Monthly Archives: December 2011

Insulin is a double-edged sword with a pointy tip

Posted on December 22, 2011 | 3 comments

Visceral fat (VAT) is bad, more VAT is worse; believe, or do a Google on it.  And it is my contention that insulin, or more specifically diets that promote insulin spikes, hyperinsulinemia, or insulin resistance, is the primary driver of VAT accumulation.

The balance between fat accumulation and fat loss is regulated by four distinct mechanisms, which are similar in myriad biological systems:

1)      Enhanced fat accumulation

2)      Reduced fat accumulation

3)      Enhanced fat loss

4)      Reduced fat loss

They are not mutually exclusive, but small shifts in any of them can cause big changes in fat mass with vast implications for metabolic outcomes.  And to complicate matters further, we are talking about two distinct fat depots which are independently regulated by those mechanisms… there are a lot of possibilities.

Subcutaneous fat (SCAT), the kind associated with a “pear” body shape, is a relatively safe place to store excess energy; i.e., safer than VAT or other ectopic depots such as liver or muscle.  And in lean healthy individuals, SCAT is more sensitive than VAT to the anti-lipolytic effects of insulin.  In other words, insulin favors the storage of excess energy in the relatively safer SCAT.  But when insulin levels spike, insulin resistance and hyperinsulinemia develop.  This causes the reverse to occur- fat mass accumulates in VAT.  Fortunately, this is completely reversible by weight loss or adopting a low-insulin diet.

Evaluation of two dietary treatments in obese hyperinsulinemic adolescents (Armeno et al., 2011 Journal of Pediatric Endocrinology and Metabolism)

This study examined the effects of two isocaloric hypoenergetic diets of identical macronutrient composition that differed markedly in their ability to spike insulin levels for 16 weeks in 86 obese Argentinian children.

CD = control diet; LIR = low insulin response diet.

Divide and conquer

After 16 weeks, the LIR group lost more weight than CD, and waist circumference declined to a greater extent.

And of particular relevance to my current thesis, the decline in waist circumference was disproportionately greater (relative to the body weight loss) in the LIR group compared to CD.

Importantly, this correlated well with a greater reduction in fasting insulin levels in LIR relative to CD.

Waist circumference is a good indicator of VAT.  Reduced insulin levels improved insulin sensitivity, which promoted a shift away from VAT- an example of how small shifts can have a big impact on the abundance of fat mass stored in one depot relative to another one.  And this also had a functional impact on metabolic outcomes- AST, a marker of liver dysfunction, was reduced to a greater extent in the LIR group relative to CD.

Why do I attribute these effects to reduced insulin levels?  Aside from the sound (IMO) biological rationale mentioned above, it occurs consistently regardless of how insulin levels are reduced.

Greater weight loss and hormonal changes after 6 months diet with carbohydrates eaten mostly at dinner (Sofer et al., Nature Obesity)

While the Armeno study in obese children (above) reduced insulin levels with a diet that didn’t spike insulin levels ever, this study did so by restricting the insulin spike to once per day, at dinner time.  This was a longer study (6 months) in an older population (39 Israeli adult police officers).

Interestingly, and similar to the Armeno study, the experimental group (dinner carbs) lost more body weight and experienced a greater reduction in waist circumference than the control group.

As in the Armeno study, the reduction in waist circumference was disproportionately greater in the experimental group than in controls, and this also correlated with a greater reduction in fasting insulin.

This was accompanied by functional improvements as well- the experimental group experienced a greater increase in HDL and a bigger decline in the inflammatory insulin-desensitizing cytokine TNF-alpha.

The similarities between these two studies is eery, especially given the markedly different patient populations (obese Argentinian children vs. obese Israeli cops) and study duration (4 months vs. 6 months).

Why do I attribute these effects to reduced insulin levels?  Aside from the sound (IMO) biological rationale mentioned above, it occurs consistently regardless of how insulin levels are reduced.

The effects of intermittent or continuous energy restriction on weight loss and metabolic disease risk markers: a randomized trial in young overweight women (Harvie et al., 2011 International Journal of Obesity)

This study compared the effects of a chronic 25% energy restricted diet (CER) to an intermittent energy restriction (IER) which reduced food intake only on Monday and Tuesday while allowing ad lib food intake for the rest of the week in 89 overweight young British women for 6 months.

