In 2012, the Journal of the American Medical Association (JAMA) published a study entitled Effects of Dietary Composition on Energy Expenditure During Weight-Loss Maintenance. While I’m guessing most readers have not read this study, I’m pretty sure most of you have heard about the results as it was all over the news this week.
I was fortunate enough to read an embargoed copy two weeks prior to publication with the caveat that I could not speak about it until it was released publicly. Furthermore, I’m friends with one of the reviewers who told me months prior that “a very interesting paper was going to hit a high–impact factor journal very soon.” Completing my disclosure, I had become acquainted with the senior investigator on this study, Dr. David Ludwig at Harvard.
This study sought to test an important question:
When an overweight or obese person loses weight, how does their choice of macronutrients impact their tendency to regain lost weight?
This is important, of course, because as most of us know that while losing weight is difficult, keeping it off is even more difficult. In fact, as the authors point out, only about 15% of people who lose 10% of their bodyweight can maintain the weight loss for up to one year. The obvious question is why?
You’ll recall from this post, that we must always obey the First Law of Thermodynamics. In other words, we accumulate stored energy (e.g., fat mass) when we are in a positive energy balance and we lose stored energy when we are in a negative energy balance.
Energy balance is a function of two variables:
- Energy input – what we eat
- Energy output – what we expend
Furthermore, energy output can be broken down into four sub-components:
- Resting energy expenditure (REE) – the amount of energy expended to stay alive at rest (e.g., energy required for basic cellular function like ion transport and respiration)
- Thermic effect of food (TEF) – the amount of energy required to process and digest food (I also include in this category the amount of energy lost as undigested material in stool)
- Activity energy expenditure (AEE) – the amount of energy expended by exercise and non-exercise movement (I consider these as two forms of expenditure)
The sum of REE, TEF, and AEE is called, appropriately, total energy expenditure (TEE).
Of these, REE is the dominant “sink” of energy output in most people, and it is generally proportional to bodyweight. I’ll cover the importance of this momentarily.
The traditional model of obesity, the so called “calories-in-calories-out” model, says that obesity is caused by the energy input terms exceeding the energy output terms. In the words of one prominent obesity researcher, “While it is mathematically true that someone who has gained weight has consumed more energy than they have expended, using the First Law to explain why someone gains weight is of little help. The First Law is descriptive but not explanative.”
I couldn’t have said that better myself. The mistake most folks make when using the First Law to explain weight gain (versus using the First Law to describe weight gain) is that they lose sight of the fact that these variables – input, REE, TEF, AEE – are linked. They are dependent on each other. They don’t exist in isolation.
Proponents of the Alternative Hypothesis argue that intake (i.e., food) plays a role on hormones and enzymes in the body that have a resulting impact on energy output, and even subsequent input. For example, eating one food over another can increase or decrease appetite, increase or decrease REE, increase or decrease AEE, and even impact TEF. While the effect on each of these may be modest in isolation, even small changes over the course of days can result in significant changes over months or years.
What does all of this have to do with this study?
The figure below shows how the study was conducted. This was a prospective design 3-way crossover study of 21 overweight or obese subjects with an average BMI of 34.4. Each subject underwent a 20-week run-in phase, which is very common in weight-reduced studies. During the run-in phase all baseline measurements are collected, including body composition by DEXA, TEE by doubly-labeled water, substrate utilization by respiratory quotient, and plasma levels of various blood markers (e.g., lipids, blood chemistry, hormone levels). No, unfortunately, lipoprotein particles were not counted.
During the 12 week weight loss phase caloric intake was reduced until each subject lost 12.5% of their starting (stable) weight. For the final 4 weeks of the run-in phase energy intake was again calibrated to hold their now-reduced-weight stable.
The figure below summarizes the data from Table 1 of the paper, showing the breakdown of macronutrients during the run-in phase and the subsequent 3 dietary interventions, each lasting 4 weeks. Again, each subject did each diet for 4 weeks due to the 3-way cross-over design. In other words, each subject spent a total of 32 weeks in the study (20 weeks of run-in and 3 x 4 weeks of each intervention diet).
How did the diets impact energy expenditure?
The figure below shows the change in REE and TEE measured for all groups. There was no difference in total physical activity or exercise, so presumably there was no appreciable change in AEE. I could not find a mention of TEF, suggesting it was not measured. These figures are a bit ugly, but they convey helpful information. Each dot represents an individual subject and the lines joining each dot allow you to see the change for each subject across the 3 diets. The blue box shows the mean change (middle of the box) with the 95% confidence interval above and below. The height of the box is therefore two standard deviations.
