Monthly Archives: November 2011

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.

 

 

calories proper

 

 

 

pizza on the docket

they’re all crooks!

or

a slice of pizza does not count as a serving of vegetables. Period.

not the worst thing for you, really just a bunch of empty calories.  definitely NOT a serving of vegetables.

The government-sponsored school lunch program is designed to provide nutrition and improve the health of our children.  And they get around 11 billion dollars (i.e., $11,000,000,000) every year to do so.  Due to the recent surge in obesity, Congress acted fast!  School lunch programs do not closely follow the dietary guidelines.  To us taxpaying voters, $11,000,000,000 of our taxes are being wasted AND our kids are suffering.   Therefore, Congress quickly changed the status of pizza to “vegetable.”  Many schools serve pizza, and thus are now more closely in line with the dietary guidelines; so our taxes are being less-wasted and our children are healthier because they are eating more vegetables! To be clear: now that pizza is a vegetable, your children are healthier.

You can’t make this shit up – it is what happens when government gets involved in nutrition.  Please, ignore the Dietary Guidelines, they are horribly misguided.  And be extremely wary of electing anyone who wants to control nutrition; or vote with your dollars, don’t buy processed food!  The message is almost always wrong and both our bank accounts and our health suffer the consequences.  I would suggest supporting nutrition education programs, but NOT IF THEY SAY PIZZA IS A VEGETABLE.  If anything, a slice of pizza should count as dessert plus 3 servings of grains :/

Isn’t it bad enough that French fries, or crisps, count as vegetables?

Admittedly, claiming “the Dietary Guidelines are horribly misguided” is a strong statement, especially when said guidelines direct how a portion of our taxes are spent AND which foods are made available to our children.  This is important.

 

calories proper

 

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Prelude to a crossover, part deux

Prelude to a crossover, part deux

The anatomy of a washout, for better or worse.

 

In blue represents the baseline data.  On the left are the subjects and their body weight prior to randomization.  At baseline in phase I, we can see that the randomization wasn’t perfect, but that doesn’t really matter so much because this is a CROSSOVER study.  Note the group who is assigned to receive active drug first weighs slightly less than those assigned to placebo (98 vs. 102 kg).

The drug causes a 10 kg weight loss and there is no relevant placebo effect.

After a treatment-appropriate washout period, we are back again at baseline but this time for phase II.  Note the body weight of subjects 1-3 at the end of phase I (89, 88, and 87 kg) has returned to normal.  Now subjects 4-6 get the active treatment and experience a similar outcome.  The final summary appears in the column on the right: even though randomization at baseline was imperfect, the differences were crushed by the superiority of the crossover design, and we see the true drug effect regardless of whether we are comparing drug to baseline OR drug to placebo.  Voila, Mucho gusto, and Kudos

 

Take II.

Everything from baseline until the end of phase I is identical to the above example.  BUT the washout period is inadequate and the group who received active drug during phase I (subjects 1-3) has not returned to baseline and thus exhibits treatment-specific spillover effects.  Subjects 1-3 are at an artificially lower body weight for the baseline measurements of phase II, so the total baseline data are reduced (97.5 kg vs. 100 kg).  Now we get a different answer if we compare drug to baseline or drug to placebo.  This example illustrates one small error, but it is grievous.  Larger errors are made, and they are worse.  at one end of the spectrum, livelihoods and intellectual progress depend on the accuracy of these data.  be prescribed a sub-optimal medication, prescribe a wrong medication, waste time, etc., etc.  failing to account for a particular confounding variable and carelessly (or otherwise) using an improper statistical technique are two very different errs.  (end soapbox diatribe).

 

 

calories proper

 

QLSCD II (or Grains IV)

WRT the Quebec Longitudinal Study of Child Development (QLSCD), I failed to adequately emphasize one major implication of their findings.  It is a point that completely and wholly illustrates the disconnect from data, empirical science, and all common sense exhibited by mainstream beliefs in calories and dieting.   gravitas

Higher intakes of energy and grain products at 4 years of age are associated with being overweight at 6 years of age (Dubois, Porcherie et al., 2011 Journal of Nutrition)

Divide and conquer

Exhibit A

 

The table above shows the percentage of underweight, normal weight, and overweight children consuming the recommended number of servings for each food group.  15.5% of underweight children, 19.1% of normal weight children, and 42.6% of overweight children meet the recommended ?5 servings of grains per day.  Grains comprise [sic]: “breads, pastas, cereals, rice, and other grains”

There is a direct relationship between body weight and the percentage of children consuming ?5 servings of grains per day, i.e., more grains equals greater chance of being overweight.

