Hello everyone.

Greetings from Montecito.

This Arrow is going to be a bit shorter than usual because all my time has been gobbled up this week with a family medical crises. I haven’t had the time even to comment on the comments, respond to emails, etc.

Also, next Thursday is the day after Christmas, so next week’s may be a little light, too, as I have all kinds of family in town.. But I’ll get back on track the week after.

Let’s get to it.

Fauci, Collins, and Vaccines

I received this poll response a few days ago.

I get these kinds of poll responses and emails all the time. I can’t remember for sure, but I don’t think anyone has made a similar request in the comments section.

I’ve been writing this newsletter now since the first Thursday in January 2021, and since I started writing it, my views of Fauci, Collins, and vaccines have changed vastly. And those of you who have been with me since the start have been able to follow my change in thinking as it happened.

Anyone coming aboard recently doesn’t have the advantage of observing my thinking evolution from week to week as I delved more and more deeply into the whole Covid pseudopandemic. I write pseudo because the powers that be had to change the definition of pandemic to get the spread of SARS-CoV-2 to qualify. The reasons for this are, let’s say, controversial. But, though I have my own suspicions, I don’t know the real reason(s) for the change in definition so I won’t elaborate.

I’m not going into detail, but Anthony Fauci lied through his teeth to Congress about his financing of gain-of-function research. Then, after he got caught out in his lie, he tried to dissemble even more by saying he didn’t really lie about funding gain-of-function research, because the definition used by his inquisitors in Congress was different than his own definition. And by his own definition, he didn’t lie.

He had an extremely powerful position in the NIH, which funds vast amounts of research. Anyone who crossed him ended up not getting funded, so few were inclined to call him out. He rounded up a group of the most influential virologists the world over—almost all of whom had decided it was highly probable that the virus was man-made and had escaped from a lab. Fauci got them on a conference call and brow beat them into writing a paper—now known as the Proximal Origin Paper—in a prestigious journal that he, Fauci, guaranteed them would be published, saying the virus originated from bats or the wet market in Wuhan.

They all complied and wrote the paper, which did not have Fauci’s name on it, though he was the moving force in getting it printed. Then, when he was questioned by members of Congress and the press about a lab leak from the Wuhan Institute of Virology, he referred to the paper by independent world famous virologists who wrote that it was not man made and didn’t come from a lab leak.

And, for reasons known only to himself, Fauci was a major mover in the lockdowns, school closures, mask mandates, and, worst of all, vaccine mandates.

There is much much more, but I don’t want to bore all the readers who have read this stuff years ago when I first wrote about it.

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But, before I get to Collins, just another anecdote about the power Fauci wielded. Robert Redfield was the director of the CDC during the first year of Covid, and in an interview with journalist Paul Thacker, Redfield said Fauci cut him (Redfield) out of all the meetings involving whether or not there was a lab leak. And, after Redfield’s tenure as head of the CDC, he became more convinced that SARS-CoV-2 came from a lab leak and not from an animal vector.

Redfield said Fauci had tremendous influence with Scientific American. When Redfield went on CNN to state his opinion that SARS-CoV-2 came from a lab leak, the Editor and Chief of Scientific American went on the attack.

Redfield said he was also trying to get a contract for a book he was going to write about his tenure at the CDC and his dealings with Fauci. He believes Fauci scotched his prospects with any mainstream publishers.

Before I go on, I have to admit that I think Redfield is a blithering idiot simply because of his mmoronic statement on masks. He’s supposedly a big-time virologist and his over-the-top advocacy of masks as preventatives against aerosols (which SARS-CoV-2 is) makes me wonder about his bona fides. Pretty much everyone—including the Cochrane Reviews—has shown that masks are worthless against aerosols, and here is the head of the CDC telling everyone masks are the best protection one can get against Covid.