A few things stand out from these results:
- The group consuming a very low carbohydrate diet had a higher REE and TEE than the low GI group, which had a higher REE and TEE than the low fat group. In other words, the fewer carbohydrates in the diet, the higher the resting and overall expenditure. This is actually the sine qua non of the alternative hypothesis: something beyond the actual number of calories is playing a role in how the body expends energy.
- As expected, given that each subject was starting from a weight-reduced state, the REE was lower for each group, relative to their baseline. REE is highly (though clearly not entirely) dependent on body mass.
- There is enormous variation between subjects by diet type. For example, at least one subject saw a dramatic increase in TEE on the low GI diet versus the other two, while another saw the greatest TEE on the low fat diet. This speaks to a theme I iterate on this blog: be willing to self-experiment until you find what works for you.
How did the diets impact metabolic parameters?
The table below shows the changes in hormone levels and metabolic syndrome biomarkers.
One thing that really jumped out at me was that it is quite likely that not one of the subjects in the study met the formal criteria for metabolic syndrome. MS requires at least 3 of the 5 parameters (blood pressure, waist girth, fasting glucose, HDL-C, and TG) exceed threshold. The thresholds are as follows:
- BP > 140/90 [No subject met this at baseline]
- Waist girth > 40 inches (men), >35 inches [Not reported, but let’s assume at least some subjects met this]
- Fasting glucose > 100 mg/dL [Not reported, but let’s assume at least some subjects met this]
- Fasting TG > 150 mg/dL [No subject met this at baseline]
- HDL-C < 40 mg/dL (men), < 50 mg/dL [No male subject met this, but it’s possible some female subjects did]
This may speak to the age of the subjects, which averaged 30.3 years, but I would have expected a worse set of baseline metabolic parameters. It also speaks to the point that just because someone is obese doesn’t mean they have metabolic syndrome and vice versa.
Tables are a bit cumbersome, so I took the liberty of graphing some of these results, mostly because I just can’t resist playing with think-cell (PowerPoint without think-cell is simply a tool for torturing people.)
I’ve explained p-values before, but let me explain the two types of p-values reported above and below.
P_overall is the p-value testing the hypothesis that the mean outcome of the three diets was equal. The smaller this value, the more likely the differences were not due to chance. As a general rule, if the p-value is greater than 0.05 we say the difference is “not significant.” Most use a more stringent requirement of 0.01 to hit the mark of statistical significance.
P_trend is the p-value testing the hypothesis that the mean outcome of the three diets showed a trend from low fat to low GI to low carbohydrate.
Not surprisingly, the low fat group experienced a significant reduction in HDL-C. It’s been documented many times that dietary fat raises HDL-C and dietary carbohydrates reduce HDL-C.
Each group also experienced a reduction in triglyceride (TG) level. Since we know carbohydrates, not fat, raise TG, you may wonder why this was even the case in the low fat group, which actually increased carbohydrate intake. I suspect it was a carbohydrate “quality” issue. I’m guessing the baseline levels reflect more sugar consumption than the low fat phase. Nevertheless, and again not surprisingly, the high fat-low carbohydrate group experienced the greatest improvement in plasma TG levels.
Insulin sensitivity was measured according to a protocol in this paper. The protocol uses time blood draws after an oral glucose challenge. The higher the index, the greater is the insulin sensitivity. Each diet improved both hepatic and peripheral insulin sensitivity and both the overall differences and the trends were significant.
If insulin is the most important hormone regulating fat metabolism and accumulation, leptin is certainly a close cousin. Leptin is a hormone secreted by fat cells that plays an important role in regulating appetite and some metabolic functions. High levels of circulating leptin can be suggestive of leptin resistance which, like insulin resistance, tends to be a marker for metabolic derangement. I’ll write a lot about leptin in subsequent posts. While leptin sensitivity was not directly measured as insulin sensitivity was, the significant reduction in circulating leptin levels suggested it was also improved in all groups, though greatest in the low carbohydrate group.
How did the diets impact inflammation?