Similar to both of the above studies, the experimental group (IER) lost more body weight and experienced a greater reduction in waist circumference than controls.

And this too was accompanied by a greater reduction in fasting insulin levels.

Function improvements occurred as well- the experimental group experienced a greater increase in insulin sensitivity and the insulin-sensitizing hormone adiponectin.

These findings further contribute to the phenomenally similar effects of reducing insulin levels, in three markedly different experimental paradigms (obese Argentinian children vs. obese Israeli police officers vs. overweight British women):

Why do I attribute these effects to reduced insulin levels?  Aside from the sound (IMO) biological rationale mentioned above, it occurs consistently regardless of how insulin levels are reduced.

Could greater VAT loss be due to weight loss and not reduced insulin levels per se?  It’s possible, but given the effects of pharmacologically reducing insulin levels (see HERE) it seems like insulin, not body weight, is the main driver.

You just gotta get those insulin levels down

  1. Eat fewer foods that spike insulin
  2. Restrict carbs to one meal per day
  3. Intermittent energy restriction

 

calories proper

 

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Posted in Advanced nutrition, Dietary fat, Energy balance, Sugar, Uncategorized

Fructose vs. The Laws of Energy Balance

Posted on December 13, 2011 | Leave a comment

Exclusively from literature featured in past blog posts, e.g. HERE and HERE, excessive fructose consumption seriously deranges metabolism.  Furthermore, fructose pre-disposes to and exacerbates leptin resistance, which is one of the most proximal causes of obesity viz. overeating.  However, this doesn’t exonerate processed foods, modern grain-based diets, or trans-fats because they frequently co-exist.  Many popular breakfast cereals contain all three, and IMO a fructose-free breakfast cereal wouldn’t do much in the treatment and/or prevention of obesity.  Just eat better.  And we might even get “low-fructose” foods on grocery store shelves in the near future (but don’t hold your breath, food companies LOVE their fructose).

Consuming fructose-sweetened, not glucose-sweetened, beverages increases visceral adiposity and lipids and decreases insulin sensitivity in overweight/obese humans (Stanhope et al., 2009 Journal of Clinical Intervention)

Consumption of fructose-sweetened beverages for 10 weeks reduces net fat oxidation and energy expenditure in overweight/obese men and women (Cox et al., 2011 European Journal of Clinical Nutrition)

Metabolic responses to prolonged consumption of glucose- and fructose-sweetened beverages are not associated with postprandial or 24-h glucose and insulin excursions (Stanhope et al., 2011 American Journal of Clinical Nutrition)

These studies came out in a few separate publications, were ultra-high budget, and used very advanced techniques to quantify energy expenditure and body composition.  AND much care was taken to ensure the subjects were truly weight stable when appropriate (inpatients for two weeks in the beginning and end of the study so all of their food intake and anthropological measurements could be assessed accurately).  The experiment consisted of feeding subjects a sugar-sweetened beverage, either glucose or fructose, equivalent to 25% of their daily energy requirements.

During the inpatient portions, subjects were fed a standardized diet of 15% protein, 20% fat, and 55% carb:

Note the differences in GI & GL (bottom two rows).   Fructose has a negligible impact on glycemia because, well, it’s fructose (not glucose), and it doesn’t magically transform into glucose after ingestion.

When left to their own free will, the patients pretty much ate the same:

In general, after a period of adaptation, their intake of other foods should have declined by 25% to compensate for the additional calories from the sugar drinks, but sugar seems to hijack the appetite set point – first row in the table above; calories were 20-25% higher, almost the exact amount of calories in their sugar drinks – therefore all subjects gained a few pounds (1% of initial body weight) (and then they went back on good behavior when they were being observed in the metabolic ward):

Herein we have the first unexpected pearl: the fructose group gained visceral fat (VAT) whereas the glucose group gained subcutaneous fat (SCAT) (eerily similar to what is seen with trans-fats!).