I’m not going to spend a lot of time on Francis Collins. He was the Director of the NIH and a running dog of Fauci’s. He famously sent Fauci an email telling him they needed to do a takedown on Jay Bhattacharya, Martin Kulldorff, and Sunatra Gupta, all of whom are famous, mainstream epidemiologist working at Stanford, Harvard, and Oxford respectively. In Collins’s words, they were ‘fringe epidemiologists’ who needed a comeuppance. Nice to know that ‘fringe epidemiologist’ Jay Bhattacharya will be the new head of the NIH.

As to vaccines, I’m not going to go into a lot of detail. Anyone can search my writings in The Arrow for vaccines and get a good idea why I think as I do. Just let me say that vaccines have side effects. If they didn’t, they would be the only medications in the history of medicine that didn’t. Even frigging aspirin kills a number of people each year. All medicines have side effects…even vaccines.

The difference is that extensive randomized controlled trials have (and the use history after the drug’s approval) demonstrated what the side effects are. If, after all this testing, it turns out that the benefits of the drug outweigh the side effects (even death in a small number of cases), the drug will probably be approved. Even after all this extensive testing, many drugs that receive FDA approval have to be removed from the market, because the side effects, once the drug is made available to millions, outweigh the benefits. And remember, this is after millions of dollars being spent on placebo-controlled testing over a number of years.

If vaccines don’t cause side effects, they are the only products in the history of medicine that don’t. Any compound you take orally or via injection has side effects. Some short term, and some long term. Why would anyone think vaccines wouldn’t?

The issue with vaccines is that they are not tested like other pharmaceuticals against a placebo. They are tested against an earlier version of the vaccine, not a placebo. If the previous version of the vaccine caused X number of adverse events, then as long as the new version doesn’t cause a significantly greater number of adverse events, then it is considered safe. But since it was tested only against a previous version of the vaccine, we don’t know if overall the adverse events are worth whatever the benefit of the vaccine is.

Essentially, we are shooting in the dark with vaccines. They are tested to see if they prevent or minimize the disease for which they are made to repel, but they aren’t tested to see if the side effects outweigh the benefits.

The only way this can be done is via a randomized controlled trial, but these have never been done. (This may surprise you. It surprised me. But it is definitely the case.)

What people have done is go back to compare the long-term health of kids who have been vaccinated against those who haven’t. The ones of those I’ve seen, clearly show that kids who have not been vaccinated have fewer issues as their lives progress than those who are vaccinated.

In 2011 researchers published a study showing that in the 30 most developed countries, those that had the highest rates of infant vaccinations also had the highest rates of infant mortality as compared to those with the lowest rates of infant vaccination.

Critics complained that the authors cherry picked the data to make it fit their hypothesis. So the researchers did a vastly larger study published last year that included all 185 countries. The difference was smaller, but still there. Those countries that had the highest rates of vaccination in infancy also had the highest rates of infant mortality.

Infants in third world countries who suffer from malnutrition and live in squalor who get vaccinated have lower childhood mortality than those who don’t get vaccinated. Which is why adding these countries to the mix of the top 30 most developed countries brought the difference down. But in countries where people don’t suffer malnutrition, and who have decent sanitation, those infants who get vaccinated die at greater rates than those who don’t.

Okay, enough.

I’ve just skimmed the surface, and I hope I did so without verbal abuse. Everything I wrote above is true and can be documented with a brief online search.

More GLP-1ra Follies

Just a couple of days ago I received a missive from one of the many newsletters for doctors that I get. It’s from Medscape Diabetes and Endocrinology and titled “No, Diet and Exercise Are Not Better Than Drugs for Obesity.” It is free, but you have to sign up to get it. I’ll excerpt the applicable parts.

The essence of the piece is in this quote:

In other words, forget about diet and exercise if you want to lose a lot of weight. It’ll never happen unless you use weight-loss drugs. So don’t even bother. Just take the shots. Because, after all, “there has never been a reproducible diet and exercise intervention that has led to anywhere near the average weight loss by those taking the obesity” injections.

Studies are validated by being reproduced. If there are no weight loss studies showing weight loss that compares to those brought about by the shots, then those weight loss studies can’t really be validated. The author of this piece is probably correct. I haven’t gone through every weight-loss study ever done, but I doubt whatever the one showing the greatest weight loss has been reproduced.

