Sugar vs. fat

Posted on April 27, 2011 | Leave a comment

This can be considered as somewhat of a follow-up to the previous post about the deleterious effect of the high fructose high fat diet in rats.  As a brief refresher, rats fed high fat were far healthier than those fed high fat and high fructose.  IOW, sugar makes everything bad happen.  In the current study, researchers first tested a high fat diet vs. a high fat high sugar diet, got interesting results, and asked the obvious follow-up question: what about high sugar alone?

A free-choice high-fat high-sugar diet induces glucose intolerance and insulin unresponsiveness to a glucose load not explained by obesity. (La Fleur et al., 2011 International Journal of Obesity)

The diets were odd, as is such when including “free-choice.” All rats received standard chow; those in a high-fat group (HF) were given a dish of beef tallow, which is high in saturated fat, while those in a high-sugar group were given a bottle of 30% sucrose (table sugar), which is composed of glucose and fructose in a 1:1 ratio (similar to high-fructose corn syrup).  Rats in the high-fat high-sugar (HFHS) group received both.

Fortunately, the researchers measured food intake with meticulous detail, so we know exactly how much sugar, fat, and calories were ingested.

For starters, when given the option of a high-fat high-sugar food, rats [and people] eat more:

As seen above (for HF) and below (for HS, closed bars), this is not the case when either are fed alone:

The increased calorie intake exhibited by the rats fed HF during week 1 was probably due to the increased caloric density of the food.  As soon as their brain detected the increased calorie influx, less food was ingested leading to a normalization of calorie intake.  This is not the case when sugar is added to the fat; considering eating butter (high fat) or icing (high fat high sugar).   Of which would you eat more?

Body weight followed a similar pattern, which HFHS gaining more weight than any other group.

Unfortunately these researchers measured fat mass by excising and weighing the individual adipose depots.  This method is inferior to most other techniques and is very inaccurate but very cheap.  So we will never know precisely how body composition was affected by these interventions.

Basal glucose and insulin were elevated in HFHS, but not HF.  Basal insulin was elevated in HS.  IOW, high fat alone did not alter body weight, basal insulin, or basal glucose, while high sugar caused an increase in basal insulin.  Thus, while HFHS is the real bad guy, HS is almost as bad.

This is roughly similar to what was we saw previously.  In accord with the body weight results, and as is usually the case, glycemic response to an intravenous glucose load was most impaired in HFHS:

Did this study bring anything new to the surface?  You be the judge.  I’m still trying to figure out why the title of the paper stated that these effects were “not explained by obesity.”  In their conclusion, the authors stated that rats fed HF and HFHS became obese, but that’s not true:

HFHS became obese, while HF remained at the same body weight as control.  The authors tried to stretch it to 1) exonerate (or not fully condemn) HS, and 2) put HF in the same ballpark as HFHS.  Things look differently when the conclusions are viewed with the data posted right next to them.  According to their data, rats on the HF diet looked similar to controls while those on HS had elevated fasting insulin:

Furthermore, and I hate to have to butcher graphs like this, but if you directly compare the results shown in  Tables 1 and 2, it is clear that rats fed the HS diet exhibited significant metabolic derangements, similar to HFHS, while HF stayed relatively healthy.

Note the results from the first experiment for glucose AUC: chow (1010), HF (872), and HFHS (1505), compared to the results from the second experiment for glucose AUC: chow (1153), and HS (1447).  In the first experiment, HFHS was much worse than chow; in the second experiment, HS was as worse than chow as HFHS was in the first experiment.

Also, basal glucose was lower in HF compared to controls (117.2 vs. 121.0), while it was higher in HS compared to controls (95.2 vs. 90.7).

Actually, these data (not the interpretation) are in close agreement with those from that previous post:

High fat high sugar (closed circles), sugar free high fat (open circles), and high fat high sugar switched to sugar free high fat (open squares).

So I’m not really sure why the authors interpreted these data to mean that HF was worse than HS and almost bad as HFHS.  This is categorically untrue.  A case of lipophobia?  Or perhaps it’s what you get when a bunch of neuroscientists try to conduct [and interpret!] a study about nutrition.  This study was done at the Rudolf Magnus Institute of Neuroscience in the Netherlands.

 

Calories proper

 

 

 

 

 

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Posted in Advanced nutrition, Dietary fat, Fructose, Sugar

Fructose vs. leptin, et al.

Posted on April 21, 2011 | 3 comments

These researchers set out to tackle a huge issue, but ended up answering 2 very small parts of 1 very important question.  That is, does fructose cause obesity?  More specifically, they were asking about the relationship between fructose, leptin resistance, and obesity.  Leptin is the homeostatic wonder protein; it is secreted from fat cells when they are full, and signals “well-fed” to the brain.  Downstream effects include increased metabolic rate, fertility, reduced appetite, and more energy.  During fasting, leptin rapidly plummets which signals “starvation” to the brain.  Downstream effects include reduced metabolic rate, infertility (not enough stored energy or food available to support a baby), and hunger.  In obesity, leptin is very high but the signal never makes it to the brain = leptin resistance.  Theoretically, if leptin sensitivity of an overweight or obese patient could be improved, their physiology would ‘normalize’ resulting in the loss of excess fat mass.

These are rat studies.

Fructose-induced leptin resistance exacerbates weight gain in response to subsequent high-fat feeding. (Shapiro et al., 2008 AJP)

In this first set of experiments, the chow-fed rats were switched to low fat diet with or without a high dose of fructose.  Prior to starting the feeding trial, all rats were leptin-sensitive (i.e., leptin injections caused a reduction in 24 hour food intake):

After 6 months, body weight, fat mass, water weight, lean mass, serum leptin levels, and food intake were identical between rats fed control and high-fructose diets:

remember, both diets are very low fat.  This may appear counterintuitive; shouldn’t a high fructose (high sugar) be more fattening than a high starch diet?  Sometimes.  Rodents, however, fatten with extremely good efficiency on a high fat diet.  So added fructose on a low-fat diet is not very obesogenic in rodents.

HOWEVER, the fructose-fed rats were leptin-resistant:

This is interesting.  Frank leptin-resistance per se, as exhibited by the fructose-fed rats, did not cause obesity, increased fat mass, or increased food intake… I’m not sure what this means.  If they were leptin resistant, shouldn’t they have eaten more or had increased fat mass?  In any case, at least we can say that fructose caused leptin-resistance in this paradigm.

Next, half the fructose-fed rats were switched to a low fructose very high fat diet.

after 2 weeks, things started to look very differently:

Rats previously fed high fructose (closed squares) gained significantly more weight than those previously fed low fructose (closed circles) (and rats that continued on their normal low fat high fructose (open squares) or low fructose control (open circle) diets still weighed the same).  This was not magical, the rats previously fed high fructose ate significantly more of the very high fat diet than rats previously fed the low fructose diet.

I am still a bit confused as to why fructose-induced frank leptin resistance had no effect on food intake or fat mass in rats fed the low fat diet.  But I find it very interesting that fructose-induced leptin resistance turned into high fat diet-induced hyperphagia and obesity despite very low fructose in the high fat diet… IOW, this may be a ‘legacy’ effect of fructose.  Fructose loads the gun…  (take away the fructose and you’ve still got a loaded gun) …

Although somewhat confusing, some of these findings are in accord with my belief that in the etiology of obesity, physiology is considerably disrupted prior to the onset of weight gain.  The source of this disruption is the diet.

This same group of researchers did a nice follow-up study in 2011.  They showed that 1) leptin-resistance increased the susceptibility to high fat diet-induced obesity (2008 study); and now they wanted to test if 2) this was reversible (2011 study).

Prevention and reversal of diet-induced leptin resistance with a sugar-free diet despite high fat content (Shapiro, Scarpace, et al., 2011 British Journal of Nutrition)

From a nutritionist’s perspective, the diets were chosen fairly well:

Divide and conquer.

For the first week, food intake was highest on the very high fat diet, and accordingly, they gained the most weight:

Looks like short-term fructose intake (@ 40% of calories) isn’t so bad when dietary fat is only 30% (HFr/HF, closed circles).

Just looking at chow (closed squares) vs. SF/HF (sugar-free high fat, open circles):

The figure on the right is fat mass assessed at day 70.  Of note, both of these diets are sugar free, and SF/HF has almost twice the fat (chow, 17% fat; SF/HF, 30% fat).  In other words, a sugar-free high fat diet does not cause obesity.  So, high fructose low fat didn’t cause more fat gain compared to high starch low fat (2008 study), and sugar-free high fat didn’t cause more fat gain compared to sugar-free low fat (2011 study).

In their next experiment, they compared HFr/HF vs. SF/HF.  These diets are very similar except that instead of fructose, the SF/HF diet contains starch.  Both contain 50% carbs & 30% fat.

Despite ingesting a similar amount of calories,

the SF/HF group (open circles) gained significantly less weight:

So fructose is significantly more fattening than starch (anyone surprised?).  And interestingly, fructose’s intrinsic fattening capacity extends beyond its caloric contribution (same total calorie & fat intake in both groups).  [Same calorie intake, more fat gain with fructose relative to starch … all calories are not created equal!]

