Category Archives: Uncategorized

Taxes, saturated fat, and HDL, Op. 71

Since red meat won’t kill you (it will make you stronger), why is taxing saturated fat still up for discussion?  The Danish proposal will add a $1.32 per pound to foods with >2.3% saturated fat; the cost of butter will increase by 30% more and olive oil by 7.1%.  I know, right?  WTF?

Again, I don’t think taxation is the solution, but for the sake of comparison: Arizona’s proposed “fat fee” would cost an extra $50 annually for childless obese patients; Rhode Island’s $0.01/oz of soda; or France’s 3.5% tax on all sugar-sweetened beverages.

Nutritionally speaking, saturated fat should be off the political chopping block; any intervention designed to reduce its consumption will do more harm than good.  In brief, here’s one example of what might happen if it worked, i.e., if dietary saturated fat consumption was reduced:

The effect of replacing dietary saturated fat with polyunsaturated or monounsaturated fat on plasma lipids in free-living young adults (Hodson et al., 2001 EJCN)

Subjects were given a high saturated fat diet and then switched to either a high polyunsaturated fat diet (trial I) or high monounsaturated fat diet (trial II).  In both cases, as seen in the table below, HDL decreased.

Alternatively, here’s what might happen if dietary saturated fat consumption was increased (in brief):

Separate effects of reduced carbohydrate intake and weight loss on atherogenic dyslipidemia (Krauss et al., 2006 AJCN)The bottom two groups in the chart above ate similar diets except monounsaturated fats were replaced by saturated fats in the last group.

As seen in the table below, saturated fat significantly increased HDL.    

So did weight loss, but I’d choose a steak over a stairmaster any day…  (daydream thought bubble: “indeed, ‘adherence’ and ‘compliance’ would be things of the past”)

If you believe HDL is important, taxing saturated fat might be a bad idea.  unless you have stock in statins.

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Paleo schmaleo, Op. 69

Brief refresher:

Paleo: lean meat, fish, fruits, vegetables, potatoes, eggs, and nuts; NO grains or dairy

Paleo carbs: fruits, veggies, nuts, and beans… NO starches, cereals, whole grains, added sugars, etc.

Paleo is GFCF-friendly

Atkins is similar to Paleo but allows fewer carbs

Mediterranean diet (from last week): whole grains, low-fat dairy, vegetables, fruits, fish, oils, and margarines (the Paleo diet improved insulin sensitivity WAY more than the Mediterranean diet in patients with CHD).

Diabetic diet (this week; see below): vegetables, root vegetables, dietary fibre, whole-grain bread and other whole-grain cereal products, fruits and berries, and decreased intake of total fat with more unsaturated fat.

Paleo vs. the “diabetic diet” in type II diabetics (Jonsson et al., 2009 Cardiovascular Diabetology).  Lindeberg designed this particular Paleo diet with a much lower carb content (32% vs. 40%) than in the previous study with CHD patients.  A cynic, who might think that some of Paleo’s benefits are due to its low carb content, might think that since traditional Paleo and the comparison “diabetic diet” have a similar carb content (42% and 40%, respectively), Lindeberg intentionally modified Paleo for this study to make sure carbs were significantly lower than in the “diabetic diet” (stacking the deck in Paleo’s favor, according to the cynic).  I can’t find any reason to disagree with the cynic, but it didn’t work out so well for Lindeberg et al.

As detailed in a series of posts about crossover studies (part I and part II), this one was botched due to: 1) what appears to be improper randomization (baseline glucose values were 7.1 and 8.6 mM); and 2) a washout period that was too short to allow one of the primary endpoint variables (HbA1C) to return to baseline.  As such, data presentation was convoluted, which said cynic might think was intentional.  But if we take it at face value, Paleo still fails.  For example, according to this figure (which is NOT crossover data), although Paleo has a lower final HbA1C, the HbA1C reduction is much greater on the diabetic diet.Paleo: 0

Diabetic diet: 1

AND weight loss was similar despite Paleo dieters consuming significantly less food (1581 vs. 1878 kcal/d):So yes, in accord with the Jonsson study (above), Paleo may have been more satiating (i.e., spontaneously lower food intake), but no, this didn’t translate to greater weight loss.  Someone needs to measure energy expenditure in Paleo dieters because it looks like this pattern of food intake either lowers basal metabolic rate or simply makes people tired (though this conclusion would be vehemently denied by Paleo loyalists).  The reduced leptin levels (Jonsson study) may have caused lower energy expenditure, but this would not entirely align with my lower-leptin-equals-higher-leptin-sensitivity hypothesis and thus cannot POSSIBLY be true :/   Alternatively, perhaps the Paleo diet really does lower energy expenditure; this would’ve been irrelevant and possibly even beneficial in Paleolithic times because: 1) they would’ve conserved more energy for “hunting” (hunter-gathers) or fleeing; and 2) weight loss was much less a concern compared to starving or being predated.