Two markers for inflammation were evaluated in this study, C-reactive protein (CRP) and plasminogen activator inhibitor-1 (PAI-1). Neither is particularly sensitive in the way, say, Lp-PLA2 is (this was discussed in the cholesterol series). Nevertheless, they give us some indication of how much overall inflammation exists in the body.
Each group experienced a significant decline in both PAI-1 and CRP, and there was no significant difference between the groups for either marker. However, the trend was (barely) significant favoring the low carbohydrate group for PAI-1 and favoring the low GI group for CRP. Sorry low fat, you didn’t win either.
I know what you’re thinking because it was the first thought that ran through my mind when I saw this table: What? Is this meaningful or is it an example of statistical chicanery? I’ll let you decide, but I’m pretty sure I know the answer.
Because I know some of you will ask, I will comment in a subsequent post on the changes seen in TSH, T3, and cortisol. These topics deserve a post of their own.
What should have been taken away from this study?
This study reproduced a number of results which have been noted for decades:
- Low carbohydrate, high fat diets reduce TG and raise HDL-C more than other diets.
- Low carbohydrate, high fat diets improve insulin sensitivity more than other diets.
It never hurts to hammer those findings home again, but the really dramatic finding of the study was the impact of macronutrient balance on REE and TEE. At previous count (circa 2011), 81 studies over the past 80 years involving 4,094 subjects for more than 1.2 million subject-days have attempted to ask this question – many of them attempting to “prove” that all calories are created equally. While none (i.e., not one) have refuted the alternative hypothesis, most of them had enough methodologic limitations that it was difficult to know for certain if the type of food – rather than the number of calories – was playing an important role.
This study, while still limited (e.g., short duration, small sample size), makes one of the more compelling cases that all calories are not created equally.
What was taken away from this study?
The embargo on this paper was lifted at 4:00 pm EDT on Tuesday, June 26, 2012. Within about 30 minutes I was being bombarded with news stories that, if you hadn’t read the study, as virtually no one actually does, would suggest that the low carbohydrate diet was the “worst” of the three diets tested. This was not universally true, in fairness to the media, but there was no shortage of this sort of commentary:
USA Today
…the authors note a downside to the low-carb diet: it appears to raise some risk factors for heart disease.
Marion Nestle, a nutrition professor at New York University, says longer studies conducted among people in their own environments, not with such controlled meals, have shown “little difference in weight loss and maintenance between one kind of diet and another.”
George Bray, an obesity researcher at Pennington Biomedical Research Center in Baton Rouge who has also studied this topic and who wrote the accompanying editorial in JAMA, says that other studies “show that you can do well on any diet as long as you stick to it. Adherence is the major key for weight loss and maintenance. There is no magic in any diet.”
The New York Times
…the low-carb diet “also had marked problems. It raised levels of CRP (c-reactive protein), which is a measure of chronic inflammation, and cortisol, a hormone that mediates stress.”
The Wall Street Journal
…the low-carb diet had the biggest boost in total energy expenditure, burning about 300 calories more per day than those on the low-fat diet — about the same as an hour of moderate exercise. But that bump came at a cost: increases in cortisol, a stress hormone, and a measure of inflammation called CRP, which can raise the risk of developing heart disease and diabetes.
Some of these comments were patently false (e.g., “the low carbohydrate diet raised levels of CRP” according to the New York Times), reflecting utter incompetence, but most of them commit a different journalistic sin: They miss the forest while machinating on one leaf.
Tragically, most people (unfortunately this includes physicians, dietitians, and politicians) have neither the time nor scientific discipline to wade through these studies and understand their implications. Instead, they rely on “reputable” journalists to translate for them.
Peter, while statins are used to lower cholesterol, we as have learned that LDL-P is more important than cholesterol, so where does this leave statins? Do statins have an impact on LDL-P? Or maybe more importantly, is the best mechanism for lowering high LDL-P the use of statins? Does the HMG-CoA reductase process that produces cholesterol also produce LDL particles? If so, that would be pretty convenient.
Will be addressing in Part X… (hopefully the final installment).
Hey man I enjoy your blog.
about 5 months ago i made a effort to get into better shape. I am 6’1 tall and weighed close to 230lbs. I joined a gym and and cut all sugar (the best I could) out of my diet. I then started lowering my carb intake .In about three months I dropped to 195lbs while continuing to increase my strenght. a Right now I eat only good carbs and consume allot of protein. My goals are to get stronger in the power and olympic lifts. I do HIT training about three times a week and weight training about 4 times a week. Im thinking trying out a ketosis state. I am wondering would ketosis (and the limiting of protein) be smart for my current goals of increasing strength? Or should I keep up a moderate carb count and higher protein count?