Next, they tested leptin sensitivity.  This was done by injecting leptin intraperitoneally and measuring food intake for the next 24 hours.  Leptin is supposed to induce satiety (i.e., reduce food intake).  Leptin injections reduced food intake in SF/HF but not HFr/HF:

Open bars are before leptin injection, closed bars are after.  So fructose caused increased fat mass and leptin resistance.  This brings up an important point.  That is, if the HFr/HF group was leptin resistant, why did they eat as much as SF/HF?  More on this below.

After 70 days on the diet, the researchers did something interesting.  So far, we have seen that fructose can cause leptin resistance on low fat (2008) and high fat diets (2011).  Next they researchers asked if removing fructose could reverse the leptin resistance.  So they took the HFr/HF group and switched half to SF/HF.  WRT body weight gain, rats fed the HFr/HF diet (closed circles) continued on their normal route while those switched to the SF/HF diet (open squares) gained significantly less weight and actually started to approach the weight of rats who were fed the SF/HF diet all along:

Importantly, this normalization was not in fact magical, the rats switched from HFr/HF to SF/HF consumed fewer calories than the other two groups:

18 days after the switch, leptin sensitivity was re-assessed:

In as little as 18 days, leptin sensitivity was completely restored by removing fructose.  This says, fairly conclusively, that in this context, fructose was sufficient to cause leptin resistance.  (… now I challenge someone to find a study showing that fructose is necessary to cause leptin resistance … necessary and sufficient are two very important factors to determine true causality on a number of levels).

To really drive this home, leptin sensitivity was assessed by injecting leptin directly into the brain:

The open circles are the controls (SF/HF injected with saline).  They showed no major changes in BW or FI, just like the leptin resistant HFr/HF rats who actually received leptin injections but were leptin-resistant (closed squares).  The rats fed SF/HF (closed circles) and those switched from HFr/HF to SF/HF (closed inverted triangles) exhibited significantly reduced food intake and they lost weight.

One small plastic wrench in the gears.  When switched from HFr/HF to SF/HF, the rats ate significantly less.  They were also more leptin sensitive.  Did they eat less because removing fructose normalized their appetite?  That would be the more attractive conclusion, however it turns out that SF/HF didn’t taste as good as HFr/HF (surprise surprise, this is why your soda is filled with high-fructose corn syrup, not starch).   When rats accustomed to chow (open squares) were given access to both HFr/HF (closed circles) & SF/HF (open circles), they chose HFr/HF:

Take-home points:  as long as there was no sugar, the high fat diet did not cause obesity.   The level of dietary fat used in this study was about 2-3x higher than standard rodent chow.  Fructose + high fat caused obesity, leptin resistance, and overconsumption; removing fructose reversed these things.  Fructose possesses an intrinsic fattening element beyond its caloric contribution.

 

 

Now back to the calorie, fat mass, food intake issue mentioned above:

the HFr/HF group was leptin resistant, had more fat mass, but ate the same amount of food as SF/HF, which led me to conclude that fructose possesses an intrinsic fattening capacity beyond its caloric value.  HFr/HF rats were eating just as many calories as SF/HF rats; and they weighed more, so if you normalized food intake to body weight, HFr/HF rats would actually be eating less than SF/HF despite gaining more weight… so how can I say they were eating too much?

For one, the end justified the means.  They were accumulating fat mass, ipso facto however many calories they were ingesting was too much.  As mentioned above, HFr/HF were eating less if normalized to body weight, but since body weight is irrelevant in the face of increased adiposity, we can say that indeed, the HFr/HF group was simply eating too much.  In this example, it is helpful to consider food intake relative to energy expenditure.  Thus, neither food intake nor energy expenditure needs to be measured, only changes in fat mass.  I believe this is valid because 1) certain dietary components, e.g. fructose, have metabolic effects beyond their caloric value, and 2) while body weight may be governed by calories, fat mass is not.  Calorie counting does not work.

 

Calories proper

 

 

 

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Posted in Advanced nutrition, Fructose

Marathon’ing

Posted on April 16, 2011 | Leave a comment

Another pearl debunked?

Liberation from the bane of cardiovascular exercise
Or
Time to hit the weights

Myocardial late gadolinium enhancement: prevalence, pattern, and prognostic relevance in marathon runners. (Breuckmann et al., 2009 Radiology)

In brief, this study showed that marathons kill.  Seriously.  And this applies to a lot of people; almost a half a million Americans participate in marathons annually.

MRI with late gadolinium enhancement (“LGE,” for short) a sensitive and powerful indicator of heart disease.  It gives few false positives and negatives.  Compared to other tests (EKG, stress tests, angiograms, etc.), if you have LGE you have a very high chance of cardiac mortality.

In this study, they recruited 102 recreational (nonprofessional) marathoners and 102 age-matched controls (~57 years of age).  All of the subjects were apparently healthy at baseline; anyone with pre-existing heart disease or diabetes was excluded.  The marathoners were hard-core: they ran in at least 5 marathons in the past 3 years and averaged 20 marathons in their life.  Furthermore, they ran ~35 miles per week.

12% of marathon runners had heart damage (as per LGE) compared to 4% of controls.  That is a pretty big difference: marathoners were 3 times more likely to have heart damage.

Does LGE affect cardiac events?

Here is a graph depicting the gravitas of LGE for marathoners:

“LE-” is the group of people with normal heart function.  Their line is almost completely straight indicating that almost 100% experienced no cardiac events.  “LE+” indicates people with LGE.  This figure basically confirms that LGE is a potent cardiac events predictor.  Over the course of the 2+ years of follow-up, 3 marathoners with LGE experienced a cardiac event compared to 1 marathoner who had normal heart function.

The numbers aren’t huge: 12 marathoners and 4 controls exhibited LGE.  4 marathoners experienced a cardiac event; 3 of them had LGE.  So marathoners were 3 times more likely to have an abnormal LGE than controls, and marathoners with LGE were 3 times more likely to experience a cardiac event than marathoners with a good heart.  IOW, a marathoner with LGE may be 9 times more likely to experience a cardiac event than a healthy control who has normal heart function.  If I were a marathoner I’d get this test done asap.  And more importantly, all of these people thought they were healthy (just like you and me); they exhibited no signs or symptoms of heart problems.

Conclusions, alternative explanations, and my take on Breuckmann’s study:

  1. Marathons are the antithesis of moderation.  They are an extremist activity.  As is running 35 miles a week.   Aerobic fitness will exhibit, like most things, an inverted U-shaped curve in relationship with mortality and quality of life.  IOW, a totally sedentary lifestyle is probably just as bad as running marathons, but running 1 mile a day or a few per week is probably beneficial.  A poor diet and sedentary lifestyle may be associated with obesity, atherosclerosis, thrombosis, whereas marathons are more like a cardiovascular-beatdown.
  2. How does marathon running kill?  Perhaps the overall stress of marathons or blood flow-induced shear stress damages the endothelial lining of vessels, which may contribute to an atherosclerotic or otherwise pathological process.  This would be exacerbated by the systemic inflammatory response associated with a prolonged high level of exertion.
  3. Then again maybe it’s all about diet:  running 35 miles per week requires a LOT of extra calories; there is bound to be some processed crap in there.  (sorry, my assumption here is that a healthy person might be able to eat a healthy 2,000 kcal diet, but if they were suddenly eating 4,000 kcal it probably wouldn’t be all the same foods as before just twice as much).  So maybe it’s the excessive caloric burden in general, or perhaps the added foods that are contributing to the problem.
  4. On the other hand, maybe they were juiced up!  I wouldn’t be surprised if running a marathon at 57 years of age required a little pharmaceutical-grade ergogenic enhancement.
  5. Last but not least, maybe their age-matched control population was not the best control group.  IOW, maybe the controls were very healthy, so anyone (including a marathoner) would appear less healthy than control.  That’s a good one.
  6. The opposite of #5.  Maybe the marathoner’s were a particularly unhealthy bunch (they were big smoker’s and drinker’s for most of their life, then gave it all up and started running… a lot of permanent damage was done prior to exercise training).

 

Fortunately for us, more data on these subjects were published a year earlier.

Running: the risk of coronary events : Prevalence and prognostic relevance of coronary atherosclerosis in marathon runners. (Möhlenkamp et al, 2008 European Heart Journal)

The marathoners are group I.  Group II is an age-matched control group and group III is a control group that was matched for other risk factors including BMI, lipid profile, and smoker status.  As a side note, this type of control population is far better than statistically adjusting for risk factors.  When data are statistically adjusted, you are no longer comparing people, per se, but rather are comparing a person to a mathematically derived variable (or something like that).  IOW, I really like Möhlenkamp’s choices for the control populations.

The most interesting numbers IMO:

Indeed marathoners had 42% higher HDL and 18% lower LDL than age-matched controls (like the controls from Breuckmann’s study).  This suggests lipid profile is a poor indicator of LGE.  And there were more smokers in the age-matched control group.  This basically strikes down my alternative explanation #5 above; the controls were not a healthier group of people.

Coronary artery calfification scores:

From these data (look at the middle of the three numbers in each column), it looks like although marathoners were more likely to exhibit LGE, they had a similar degree of coronary artery calcification compared age-matched controls.  Furthermore, marathoners had significantly more coronary artery calcification than the controls that were matched for other risk factors, which more implies marathon running per se increases coronary artery calcification.