The Paleo diet is interesting in that it eludes low-carb status by selectively excluding grains, and I’m pleased that high quality studies (randomized crossover) are at least being attempted, but data thus far suggest we haven’t found anything magical about Paleo (yet)… just need better studies, especially those controlling for total carb content.

Paleo:

+1 for excluding grains, but not much else

 

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Yogurt black belt test, Op. 65

Proper yogurt can serve as a delicious and healthy addition to any meal of the day.  It contains probiotics, whose role in promoting a healthy gut flora and overall well-being is widely appreciated.  As such, yogurt can be considered an acceptable source of a little bit of sugar in your diet.  (I don’t say that very often… actually, that was probably the first time.)

BUT (you had to know there was a “but”) there are a lot of caveats.  First and foremost is selecting the best yogurt product, since not many people are down with DIY fermentation (which is unfortunate given its tremendous ease).  The yogurt with the most gravitas on the market: FAGE.  It’s supposedly Greek, but I’d say given it’s macronutrient composition, it’s more Spartan.  There are considerable differences between the plain and fruity varieties worth considering.  For example, one serving of plain contains 190 kcal, 10g fat, 8g sugar, and 19g protein, whereas one serving of the blueberry-flavored variety contains 170 kcal, 6g fat, 16g sugar, and 11g protein.  twice the sugar! This is unacceptable, primarily because while I’m not really clear what’s in the “blueberry fruit preparation” that’s listed in the ingredients, I’m sure it’s not real blueberries.  Since real blueberries have negligible protein, we can assume the total protein content of the final product is entirely from the yogurt; therefore, their ambiguously named “blueberry fruit preparation” contributes about 27 grams to the entire 150 gram serving.  This adds 12 grams of sugar, whereas 27 grams of real blueberries would provide only 3 grams of sugar (and some fiber and phytonutrients).

And pass on the 0% fat version; one serving contains all of the sugar but none of  the healthy fats that slow down sugar absorption and contribute to satiation.

On to more pressing, or ‘popular,’ matters.  Dannon is the most widely purchased yogurt on the market.  One serving of plain Dannon yogurt contains 160 kcal, 8g fat, 12g sugar, and 9 grams of protein (less protein and healthy fats, and more sugar than its Spartan counterpart).  Their vanilla-flavored variety has a whopping 25 grams of sugar (and it’s certainly not natural dairy sugar…).  One serving of blueberry-flavored Fruit-on-the-Bottom contains 140 kcal, 1.5g fat, 26g sugar, and 6g protein.  If you added real blueberries to the plain variety this would only yield 15 grams of sugar (still more than FAGE, FTR).  Again, this additional sugar is not coming from real blueberries; unlike FAGE, who disguises their mystery flavor as “blueberry fruit preparation,” Dannon doesn’t even try to hide it.  Right in the ingredients list you’ll find strike 1: sugar, strike 2: fructose syrup, and strike 3: high fructose corn syrup (I honestly don’t know why that’s listed as three separate ingredients.  It’s like they’re trying to boast about it).  I feel pre-diabetic just reading it.  Yoplait is just as bad (high sugar and low protein); come on, Trix -flavored yogurt?  Really?

With regard to promoting a healthy gut flora:  Dannon contains only 1 probiotic strain: L. acidophilus; Yoplait has 2: L. bulgaricus and S. thermophiles; FAGE has 5, L. acidophilus, L. bulgaricus, S. thermophiles, Bifidus, and L. casei.

FAGE: winner.

 

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Diet, diabetes, and death (oh my)

Fatty acid face off: saturation vs. chain length
or
an homage to pioneers of nutrition research

While both fats contain a lot of 8-12 carbon fatty acids (C8-C12), coconut oil contains more of the 12-carbon fatty acid “lauric acid” whereas medium-chain triacylglycerols (MCT) have more of the 10-carbon fatty acid “capric acid.”  Both exhibit remarkably protective effects against diabetes and this has been known for quite a while.  Coconut and MCT oils are also phenomenally ketogenic, which contributes to their healthful effects (although this eluded early researchers).

