to think so.

Red wine, and not the individual components, lowered several pro inflammatory molecules in the blood.  Collectively called "chemokines," CD40a, IL-16, MCP-1, and VCAM-1 were all reduced.  These molecules help immune cells cling and penetrate into blood vessel walls, where they form fatty plaques.  Other effects were specifically attributed to the components of the wine.

Two daily shots of gin made immune cells less sticky, while also increasing IL-10, a potent anti-inflammatory molecule that mellows-out aggressive immune cells.

And the polyphenols themselves lowered IL-6, an acute-phase protein that stimulates the liver to secrete C-reactive protein, which is routinely measured by doctors to assess heart disease risk.  Grape juice also contains polyphenols, but all of the sugar without the fiber likely makes the teetotaling drink more harmful than helpful.

Of course, the participants probably didn't drink every last drop; some of the soda, although perhaps not the wine, surely went right down the sink.  And we don't know exactly how important it is to decrease esoteric molecules like IL-16 from 478pg/ml to 450pg/ml.  But we do have a certain degree of confidence in deciding what we should drink.

We can say that soda consumption does indeed cause fat to be deposited throughout the body.  And the benefits of drinking wine could very well be attributable to the wine itself; and at the very least, some merlot doesn't appear harmful to the heart.

It looks like the cherry-brandy diet Coke highball I made the other weekend was an even better idea that I originally thought!

Not-So-Placebo Controlled Trials

Source: Paul at FreeDigitalPhotos

Imagine that you are a new parent.  Your wife has had diabetes since childhood, and after being tested, you find out that you have a diabetes-associated HLA genotype.  Together, this means that your newborn has a genetic risk for diabetes (I'm referring to type 1 or insulin-dependent diabetes for this post), and if the right environmental exposure is present, she could develop the disease.  Being as proactive as you can, you search for available clinical trials that are testing interventions to prevent diabetes.  You acknowledge that it is important to advance scientific understanding of medicine and disease, but let's be honest, you are primarily concerned with your child.  Given that you are not the only participant with this mind-set, how are these experiments influenced by the evolving and sentient nature of human subjects?  After all, lab rats don't know what hypothesis is being tested, but you just might.

Randomized placebo controlled trials (controlled trials, for short) are arguably the best experimental design to understand human biology and behavior.  Unlike observational epidemiology, such as cohort studies that can only observe causal associations, controlled trials are able to determine the causality between an exposure - think treatment - and an outcome.  This is because controlled trials best approximate the "counter-factual," which is the impossible ideal where by the same subject is studied at the same time with and without an exposure.  This is accomplished by randomizing sufficiently large numbers of people into a control and an experimental group(s) so that any confounding characteristics are equally distributed between the groups to remove their influence on the exposure-outcome relationship, thereby studying the "same people" at the same time.  However, this assumes that both groups maintain randomization.

If a trial is sufficiently long enough, let's say between six months and a year or more, then some number of subjects will drop-out of the trial.  As mentioned above, subjects are randomized into two or more groups, and then demonstrated to be identical at baseline, at least for any known pertinent characteristics.  For example, groups will be shown to have the same distribution of body mass indexes, age, and exercise level.  However, as the trial progresses, some subjects will leave the trial, which could result in 20% to 40% attrition.  Every publication of a trial documents the similar baseline characteristics, the number of subjects in each group, and the attrition (See figure).  Researchers must keep track of who leaves the trial and why.

A particular concern is that subjects will learn what group they are in, and if they are unsatisfied with their assignment, may leave the trial and create a selection bias since the groups are no longer identical.  This is one of the reasons to blind the subjects, that is, give them a placebo treatment.  But this is usually easier said than done, especially in dietary trials, which is a concern for this blog.  One problem is that there is no sugar pill equivalent for a diet since we are all very aware of what are eating and have some idea, whether right or wrong, of the nutritional implications of our food. The A-to-Z trial* compared the effectiveness of several popular diets, including the Atkin's Diet and the Ornish Diet, on weight-loss and metabolic parameters.  The Ornish Diet is essentially a whole-foods nearly-vegan diet, while the Atkin's Diet is, well, the Atkin's Diet.  Unless you've been living under a rock, you would already have a preconceived notion of the Atkin's Diet and might even leave the experiment after watching an episode of Dr. Oz half-way through the trial.  Fortunately, the investigators documented how many people left the trial and why, and there was no apparent bias.  Although the degree of adherence was another matter.

The placebo is also necessary to ensure that the groups are exposed to the treatment in the desired manner - this is to avoid what is known as bias by differential misclassification.  This was particularly evident in a primary prevention trial for children at high risk for diabetes.  Previous non-trial research has shown a link between the age of dietary gluten introduction and the onset of diabetes, so that researchers hypothesized that delaying the introduction of gluten from 6 months of age to 12 months of age would prevent pancreatic islet auto-immunity and subsequent diabetes.  While this was a preliminary study designed to test the feasability and safety of such an intervention, it was still disappointing that the results were null.  But for our purposes, it demonstrated the placebo problem.  Subjects, by and large, did not leave the trial, but many opted to ignore their assignment.  At least 15% of subjects switched from the control group to the experimental group because they perceived a greater chance of success by delaying gluten exposure.  The researchers did analyze the initial assignments, and also the "new" experimental and control groups to avoid the differential misclassification bias.  However, this later analysis means that the groups were no longer randomized and that the experimental group now had a greater proportion of participants that are willing to switch groups - and any other characteristics (highly motivated, well-informed, aggressive?) that these types of people have.  Thus, the actions of the subjects can make our initial assumptions, that both groups are random and appropriately exposed, invalid, or at least, less valid.

Experimental trials are the most sound means for determining the causal association between an exposure and an outcome, but they still have inherent flaws that we must anticipate.  And his extends beyond mere methodological considerations and offers practical implications.  First, it may help to interpret clinical trials based on how well you match the study population - are you the same sex, age, BMI classification, and would you yourself participate in such a trial (this may help gauge your behavioral similarities with the study).  Second, the results of an experimental trial should not be regarded as dogma.  One clinical trial with little grounding in basic science (e.g. homeopathic medicine) likely needs much more investigation;  whereas several clinical trials that refute modest epidemiological evidence (e.g. dietary saturated fat and heart disease risk factors) probably clarify the true association.  We cannot study ourselves as though we are lab rats, so let's not interpret our studies as though we are.

*In case you didn't look at the link, the Atkin's Diet surpassed all of the other diets, including the Ornish Diet, in terms of weight loss and improvements in cholesterol/metabolic markers after one year in pre-menopausal women.  Perhaps this changes your preconceptions a bit?