But does this mean you can’t lose as much, if not more, weight via diet than you can on Wegovy, Ozempic, etc?

To understand, let’s look at a little inside baseball as related to weight loss.

First, you’ve got to understand how weight-loss studies are conducted. Most of them start by recruiting subjects through newspaper ads or other media. In many of them, people get paid a fee to participate. And they usually get a pretty good medical going over, complete with a physical exam, a plethora of lab work, EKGs, and other testing.

Then they get randomized into a study group and a control group. Depends upon the dietary study, but typically one diet is compared to another.

Over the course of the study, they get continuous counseling by dietitians and other healthcare professionals. Often they even get food packets that they pick up or, in some cases, have delivered to their homes. Some just have precise dietary instructions.

The first problem is the make up of the diet. Is it high-carb, low-fat, low-carb, high-fat, low-cal, whatever? Usually one of these is compared to another. The problem arises in the subjects’ dietary likes and dislikes.

Let’s say you’re a real carb junky, and you don’t particularly enjoy meat. You live on pasta and bread and cereal. And you get randomized into the low-carb, high-fat group. It happens. Randomization is random. People don’t get to decide which group they’re going to be randomized into. If they do, it isn’t randomized; it is self selected.

Despite all the excitement at the start of the diet, that enthusiasm wanes over time. Especially if you’re a carb junky and you get randomized into the low-carb, high-fat diet. Or the opposite. If you’re a steak and eggs person and find yourself in the bagels-and-no-cream-cheese arm of the study.

This is why virtually every dietary study ever done shows the following trajectory. Weight falls off quickly, then slows a bit, bottoms out, then starts coming back on toward the end of the study.

People who are not particularly motivated don’t stick well on diets for the long term. Those who get randomized into the arm of the study filled with foods they don’t particularly like stick the least well of all. Many of them simply abandon the diet, but they can’t be removed from the study data even though they didn’t complete the study. More about why in a bit.

Studies of weight-loss drugs are similar, but also much different. Subjects are randomized into two groups. Both groups are encouraged to follow some sort of basic diet, but the diet is the same for both. The only difference is in the contents of the shots they give themselves. The control group has a placebo, while the study group has the real medication.

Subjects who follow the Wegovy, Ozempic, Mounjaro, etc. studies don’t really have to make choices between diets. They just have to give themselves a pre-loaded shot periodically. Following this program requires virtually no willpower. The medications reduce hunger, so people eat much less than they did before they started the program. And, of course, they lose weight. At least those on the real drug.

The stats show that if they quit the shots for some reason, the weight starts going back up. And if they don’t quit, the weight loss bottoms out typically before they hit their goal. But they do lose a fair amount of weight. And they do it without a lot of will power.

I’ve written before about the Public Health Collaboration, a group in the UK that studies diets for weight loss and type 2 diabetes. They constantly go through the literature on the prowl for studies comparing low-carb, high-fat diets to low-fat, high-carb diets or even low calorie diets. They then add them to their database and keep a running tally of how the carb-restricted diets stack up in terms of fat loss and reduction of type 2 diabetes symptoms.

As of now, they have tabulated 71 studies that meet their criteria in terms of length of study and carb restriction. Of those 71 studies (all RCTs, by the way), 62 show that those subjects on the low-carb arm lost the most weight, while 7 of the 71 show those on low-fat diets lost more weight. In doing an analysis for statistical significance, for those who care about those things, 32 out of the 71 studies showed statistically significant weight loss by those following low-carb diets. None of those on the low-fat arm in any studies showed a statistically significant weight loss. That’s 0 out of 71. Two were tied.

Here they display their data between the two diets graphically.

As you can see, subjects following the low-carb diet in these randomized controlled studies did much, much better than those following low-fat or low-calorie diets.

But they didn’t lose as much as those on Wegovy, Ozempic, and the rest.

Why is that?

A couple of reasons.

The first reason is the one we discussed first. If you’re randomized to a diet you don’t particularly like, you won’t follow it as you should, and you may well drop out.