Check my posts on ketosis and exercise.
https://www.readability.com/articles/fmeto4wl
NY Times discussing the study.
Joe, “discussing” is putting it mildly. This is called journalistic sloppiness at its finest. Virtually all of it is incorrect, and Ms. Kolata didn’t even fact check it with Dr. Ludwig. Keep an eye open for his response.
Dr. Attia, was referred to your site last night and am now hooked! I was wondering if you were aware of any diminishing-returns effects of low-carbohydrate diets? Weight-loss seems to be quite drastic in those with a waistline of, say, 36-inches, but do you think someone carrying 8lbs of ‘extra’ fat will have difficulty losing it on a diet alone? I know Gary Taubes talks about losing weight without exercising but he doesn’t address this particular issue.
Very tough to say without knowing a lot more, Gustavo. Sure, you’ll typically see a greater response in those with more fat to lose, but this is not a universal truth.
Let’s talk about the statistics for a minute. You mentioned chicanery regarding the CRP and PAI-1 in your post, and you are correct. They’ve done 3 stat tests here: the overall difference test (a necessary, but limited, test – tells you that there are differences across groups, but does not tell you which groups are different from which); if the overall difference test is significant, they did post-hoc pair-wise tests of every possible pair-wise comparison (3 in total: lc v. lg, lc v. lf, lg v. lf), using a Bonferroni adjustment, which is fine; then this trend test. It is not clear how they constructed that trend test, and they do not provide enough info in the paper to judge the validity of the test (this may be obfuscation, or perhaps just lack of attention to detail).
As might be expected, the devil is in the details, more specifically, the footnotes in Table 3. They report the p-value for the overall difference test, and then the pair-wise test results are reported in footnotes b and c. You’ll see that for several outcomes, the low glycemic diet does not differ from either of the other 2, but presumably the low carb and low fat diets do differ. You can verify this by comparing means for a particular diet to the CIs for the other two diets – generally, if the mean for one is *inside* the CI for another, then they are NOT different from each other (statistically; though sometimes, if the numbers are close, you’ll get p-values of 0.03 or 0.04 or so). This is your basic, ANCOVA-type approach for analyzing experimental data – you compare the groups of interest to see if they differ. The trend test seems inappropriate here, even though we don’t really know how it was constructed. Sounds like they tried to force what may have been up to 6 different sequences of diet progression into an artificial, single sequence trend, which doesn’t seem appropriate.
Now lets look at the tests that matter with respect to PAI-1 and CRP: PAI-1 improved in each diet when compared to baseline; however, the groups simply do not differ from each other, despite the borderline significant ‘trend’ effect (which is essentially meaningless without some more information about how it was constructed). You’ll note that the means for each diet all overlap with the CIs for the other 2 diets, and the overall effect is not even significant. Furthermore, CRP did not change relative to baseline (all the diet means are inside the baseline CI), for any of the diets; AND, more importantly, CRP does not differ across the groups, even in terms of the far less stringent overall difference test (p = 0.13). For the authors to call out this effect in the Comment section discussing CRP is kind of silly; for the press to obsess on it is simply ludicrous.
I look forward to your post on cortisol though, because that one did indeed appear to look the worst for low carb.
-Chris
Hi Chris,
Good points, but actually we *can* know exactly how they constructed the trend test and precisely what it is testing (see my comment on this from July 5). The authors state in the paper that they “constructed a test for linear trend across diets, proceeding from highest to lowest glycemic load … assuming equal spacing.” The last part of this is hidden in the footnote for Table 2. This test is conducted using a contrast in a linear model, where a contrast is simply a linear combination of the model parameters (in this case, the model-estimated means for each diet) such that the contrast coefficients sum to zero. In this case, the appropriate contrast coefficients for testing linear trend with equal spacing are -1, 0, 1, or any multiple thereof (e.g., using -0.5, 0, 0.5 would lead to exactly the same test statistic). That is, the contrast for linear trend takes -1 times the mean for the LF diet, 0 times the mean for the LGI diet, and 1 times the mean for the LC diet… this is just a pairwise comparison between the LF and LC diets! But the authors said that they wouldn’t do such pairwise comparisons unless the overall null hypothesis could be rejected, which was not the case for CRP. AND, they said that when they would do pairwise comparisons, they’d use a Bonferroni correction… they didn’t for CRP. As such, their data provide NO EVIDENCE for a difference between diets with respect to mean CRP. None. They should never have even discussed this, per their own pre-specified analysis plan.