Exhibit A:

The glucose group actually gained slightly more fat mass than the fructose group, but most of the excess weight was deposited in the relatively inert SCAT, or “extraabdominal” regions.  The fructose group, on the other hand, gained it all in VAT (apple, not pear).  Abdominal fat and waist circumference increased significantly in the fructose drinkers.  FYI that is very interesting.  And it wasn’t caused by individual differences- it’s not like some people were more predisposed to gain more VAT than SCAT; these subjects were randomized.  Diet, or more specifically, dietary sugars caused this differential fat storage.  Amazing.

Exhibit B:

This figure shows the differences in fat gain.  The glucose group gained less VAT than SCAT, while the fructose group did the opposite.  Genetics had nothing to do with this.  It is diet.  It is nutrition.  For the love of God people, it is nutrition.

In lieu of the recent publication by Dr. Bray, it is interesting to note the second pearl: an example of the irrelevance of the laws of thermodynamics (universal) with respect to the Laws of Energy Balance (conjured up by yours truly).  Namely, energy expenditure is affected by the diet… IOW, the laws of thermodynamics are not violated, but all calories are not equal (THERE. I said it… on the record, in cyberspace, for all of eternity).

This nuance is introduced in figure 2:

Divide and conquer

On the left, fat oxidation is slightly lower in the glucose group.  This is expected, because carb oxidation should have increased due to the increased carb consumption (in the form of the glucose drink).  But as seen in the right panel, fat oxidation declined significantly in the fructose group.  From this, we would expect fat gain to be greater in the fructose group compared to the glucose group … but it wasn’t.  Artefact?  Error in measurement?  I don’t know how, but this appears to be a violation of the Laws of Energy Balance (which is impossible).  UNLESS energy expenditure declined more in the fructose group than in the glucose group.

Exhibit C:

And it did!  Both groups increased their sugar consumption (by design), and energy expenditure declined in both groups (they all gained weight).  The fructose group gained about a pound less than the glucose group, but consumed slightly fewer calories on average.  So the reduced fat oxidation didn’t enhance fat gain in the fructose group because food intake declined proportionately; and they maintained energy balance relative to the glucose group because energy expenditure was slightly lower (this is complicated).

To be clear, the fructose-induced VAT deposition is not explained by reduced fat oxidation as that would imply less fat gain overall, relative to the glucose group (which didn’t happen).

Fructose-induced VAT deposition is a product of the deranged nutrient partitioning caused by fructose itself.  It’s a dangerous lil’ bugger.  How does fructose conspire with the metabolic machinery to selectively enhance visceral adiposity?  Not sure, but it might have something to do with insulin.  Glucose but not fructose stimulates insulin secretion, and SCAT is more sensitive to the anti-lipolytic effects of insulin than VAT.  The overall fat gain was similar in both groups, in accord with the Laws of Energy Balance.   But insulin tends to drive fat into metabolically safer SCAT.  An example of this concept in practice can be seen by looking at obese insulin resistant people.  In this population, SCAT is less responsive to insulin, relativeto lean people, and indeed, they have significantly greater visceral fat mass.  So fructose doesn’t trigger an insulin response, which means excess calories are less likely to be stored in SCAT, but since this can’t violate the Laws of Energy Balance the calories must go somewhere…  deposited into the notorious VAT bank where they not only still make you fat but also initiate a storm of metabolic abnormalities.

 

calories proper

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Posted in Advanced nutrition, empty calories, Energy balance, Fructose, Sugar, Trans fat

Body Mass Index, Op. 42

Posted on December 6, 2011 | 1 comment

BMI: everything you wanted to know but were afraid to ask

or

More than your friends know about the BMI

BMI, or “body mass index,” is an index of adiposity and is calculated as body weight divided by height-squared (kg/m2).

BMI is usually divided into 4 categories: underweight, healthy weight, overweight, and obese.  These categories have specific meanings and there is a physiological rationale for culture-specific cutoff points.  In the United States, healthy weight is classified as BMI 20-25 and obesity is BMI > 30.

N.B.  The rationale for setting the obesity cutoff at 30 and not 28 or 32 is that Americans with BMI < 30 experience fewer health problems, e.g., lower rates of diabetes, than those > 30.  It is NOT the average, nor does it have anything to do with odd biological phenomena like adipocyte cell number or maximal obesity capacity.  The cutoff for Chinese is ~25 because > 25 is associated with significantly more health problems than < 25.  The cutoff for Chinese (25) is not lower than Americans (30) because Chinese are on average leaner.  Chinese ARE leaner, but that’s not why the cutoff is lower.  The cutoff is lower because it is based on disease risk, not average body weight.