Let me digress here to explain something else before we get to the other reason.

You might be wondering why the researchers in these studies just don’t let the subjects follow the diets they would prefer? Why not keep recruiting subjects until you have, say, 50 who like low-carb foods and want to be in the low-carb arm of the study and 50 who are carb lovers and would prefer to be in the low-fat/high-carb arm of the study? That would negate the problem of sticking people on diets filled with foods they don’t really like and will probably not follow very well.

It would also negate the randomization. The researchers wouldn’t know if people who inherently prefer meat, eggs, and cheese might just be people who lose weight easily. Or it could be the carb lovers who are easy losers, though I would doubt it. The only way you can really randomize is if you just do it by the numbers, not by who likes what. That simply throws another variable into the mix, and anyone who had even a sliver of scientific training would identify the problem and trash the study.

The second reason these kinds of studies typically show less weight loss than they should is the way they must be done to be published. There is an analytic technique called Intention to Treat Analysis (ITT) that throws a wrench in the works.

Let me explain.

ITT is a means of analyzing data that almost all journals require. When you learn what it involves, you will be as amazed as I was when I first learned about it 20 years ago.

What ITT makes researchers do is count all those who get randomized into either the control group or the study group even if they drop out along the way.

That’s right. Let’s say 100 people are randomized into two groups of 50 subjects each. One group, the study group, is assigned to a particular medication while the other group of 50, the control group, gets a placebo. In this fictional study, we’re going to look at blood pressure, because the drug being evaluated is a blood pressure drug.

The subjects getting the placebo don’t really have many problems, because they’re getting basically a sugar pill. A very few may develop symptoms that they attribute to the placebo, and they may drop out of the study, i.e., fail to pick up their meds (placebo) and fail to return for follow up.

Those subjects in the study group who are taking the real drug will have more issues. Remember, every drug or medicine of any kind causes some side effects for some people. So this test drug has some bad side effects for 15 of the people on the study, and, consequently, they quit taking it and bail out of the study.

Each subject in this study has particular blood pressure at the start, is tested monthly (or whatever) throughout the study, with a final blood pressure at the end. The researchers can subtract the beginning blood pressure from the final blood pressure, and if there is a large enough difference to matter, conclude that the drug is effective.

But this is where ITT comes into play.

In looking at the placebo group, the researchers find that only three people dropped out. But in the study group, the ones who got the real medication, 15 dropped out.

A reasonable person would say, okay, 35 subjects made it all the way through to the end. Let’s look at the change in the blood pressures of those 35 people to evaluate how the drug worked. If there is a significant drop in blood pressure in those who have taken the drug till the end of the study period, then the researchers can say the drug works.

But that’s not how it works with ITT.

ITT requires that data from ALL the subjects who started on the study are counted, even if they’ve dropped out. They simply use the last numbers they have before these subjects dropped out. Let’s say the study was a year long, and let’s say all 15 dropped out by the third month. Those probably won’t have the same drop in blood pressure as those who stayed in for the duration and took their meds religiously.

When the results of those who did not finish the study are combined with the results of those who did, the overall results will be lower than if just the results of those who completed the study were tabulated.

If the lack of blood pressure lowering in the folks that drop out are enough to drag the overall results below the level of statistical significance most research is based on, then the drug would be deemed ineffective…even though it was effective for the people who actually took the medication.

It sounds insane, but there is some basis for ITT, at least in drug studies, that I don’t have time to get into right now.

But it is, in my view, at least, terrible for diet studies.

Here’s why.

Just about everyone loves carbohydrates. Diets that contain a lot of carbs are easier for most people to stick with than those filled with protein and fat. For instance, I’ve seen diets in which bagels are recommended for breakfast. But not bagels with cream cheese or bagels with butter. No, the recommendation is bagels with jelly or jam. There is no fat in jelly or jam.

I’m a diehard low-carber, but though I wouldn’t actually do it, I would dearly love to go face down in a bunch of bagels and jam.