Experimental diabetes and diet (Houssay and Martinez 1947 Science)

This study used alloxan to deplete insulin-producing beta-cells rendering these rats essentially type I diabetic.  In the first experiment, they injected alloxan and counted how many rats were still alive after one week.  This study is cruel by today’s standards, but things were different in 1947.  It does, however, provide valuable information as the rats were also being fed one of 16 (16!) different diets.  The major finding was that all the rats fed lard died (d, e, and i in the table below), while all those fed coconut oil survived (o in the table).  And additional coconut oil, methionine, or thiouracil, but not protein, sulfanilamide, or choline reduced the deadliness of lard.  Both lard and coconut oil contain saturated fat, but lard has longer chain fatty acids and more unsaturated fat than coconut oil suggesting fatty acid chain length and/or degree of unsaturation may be important.

In the follow-up experiment, rats were rendered diabetic by surgical removal of 95% of their pancreas and fed high carb, high protein, or high lard diets (a, b, and d from the table above).  In agreement with the first experiment, lard is bad news.  On the other hand, whereas a high protein diet wasn’t helpful for alloxan diabetes, it was remarkably protective in pancreatic diabetes. 

Influence of diet on incidence of alloxan diabetes (Rodriguez and Krehl 1952)

These researchers measured mortality and diabetes incidence in alloxan-treated rats and found that: 1) coconut oil is protective against mortality and diabetes; 2) lard is not; and 3) high protein is modestly protective.  IOW, these data confirm Houssay’s from 5 years earlier.These authors added some information to the picture by measuring body weight and showing that the protective effect of coconut oil is not due to reduced body weight, because these coconut oil-fed rats weighed as much as those fed a low protein diet, and low protein diet-fed rats fared rather poorly.

To add yet more information to the picture (kudos!), they fed rats diets containing the most abundant fatty acids found in coconut oil (caprylic acid) or lard (palmitic acid) and showed that coconut oil’s benefits may be due to caprylic acid because this fatty acid alone was similarly protective against mortality and diabetes.  They also showed lard’s malevolence is not due to palmitic acid because these rats were almost just as protected as those fed caprylic acid.  This somewhat excludes a role of fatty acid length as caprylic acid has 8 carbons while palmitic acid has 16, but both are fully saturated (suggesting a possible detrimental role for unsaturated fatty acids [?]).

So why is coconut oil so good?

One possible reason:  saturated fatty acids are protective, which is supported by the beneficial effect of coconut oil, caprylic acid, and palmitic acid.  Similarly, lard and Swift’ning have a lot of unsaturated fats and both were detrimental.

Unsaturated fatty acids and alloxan diabetes (Rodriguez et al., 1953 Journal of Nutrition)

Rats fed saturated fats of varying chain length were remarkably more protected than those fed unsaturated fats.  Lard has a lot of oleic acid, and rats fed oleic acid didn’t do so well; corn oil is predominantly unsaturated fat and rats fed corn oil were phenomenally unhealthy.  They also showed that rats fed stearic acid (18-carbons, fully saturated) were much healthier than those fed oleic acid (18-carbons, monounsaturated). While none of these studies explored the ketogenic effects of C8-12 fatty acids, they clearly demonstrated that saturated fatty acids of any chain length are good for diabetics, while unsaturated fatty acids are bad.  Good sources for C8-10 fatty acids are MCT oil and goat’s milk, and a good source for C12 fatty acids is coconut oil.

As to the role of ketones, which I think is quite important… to be continued

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Gluc-a-gone wild, Op. 60

optional pre-reading

Q. What happens to a type I diabetic when you 1) withhold insulin, 2) provide insulin, or 3) withhold insulin and suppress glucagon?  (Charlton and Nair, 1998 Diabetes)…

A. You learn glucagon is the bad guy.

Divide and conquer

Zero insulin makes you hyperglucagonemic, hyperglycemic, and ketoacidotic (see first column).  Insulin cures all of these things (second column), but they aren’t caused by insulin deficiency, per se… they’re caused by high glucagon, which itself is cured by insulin (second column) and SRIH (somatostatin, third column).  Cure the hyperglycemia by inhibiting glucagon and pathological diabetic ketoacidosis suddenly becomes physiological ketosis.