This is complicated.  Trust me.

Example #1.  An American living in the United States with BMI 27.5 is classified as overweight, but a Chinese with BMI 27.5 is classified obese.  These terms have more to do with disease risk than body weight.

Example #2.  On average, Americans are fatter than Chinese.  But Chinese have higher rates of diabetes.  Chinese get diabetes at a lower BMI, on average, than Americans.  THIS is why the obese BMI has a lower cutoff for Chinese.  Pound for pound, Chinese are more diabetic than Americans and studies like Ni-Hon-San suggest this is not genetic, but rather environmental or dietary.

BMI 101, Americans are good at getting fat without diabetes.  And they’re really good at getting fat, but that’s got nothing to do with BMI category cutoffs.

Deriving ethnic-specific BMI cutoff points for assessing diabetes risk (Chiu et al., 2011 Diabetes Care)

Divide and conquer

No study more robustly exhibits the gravitas of BMI categories.  From this relatively random sample, whites and blacks arethe heaviest, followed by South Asians (Indian, Pakistani, etc.), and then Chinese, who are the leanest.

Table 1.

If the obesity cutoff is universally set at 30, then 16.5% of whites, 14.7% of blacks, and 6.9% of South Asians, and a paltry 2.2% of Chinese are “obese.”  And THIS is why the obesity cutoffs are not universal.

Table 2. Incidence of diabetes (per 1,000 person-years)

The first row in Table 2 shows the incidence of diabetes among those at a healthy body weight.  It is fairly low, in all except for those hailing from the diabetes capital of the world, India, whose unlucky population experiences 3x more diabetes than whites (cough cough diet cough).  But now focus on the first column, diabetes incidence in whites with a healthy body weight (4.1) vs. overweight (10.0) vs. obese (25.6).  There is a clear increase across the groups which confirms the usefulness of these specific BMI cutoff points for this population.

But what happens when these cutoffs are applied to Chinese (Table 2, third column)?  Holy crap, 79.6% of Chinese with BMI 30 have diabetes!!  Actually, overweight Chinese are almost as diabetic as obese whites (despite being ~25 pounds lighter).  But while this clearly shows a BMI-independent predisposition toward diabetes among Chinese (cough cough diet cough; as blogged about previously), it also demonstrates the futility of universal BMI cutoff points.  Since the cutoffs are based on health risks, to say someone is obese should be associated with a specific risk.  If the cutoff was universally at BMI 30, then we could conclude nothing meaningful about the incidence of diabetes in obesity since the range would be enormous (25.6 – 79.6).  In other words, the word “obese” would be rendered useless by universal BMI cutoff points.

Table 3. Incidence of diabetes (per 1,000 person-years)

 

Lowering the obese cutoff to 27.5 for Chinese levels the playing field.  30.9% of Chinese with BMI > 27.5 have diabetes, which is roughly equivalent to the 25.6% of whites with BMI > 30.  Now we can clearly say “obesity” is associated with increased risk for diabetes, and this risk is increased equally in an ethnic-dependent manner.  By normalizing to disease burden, BMI categories have significantly more clinical value.

Clearest depiction of this concept:

To most accurately normalize BMI categories, one could ask at which BMI certain ethnic groups experience the same diabetes incidence as obese whites, for example.  By graphing the data in this manner, it is clear that Indians, Chinese, and blacks aren’t very good at getting fat.  Just a few extra pounds and here comes the diabetes.  At least for Chinese, it’s a good thing they tend to stay lean (Table 1).

And this isn’t restricted to diabetes…

Are Asians at greater mortality risks for being overweight than Caucasians?  Redefining obesity for Asians (Wen et al., 2008 Public Health Nutrition)

 

 

The relative risk for all-cause mortality increases with body weight.  As per the figure above, e.g., a 225-pound American has the same relative risk for all-cause mortality as a 200-pound Chinese.  Alternatively, a Chinese with BMI 25 has the same relative risk for all-cause mortality as an American with BMI 30.  Thus, ethnic specificity is mandatory to establish clinical meaningfulness to the word “obesity.”

 

calories proper

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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