You don’t find many people who wouldn’t. I love steak and eggs, but I suspect the majority of people would choose the bagels and jam. Especially if that was recommended as an allowed choice in a dietary study.

We know from the list of RCTs tabulated by the Public Health Collaboration above that low-carb diets perform better than low-fat/high-carb diets. And all of those were done using ITT calculations.

I wrote about ITT years ago in my blog, so I’ll excerpt a bit to demonstrate what happens with ITT calculations and diet.

I analyzed one specific study in great detail. In this RCT the low-carb diet was pitted against a “conventional weight loss diet,” read low-cal/high-carb, in “severely obese adults” for a year.

At the end of the study, there was no difference in weight change between the groups. There actually was. Those on the low-carb diet lost more, but it didn’t reach statistical significance as measured by the p value, which was 0.2. If you’re looking—as all researchers do these days—at p values, the p must be equal to or below 0.05.

So, in this study the low-carb diet—although the low-carb subjects lost more weight—did not clear the statistical hurdle required for a victory. So the study was described by the press as proof that low carb diets didn’t work any better than conventional diets for weight loss.

(A bunch of other parameters improved to statistically significant amount in those in the low-carb arm of the study, but not weight loss.)

But when I analyzed the actual data and backed out those who hadn’t completed the study, there was a major difference. The study included the number of dropouts and the weights at which they dropped out.

From the study:

So, those on the low-carb arm who dropped out of the study prematurely lost less weight than those who made it through to the end, whereas those who dropped out on the conventional diet lost about the same as those who stayed for the duration.

Here was my commentary to the above.

When the researchers averaged in those who dropped out, the results were vastly lower than what subjects experienced who followed the diet to the end. They were lower enough, in fact, to drag down the average weight loss to a number still higher than those lost on the conventional diet, but not enough to reach the vaunted p value of equal to or less than 0.05.

As any one with a brain can see from what happened here, those who followed the low-carb diet lost significantly more weight than those who followed the conventional diet.

The key phrase is “those who followed the low-carb diet.” It’s not fair to include the numbers of those who drop out against those who persevered to the end. But that’s what ITT does.

Remember that next time you look at a dietary study.

Now, circling back to the GLP-1ra studies.

In those studies, people are encouraged to go on some sort of weight-loss diet, probably a conventional weight loss diet. Subjects in both arms of the study get basically the same diet, the only difference is that one group gets the GLP-1ra shots while the other gets placebo shots. No one knows who gets which shots—the mechanisms for self injecting are exactly the same.

Since there is no real effort in following a conventional diet and the shots are all administered the same, there is really no reason for many people to drop out. Or, if they do, they probably drop out in similar numbers. So the ITT calculations aren’t altered much by the dropouts.

Low-carb diets, on the other hand, are more difficult to follow for most people. So, thanks to ITT calculations, the results in those who do follow them are typically lower thanks to the dropouts.

Those of us who do follow low-carb diets regularly don’t think they’re that hard to follow. But for the rank and file, they are more difficult to adhere to.

I recently watched a Huberman podcast with Chris Palmer, who is a professor of psychiatry at Harvard. Dr. Palmer related a pilot study he had done with Alzheimer’s patients who did great on ketogenic diets. But there were a lot of dropouts because the subjects didn’t like the diets. Huberman asked him if the great results of the pilot study garnered funding for a bigger study, and Palmer said no. The NIH wouldn’t fund it. Their rationale was that it didn’t matter how well the diet worked, if few people were willing to follow it, there was no reason to invest in a study.

Years ago, I wrote about a similar study done in Germany, but for those with terminal cancer. In order to get into the study, the subjects had to have undergone every cancer therapy available—chemo therapy, radiation, and surgery. Basically, they had been sent home to die.

Those who did decide to do the study, which was basically a carb-free diet, did well.

But what stunned me was the people who dropped out.

These people had been thrown a lifeline, yet they just couldn’t give up sweets to stay alive.

That’s why more people drop out of the low-carb arms of studies than do out of the higher-carb diet studies.