Uncontrolled diabetes also wastes muscle:Zero insulin makes you hypermetabolic and increases amino acid oxidation.  Insulin cures this, but again, it appears to be driven by hyperglucagonemia, not insulin deficiency.

Glucagon directly correlates with energy expenditure, and this isn’t the good metabolic rate boost sought by dieters, it’s the type that indiscriminately burns everything including muscle.  High protein diets also increase energy expenditure, but in pathological hyperglucagonemia, the amino acids come from muscle, not food.

The above mentioned study is most relevant to type I diabetes.  The following study is about glucagon and the far more common type II diabetes (Petersen and Sullivan, 2001 Diabetologia).

The effects of hyperglucagonemia can be blunted by glucagon receptor antagonists (GRAs).  In the figure below, a GRA (Bay-27-9955), was administered immediately prior to a glucagon infusion.  The GRA significantly reduced blood glucose levels, an effect largely attributed to the reduction in endogenous glucose production:One of the ways GRA’s accomplish this is by keeping glucose tied up in hepatic glycogen instead of flooding into the plasma (Qureshi et al., 2004 Diabetes; “CPD” is the GRA used in this study).  The figure on the left is primary human hepatocytes; on the left is in mice.Another way of looking at this is in mice chronically treated with glucagon or glucagon plus a GRA.  Glucose tolerance is obviously deteriorated by glucagon treatment, but is completely restored by a GRA (Li et al., 2008 Clinical Science):

One of the most severe side effects of diabetic hyperglycemia is nephropathy, which is similarly cured by GRA treatment:

The physiological role of glucagon is to prevent hypOglycemia; but hypERglycemia is the problem most of the time.  Don’t get me wrong, hypOglycemia can be deadly, but 1) it’s not nearly as prevalent as hypERglycemia, and 2) inhibiting glucagon doesn’t cause hypoglycemia, there are a battery of counterregulatory hormones that prevent hypoglycemia.

Furthermore, reducing glucagon action isn’t limited to glucagon receptor antagonists (GRAs), leptin and amylin can do it too!

And while gastric bypass surgery is easily more extreme than GRA’s and leptin or amylin therapy, it’s magical effect on diabetes remission might also be partly attributed to glucagon suppression (Umeda et al., 2011 Obesity Surgery):

Convinced yet?

 

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USDA vs. nutrition, round II

The school lunch program is screwed.

First the USDA modifies the definition of a vegetable to include pizza.  Now they significantly altered their standards for school lunches to include fewer healthy foods and more USDA-approved ones (see report at the USDA’s website).  In brief, this move further reduces the nutrition of school lunches and will likely do more harm than good.  Here’s why:

In this cross-sectional Swedish study, parents recorded 7-day food diaries for their 4-year old children who then went in for a regular checkup.

Metabolic markers in relation to nutrition and growth in healthy 4-y-old children in Sweden (Garemo et al., 2006 AJCN)

On a 1,400 kcalorie diet, these children were consuming roughly 15% protein, 33% fat, and 52% carbs (about 20% of which came from sucrose).  That seems like a lot of calories, but besides playing all day, 4 year old children are also growing at an incredible rate.

Interesting finding numbers 1 & 2:  Children who got most of their calories from fat had the lowest BMI (i.e., they were the leanest), and the opposite was observed for carbs.

When divided into groups of normal weight vs. overweight and obese, some interesting and non-intuitive patterns emerged.  For example, lean kids don’t eat less food; but they do eat fewer carbs and less sucrose (and make up the difference by eating more fat and saturated fat).

Some of the weaker correlations showed:
-total calorie intake was associated with growth (logical)
-total carbohydrate intake was associated with increased fat mass (unfortunate yet also logical)
-total fat intake was associated with decreased fat mass (interesting)

And those who ate the most saturated fat had the least amount of excess body fat. (more on this below)

Fortunately, in a young child, a poor diet hasn’t had enough time to significantly impact their metabolic health; as such no macronutrient was associated, either positively or negatively, with insulin resistance [yet].

In a more appropriately titled follow-up, Swedish pre-school children eat too much junk food and sucrose (Garemo et al., 2007 Acta Paediatrica), Garemo reported that most of their carbs came from bread, cakes, and cookies, while most of the sucrose came from fruit, juices, jam, soft drinks, and sweets.  And WOW, go figure- most of the fat came from meat, chicken, sausage, liver, eggs, and dairy; NOT vegetable oils.

