Category Archives: Advanced nutrition

Research studies, hypotheses, data, etc.

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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Posted in Advanced nutrition, Orlistat

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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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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the poor, underestimated glucagon

Posted on February 21, 2011 | 4 comments

it appears that the time for the indirect pathway for glycogen synthesis may finally be here.  Or perhaps it is insulin’s peripheral anti-gluconeogenic actions.

this topic deserves at least 1 lecture in a graduate level nutrition course: Glucagon receptor KO prevents type I diabetes in mice

Background information
When blood glucose is high after a meal, it is cleared by three tissues.
1)      Liver
2)      Muscle (insulin-dependent)
3)      Adipose (insulin-dependent)

Insulin stimulates glucose clearance but also suppresses glucagon.   b-cells surround the capillaries within pancreatic islets; blood flows through the b-cells first than through the a-cells.  So if glucose is high, it triggers insulin release from the b-cells, which then bathes the a-cells and inhibits glucagon.

Glucagon is a counter-regulatory hormone, i.e., it acts to increase blood glucose.  If you asked me before reading this paper, I would have said glucagon is important in fasting; necessary to maintain blood glucose at a level compatible with brain glucose uptake.  How?  By stimulating hepatic glucose production (glycogenolysis and gluconeogenesis).  That’s all.  After a carbohydrate containing meal, there’s less need for hepatic glucose production (HGP), so insulin inhibits glucagon.  After a high protein meal, maybe you still get a little bit of glucagon to help with excess amino acids.  End of story.

Divide and conquer.

There were four important groups of mice:

1)      Wild-type (normal, “WT”)
2)      Type I diabetic (no insulin, “STZ”)
3)      Glucagon receptor KO (no glucagon activity, “Gcgr-/-”)
4)      STZ Gcgr-/- (no insulin or glucagon activity)

STZ (streptozotocin):        b-cell toxin, mimics type I diabetes (no insulin), Ketoacidosis, weight loss despite hyperphagia (2-fold increase in food intake!), Decreased fat mass (due to lack of insulin)

Gcgr-/-:  Decreased blood glucose levels as expected, Improved glucose tolerance (somewhat expected), Ultra-high glucagon levels but confirmed no glucagon activity

Table 1.  The basics.

  • Insulin levels are practically nil in all STZ-treated mice
  • As expected, glucagon is increased in STZ-treated mice, because insulin usually suppresses glucagon
  • Glucagon levels are astronomical in Gcgr-/-

we know from previous studies that Gcgr-/- have lower fasting glucose & better glucose tolerance (Gelling et al., 2003 PNAS)

Back to the data.  Rodent adipose tissue physiology 101:

Fasting & Fed Free Fatty Acids

1st panel: normal WT mice.  Feeding increases insulin which suppresses lipolysis, thus FFA levels go down.

2nd panel: STZ (type I diabetic).  No insulin, so no change in FFA levels after feeding.

3rd panel: Gcgr-/-.
1) Higher fasting FFA compared to WT mice? (compare the two blue outlined boxes);  2)  Feeding increases insulin which suppresses lipolysis, thus FFAs drop; 3) Greater relative suppression of FFA (due to higher basal FFA) suggesting increased adipose tissue insulin sensitivity?

4th panel: STZ Gcgr-/-
1) Reduced fasting FFA compared to WT STZ, suggests a lipolytic role for glucagon, but why is this restricted to STZ mice?  2) No effect of feeding on FFAs because no insulin (just like in WT mice)

A lipolytic role for glucagon is confirmed by comparing the bars circled in red.

Compare the fasted to fed columns in each panel: all four panels are in accord with what we know about the anti-lipolytic effects of insulin.

Not sure why fasting FFAs are lower in STZ Gcgr-/- compared to nondiabetic Gcgr-/-.  Any ideas?

Oral glucose tolerance is improved in Gcgr-/- (compare WT [closed circles] to Gcgr-/- [open squares]), and insulin response is identical (not shown).

Glucose tolerance is improved in STZ Gcgr-/- mice.  (compare STZ Gcgr-/- [closed triangles] to Gcgr-/-[open squares]).  But STZ mice have no insulin!

This suggests that glucagon causes postprandial hyperglycemia and the primary role for insulin is to suppress glucagon (as opposed to facilitate glucose uptake in muscle, for example).  In other words, without glucagon driving up hepatic glucose production (HGP), there won’t be high postprandial glucose, and therefore it doesn’t matter if insulin is present.

Where does the dietary glucose go?  Probably not so much into muscle because there’s no insulin (there is still controversy, but there is evidence that insulin signaling in muscle is important for glucose tolerance; see MIRKO mice , although this isn’t entirely clear).