Furthermore, given the increased cardiac events in marathoners compared to age-matched controls (Breuckmann’s study), these results suggest that LGE is a more powerful indicator of risk than increased coronary artery calcification.

Coronary artery calcification is not a bad indicator, however:

The green line indicates event-free survival in runners with the least coronary artery calcification (they experienced zero cardiac events).  The blue dotted line is runners with intermediate coronary artery calcification, and the red dashed line is runners with the most coronary artery calcification.  This graph basically shows that the extent of coronary artery calcification is a pretty good predictor of cardiac events.

 

Interestingly, coronary artery calcification was not associated with years of running, miles per week, or number of marathons.  This is odd because coronary artery calcification was much worse in marathoners compared to risk-factor matched controls.   And number of marathons was significantly associated with LGE.  Does this mean that simply being a marathoner worsens coronary artery calcification, and the more you run worsens LGE?  I don’t know enough about these measurements to speculate on their pathological relationship, but in general, they are both pointing in the same direction.

But what about cardiac events in the risk-factor matched controls?  “data not shown”

 

 

More conclusions/alternative explanations:  going back to point #5 (above) regarding the possibility of an extra-healthy control group (which was subsequently de-bunked by comparing their lipid profiles and smoking history), it is also possible that this was a particularly unhealthy group of marathon runners (back to explanation #6) …  There were a LOT of former smokers; maybe it is people who started caring about their health, so they quit smoking and started running.  This could also possibly explain why coronary artery calcification was associated with marathons but not weekly running distance, number of marathons, etc.  IOW, former poor diet or lifestyle habits caused the coronary artery calcification and caused these subjects to start running (a bona fide confounding factor).  This may be supported by considering how these studies recruit their subjects.  Which marathoner is more likely to enter into this study, which entailed a labor-intensive comprehensive battery of cardiovascular and blood tests?  The recreational runner who has been healthy their whole life, to whom running is simply a hobby; or the runner who gave up their former poor diet and lifestyle to begin a health crusade and is now totally obsessed.  I think the latter has more motivation to sign-up.

But none of that explains the correlation with all measures of the marathoner (miles ran per week, number of marathons, etc) and LGE.  The LGE data suggest that marathons (training for and running in) are pathologically related to heart function.  And we still can’t rule out a role for diet!  Marathon training/running burns a LOT of calories.  Maybe it’s something their eating?  No food intake data were collected or reported in either study (but we know that unless these guys were losing weight, their food intake increased to match their expenditure; we just don’t know what they were eating).

Alternatively, maybe it’s not what they ate, but simply that they were eating so much more… the “rate of living” theory said that increased energy expenditure causes aging, disease, and death via free radicals.  Thus, caloric restriction, in which both food intake and metabolic rate are markedly reduced, improves longevity.

“Keep a quiet heart, sit like a tortoise, walk sprightly like a pigeon, and sleep like a dog.”  -Li Ching-Yuen (1677-1933)

 

Calories proper

 

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Posted in Advanced nutrition, Exercise

Fat cats or trans fat blog, take II

Posted on April 10, 2011 | Leave a comment

Fat cats
or
Trans fat blog, take II

Protein intake during weight loss influences the energy required for weight loss and maintenance in cats. (Vasconcellos et al., 2009 Journal of Nutrition)

I am flabbergasted at how this study played out.  Regardless of whether the eloquence was intentional or not; a wonderful demonstrate that “all calories are not created equal.”

Study design: They started with obese cats and fed them one of two weight loss diets.  The goal was to lose 20% of their body weight at a rate of 1% per day.  Therefore, they were given more or less calories to meet that goal.  The rate of weight loss was controlled.

The high protein diet contained 33% more protein than control (21.4 g/mJ vs. 28.4 g/mJ).  To balance out calories, the control diet had more starch.

During the weight loss phase, both groups lost 20% of their initial body weight.  The high protein group lost almost 50% more fat than control!  Accordingly, the high protein group lost 64% less lean mass than control.  So only a 33% boost in protein during a hypocaloric diet caused drastic effects on body composition.

Body composition:

LM, lean mass; FM, fat mass.

The best part: remember, they were being fed on the basis of 1% weight loss per day.  The high protein group actually required 13% more food than control during the first half of their weight loss and 6% more during the second half.… in other words, if they were given the same amount of calories, the high protein group would have lost weight too quickly.  So the high protein group lost more fat and less muscle despite eating more!  Sounds like a pretty good deal, right?

Cats in the control group lost 1.65 grams of fat mass for every gram of lean mass lost.  Cats on the high protein diet lost 19.4 grams of fat for every gram of lean mass (over 10 times more).

Food intake data (ME = metabolizable energy, just think of it as calories):

It gets better.  Now all the cats are 20% reduced body weight.  Recall that muscle is the main driver of metabolic rate…

During the next phase of the study, the cats were fed enough to keep them weight stable for 4 months.  Because of their high protein diet, cats in that group finished the weight loss phase with more muscle and less fat than control.  During the maintenance phase, they were all fed the same diet, therefore any differences between groups during maintenance was due to the changes that occurred during weight loss (because diets are the same now).  Cats that lost weight via high protein diet required ~16% more calories per day to maintain their weight compared to cats that lost weight on the control diet.  So they got to eat more during weight loss, ended up with less fat mass, more muscle mass, and now have to eat more to maintain their new weight! (presumably because of their increased muscle mass).

This study was in cats, a carnivorous species, so there may have been a species-nutrient interaction; however, these findings while more robust are in agreement with what is seen in humans.  High protein dieters fare better in the short and long-term than low calorie dieters.

I think this study brilliantly illustrates that a calorie is not a calorie.  Dietary protein and carbohydrate may provide 4 kilocalories per gram when burned in a bomb calorimeter, but they are not equally fattening.

That’s about all for the coolness of energy balance in this cat study, but there is one other relevant topic with implications for human body composition.  Cats are carnivores and  experience a greater insulin response to protein than to carbohydrates…

Comparison of three commercially available prescription diet regimens on short-term post-prandial serum glucose and insulin concentrations in healthy cats. (Mori et al., 2009)

This study design was not nearly as eloquent as Vasconcellos’ (above).  They basically wanted to measure the insulin and glucose response to three different meals.  So it was a triple crossover (each cat tested each meal with a one week washout in between).  They were healthy cats.

The meals were:

  1. (C/D) Low protein, high fat, high carbs, low fiber
  2. (M/D) High protein, high fat, low carbs, high fiber
  3. (W/D) Low protein, low fat, high carbs, high fiber

Diet 1 was a relatively standard control diet.  Diet 2 was Atkins-esque and is used to treat feline obesity and related disorders.  Diet 3 was another generic therapeutic diet.

Protein:  2 > 3 ? 1

Fat: 2 > 1 > 3

Carbs:  1 ? 3 > 2

C/D = diet 1 (control)

M/D = diet 2 (Atkins, usually in red)

W/D = diet 3

Glucose responses were relatively similar:

Diet 3 (W/D, inverted triangles) had modestly a greater glucose response, while diet 2 (M/D, Atkins diet, open circles) had the lowest.  This isn’t entirely surprising because diet 2 had the least carbs, while diet 1 had the most.

Here’s the interesting part:

Diet 2 (M/D, Atkins, open circles) had the largest insulin response despite the least carbs!   Diets 1 (C/D) & 3 (W/D) had the most carbs, but Diet 2 (M/D, Atkins, open circles) had the most protein.

 

This is almost exactly in line with what was seen in Vasconcellos’ study (above):

The average insulin levels over the entire weight loss period was 36.5 pM in the control group and 39.1 pM in the high protein group.

These studies were performed in cats, who evolutionarily and genetically differ markedly from humans.  Their status as true carnivores makes it difficult to extrapolate the results to humans.  But there is a large group of scientists, journalists, and bloggers, etc. who implicate insulin per se as the cause of obesity.  In cats, a high carbohydrate diet (standard store-bought dry food) causes obesity, and a high protein diet is an effective treatment.  Furthermore, a high protein diet causes just as favorable changes in body composition in cats as it does in humans.  But the high protein diet is markedly more insulinogenic in cats.  There are a few possible alternatives explanation of which I can think.

1-It might be the carbohydrates and not necessarily the insulin… that possibility agrees with the observations in both species… in humans, we know that a carb-rich diet is associated with obesity and we think it is due to insulin’s role in fat storage… in cats, we know that a carb-rich diet is associated with obesity but as seen in these two studies, it is probably not due to insulin.

2-Alternatively, maybe insulin is obesogenic only when accompanied by high carbs.  That would explain why insulin is obesogenic in humans (whose high insulin levels are associated with a high carb diet) but not cats (whose high insulin levels are associated with a high protein diet)… but this wouldn’t explain why type I diabetics are usually thin but get fat deposits around their insulin injection sites (which suggests that insulin directly promotes fat storage and doesn’t need carbs).  However, type I diabetics are frequently hyperphagic, so maybe the high carbs are present.

Aargh, a clear conclusion can’t be drawn to tie together all of the observations, but option 2 comes close.  N.B. I personally believe other dietary factors like processed foods, industrially produced trans fats, high fructose corn syrup, and grains probably have a big role in insulin resistance, which is associated with obesity, but I’d still like to see a clean cut demonstration of this across all species, or at least mammals, or at least in primates.