And in a mammoth dissertation, Eriksson (2009) confirmed many of these findings in a larger cohort of 8-year old Swedish children and had this to say about dairy fat:

The open boxes represent overweight kids, the closed boxes are lean kids.  Going from left to right, in either the open or closed boxes, BMI declines with increasing intake of full fat milk (perhaps parents should reconsider skim milk?).  Eriksson also confirmed that saturated fat intake was strongly associated with reduced body weight.  Interestingly, she mentioned that food intake patterns are established early in life, so it might be prudent to remove sugars and other nutrient poor carb-rich foods, and introduce nutritious whole foods as early as possible.  I’m not exactly sure how she assessed patterns of food intake establishment, but it seems logical.  Especially in light of the following study… we’ve seen 4 year olds, 8 year olds, and now we have 12-19 year olds.  The relationship between diet and health is consistent across all age groups.

Virtually all of the above data in Swedish children seem to suggest dietary saturated fat, whether it’s from beef, sausage, eggs, whole fat dairy, or liver (i.e., WHOLE food sources; NOT hydrogenated vegetable oils), is associated with reduced fat mass.  Metabolic abnormalities were not present, probably because the children were simply too young (although body weight seems to respond relatively quickly, other downstream effects of poor nutrition take years to accumulate before symptoms develop).

An American study about nutrient density and metabolic syndrome was recently published.  These kids were exposed to poor nutrition for just long enough to experience some of those malevolent effects.

Dietary fiber and nutrient density are inversely associated with the metabolic syndrome in US adolescents (Carlson et al., 2011 Journal of the American Dietetic Association)

The figure below divides fiber (a proxy for good nutrition; i.e., leafy vegetables, beans, etc.) and saturated fat into groups of least and most amounts comsumed. The lowest fiber intake was 2.9 grams for every 1,000 kcal, and 9.3% of these kids already had metabolic syndrome; the highest fiber intake was 10.7 grams / 1,000 kcal and 3.2% had metabolic syndrome.  Thus, consuming a fiber-rich [nutrient dense] diet is associated with a significantly reduced risk of metabolic syndrome.

The next rows are saturated fat.  The lowest saturated fat intake was 6.9 grams / 1,000 kcal and 7.2% had metabolic syndrome; the highest saturated fat intake was 18 grams / 1,000 kcal and 6.7% had metabolic syndrome…. huh?  While it didn’t reach statistical significance, the trend for saturated fat paralleled that of a “nutrient dense” diet.  Is it possible that saturated fat might be part of a nutrient dense diet?   if saturated fat comes in the form of red meat, liver, eggs, etc., then yes, it is part of a nutrient dense diet.  This conclusion evaded both the study authors and the media.

In 4 and 8 year old Swedish children, those who ate the most saturated fat had the least excess fat mass.  In 12 – 19 year old American adolescents, those who ate the most saturated fat had the lowest risk for metabolic syndrome.

Is it too much of a stretch to connect these ideas by saying that in the short run, a low saturated fat (nutrient poor, carb-rich) diet predisposes to obesity; and in the long run it predisposes to metabolic syndrome  ???

Collectively, these data suggest a diet based on whole foods like meat and eggs, including animal fats, with nutrient dense sources of fiber (e.g., leafy vegetables) but without a lot of nutrient poor carb-rich or high sugar foods, may be the healthiest diet for children.  

Flashback: recap of “USDA vs. nutrition, round I”
USDA: 1
Nutrition: 0
They made pizza a vegetable and insiders suspect that next they’ll try to make it a vitamin.

USDA vs. nutrition, round II

USDA: replacing normal milk with low fat milk
nutrition: full-fat milk was associated with lower BMI in both lean and obese children (see the Eriksson figure above)

USDA: increasing nutrient poor carb-rich options
nutrition: this was associated with increased fat mass in children (Garumen et al., see figures above)

USDA: reducing saturated fat as much as possible
nutrition: reduced saturated fat was associated with excess fat mass in children and metabolic syndrome in adolescents.

Such changes will have an immeasurable long-term impact if children grow up thinking these are healthy options.  Finally, this blog post does not contain a comprehensive analysis of saturated fat intake and health outcomes in children, but the USDA’s new regulations should have been accompanied by one.  In other words, these regulations should not have been based on the studies discussed above, but the studies discussed above should have been considered when the USDA was crafting their recommendations.  Obviously, they weren’t.