Liver glycogen?    glucagon actively inhibits hepatic glucose uptake and stimulates HGP (?)

So, to recap:  1) In the absence of glucagon, oral glucose tolerance is normal because there is no great stimulus for HGP; insulin is dispensable.     2) Point #1 is supported because without insulin, oral glucose tolerance is normal as long as there is no great push on HGP (i.e., STZ Gcgr-/-).     3) If glucagon is present, then insulin decreases glycemia by inhibiting glucagon which reduces HGP.  If glucagon is absent, then insulin is not necessary because the liver takes up more of the dietary glucose.  Clear as an unmuddied lake.

STZ Gcgr-/- Probably lower gluconeogenesis compared to STZ wild-type because: lower ketones compared to STZ wild-type, and lower fasting glucose compared to STZ wild-type.  Lower fasting FFAs compared to STZ wild-type, confirming lipolytic effect of glucagon.

Gcgr-/- Elevated fasting FFAs compared to WT, suggesting lipolytic effect of glucagon is restricted to STZ-treated mice.  Reason unknown, any ideas?

OK, like John Dewey now, in the normal situation: dietary glucose is absorbed but glucagon inhibits it from entering the liver at first, so the dietary glucose adds to the glucose produced via gluconeogenesis/ glycogenolysis causing a high peak in blood glucose.  Insulin inhibits glucagon lowering HGP and blood glucose comes down  (role for insulin signaling in muscle?).    Without glucagon: dietary glucose absorbed and is mainly incorporated into liver glycogen; some gets by but HGP is lower, so the total glucose peak is blunted.  Insulin is unimportant in this model, which is why glucose tolerance is the same in Gcgr-/- and STZ Gcgr-/- mice.

So insulin makes you fat and glucagon makes you hyperglycemic (blind, renal failure, neuropathy).   ‘damned endocrine pancreas.

Flashback 1987 – Glucagon levels elevated in lean and obese type II diabetics; maybe this is why they are hyperglycemic?  Less to do with skeletal muscle insulin resistance, more to do with glucagon & HGP?     Reaven et al., 1987 Journal of Clinical Endocrinology and Metabolism

Flashforward 1999 – The almost exact same study was done 10 years ago in humans  (Shah et al., 1999 AJP)  “Impact of lack of suppression of glucagon on glucose tolerance in humans.”  The authors insisted on using the double negative, which is annoying, so instead of saying “lack of suppression of glucagon,” I refer to this condition as “high glucagon.”

Divide and conquer.

No time for background:  10 healthy patients.  Endogenous insulin and glucagon were inhibited by infusion of somatostatin.  At time zero, glucose infusion begins at a rate designed to mimic a 50 gram oral bolus.  Then they infused either a high dose of insulin to mimic the “nondiabetic” response, or a low dose of insulin to mimic the “diabetic” response.  In each of these conditions, glucagon was either co-infused the entire time (“high glucagon”) or delayed for 2 hours (“suppressed glucagon”).

So the 4 groups were: High insulin & low glucagon, High insulin & high glucagon, Low insulin & low glucagon, and Low insulin & high glucagon (diabetic).

Insulin infusions looked like this (Fig 1):

Top graph is nondiabetic, note the high insulin peak.  Bottom graph is diabetic.

Glucagon infusions were pretty much identical for nondiabetic & diabetic conditions (Fig 2):

Top graph is nondiabetic, bottom graph is diabetic.  NOTE: Figures 1 and 2 simply reflect the experimentally produced conditions (that is, the infusions), they are not a physiological effect.  Secretion of endogenous insulin and glucagon was inhibited by somatostatin so the only insulin and glucagon in the blood is what the researchers put there.

Then they gave ‘em glucose (Fig 4).  Please just focus on the top graph first.  Here’s what the glucose levels looked like in nondiabetic conditions.  In other words, high insulin [similar to nondiabetic WT (open circles) & Gcgr-/- (closed squares) above]:

Both Gcgr-/- mice and humans with suppressed glucagon have normal or improved glucose tolerance when insulin is present in nondiabetic doses (top graph).  Now look at the bottom graph; it is what happens in diabetic conditions (just like STZ-treated Gcgr-/- [open circles]; STZ-treated WT mice [closed squares])

We don’t have the data for STZ-treated WT mice, but most assuredly there is higher glucose levels, similar to what is seen in Fig 4B (low insulin, high glucagon [closed squares]).  So glucagon is not bad if insulin is there to defend you (Fig 4A).  Without adequate insulin (Fig 4B), high glucagon causes hyperglycemia.

This was shown to be primarily due to differences hepatic glucose production (Fig 6):

This is an important graph.   Top graph (Fig 6A): (nondiabetic) high insulin suppresses HGP.