 

Calories proper

…

 

 

OK, so maybe protein is just as insulinogenic as carbs in humans too:

A high-protein diet induces sustained reductions in appetite, ad libitum caloric intake, and body weight despite compensatory changes in diurnal plasma leptin and ghrelin concentrations (Weigle et al., 2005 AJCN)

open squares, controls; closed circles, isocaloric high protein, open triangles, ad lib high protein

 

 

One more wrench in the gears!

This last one is a total doozy.  I feel double-crossed.  never saw it coming.  To the best of my knowledge, industrial trans fats have never failed to maim those who ingest them.  until now.

 

 

Effect of trans-fat, fructose and monosodium glutamate feeding on feline weight gain, adiposity, insulin sensitivity, adipokine and lipid profile. (Collison et al., 2011 British Journal of Nutrition)

This study was “different;” they fed pregnant/lactating cats one of four diets and then weaned the kittens onto the same diet as their mother.  In brief, the diets were:

Control: standard low fat diet

A) Control + MSG (~200mg/kg)

B) High trans fat & fructose

C) High trans fat & fructose + MSG

 

The diets are kind of sketchy, so here are some generalities: we can compare diet A to control and diet C to diet B to see the effects of MSG, and we can compare diet B to control and diet C to diet A to see the effects of high trans fat & fructose.

The whole story can be summed up in the following table (which has been heavily edited):

First, please note the red circle.  Body-fat increased 378.38% in control kitties and 576.50% in those fed MSG!!!  MSG is the devil for cats (?).  Interestingly, MSG had no effect on cats fed a high fructose and trans fat diet (302.59% vs. 277.32%)… (??) actually, all cats fed a high fructose & trans fat diet accumulated less fat mass than low fat fed cats.  (???)  This is in agreement with the findings above; cats become obese on a low fat high carb diet and remain lean and muscular on high fat high protein.  I’m surprised the fructose had no effect.  I’m also a little surprised that MSG was far worse than high fructose & trans fat.

Second, please note the arrows.  The red arrows show the effect of MSG on liver enzymes.  In both low fat and high fructose & trans fats, the addition of MSG markedly improved the liver enzyme ALT. The blue arrow shows that high fructose & trans fat is bad for the liver, in agreement with human and rodent data, but this is completely ameliorated by the addition of MSG [in cats] (????).

I have no idea how to interpret these findings from a biological standpoint, but I think it might have something to do with cats being true carnivores.  Cats need meat to live.  MSG is a meat-mimetic; that is, it tastes savory, better than meat, but does not provide any of the nutrients.  I don’t know how MSG would enhance fat gain but improve liver enzymes in cats on a low fat diet, but I think cats on a low fat diet is another problem because a carnivorous diet is not low fat.  And most troubling, trans fats aren’t bad for cats!  Maybe since cats generally eat a relatively high fat diet, the addition of a few grams of trans fats are well tolerated (because they comprise a small fraction of the total fat intake).  Trans fats were shown to be harmful in rodents & rhesus monkeys, two species who consume a low fat diet in their natural habitats.  Since humans are omnivores, does this mean that trans fats are worse for monkeys & rodents than they are for us?  IOW, does extrapolating the results from rodent studies to humans inevitably exaggerate the harm of trans fat?  Food for thought.

 

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April Fool’s day

Posted on April 6, 2011 | Leave a comment

Please forgive me in advance for the crass humor in this post.

Have obesity researchers given up?  Their most recent advice: shit your pants or eat shit.

Exhibit A. The drug formerly known as Zenical and Orlistat, currently marketed OTC as Alli, is one the only medications FDA approved for the treatment of obesity.  The results from Orlistat’s weight loss trials are unequivocal (figure below).

But the list of “Common Adverse Events” is horrendous.  “Common” means it happens in over 5% or 1 in 20 patients.  This was taken directly from the prescribing information.

Exhibit B.

The gut microbiota consists of millions of organisms that reside in the intestines and they are intricately associated with the health of its host.  The microbiota differs markedly between obese and lean people.  In animal studies, transfer of an obese mouse’s microbiota to a lean mouse makes the latter gain fat mass, suggesting a causal relationship.  (if your interested, check it this and this)  Recently, however, scientists have taken it to the next level.  The complete abstract is below.  Fecal transplants (from lean healthy donors) have remarkable effects on glucose tolerance and insulin sensitivity.  I shit you not.

Metabolic effects of transplanting gut microbiota from lean donors to subjects with metabolic syndrome (Vrieze et al., 2010 EASD)

Recent data in animal models revealed that obesity is associated with substantial changes in composition and metabolic function of gut microbiota. Moreover, colonization of germ-free mice with faeces harvested from obese mice resulted in a significantly greater increase in total body fat than colonization with a ‘lean microbiota’. However, data on the role of gut microbiota in human obesity are scarce. Thus, our aim was to examine the effect of faecal infusions derived from lean healthy donors on gut microbiota composition, glucose and lipids in metabolic syndrome.  This study was a double-blind, randomised controlled trial. A total of 18 male subjects with newly diagnosed metabolic syndrome (BMI?30 kg/m2, FPG>5.6mmol/L, TG>1,6 mmol/L with no medication use) underwent jejunum biopsies and subsequent polyethylene-glycol bowel lavage through duodenal tube followed by random assignment to either allogenic (from lean male donors with BMI<23 kg/m2, n=9) or autologous faecal transplantation (reinfusion of own collected faeces, n=9). We studied changes in sigmoidal microbiota composition and fasting lipid profiles at 0.5, 2, 6 and 12 weeks after faecal transplantation. Weight, jejunal gut microbiota (epithelial biopsy) and glucose metabolism (peripheral and hepatic insulin sensitivity as assessed by hyperinsulinemic euglycemic clamp with stable isotopes) were studied before and 6 weeks after transplantation.  Lean subjects were characterized by different sigmoidal gut microbiota compared to obese subjects (by HITChip phylogenetic microarray analysis). Fasting levels of TG-rich lipoproteins (TG/ApoB ratio) were significantly reduced following donor faeces (1.43 ± 0.21 to 1.11 ± 0.18, p<0.01) with no effect after autologous faeces infusion. Resting energy expenditure and basal endogenous glucose production (EGP) did not change in both groups after faecal infusion. Although weight remained stable, an improvement in both peripheral (Rd) and hepatic insulin sensitivity (suppression of EGP) was found 6 weeks after allogenic faeces (median Rd: from 26.2 to 45.3 ?mol/kg.min, p=0.02 and EGP suppression: from 51.5 to 61.6 %, p=0.08) while no significant changes were observed in the autologous treatment group (Rd: from 21.0 to 19.5 ?mol/kg.min and EGP suppression: from 53.8 to 52.4 %, ns). Changes in jejunal microbiota are currently analyzed. Lean donor faecal infusion improves hepatic and peripheral insulin resistance as well as fasting lipid levels in obese individuals with the metabolic syndrome underscoring the potential role of gut microbiota in the disturbances of glucose and lipid metabolism in obesity. Our data could provide pathophysiological insight in the metabolic deviations in obese subjects and a rationale for therapeutic intervention.

 

For the record, those changes in insulin sensitivity are fairly robust, especially compared to control.  And perhaps the title of this post was too crude; the fecal transplants were administered through a nasogastric tube that goes in through the recipient’s nose and down their throat, so they’re not technically “eating” it.  By the way, those infusions consisted of 300mL (1 ½ cups) infused slowly over the course of an hour, every day for 9 days.  The environment within: how gut microbiota may influence metabolism and body composition (Vrieze et al., 2010 Diabetologia).

 

 

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Orlistat blog

Posted on March 31, 2011 | 2 comments

Orlistat blog.

This post is a little long, but the conclusion was bold enough to warrant inclusion of enough studies to independently demonstrate each point.   Reducing calorie and dietary fat intake will help weight loss, but it’s not the only way.  The goal for this blog entry is to compare the results from studies on Orlistat, which pharmacologically inhibits dietary fat digestion, with the results from low fat and low carb diet studies.  In brief, yes, reducing dietary fat and calories pharmacologically via Orlistat or voluntarily via low fat diet causes weight loss, but the metabolic improvements that are usually associated with weight loss are considerably attenuated because of the reduced dietary fat.  IOW, for any given amount of weight loss, the metabolic improvements are greater for a diet that restricts carbohydrates (sugars, whole grains, refined grains, cereal fibre, etc.) than for a diet or drug that restricts fats (e.g., low-fat diet, Orlistat, etc.).

Note: Orlistat is biologically similar to a low fat diet.  With the low fat diet, dietary fat intake is reduced voluntarily; with Orlistat, dietary fat digestion is reduced pharmacologically.  With both, the amount of dietary fat that gets into the body is reduced.

These studies are relatively similar in study design, subject population, duration, and how the interventions were administered.  They all lasted at least one year, except the Yancy study, which was only 48 weeks but had to be included because it makes an important connection.

Orlistat (low fat) studies: Krempf, 18 months; Hauptman, 24 months.   Low carb vs. low fat: Stern, 12 months; Foster, 12 months.  Low carb vs. Orlistat: Yancy: 48 weeks.