Mark, thanks again for very good explanation of this issue.
I have to admit that this idea of the microflora in the gut and their effect on obesity is one I have been wondering about a great deal. I really think this concept makes an important leap to explain several unanswered questions in the obesity debate.
With DNA analysis we are now becoming aware of the role of the literally trillions of bacterial cells that inhabit our body most of which were previously unknown because they cannot be cultured in the lab.
https://www.nytimes.com/2012/06/14/health/human-microbiome-project-decodes-our-100-trillion-good-bacteria.html?hpw
One important question is that we always speak of peoples genetic differences in their propensity to obesity, but humans of different races, for instance, have a genetic make up that is more than 99% identical. What about the genetic make up of the human biome? Here we can perhaps have important species completely wiped out by an improper diet. While human genetics may vary only slightly the human biome could vary greatly between individuals. Further, as the above reference article points out, the beginnings of the human biome are passed from mother to sterile child at birth, creating a kind of quasi-heredity.
And as in the bacteria that cause dental caries, the article referenced by g2sb above (thank you!) points out another very interesting idea. The insulin/obesity connection does not consider that in the paleo diet, or the diet without processed foods, all sugars, fats, carbohydarates or proteins are found within cells and not as pure compounds. This goes beyond such notions as the glycemic index as the abstract points out. The thesis questions whether the flora in the gut can be considered “the forgotten organ” and an organ that is susceptible to being radically changed by our diet, especially pure sugars encountered outside of cells, for instance. This I think goes beyond just the notion of inflammation.
Peter, thank you so much for tolerating and even reading my posts. I am aware that they sometimes verge on rants, and I certainly do not want you to feel I am trying to hijack this very important forum.
Martin, your insights are great and add to a great discussion thread. Thank you.
Peter, the mention of gastric ulcers above in the thread reminded me of the story of Barry Marshall and of what you are up against in your battle to overturn years of bad science. I guess your extreme regimen of diet and exercise might be considered by some as analogous to drinking a Petri dish containing cultured H. pylori.
Yes, this example is one of my favorites — and resulted not only in a Nobel Prize, but a fundamental way a disease was treated. Unfortunately, this was (and is) a much easier problem to treat, as it’s simply treated by a single pill.
In the same topic as this I would value your investigation on the latest Prospective Cohort Study: Low Carbohydrate-High Protein Diet and Incidence of Cardiovascular Diseases in Swedish Women. I seems they have neglected to factor in for a Low Carbohydrate-Moderate Protein-High Fat Diet?
Read this: https://www.marksdailyapple.com/is-it-time-to-retire-the-low-carb-diet-fad/#axzz20VkYyILs
EVERYONE…read this. ESPECIALLY the last figure.
Ha! Colin Campbell needs to give that girl an honorary Ph.D.
Hopefully, it is not against protocol here to note that Gary Taubes is featured on this week’s EconTalk podcast:
https://www.econtalk.org/archives/2012/07/taubes_on_why_w.html
(I am sure it is great, but I won’t have a chance to listen to it for a few days…)
I figure that this only fair as it was Gary’s original interview on EconTalk back in November:
https://www.econtalk.org/archives/2011/11/taubes_on_fat_s.html
That was the AHA! moment for me and led me to read his books and eventually discover Peter’s site.
Peter,
I have been reading a lot of research articles about the effects of hormones on the body at the cellular level (as well as Good Calories, Bad Calories) and their effects on lipoprotein lipase in storing fat in adipose tissue. Research by a woman named Greenwood talks about how when progestin is added to Zucker rats the LPL activity on the fat cells increases. Does this mean that women who are on birth control that contains progestin (or progesterone) will likely have difficulty losing weight even if on a low-carb diet? Insulin would no longer be present to shuttle the glucose/fat/protein to the fat cells, but progesterone’s affect on LPL (the gate keeper to the cells) might combat the gains that could be made on a low carb diet. I ask because I have been doing the low carb diet for 5 months now, 20-25g of carbs daily with an increase in fat in the recent months (cream cheese, coconut oil, etc). Granted I am 5’11 and weigh 150lbs (where I should be for my BMI), but I would like to tone up and lose the excess fat. However, my weight has not budged and I was curious to know if it had to do with the progesterone in my birth control? Do you know much about other hormones’ affects on fat metabolism?