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Holiday feasts, the freshman 15, and damage control

Holiday feasts, the freshman 15, and damage control, Op. 54

overeating ANYthing is a bad idea.  But as demonstrated in this recent study, WHAT you overeat has a big effect on how your body responds.  The overfeeding protocol studied was pretty intense, ~1000 excess kilocalories per day for 8 weeks.

Effect of dietary protein content on weight gain, energy expenditure, and body composition during overeating (Bray et al., 2011 JAMA) Healthy people where fed hypercaloric low, medium, or high protein diets.  It’s impossible to isocalorically change one macronutrient without inadvertently changing the others.  With regard to study design, this is always a tough decision, and in this study they exchanged protein for fat:

Divide and conquer

As seen in the monster-table above or simplified table below, the high protein group gained the most weight despite eating no more than the other groups; but this weight was comprised of significantly more lean body mass than in any other group. 

High protein dieters also expended more energy but still gained more weight!  Importantly, however, much of that weight was muscle.  The increase in energy expenditure is likely due to dietary protein-specific effects: 1) high metabolic cost of increased protein turnover, 2) elevated metabolic rate associated with more muscle, and 3) increased diet-induced thermogenesis.  The low protein group, on the other hand, lost muscle and gained more fat than any other group.

an aside: the energy expenditure measurements taken during overfeeding should be taken with a grain of salt, shot of tequila, and suck of a lemon because the accuracy of such measurements usually require weight-stable conditions; overfed subjects were gaining weight and in positive energy balance.  In other words, the assumptions required for doubly-labeled water to assess energy expenditure during weight-stable conditions are likely not met during weight gain (which is further complicated by the fact that the different groups were gaining different types of body weight [fat vs. fat-free mass]).  But the body composition data are probably OK (see below).

Furthermore, while it may seem like the Laws of Energy Balance were violated in this study, I assure you, they were not.  This study was not designed to test them, as evidenced by the author’s failure to conduct a comprehensive assessment of energy balance.

The high monetary cost of high protein foods (e.g., steak) is matched by the high energetic cost of their assimilation.  By increasing protein intake, energy expenditure rises in parallel.  This is most likely due to a combination of factors (mentioned above), and the result, at least in this study, is increased lean body mass.   The low protein diet, on the other hand, didn’t increase energy expenditure and resulted in more fat gain.  N.B. the absolute amount of protein consumed by the low protein group (47 grams) was too low to maintain muscle despite ingesting 40% more total calories.  In other words, the low protein dieters actually lost muscle mass while gaining fat!!

conclusions

1. THE media always screws up things (no thanks to Dr. Bray’s discussion).  The headlines should’ve read: “Dietary protein increases lean body mass more than total calories increase fat mass.”  That headline would’ve taken the focus away from the calorie debate by highlighting an important macronutrient effect.  This is important, IMHO, because body composition is a very important factor determining metabolic outcomes and quality of life, and is often overlooked (e.g., BMI).

2. While excess calories are necessary to increase lean body mass, excess protein has little effect on fat mass.  “Excess protein has little effect on fat mass” would’ve been another great headline.  But it wasn’t.

Most of the excess energy consumed by the low protein dieters was stored as fat, while in the high protein dieters it was invested in muscle and burned off.  Although it’s a little too late to prevent holiday feast-induced weight gain (or the freshman 15 for that matter) these data suggest that whenever possible, filling up on the highest protein foods available will cause the least fat gain.  Increased dietary protein -> increased lean body mass –> increased metabolic rate (you burn more fat in your sleep!)

Dietary protein doesn’t require a prescription and is a potent nutrition partitioning agent.  But as mentioned above, WRT energy balance, this study was not perfect.  So, why do I believe the effects of dietary protein are true despite the methodological flaws in Dr. Bray’s assessment of energy balance?  Because they are consistent in a variety of conditions.  For example, the remarkable effects of a high protein diet on body composition prevail even during underfeeding (aka going on a diet), a completely opposite paradigm.

Skov and colleagues tested hypocaloric high vs. low protein diets for 26 weeks and confirmed that even during negative energy balance, dietary protein favors lean body mass at the expense of fat mass (Skov et al., 1999 International Journal of Obesity)

And similar results, albeit less robust due to the shorter duration, were found in a study by Layman and colleagues in as few as 10 weeks (Layman et al., 2003 Journal of Nutrition)

During overfeeding, high protein diets cause greater increases in lean body mass and energy expenditure, and prevent excess fat accumulation relative to low protein diets.  During underfeeding, high protein diets lead to a greater retention of lean body mass and more fat loss.  Nutrient partitioning 101. All calories are not created equal.