Round 1. Orlistat vs. Placebo

Weight reduction and long-term maintenance after 18 months treatment with orlistat for obesity. (Krempf et al., 2003 International Journal of Obesity Related Metabolic Disorders)

700 obese subjects, baseline characteristics:

As expected, impressive weight loss in the Orlistat group:

Orlistat group lost 8% of their initial body weight, compared to 3% in the placebo group… Orlistat weight loss was 2.5x greater than placebo.

Important numbers: (Placebo vs. Orlistat)

Fasting glucose: -0.29 mM vs. -0.86 mM

HDL: +31.5% vs. +38.2%

TG: -15.6% vs. -24.4%

 

Orlistat in the long-term treatment of obesity in primary care settings. (Hauptman et al., 2000 Archives of Family Medicine)

Same basic outline as Krempf study (above). 600+ obese subjects, baseline characteristics:

results:

To compare directly with the Krempf study: by week 76 (18 months), Orlistat group lost 7% of their initial body weight compared to 3% in placebo, just over twice as much.  To compare with the rest of the data in this study: by week 104 (24 months), Orlistat group lost 5% of their initial body weight compared to 2% in placebo.  Orlistat group lost 2.5x more weight than placebo.

Important numbers: (placebo vs. 120 mg Orlistat [dose used by Krempf])

Fasting glucose: +0.24 vs. +0.16 (yes, fasting glucose actually increased in the Orlistat group)

HDL: +7.7 vs. +5.8%  (yes, HDL improved more in the placebo group compared to Orlistat)

TG: -3.0% vs. +13.5% (yes, TGs actually increased in the Orlistat group)

 

 

Round II. low carb vs. low fat.

The effects of low-carbohydrate versus conventional weight loss diets in severely obese adults: one-year follow-up of a randomized trial. (Stern et al., 2004 Annals of Internal Medicine)

Weight loss:

Low fat dieters lost 2% of their initial body weight, and low carb dieters lost 4%.  Although this study was shorter (1 year, compared to 1.5 years in Krempf and 2 years in Hauptman).

Important numbers: (Low fat vs. low carb)

Fasting glucose: -1.11 vs. -1.55

HDL: -12.3% vs. -1.9%

TG: +2.7% vs. -28.2%

 

 

 

http://www.ncbi.nlm.nih.gov/pubmed/12761365

A randomized trial of a low-carbohydrate diet for obesity. (Foster et al., 2003 NEJM)

Baseline characteristics:

Weight loss:

and the data:

Important numbers: (Low carb vs. low fat)

Fasting glucose: ?

HDL: +11% vs. +6.0%

TG: -17% vs. -0.7%

 

Round III. Low carb diet vs. Orlistat

A randomized trial of a low-carbohydrate diet vs orlistat plus a low-fat diet for weight loss. (Yancy et al., 2010 Archives of Internal Medicine)

This study actually pitted a calorie unrestricted low carb diet directly against Orlistat.  Over 100 subjects were included, the details are in line with the above studies.

Body weight: Low carb group, 124 kg -> 113 kg, they lost 9 % of their initial body weight; Orlistat, 119 kg -> 109 kg, they lost 8 % of their initial body weight

Important numbers: (Low carb vs. Orlistat)

Body weight:   -9.2%   vs. -8.1%

Fasting glc:      -9.74    vs. -3.26

HDL:                +10.3% vs. +8.7%

TG:                   -19%    vs. -15.7%

 

 

Summary

Krempf:           placebo vs. Orlistat

Body weight:   -3%      vs. -8%

Fasting glc:      -0.29    vs. -0.86

HDL:                +32%   vs. +38%

TG:                   -16%    vs. -24%

 

Hauptman:      placebo vs. Orlistat

Body weight:   -2%      vs. -5%

Fasting glc:      +0.24   vs. +0.16

HDL:                +7.7     vs. +5.8%

TG:                   -3.0%   vs. +13.5%

 

Stern:               low fat vs. low carb

Body weight:   -2%      vs. -4%

Fasting glc:      -1.11    vs. -1.55

HDL:                -12.3% vs. -1.9%

TG:                   +2.7%  vs. -28.2%

 

Foster:             low fat vs. low carb

Body weight:   -3%      vs. -4%

Fasting glucose: ?

HDL:                +6%     vs. +11.0%

TG:                   -0.7%   vs. -17%

 

Yancy:              low carb vs. Orlistat

Body weight:   -9.2%   vs. -8.1%

Fasting glc:      -9.74    vs. -3.26

HDL:                +10.3% vs. +8.7%

TG:                   -19%    vs. -15.7%

1. In the Krempf Orlistat study, Orlistat caused more weight loss than placebo, and was modestly better at reducing fasting glucose and TGs and increasing HDL.

2. In the Hauptman Orlistat study, Orlistat caused more weight loss but fasting glucose actually increased relative to baseline, the increase in HDL was less than in placebo, and TGs actually increased relative to baseline and placebo.

3. In the Stern low carb study, the low carb diet caused more weight loss than the low fat diet, and the low carb diet lowered fasting glucose modestly better than low fat diet.  Changes in HDL and TGs were significantly better in the low carb group.

4. In the Foster low carb study, the low carb group lost modestly more weight than the low fat group, and the changes in HDL and TG were significantly better in the low carb group as well.

5. In the Yancy low carb vs. Orlistat study, the low carb group lost modestly more weight, fasting glucose decreased almost twice as much in the low carb group, and HDL and TG improved significantly more in low carb relative to Orlistat.

 

Reducing body weight by cutting calories and reducing fat intake (via Orlistat [Krempf, Hauptman, Yancy] or low fat diet [Orlistat studies, Stern, Foster]) consistently produces inferior changes in the metabolic landscape compared to reducing carbohydrate intake (Stern, Foster, & Yancy).  Orlistat caused more weight loss compared to placebo (Krempf, Hauptman), but not compared to a low carb diet (Yancy).

Dietary fat increases HDL.  Replacing carbs with dietary fat reduces TGs.  These things occur independently from weight loss, although weight loss is greater on a low carb diet compared to a low fat diet.   IOW, reducing carb intake causes more weight loss and superior changes in risk factor profiles compared to reducing calorie and fat intake regardless of whether fat is reduced via dieting (low fat diet) or pharmacologically (Orlistat).

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GB Grains blog, take II

Posted on March 25, 2011 | Leave a comment

GB Grains blog, take II

The effect of increasing consumption of pulses and wholegrains in obese people: a randomized controlled trial. (Venn et al., 2010 JACN)

I like this study because of its thorough dietary intervention.  The researchers provided a lot of the food, had frequent meetings, checkups, and dietary counseling sessions.  They even sponsored cooking lessons and supermarket tours!  Those are all definitely strengths, in addition to the ultra-long study duration of 18 months.  Both groups were advised to eat low fat diets, but the intervention group was specifically instructed to eat more whole grains.  To supplement their diets, the intervention group was given rolled oats and rye, wholemeal flour breads, etc., while the control group received cornflakes, cans of fruits & vegetables, refined grain breads, etc.

How’d they do?  As seen in figure 1 (below), the diet was followed quite well.

Figure 1.  Everybody tried to eat healthier in this study, so whole grains increased in both groups.  But it was significantly higher for most of the time in the intervention group.  Everybody also ate fewer calories.  And since whole grains are both carbohydrates and fibrous, consumption of these increased in both groups, but more so in the intervention group.

To make a long story short, both groups lost approximately equal amounts of weight with the treatment group losing slightly more than control.  The interesting thing is that we would have expected these weight losses to be accompanied by all-around improvements in health.  But they weren’t (reminiscent of the Orlistat trials).  Fasting glucose is a surrogate for insulin sensitivity.  Fasting glucose increased in both groups:

Figure 2. Metabolic outcomes.

Both groups lost weight.  Dietary carbohydrates are linked with insulin resistance, and although the % of calories from carbohydrates increased in both groups, the absolute amount decreased because of the large reduction in calories.  So they were eating fewer grams of carbohydrates and losing weight… So WHY did blood glucose increase?  I’d be willing to bet whole grains had something to do with it.  Whole grains increased significantly in both groups.  There’s something creepy about whole grains, like how every correlation between them and good health is attenuated after adjusting for confounding lifestyle and dietary factors.  Healthy people eat whole grains, but whole grains don’t healthify.  Possible suspects include lectins and gluten.

Just like DART, the Venn study was a randomized controlled intervention study, which is very powerful study design.

However the Venn study was a weight loss study, which is very different from free-living individuals eating ad libitum in ‘energy balance.’

Enter: the Jiangsu Nutrition Studies.  These epidemiological observational studies have been going on for a while and their goal is to identify dietary patterns that are associated with weight gain.

disclaimer: in general, when coming upon a study of “dietary patterns” I turn around and run away.  The data are usually so manipulated that they no longer reflect what a person actually eats.  I’m making an exception here because Jiangsu  demonstrates an interesting point.  Briefly, they were able to differentiate “diets devoid of whole grains” from “diets rich in whole grains,” and two other dietary patterns that couldn’t be characterized by their whole grain content.