Bottom graph (Fib 6B): (diabetic) insulin is not necessary to repress HGP when glucagon is low (open circles).  But if glucagon levels are high and there is low insulin, HGP is high, causing the hyperglycemia seen in Fig 4B.  Interestingly, with low glucagon (open circles), HGP is suppressed to a similar level by high (top graph) or low (bottom graph) insulin (~5umol/kg/min).  High glucagon is sufficient to cause hyperglycemia.  Is it essential?  So high glucagon is why type II diabetics have postprandial hyperglycemia?  (this seems to downplay the role of skeletal muscle insulin resistance [?])

Furthermore, high glucagon (closed squares) impaired insulin’s ability to suppress HGP (compare, in both the top and bottom graphs; HGP is higher with the closed squares (high glucagon) than with the open circles (low glucagon).  This means that insulin inhibits HGP by inhibiting glucagon; it takes really high insulin to counter the effects of high glucagon (with high glucagon [closed squares] HGP could only be suppressed by high insulin [Fig 6A closed squares are suppressed; Fig 6B closed squares are not suppressed) …. Suppressing glucagon in the diabetic insulin milieu (open circles, bottom graph) was sufficient to restore glycemia down to the same levels as nondiabetics (Figure 4 [open circles], glucose peaks at 8mM in both conditions)… so is that why insulin inhibits glucagon?  Or is it how insulin suppresses HGP?  Does it matter?

I think it might, there may be fundamentally important difference.   If it’s how insulin inhibits HGP, then that excludes the possibility that insulin reduces HGP by decreasing amino acid and glycerol release from muscle and adipose, respectively.  It means that insulin acts on the liver, not peripheral tissues, to repress HGP (assuming that in humans, the liver is the major physiological target of glucagon).

But alas! It looks like we won’t be needing to grapple with such questions today.  The researchers found that gluconeogenesis was nearly equally suppressed in all conditions suggesting that the major contributor to glucagon-induced glycemia was glycogenolysis (in this model).

The poor, underestimated glucagon

Calories, proper

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Posted in Advanced nutrition, Uncategorized

USDA Guidelines

Posted on February 7, 2011 | Leave a comment

Extra, extra, read all about it

USDA press release – “New dietary guidelines to help Americans make healthier food choices and confront obesity epidemic”

the new message? “eat less and move more.”  Sound familiar?

But is this really the best message?  I mean, they didn’t have to do any research or studies or anything, because, well, it’s obvious.

This paper is a nice example of how it might not be so obvious.

These researchers took two groups of mice, fed one group “ad libitum” (AL), meaning, the mice ate as much as they wanted, and fed the other group 5% less (calorie-restricted, CR).  For the record, 5% is not very much; it’s about 100 fewer calories for you or me.  And apparently, it’s not enough to cause weight loss:

the experiment was performed twice, and the results are in panels a & b above.  The top lines represent body weight and you can see that there is virtually no difference between the groups.  Look a little further down, however, and you will see that the calorie-restricted group actually lost lean mass (skeletal muscle).  Aargh.  If that isn’t bad enough, mathematically it doesn’t exactly work out unless the mass of something else increased to balance it.  Look further down on the figure, their fat mass actually increased.  Sounds like the worst diet ever.  They didn’t get fatter, they got fattier.

1. Why did they lose muscle?

2. How did they gain fat while being underfed?

What is going here ?

To a large degree, muscle mass determines energy expenditure, which in turn is roughly equivalent to your food intake.  So there is a good relationship between the amount of muscle you have and the amount of food you eat.  As seen above, the opposite appears true also.  By reducing food intake, muscle mass declined, apparently to match the new (lower) food intake level.   So, for now, we can blame the muscle loss on the calorie deficit.

What about the fat gain?  Fat gain is very unlikely if you are in a hypocaloric state… so maybe the mice weren’t technically hypocaloric.  They researchers measured resting energy expenditure (REE) and found that their metabolism was significantly lower.

In other words, the 5% calorie restriction would have been hypocaloric for “ad libitum” fed mice, but once on the diet, their metabolic rate slowed down so much that 5% fewer calories was no longer hypocaloric.  What caused this reduction in metabolic rate?  The researchers then measured physical activity and found the mice certainly weren’t less active, and were actually more active at some time points.

Since they weren’t less active, the reduced energy expenditure must have been in the form of a lower metabolic rate.  In other words, they didn’t get tired or lazy, their body’s intrinsic metabolic rate simply declined.  This was likely related to the loss of muscle mass, but admittedly, these data are a bit cryptic.

To summarize: 5% calorie restriction -> muscle loss -> lower energy expenditure -> reduced metabolic rate -> increased fat mass ?     Well yes, all of those things occurred, but whether or not they necessarily occurred in that sequence is less clear.