My bet is that if a good quality low-carb diet were to compete against those following a conventional diet and taking GLP-1ra shots, the results would be the same. Or maybe even better in the low-carb arm. I’m certain the low-carbers would be healthier at the end because they would have maintained their muscle mass much better.

When recommending diets to patients, doctors should engage in the following conversation. You need to lose some weight. I can put you on one of two diets. One is pretty easy to follow, probably won’t be a big change for you, and you’ll lose a little weight. The other is more difficult to follow, but you’ll lose a lot of weight and change your metabolic parameters a lot. Which one would you prefer to go on?

Odds and Ends

• New research reveals that Neanderthal genes entered our own DNA within the past 50,000 years, rewriting the story of how Homo sapiens spread across the world.

• Nature’s picks for the best science images of 2024.

• Archaeologists discover a "unique" ancient Roman drinking vessel containing lard mysteriously embedded in an Anglo-Saxon burial from the early 6th century A.D.

• Scientists in the UK have created a diamond battery supposedly capable of lasting millennia. Made from synthetic diamonds encasing radioactive Carbon-14, it converts decaying electrons into electricity. It would be great for pacemakers.

• A biologist explains how a black fungus might be healing Chernobyl by drinking radiation.

• Ever wonder why the background in old sports photos is a hazy blue? Take a guess as to why.

• Pediatric ER doctor shares which gifts not to give to kids this Christmas season…if you want to keep them out of the ER.

• Egypt declares victory over malaria after 100 years: The disease that claimed Tutankhamen eradicated at last.

• By creating “green corridors” mimicking the natural forest, Bogota, Colombia is driving down temperatures while sucking up CO₂—and could become five degrees cooler over the next few decades.

• Of all the new Illinois laws going into effect on Jan 1, 2025, this one is my favorite: Replace the term “offender” with “justice impacted individuals” in the Illinois Crime Reduction Act. Orwellian, no? I’m sure it will greatly reduce crime.

• An attack in early Bronze Age England around 4,000 years ago suggests that at least 37 people may have been “systematically dismembered” and eaten.

• Fascinating short time-lapse video showing a pine cone turning into a pine tree.

• Do changes in dosage of Mexico’s fentanyl production explain the drop in US overdoses?

• Europeans sent many diseases that killed many immunologically naive denizens of the New World, but the American Indians returned the favor by sending syphilis back their way.

• Terrific post by one of my favorite authors on why empathy makes us cruel and irrational.

• Culture Critic opines that Rome didn’t fall as a consequence of disease or invasion, but by lack of replacement birth rate instead.

• The drugs young bankers use to get through the day…and the night. There is always a cost.

• The safest and the least safe methods of travel. I knew the safest was pretty safe, but I had no idea it was as safe as it is.

• Homeowner in New York notices something weird sticking up in his backyard. Ends up being a full fossil of a Mastodon jaw. Why does this never happen to me?

• Can squirrels become carnivores? Apparently they can. At least in California.

Newsletter Recommendations

Video of the Week

I’ve got a couple of VOTW this week. One inspired the other. First, MD sent me the video right below. It’s incredible how much time this guy takes to do something so ephemeral. And so at the mercy of the weather. A sudden snow squall halfway through could have destroyed hours and hours and hours of snowshoe slogging. And I have snowshoe slogged…once. It is hugely demanding. I wouldn’t want to do it for any distance.

While watching this, I got to wondering what would happen to this guy in the middle of nowhere if the temp dropped and it started to snow. I took to YouTube for an answer, and it didn’t disappoint. I found a whole hoard of sleeping rough in the snow and cold. This is one I enjoyed. Why would people do this? As far as these kinds of adventures go, I much prefer to watch than to do. But, I have to say, now I know what to look for and what to do if trapped in the snow in the freezing cold. Enjoy?

Time for the poll, so you can grade my performance this week.

That’s about it for this week. Keep in good cheer, and I’ll be back next Thursday.

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This newsletter is for informational and educational purposes only. It is not, nor is it intended to be, a substitute for professional medical advice, diagnosis, or treatment and should never be relied upon for specific medical advice.

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