Vegetable-rich food pattern is related to obesity in China. (Shi et al, 2008 International Journal of Obesity)

Dietary pattern and weight change in a 5-year follow-up among Chinese adults: results from the Jiangsu Nutrition Study. (Shi et al., 2010 British Journal of Nutrition)

They somewhat humorously defined four major dietary patterns:

Divide and conquer.

 

Table 1.  Dietary patterns.  Focus on the foods with the biggest “Factor loading,” as these are the most important foods that define each pattern.  In the traditional diet, for example, presence of rice (0.78) and absence of wheat flour (-0.75) http://en.wikipedia.org/wiki/Wheat_flour are the two most important factors that distinguish the traditional dietary pattern.  Presence of whole grains (0.56) is what most defines the vegetable-rich pattern.  Those are the two I think are of most interest: traditional dietary pattern is defined by an absence of wheat flour, while the vegetable-rich diet is defined by an abundance of whole grains.

In 2002, the food intake data were collected and analyzed.  For each dietary pattern, subjects are divided into quartiles based on their adherence to each respective dietary pattern.  IOW, every subject is ranked on their adherence to each dietary pattern.  For example, you might rank very high for macho, intermediate for vegetable-rich, and low for traditional and sweet tooth. You are ranked by your adherence to each dietary pattern.

To analyze the effect of a dietary pattern on a specific health parameter, investigators compare the prevalence of that parameter outcome across quartiles of each dietary pattern.  If there is no association between a specific dietary pattern and the health parameter, it would be similar across quartiles.  If, OOTH, the parameter increases or decreases across all 4 quartiles, then there is a correlation.

At baseline (2002) and follow-up (2007), the subjects were weighed.  The figure below depicts weight change between 2002 and 2007 and is divided into quartiles of each dietary pattern.

5-year weight change across quartiles of each dietary pattern.  Can you spot which two of the four dietary patterns were significantly associated with weight change?

 

Traditional diet, defined by the absence of wheat flour (top left).  People who were the most adherent to the traditional diet (“Q4”), meaning they never touched wheat flour, gained the least amount of weight over those 5 years.  Conversely, people who were the least adherent to the traditional diet (“Q1”), i.e., those who ate the most wheat flour, gained the most weight over those 5 years (~2.0 kg).

Vegetable-rich diet, defined by an abundance of whole grains (bottom right).  People who were the most adherent to the vegetable-rich diet, meaning they ate plenty of whole grains, gained the most weight over those 5 years (“Q4,” 1.6 kg).  Conversely, people who ate the least whole grains gained the least weight over those 5 years (“Q1,” 0.4 kg).

It gets worse.

The prevalence of frank obesity (BMI > 30) according to adherence to the vegetable-rich (high whole grains) diet:

Obesity is far more prevalent among those consuming the most whole grains compared to the least.  To make a stretch, people who ate the most whole grains were twice as likely to be obese (bottom row, first [6.9] compared to fourth [15.0] quartile).

Whole grains are associated with frank obesity in the total population, but they are really really associated with obesity in folks between 31 and 45 years of age:

People aged 31-45 with the highest intake of whole grains were 3.66x more likely to be obese than people with the lowest.

The Jiangsu Nutrition studies are observational, but prospective.  The Venn study (above) and DART are randomized intervention trials.  Obesity (Jiangsu), elevated fasting glucose despite weight loss (Venn), and all-cause mortality (DART)… Collectively, these findings suggest that whole grains should be abandoned, or at least demoted to “consume sparingly.”  But their elite status among dietitians and health advocates prohibits this.  Divide and conquer?

 

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Fish blog, take I

Posted on March 19, 2011 | 5 comments

Fish blog, take I

Eat fish people.  No, don’t eat “fishpeople,” … nevermind.  I am a strong proponent of eating salmon so this blog was created to figure out which is the best kind to eat.  Priorities are 1) least toxins and 2) best fatty acid composition.

Round 1. Metals in salmon: Farmed vs. wild

A survey of metals in tissues of farmed Atlantic and wild Pacific salmon (Foran et al., 2004)

Farmed Atlantic salmon were sourced from North American commercial suppliers and included salmon from British Columbia, Chile, Maine, and Norway.  They got 10 fish from 3 different suppliers in each region: 4 regions x 3 suppliers/region x 10 fish = 120 fish. Species: Farmed Atlantic (didn’t realize that was a species…  this is one of those “a-duh” moments).

Wild salmon were from suppliers in Alaska, British Columbia, and Washington for a total of 6 batches of 10 fish.  Species: chum & coho.

Methods: BORing

Results:

Divide and conquer.

The amount of metals in Farmed Atlantic (filled bars) and wild salmon (open bars):

Co, cobalt; Cu, copper; Sr, strontium; Cd, cadmium; Pb, lead; U, uranium; As, arsenic; MeHg, methylmercury.

The authors noted some statistically significant differences in cobalt, copper, cadmium (all modestly higher in wild salmon), and the nontoxic “organic” arsenic (higher in farmed salmon), but while those differences may be significant statistically, they don’t look significant physiologically.  (if the authors wanted to make the differences look bigger, perhaps they should have opted for a linear ordinate; or maybe they just wanted to squeeze everything in one figure instead).  Interestingly, similar levels of these metals were found regardless of where the salmon came from.  I would’ve imagined a Farmed Atlantic salmon from Norway would be vastly different than a Farmed Atlantic from British Columbia.  Guess not.

As expected, mercury content was correlated with body size (higher up on the food chain, more mercury accumulation), but this is pretty much meaningless to the consumer because we have no idea of the fish’s weight when it was intact.  IOW, the salmon on the bottom (figure below) would have less mercury than the one on the top,

But I have no way of knowing who these came from (salmon fillets):

Fortunately, salmon is a relatively “clean” fish, so it doesn’t really matter.

Back to the data.

Oddly, the authors noted that wild salmon were longer than Farmed Atlantic, but mercury content didn’t correlate with length, only body size (fatness? muscularity? weird).

acceptable levels for metals in fish:

Fig 2

 

Farmed Atlantic and wild (Coho & Chum) salmon were equivalent and well beneath both the FDA and the far more stringent EPA’s limits.  On a side note, I learned that the FDA allows a higher amount of contaminants because they are talking about exposure to each contaminant individually.  The EPA is stricter because they are taking into consideration the fact that we are exposed to multiple contaminants simultaneously (“toxic world,” and all that jazz).  For example, you would be safe consuming a fish with 76.0 mg/kg inorganic arsenic if that were the only toxin to which you were exposed.  But when multiple toxins are present, as they most likely are in our diet, the cutoff for inorganic arsenic is set at 0.002 mg/kg.  The FDA allows 38,000 times more inorganic arsenic than the EPA; that seems grievously negligent but in reality, the amount in commercial fish is significantly lower.  It’s like saying you must be at least 2 inches tall, by the EPA’s standards, or 5 inches tall, by the FDA’s standards, to go on a rollercoaster ride.

One last note: the limit for methylmercury consumption is ~0.4 ug/kg/d, which is approximately 28 ug/d (for a 70 kg or 154 lb person).  Even the most toxic salmon has methylmercury  <100 ug/kg, meaning you can safely eat ~300 grams (10 ounces or about 3 servings) of salmon per day.

 

Round 2. Pesticides: Farmed vs. wild salmon

Global Assessment of Organic Contaminants in Farmed Salmon (Hites et al., 2004 Science)

These researcher went big-time, 700 fish! (appr. 1 ton of salmon)

Sources:

  1. Farmed Atlantic salmon: 8 major commercial suppliers.
  2. Wild Pacific salmon: chum, coho, chinook, pink, & sockeye from 3 different regions
  3. My personal favorite: Farmed Atlantic salmon fillets purchased by undercover secret agents in 16 cities in North America and Europe (Boston, Chicago, Denver, Edinburgh, Frankfurt, London, Los Angeles, New Orleans, New York, Oslo, Paris, San Francisco, Seattle, Toronto, Vancouver, and Washington DC.)
  4. They even analyzed samples of fish food covering over 80% of the global supply

Side note: even if your exact city or region isn’t on this list, I suspect the conclusions can be reasonably applied to just about everywhere.

Results:

Fig 3

 

Figure 3. Contaminants present in Farmed (red) or wild (green) salmon.  It looks like for every contaminant Farmed and wild are similar, but Farmed always has a little more (beware of the deceptive log scale)

Fig 4

 

This figure is very busy.  Concentration of contaminants in Farmed (red), supermarket Farmed Atlantic fillets (yellow), and wild (green) salmon.  Focus on the cities listed at the bottom: the ones toward the left (Europe) are ultra-toxic; the ones on the right (Pacific [Alaska]) are the most safe.  Conclusion from these data: Wild Pacific is safe, Farmed Atlantic is intermediate, and anything European is toxic.  Avoid Scottish salmon like the plague.  And microwave popcorn.

WRT farmed salmon, it looks like most of the problem is with the fish feed:

Figure 5.  Contaminants in fish feed.  European fish food is bunk (red bars).  Pacific (BC British Columbia, Chile) and Atlantic (E. Canada) fish foods are OK (both in gray bars).

 

Conclusions:

WRT metals (Foran study): no difference between Farmed Atlantic and wild Pacific

WRT contaminants (Hites study): wild Pacific (Alaska and British Columbia, also Chilean) is good, supermarket Farmed Atlantic fillets are OK, and European is bad.