Major fundamental finding: the mice ate less, moved more, and got fattier.

Eating less and moving more is not the answer.

calories, proper

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more clips from TPMC

Posted on January 11, 2011 | Leave a comment

Calories in the body are a measure of how much energy a given food provides when it is “burned.” The extent to which this occurs comprises the “metabolic rate.” Let us take, for example, a 160 pound woman who expends 2,000 calories per day. If weight-stable, she is consuming 2,000 calories of food per day, and is creating 2,000 calories of heat per day (that is not a coincidence). The calories expended provide energy for all aspects physical life, breathing, daily activity, walking, eating, etc., etc. Extra calories are used to fuel physical activities, as opposed to non-physical activities such as reading… even if you are reading really, really fast. More intense activities like running or climbing stairs require more energy, and thus burn more calories, relative to lower intensity activities like walking or sitting on the couch. These calories can be derived directly from the food in your most recent meal, or from the body’s storage depots. So, basically, you consume calories in the form of food, and expend calories in the form of mechanical work and heat. That is calorie balance.

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HART-D trial: aerobic, resistance, or both?

Posted on January 6, 2011 | 2 comments

calories, proper. Sticking to the data-
Today’s study: aerobic exercise is good for you, but bad for muscular size and strength.

The HART-D trial was a 9 month exercise study which compared resistance (RT), aerobic (AT), and combined (combo) training. Combo did a lot less resistance exercise than RT, and a little less aerobic training than AT. The primary outcome measure was glycated hemoglobin, which combo improved the most, but I am primarily interested in Table 4.
Effects of Aerobic and Resistance Training on Hemoglobin A1c Levels in Patients With Type 2 Diabetes

To start out with my conclusion, I think this study shows that aerobic training is bad for muscle, fat loss, and strength (albeit due to a very small statistically non-significant extent). In short, aerobic exercise is softening (in a small statistically non-significant manner).

The exercise interventions were good.

Aerobic training (AT & combo groups)
150 min/wk of moderate intensity (50-80% VO2max) (?10-12kcal/kg BW*week)

Aerobic: 12 kcal/kg*wk x 98.2 kg = 1170 kcal /wk 623.7-681.9 MET/min*wk
Combo: 10 kcal/kg*wk x 100.6 kg = 1006 kcal /wk 532.0-572.8 MET/min*wk

Resistance training (RT & combo groups)
Days/wk sets x exercises ( x 10-12 reps)
RT: 3 2 x 4 upper, 3 x 3 lower, 2 abs
Combo: 2 1 x 4 upper, 1 x 3 lower, 1 abs

Basically the combo group did about 33% of the resistance training and 80% of the aerobic training as RT and AT, respectively. They all exercised for 140 minutes per week which burnt approximately 1200 kilocalories. In brief, the combination regimen achieved the biggest reduction in glycated haemoglobin while simultaneously reducing blood glucose-lowering medications more than any other group. That is a pretty nice finding, but I think the data presented in Table IV are of critical importance to anyone concerned with body composition, physical performance, and quality of life in general.

Observations:
Aerobic training alone reduced fat-free mass… Their muscles got smaller (3rd row, 4th column).
Aerobic training alone reduced strength… They got weaker (3rd row, 5th column).
The aerobic training alone group lost the least amount of fat mass despite reducing their food intake the most (3rd row, 1st and 3rd columns). I repeat: the aerobic training alone group lost the least amount of fat mass despite reducing their food intake the most…
I can’t entirely explain this, however I would guess that either:
1) Aerobic exercise makes you so tired for the rest of the day that you just lie around doing nothing, expending much less energy. Or
2) Measurement errors, which might be important considering the small overall differences between groups.

Moving on,
Any group that resistance trained (RT & combo) lost more fat mass and gained more strength than those who didn’t (aerobic only)
Resistance training alone increased fat-free mass (also confirms the adequacy of the resistance training regimen… in other words, if muscle mass & strength didn’t respond to the resistance exercise, I wouldn’t consider this a good study.)

Resistance training in the combination group probably would’ve increased muscle mass if it wasn’t for that darned muscle-burning aerobic exercise 🙂

The combo group lost the most fat while reducing their food intake the least. From this I would recommend combination training, however this group also had no improvements in muscle mass and only a modest increase in strength, two things that are very important for quality of life.

In conclusion, resistance training prevailed in this study. Aerobic training is still important for patients with congestive heart failure, so combination training may be more appropriate for this population (and for people with chronic hyperglycaemia), however for the rest of us, resistance training is superior.

Thoughts?

Calories, proper.

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Posted in Advanced nutrition