Exhibit B

 

This table shows the odds for being overweight at 6 years of age in increasing quintiles of how many calories consumed daily two years earlier.  The crude odds risk (first column) shows a poor relationship between calorie intake at 4 years old and risk of being overweight 2 years later.  I say “poor” because the risk is non-significantly lower in the second quintile, higher in the third, lower in the fourth, but much higher in the fifth quintile (3.15x more likely to be overweight for the biggest eaters compared to the littlest eaters).  These data are unadjusted and could be confounded by a variety of factors.  Thus, the significance level of the trend is high p=0.0007.

The second column is similar to the first, but is adjusted for many known confounders: birth weight, physical activity, mother’s smoking status during pregnancy, annual household income, and number of above normal weight parents.  As such, the degree of statistical significance was reduced from 0.0007 to 0.001.

The third and most important column is further adjusted for body weight at 4 years of age, and shows that calorie intake is no longer associated with body weight at 6 years of age.  In other words, being overweight at 4 years old predicted being overweight at 6 years old better than calorie intake (and physical activity).

In the authors’ own words [sic]: “The only food group significantly related to overweight was grains.”  No association was observed for overweight risk with vegetables and fruits, milk products, or meat and alternatives.

IMHO, the observation that being overweight at 4 years old was the best predictor for being overweight 2 years later is remarkable… body weight status at 4 years old is a more important risk factor than both physical activity and calorie intake.  The only ‘controllable’ variable  is grains; i.e., you can’t change whether or not your child was overweight at 4 years of age, and physical activity and calorie intake doesn’t matter.  But grain consumption seems to matter, and it is something that can be controlled.

What is it about grains?  I don’t know, exactly, but it’s not simply that they’re carbohydrates because elevated carbohydrate intake didn’t increase risk for being overweight.

Exhibit C

 

 

“Eating less and moving more” is not the answer.  Nutrition matters, not the guidelines.

 

calories proper

 

 

Prelude to a crossover study I

A well-designed but poorly executed crossover study is always lamentable, but never so much as when it was intending to test an interesting hypothesis, in a human population.

Enter: the crossover study.

IMHO, a crossover is the superior human study design.  When properly executed, crossover study data are straight-forward, lack confounding, and the interpretation benefits from a reductionist simplicity that approaches that of an animal study.

In brief, a cohort is randomly divided into two subgroups, half receive active drug and half get placebo for the first treatment period, then after a brief washout the groups switch and receive the opposite treatment.

The subjects never know if they are receiving active drug or placebo, which prevents the placebo effect, but more importantly each subject actually receives both treatments, active drug and placebo.  So we get to compare how they respond to drug with how they respond to placebo.  Although it is expensive and labor intensive, the crossover study design provides great statistical power with a relatively small sample size.  I am far more likely to accept crossover data at face value, because given a treatment-appropriate washout period, confounders are negligible.

With less brevity, as seen in the table below, a simple crossover study consists of two treatment phases divided by a washout period.

 

 

The most important, critical times to acquire data are

1)      Baselines: immediately prior to phase I (1a and 1b) and II (1c and 1d).  1a and 1b are averaged together because they represent baseline subject characteristics before any treatment.  Importantly, after the washout period time points 1c and 1d represent an identical scenario and are included as baseline subject characteristics.  E.g., baseline characteristics for subject 1 will be an average of measurements made at time points 1a and 1c.  The same goes for subjects 2-10, and all these values are averaged together and represent the baseline.  IOW, there is ONE set of baseline data that includes EVERYONE.

2)      Active drug finals: data taken immediately following active drug treatment periods (2a and 2d) are combined and represent drug effects.

3)      Placebo finals: data taken immediately following placebo treatment periods (2b and 2c) are combined and represent placebo effects.

 

The relevant comparisons are:

1)      Final values: active drug vs. placebo.  These are the most common and usually most relevant data reported.

2)      Final vs. baseline

  1. Placebo: any differences between the final placebo time points (2b and 2c) and baseline are bona fide placebo effects
  2. Drug: any differences between final drug time points (2a and 2d) and baseline roughly correlate with drug effects, but need to be compared with placebo effects to determine the relative contribution of each component.

A treatment-appropriate washout period is necessary to minimize any spillover effects.  For example, take a 100 kg subject in a weight loss trial who is randomized to receive active drug for phase I and loses 10 kg.  After phase I they will likely regain the lost weight, and this should be complete prior to phase II.  If not, baseline body weight will be artificially low, and the “placebo effect” will appear to be weight gain, enhancing the apparent benefits of the weight loss treatment in question.  The effects of improper washout periods are difficult to predict, but clearly don’t improve accuracy.  To further the point with an exaggerated extreme example: a drug that does nothing would look great against a placebo that caused weight gain.

In conclusion, it’s hard to mess up a crossover study short of grievous errors, but they happen.  Alternatively, some treatment effects may be attenuated by a rigorously designed and executed crossover study, but they are rarely exaggerated, which I believe is the better side with which to err.

 

 

calories proper