 

Round 3.  Fatty acid composition as per www.NutritionData.com

 

Atlantic: Farmed vs. wild

Pacific coho: farmed vs. wild vs. silver Alaska native

Alaska: Silver native vs. King chinook

 

Total EPA + DHA:  1st place goes to farmed Atlantic: 1,966 mg EPA + DHA per 100 grams.  On average, farmed salmon contains more EPA + DHA than wild salmon.

2nd place goes to silver Alaska native coho: 1,876 mg EPA + DHA

3rd place goes to wild Atlantic 1,436 mg EPA + DHA

Lowest were: wild Pacific coho (1,085 mg), Alaska King Chinook (1,150 mg), and wild Pacific Sockeye (1,172 mg).  (all three are Pacific.)

-Farmed salmon has more EPA + DHA than wild salmon

-Atlantic salmon has more EPA + DHA than Pacific salmon

And there were even species-differences:  Alaskan Silver native coho (1,876 mg) had much higher EPA + DHA than Alaskan King Chinook (1,150 mg).

EPA/DHA ratio: not entirely sure about the significance of this, but perhaps EPA is slightly better for physical health while DHA is slightly better for mental health (?) (future blog post topic?)

Average 0.6 (all salmon have slightly more EPA than DHA).  Most EPA (highest EPA/DHA ratio): Farmed Atlantic & wild Pacific Sockeye (0.8).  Most DHA (lowest EPA/DHA ratio): wild Atlantic (0.3) & Silver Alaska native coho (0.4)

 

Conclusions:  WRT contaminants, wild Pacific seems best, farmed Atlantic is OK, and European is bad.

WRT EPA + DHA, Farmed Atlantic and silver Alaska native coho were best and wild Pacific was the lowest.  IMHO the benefits of DHA & EPA outweigh the malefits of contaminants because the dose of EPA + DHA in a serving of salmon is sufficient to reap many of the benefits of EPA & DHA, while the dose of contaminants is too low to cause harm.  Therefore I’m going to stick with Farmed Atlantic.  OOTH if silver Alaska native coho is similar to Kodiak salmon (which I think it is), then it has the lowest contaminants as per the Hites study and 2nd highest EPA DHA as per nutritiondata.

Winner: wild Pacific Kodiak or silver Alaska native coho

2nd place: Farmed Atlantic

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Posted in Advanced nutrition, Fish

GB Grains blog, take I

Posted on March 16, 2011 | 6 comments

What are whole grains?  Short answer: grass seeds.  Long answer: see below.

Grains are made of bran, germ, and endosperm.  Refined grains are endosperm only (GLUTEN and starch).

Germ is the embryo.  Bran is the shell, lots of fiber & vits.  Has unsaturated fatty acids which can go rancid so bran is removed to improve shelf life of refined grains.

Moving on to the data:

Round 1. Grains et al. vs. mortality

Effects of changes in fat, fish, and fibre intakes on death and myocardial reinfarction: Diet and reinfarction trial (DART) (Burr et al., 1989)

The Diet And Reinfarction Trial (DART) is one of the most historically important nutrition INTERVENTION studies.  Intervention studies are where investigators actually go into a population and ask them to change something about their diet; they are intended to determine causation and thus the best advice to give the target population.  The findings from DART, in particular, are crucial & too often ignored.

DART included ~2,000 men, ~56-57 years of age, who recently suffered a heart attack and the intervention was simple.  They were randomly assigned to receive one of three dietary recommendations:

  1. < 30% dietary fat
  2. > 2 servings of fatty fish per week
  3. > 18 grams of cereal fibre (whole grains)
  4. Control group, received no advice

If you’re dispensing nutrition information, then you are intervening just like these researchers.  It stands to reason that the recipients of your advice might respond similar to the DART subjects.  Pay attention.

Just like real patients, the DART population mostly did what they were supposed to.  The fat group decreased dietary fat intake from 35.3% to 32.1%.  The P/S ratio reflects the ratio of polyunsaturated to saturated fat.  The recommendation was to increase this ratio by either increasing polyunsaturated fat consumption or decreasing saturated fat consumption, which was accomplished.

The fish group increased their fish intake from less than one serving per week on average up to the recommended 2 servings (however, 14%-22% of the patients couldn’t tolerate fish and were allowed to take fish oil capsules instead).  The table is showing EPA as a proxy for fish intake; salmon has about 0.5 – 1 gram per serving.

And the fibre group doubled their cereal fibre (whole grains) intake from 9 up to 19 grams per day.

So remember three things: fat, salmon, and cereal fibre.

Gravitas (see below):

Divide and conquer.

 

10.9% of the men who were advised to decrease dietary fat intake died within 2 years while 11.1% who received no such advice died.  That is an absolute risk reduction of 0.2% and a relative risk reduction of 2%.

Absolute risk = 10.9% – 11.1% = -0.2%

Relative risk = (10.9% – 11.1%) / 10.9% = -2.0%

 

9.3% of men who ate more fish died compared to 12.8%!! That is an absolute risk reduction of 3.5% and a relative reduction of 27%.  27% reduced relative risk is huge.  Eat salmon.

Last but not least, 12.1% of men who increased their consumption of cereal fibre died while 9.9% who didn’t died.  IOW, increased cereal fibre caused a 2.2% increase in absolute risk and a 22% increase in relative risk.

In summary, reducing dietary fat had no effect on mortality.  Eating more salmon drastically reduced mortality.  And eating more cereal fibre increased mortality.  Cereal fibre is bad for you?

Cereal fibre comes from whole grains, which are different from other fibrous foods like broccoli and spinach.  Cereal fibre, and whole grains in general, are suspect.  These were men in their 50’s who had a heart attack, so the results may not apply to everyone, but there is another way to look at it.  The DART population had two immediately relevant risk factors: age and a cardiovascular event.  I propose cereal fibre may have been simply another insult to their health profile.  IOW, the mortality risk for increasing cereal fibre might be > 22% for a population with more than two risk factors and < 22% for a population with fewer risk factors. IOW, cereal fibre is bad but won’t kill a healthy person.  Indeed, we see this every day; many people who lead a healthy lifestyle consume whole grains and are fine.  Perhaps the whole grains aren’t what is making them healthy.

Another remarkable finding of this study was the effect of increasing fatty fish intake:

The survival curve: the life-saving effect of increased fish intake are almost instantaneous; by 3 months there is already a noticeable reduction in mortality in the fish group.  That is more substantial than what has been shown in any single trial of statin drugs.  Gravitas.

Summary of DART: Eat salmon, not grains.  And dietary fat doesn’t matter.

 

Support for the theory that whole grain consumption is simply a habit of healthy people, not what actually makes them healthy:

Whole grains, bran, and germ in relation to homocysteine and markers of glycemic control, lipids, and inflammation (Jensen et al., 2006 AJCN)

This paper includes data from the Health Professionals Follow-Up Study, a huge epidemiological study on food intake data and a variety of endpoints.  HPFS = >50,000, all male doctors, established circa 1986

Divide and conquer.

Table 1: lifestyle and dietary characteristics

The table above is divided into three columns.  On the right are people with low grain intake (4.9-11.9 g/d).  Middle is intermediate grain intake.  Rightmost column is high grain intake (38.6 – 50.9 g/d)

Healthy people eat a lot of whole grains (and exercise more, maintain a healthier body weight, smoke less, eat more fiber, eat less trans fats, and eat more vegetables).  All of those factors are directly correlated with the consumption of whole grains.  IOW, scientifically, this makes it very difficult to differentiate true health-promoting effects of grains because there are a lot of bona fide confounding factors.

For example:

1)      low insulin levels are good.

2)      People who eat a lot of grains are all-around healthy

3)      There is a good inverse correlation between grains and insulin level, suggesting that grains may actually be healthy and not something that is coincidentally ingested by healthy people; note the high degree of significance (“0.01,” red arrow)

BUT once the data are statistically corrected for confounding lifestyle factors such as smoking, body weight, and exercise, the association between grains and insulin gets weaker (“0.06,” middle row, red arrow)

And when the data are further corrected for confounding lifestyle and dietary factors such as vegetables and sugar, the association is no longer significant (“0.13,” bottom row, red arrow):

So in some cases, like with insulin, high grain intake is most likely a marker for a healthy person; the grains themselves aren’t what makes these people healthy, it is the lifestyle and dietary things that healthy people do.  In hindsight this shouldn’t have been too unexpected, because foods high in grains are carbohydrate-rich, after all, and carbohydrates drive insulin secretion.  So we shouldn’t be terribly surprised that higher carbohydrate consumption is not associated with lower insulin levels.

Moving on.

C-reactive protein (CRP) is a general marker of inflammation and an excellent marker for an assortment of morbidities and mortality… (IMHO CRP a better marker than LDL).

Note the similar trend: There is a strong correlation between CRP and grains (top row, last column, “0.03”).  But the correlation is weakened by controlling for lifestyle factors (middle row, “0.32”) and further weakened by controlling for diet (bottom row, “0.63”).  Thus, while someone who eats a lot of grains also has relatively low systemic inflammation, the grains are most likely not playing a causal role.