 

or


??

 

 

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Insulin is a double-edged sword with a pointy tip

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

 

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Body Mass Index, Op. 42

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.”

 

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the great difficulties of weight loss

As usual, the media buzz and author’s own interpretations are inaccurate, exaggerated, or downright bizarre, but the study was fairly well-executed so it isn’t without a few novel insights.  Actually, some of their findings are quite interesting.

Long-term persistence of hormonal adaptations to weight loss (Sumithran et al., 2011 NEJM)

50 obese patients underwent an intensive ultra-low calorie diet to lose ~15% of their body weight in 10 weeks, and returned one year later for a battery of testing.  Two common problems with this type of dietary interventions studies are 1) failure to achieve significant weight loss, and 2) weight re-gain.

The first problem was solved by removing all food-based decisions by providing the subjects with a nutritionally adequate liquid diet (Optifast VLCD, Nestle; nutrition information).  By nutritionally adequate, I am specifically referring to vitamins and minerals…  the calories in Optifast VLCD (150 kcal tid) are comprised of 46% protein, 14% fat, 39% carbs… 24% of the total calories come from sugar.  This plus 2 cups of “low-starch vegetables” is all the subjects consumed during the weight loss period.  The macronutrient ratios extreme hypocaloric level is incompatible with anything normal (e.g., the Minnesota Starvation Studies).  So while this is not a recommended weight loss strategy (not viable for the long-term, horrible side effects due to fatty acid deficiency, etc.), it is certainly effective.

The second problem was solved by [brilliantly] excluding data from participants who dropped out or who failed to maintain the weight loss.  Also known as a “completer’s analysis,” this is the bane of dietitians, many of whom prefer the “intention to treat” (ITT) analysis.  ITT includes data from every subject who began the intervention and is justified because it is said to reflect what would actually happen to a group of real-life patients.  I usually dislike ITT because it considerably dilutes the actual effects of the intervention with data from subjects who didn’t complete the intervention.  In this study, ITT is particularly inappropriate because the authors wanted to see the effects of long-term adaptations to weight loss; if the patients dropped out because of inadequate weight loss, then their biochemical variables do not reflect long-term adaptations to weight loss, which was the whole point of the study.  In more complicated cases, dropouts aren’t random, so the results may be restricted to a very specific mystery group of people (i.e., NOT the people you to which you think they apply).  Thus, the second problem was solved by the author’s choice of statistical analysis.

Divide and conquer

Figure 1.

They started out at 96.3 kg (~212 lbs) with 51.6% body fat (FTR, that is a LOT of excess fat mass) and lost 13.5% of their initial body weight during the 10-week weight intervention (pretty good for diet alone), and gained half back by the end of 1 year (disappointing but common).

#1. The authors stressed throughout the entire manuscript that the subjects were weight-stable at a reduced body weight for the subsequent year; it was built-in to the intervention (as described in the Methods section), and it was consistently referenced in the discussion.  However, according to Figure 1, this is horribly incorrect.  In fact, I would say the subjects were in a positive energy balance for the entire year.  This doesn’t mean the study is worthless; it just means that we aren’t talking about people who lost 30 pounds and kept it off.

#2a.  These subjects were 56 ± 10 years old and probably spent a few decades with their excess adiposity.  Forgetting about point 1 (above) for the moment, 1 year in a weight-reduced state is far from “long-term,” relative to the amount of time they were heavier.  If someone has 50 excess pounds of fat mass for 25 years, do you expect everything to go back to normal after a year at a slightly lower body weight?  No.  It is interesting to see what is happening at that time point, but is not what I would consider long-term.  I’d say most of their biochemical indices reflect the preceding 25 years, not the past year.  Are there permanent metabolic derangements in weight-reduced people?  Perhaps, but I don’t think we are seeing what the authors claim to be showing us.

#2b. WRT point #1, obesity doesn’t happen overnight.  It happens over years of maintaining a positive energy balance.  Thus, these subjects were in a positive energy balance for a long time, then underwent 10 weeks of energy deficit-induced weight loss, then returned to a positive energy balance.   With that in mind, these data hardly reflect “long-term” adaptations to weight loss.

Not many data were presented.