Corrie, the effect of hormones on LPL is significant as you note. The effect of E and P on LPL (in addition to insulin’s dramatic effects) may partially explain fat differences in men and women, and pre- and post-menopausal women. However, there could be many reasons to explain the lack of weight loss. Figuring it out would require a lot more detective work.
I read through your personal testimony and read about your results. Did you notice once you began a more drastic Carb restriction that it was difficult to slim down in your abdominal area, or that it took longer? Since you mentioned that’s where your body chose to store fat. How long did it take to get to the waist size you are now? Do you think there is a set point for everyone as far as weight goes? Maybe there’s a certain weight that your body likes and doesn’t want to lose anymore fat? Or is there always room for improvement on a high fat low Carb lifestyle, it just requires a more strict restriction of carbs, or possibly resistance or weight exercises added with it?
I think there certainly are set points. It’s taken 3 years to take 5 inches off my waist in a manner I deem “sustainable” for me, and it has not been a monotone decrease, either. There are many plateaus, of course, so you’ve got think LONG term…as in a lifetime of change and tweaking.
Hi Peter — Thanks for the great review of the study and engaging comments. It didn’t look like anyone had posted the response to the Kolata article by Drs. Ludwig and Ebbeling in the above comments. Here it is:
https://www.nytimes.com/2012/07/17/science/diet-study-authors-reply-1-letter.html
Thanks very much for this, Andrew. Kind of amazing that NYT would run a LOOOONG piece on this featuring someone who hadn’t read the paper *without* contacting the author of the study…then only give him 150 words to respond. Better than nothing, I guess.
Has anyone considered the worlwide impact of these diets on the Earth? Can we support this for everyone or are we just talking about an elite class that can personally afford these menus along with our big screen TV’s and iPhones? Are there other healthy options?
Lots of folk have, but largely with incomplete and inconsistent science. I think this is an important question, but it is a distant second place to the first-order question: what should we be eating to be healthy. If we don’t know the answer to that, what difference is sustainability? These issues get confused too often.
Yeah, that’s a fair question, Keith, and it’s one I seem to hear a lot from family and friends. Like “well, there’s not enough land on earth or air in the sky for all the animals we’d need and methane they’d fart out if everyone ate as much meat as you, John” Sustainable choices are things I want to consider as I continually tweak my diet, but on the other hand, I also don’t want to be that guy who says “Yeah, sure, LCHF with lots of animal proteins/fats is better for you, but it’s not sustainable…so let’s keep eating stuff that makes us obese, gives us heart disease, diabetes, cancer and kills us prematurely. After all, that’s more sustainable. You see my struggle?
Peter. Thank you for this and your work. Simply fantastic.
Thank you, Jade.
great articles. just also read the cholesterol blog. just got home today with my new heart stent due to a clogged artery (plaque). Now , how do I now continue to convince myself, my doctors and family, that despite needing this stent , which really came at a shock to me. discovered the blockage when i increased my exercise regime. I am s. fla and am not feeling like I will get any help maintaining a low-carb, high protein regimen . how do I get my doctor on board with me ? immediately the statins are out and have been read the riot act !
Not sure. I guess you hope they are open minded enough to learn new science.
I am encouraged by seeing medical people like yourself actively involved . maybe when it goes more mainstream, it would benefit all of us, you have the advantage of access to the proper tests of true measure of health, not some cookie cutter formula and knee jerk “standard ‘ test’s we all get shuffled into.
my breakfast in the heart wing this morning was scrambled egg with a piece of pound cake?? oatmeal?? lunch was something breaded , white rice, margarine? how are we to get /stay healthy when this is their idea of “good eats” for me? it is maddening ! hope to get some support, feel like , now my life REALLY depends on it!
I found this statement confusing: “Not surprisingly, the low fat group experienced a significant reduction in HDL-C. It’s been documented many times that dietary fat raises HDL-C and dietary carbohydrates reduce HDL-C.”
If dietary carbs reduces HDL-C, why is it “not surprising” that low fat reduces “HDL-C.” Sorry for the confusion.
If you lower fat, what do you add to make up for it? 🙂