So where does this leave us?  The things that were true in 1989 and 2006 are probably still true today.  Eat salmon, not grains.  And fat doesn’t matter.

Will whole grains kill a healthy person?   No.

Whole-grain intake is inversely associated with the metabolic syndrome and mortality in older adults (Sahyoun et al., 2006 AJCN)

This was a population of >500 healthy people, 60-98 years of age, established circa 1981.

The usual suspects:

Same as above, people who eat more whole grains smoke & drink less, exercise more, eat less saturated fat; all things that are common amongst “healthy” people (but may or may not actually be what is making them healthy).

But to make a long story short:

Hey!! They are the only data I want shown!

This was a much smaller study, but supports the theory that grains may only be detrimental to people with more serious risk factors (like a previous cardiovascular event [e.g., heart attack, etc.]).

DART was an intervention study, and therefore was more powerful and meaningful. The other studies were observational.  Will you still recommend whole grains?

 

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Posted in Advanced nutrition

BAT blog, take I

Posted on March 12, 2011 | Leave a comment

Brown adipose tissue (BAT) has classically been thought of as a thermogenic tissue in rodents, infants, and to a small degree in adults.  BAT is brown because of its rich vascular supply; it’s not a fat storage organ like it’s cousin white adipose tissue.  BAT burns fat and wastes the energy as heat.  Cold temperature activates BAT, which we know is true for humans

Cold-activated brown adipose tissue in healthy men (van Marken Lichtenbelt et al., 2009 NEJM)

BAT in humans, the dark areas represent regions of brown adipose tissue (BAT).

The characteristic of BAT introduced in this paper is that of a Hoover sucking lipids out of the blood, but instead of swirling around in the Windtunnel, they are disposed of into a region of space in which nothing, not even light, can escape (except maybe heat).  For those of you at home, this means if you spend the day swimming in a cold pool or the ocean and then consume a fat-rich meal, virtually all of the lipids will be diverted into BAT where much of their energy will be dissipated as heat.

Brown adipose tissue activity controls triglyceride clearance. (Bartelt et al., 2011 Nature Medicine)

This paper had such an astounding umph…

Below is the profile of plasma lipids from fasted mice at either room temperature (control) or after 24 hours at 4C (39F):

Figure 1a: mice at 22C or 4C.  Cold mice have much lower triacylglycerols (TGs) and moderately lower glycerol.

Problem #1.  Less glycerol generally means lower lipolysis; cold mice should have more glycerol because: cold -> sympathetic nervous system activation -> catecholamines -> lipolysis -> more glycerol and FFAs (?)

It’s so early in the post, but here is the gravitas.  The meat and potatoes.  Cold mice experience virtually no lipemic response to an oral bolus of 100uL olive oil.

I love the author’s choice of dollar signs to denote statistical significance.

Problem #2.  3H-triolein was mixed in the olive oil, and accordingly plasma 3H rose in parallel with TG’s in control mice.  But plasma 3H also increased in cold mice despite no increase in TGs.

Plasma 3H mirrors TGs in control but not cold mice.

The lipid profile of plasma 3H 2 hours after the gavage is shown below (Figure s3)

In the controls (normal mice at room temperature, open circles), most of the 3H is recovered in TRLs (chylomicrons and VLDL) and HDL.  The only 3H recovered from the plasma of cold mice was in the flow-through.  For some reason the authors are calling this fraction “degraded oleate,” but in my experience everything that’s left in the sample dumps out together in the end, including free fatty acids. The authors claim this “degraded oleate” is comprised of partially oxidized (chain-shortened) 3H-fatty acids and 3H20, which it could be, but it could also be unmetabolized 3H-oleate.  More importantly, however, is that it was similar in both groups.  So the cold didn’t enhance the appearance of these mysterious 3H molecules.  IOW, there is still a lot of 3H unaccounted for…  So, where did it go?

Divide and conquer

Figure 1e.  2 hours after the oral gavage, tissues were collected and checked for radioactivity

As expected, liver and muscle took up the most.  Liver should have a higher fractional uptake, but muscle takes up more overall simply because it is a much bigger organ….   Enter: BAT.

Problem #3.  Figure 1e is showing uptake per total tissue, and given the authors claim that both liver and BAT are of equal weight (1.4 grams), that is a ridiculously high fractional uptake in cold BAT.

Suspicions confirmed:  when expressed as fractional uptake, it is ridiculously high in BAT:

Figure s4: As expected, liver is higher than most tissues, except for BAT, which exhibits a phenomenally high fractional uptake in both control and cold mice.  If it’s true that BAT really clears more TG than any other tissue, even at room temperature, then I learned something shocking today.

Problem #5.  Why have I never heard of BAT’s role in clearing chylomicron TGs?

Inconsistency (or Problem #6):  Figure 2d.  Tissue distribution of 3H 15 minutes after i.v. injection of 3H-triolein-labeled chylomicrons.  Note cold liver and BAT are approximately equal at around 5,500 cpm per total tissue.

Below, they tested lean and obese mice in the same type of experiment:

Figure 4i.  Cold liver is about 3x higher than cold BAT.  The units are different, first experiment is cpm per total tissue, second is cpm per gram (“c.p.m. x g” ?) or fractional uptake… but from the first two figures 1e & s4, total and fractional uptake is higher in BAT than liver.  Why is this result flipped around?  Actually, the values for BAT are pretty much the same in both experiments.  But the value for liver is almost 10x higher when corrected for the “weight of the excised tissue.”  The experiment was the same, the data are expressed differently (which is OK), but the results seem different.

Moving on.  Maybe I’m just nit-picking but there are definitely some anomalies that warranted at least a brief mention.  For example,

1. what are those tritiated molecules that are lurking in the cold plasma (figure s2)?

It’s not exclusively “degraded oleate” (whatever that means).  Perhaps some of it is degraded oleate, of which 3H20 and chain-shortened 3H-fatty acids are possibilities, but it could also be native 3H-oleate (or 3H-di- or mono-acylglycerol?).

2. In what form is the 3H remaining in BAT after 15 minutes?  I suspect it is 3H-fatty acids (but from other data they present it could actually be intact 3H-triolein). 15 minutes is too soon for the bulk of it to be 3H2O, which would reflect an extremely high fatty acid rate considering the position of the label is [9,10-3H]oleate.

3. And WHAT about at 2 hours??? 2 hours is way too long for any of the 3H to still be in fatty acids, 15 minutes maybe, but 2 hours? This is BAT after all, isn’t it supposed to be oxidative especially at 4C?

3a. It shouldn’t be 3H2O either because water doesn’t accumulate inside of adipose tissue of any color.

3b. Membranes?  But why would BAT suck fat out of the blood membranes remodeling (especially when it’s so cold!)

4. Does BAT really weigh as much as liver?  Given their densities, this would mean the brown adipose tissue depot is significantly larger than the liver.  It seems like these guys would be walking around with little humpbacks (like a camel), especially after a bolus of olive oil in the cold!

Anyway, the data are staring me straight in the face, but I’m still having trouble swallowing this new characteristic of BAT.  In the meantime, flashback 1957:

TISSUE DISTRIBUTION OF C14 AFTER THE INTRAVENOUS INJECTION OF LABELED CHYLOMICRONS AND UNESTERIFIED FATTY ACIDS IN THE RAT (Bragdon et al., 1958 JCI)

The fate of 14C from [14C]palmitate-labeled chylomicrons in rats.  The time point in this study is 10 minutes; in the Bartelt BAT study above it was 15 minutes, but that shouldn’t matter too much.

Total tissue uptake.  Most of the 14C was recovered in the liver, in agreement with Bartelt’s Figure 2d, but a significant amount is also recovered in muscle and fat, which is not the case in Bartelt’s Figure 2d.

Uptake per gram of tissue (fractional uptake).  These results are drastically different than Bartelt’s.  Liver, heart, and spleen exhibit the highest fractional uptake.  In Bartelt’s figure 4i it’s all liver.  Is this difference due to a rat/mouse thing? 10 vs. 15 minutes?  In any case, both papers agree that a lot of dietary fat/chylomicron-fatty acids end up in the liver.  In mice, maybe it’s liver and BAT, in cold mice maybe it’s all BAT, but in rats maybe it’s liver, heart, and spleen.

In humans, at least from one paper, it seems like fractional TG/FA uptake is approximately equal in fat and muscle (in agreement with mice but not rats where fat takes way more than muscle).

Preferential uptake of dietary Fatty acids in adipose tissue and muscle in the postprandial period. (Bickerton et al., 2007 Diabetes)

This study utilized the arterio-venous balance technique to measure the fractional uptake of [U-13C]palmitate from a mixed meal across subcutaneous stomach fat and a forearm muscle.  However, in Frayn’s Biochemistry textbook adipose is said to take up 4-5 times more than muscle overall which is more on par with the mice and rats cited above.

And in this rat study, whole tissue adipose uptake of 14C-oleate was higher than muscle and liver at 2 hours: Trafficking of dietary oleic, linolenic, and stearic acids in fasted or fed lean rats. (Besseson et al., 2000 AJP)

I guess I’m just trying to keep my mind off of the whole BAT thing.  Come to think of it, I’ll just do the experiment myself (in mice) to see if BAT really plays such a large role in TG clearance.  will post the results.

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