 

Interesting finding #1:

 

These data confirm my critique in point #2 (above), i.e., the subjects were not weight stable.  Their pre-diabetic state (glucose ~5.9) was fully recovered, albeit at a lower body weight (88.3 vs. 96.3 kg).  This is not a good thing.  If the subjects were stable at a reduced body weight, then their fasting glucose would have remained low.  Actually, I think these data support the yo-yo dieting theory; these subjects will be more insulin resistant when they returned to their normal body weight than they were at the beginning of the study… Indeed, I predict their fasting insulin will exceed 17.7 mU and glucose 5.9 mM when their body weight [inevitably] fully recovers, unfortunately.

Interesting finding #2, it doesn’t look like adipose insulin sensitivity was really affected by the intervention:

 

Non-esterified fatty acids (plasma free fatty acid levels, “NEFA”) moved inversely with insulin, to a tee.  This probably supports the notion that adipose insulin sensitivity is normal in obese subjects prior to diabetes.  And these were obese but otherwise relatively healthy subjects, probably nowhere near frank diabetes.

Here is where the author’s data interpretation starts to go off-the-wall.

 

Leptin is secreted from fat cells to signal the brain that energy stores are full.  The authors claim leptin, an appetite-suppressing hormone, is still excessively reduced in the weight-reduced 1 full year after weight loss.  This is would be predicted to elevate hunger levels and drive weight regain.  The media buzz jumped all over this, in agreement with the author’s own interpretation, and said this is one of the reasons why so many dieters fail.  However, I would argue that 1) leptin was highest at baseline (when fat mass was the highest), 2) leptin was lowest at week 10 (when fat mass was lowest), and 3) leptin was intermediate at week 62 (when fat mass was intermediate).  Thus, leptin was properly regulated.  Furthermore, as leptin is correlated with fat mass, leptin shouldn’t return to baseline levels until fat mass returns to baseline levels.  Leptin 101.  They should be experiencing an intermediate level of hunger at week 62.

But alas, this is not happening.  The authors performed a battery of psychological tests to assess post-weight loss appetite.  Although psychology is not my forte, these data seem straight forward and extremely important:

 

“Hunger” is increased by week 10, exactly as expected for subjects that just lost 14% of their body weight in 10 weeks on a semi-starvation diet.  But even after they’ve regained half of the lost weight, they’re still just as hungry.  And the “urge to eat” is even starting to decline.  So it appears that they are adapting quite well.  Extremely well, in fact.  They spent 20 years overeating to maintain a huge amount of excess fat mass and in 1 short year their appetite is already starting to adjust to match their lower body weight.

The lower leptin levels immediately after weight loss, at week 10, reflect the starvation response and most likely had something to do with their increased hunger levels.  But the authors noted there was no correlation between hunger and the degree to which leptin declined.  In other words, if two people both lost exactly 14% of their body weight, and one person’s leptin dropped half more than the other, they weren’t half hungrier, meaning leptin isn’t exactly related to appetite.  Furthermore, and of utter importance, leptin levels didn’t correlate with weight regain; people who were hungrier were no more likely than anyone else to regain weight.  Everybody gets hungry after they lose weight; they are not more or less disadvantaged than others because of dysregulated hormones- the hormones and hunger responses were intact.  They may have even been adjusting to facilitate maintenance of a lower body weight, but this is not a “cool” conclusion, so it wasn’t entertained by the authors and certainly not by the media.

A speculative pearl: perhaps the markedly less hunger than expected based on the lower leptin levels is due, in part, to the lower insulin and free fatty acids.  These subjects were tapping into their stored fat, which may have compensated for the reduced energy intake.

I’m not saying that a 210 pound person can eat just as much as a 180 pound person if they too want to weigh 180 pounds.  No, depending on how fast they want to lose weight, they might need to eat less or markedly differently from their current diet.  But to consider that a disadvantage is backwards.  For a person to go from 180 to 210 pounds they ate more than a 210 pound person (it takes a lot of additional energy to lay down all that excess fat mass! … a lot of it just burns off).  Is that an advantage?  It is as much of an advantage as the disadvantage of a lower metabolic rate in weight-reduced people.  The data simply don’t tell a sad story about how hard it is to lose weight, they tell a clear story about energy balance.  The efficiency of investing excess energy from overeating in fat mass is matched during weight loss. It might not be “easy,” but the deck isn’t unfairly stacked.  They regained weight not because they were hormonally hungrier, they most likely regained it for the same reason they had it in the first place.

 

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