Obesity, Blood Pressure, and Study Design

This entry had better save someone’s life because WordPress ate the entirety of the first draft and half the second draft and it was not easy to recreate.

Health at Every Size claim: high blood pressure (hypertension) is not caused by fat, but by dieting (which, because fat people diet more, creates the illusion that fat causes high blood pressure.  This is that confound thing we talked about last week).
My reading: The first source it cites does not quite say this: they say that current diet affects blood pressure, regardless of weight (which is actually even better news).*  This study (PDF)  purely retrospective and found both weight cycling and waist:hip ratio (a measure of fat gain around the waist, and due to the design of the study, a measure of fat itself**) to be significant (also, sample size was very small).  The next 2 studies were in obese spontaneously hypertensive rats.  Using animal models bred to have the disease you are studying is very common  because it reduces the number of subjects needed to generate a statistically significant result, but if the model is suffering from a different root illness that merely causes identical symptoms (e.g. rats bred for leptin deficiency lose fat when given leptin, but leptin-sufficient humans do not).  The last source is a book with a name like a perfume store.
Verdict: claim not proven.

HAES claim:  hypertension is only associated with bad outcomes in thin people.  Fat people with hypertension actually live longer.
My reading: This appears to be true, but all the studies were retrospective, none studies backed out smoking as a confound, and half were in men only.  None looked for a dose-dependent effect, but dumped people in two or three buckets and compared outcomes within them.  The technical term for this is categorical analysis (the alternative is numeric analysis), and it is almost always a bad choice. Here’s why:

 

START STATISTICS  FIELD TRIP

Let’s imagine that weight affects, and is the only thing to affect, longevity, and that it does so in an exceedingly simple way

Statistical analysis by MS Paint
Statistical analysis by MS Paint

 

Any idiot can draw the correct inference from this graph: being extremely overweight or underweight is bad, being anywhere in the middle range is equally good.  Let’s imagine three naive scientists attempting to study the relationship between weight and longevity.

Scientist A gathers an equal number of people at every weight and sorts them into “low” or “high” weight, with the divider being the exact center of that trapezoid.    They conclude weight has no impact on lifespan.

Scientist B draws the low/high line in the same place, but has slightly more extremely overweight people than extremely underweight people (which is what will likely happen in the real world, because 100 pounds overweight is a struggle, but 100 pounds underweight is dead).  They conclude being overweight increases chance of death.

Scientist C gets and even distribution of weights, but draws the line to the left of the exact center.  Their low-weight group now has a higher percentage of extremely underweight people than the high-weight group has of extremely overweight people.  They conclude being underweight increases your chance of death.

And those were good intentioned scientists.  A bad intentioned one can gather their data first and set cut off points after to prove almost whatever they want.

END STATISTICS FIELD TRIP

Verdict: After all the crap I’ve given HAES for misrepresenting studies, you might think I’m about to do that again.  But actually I find it perfectly plausible that all the studies on weight and blood pressure use categorical analysis, because it’s incredibly common in medical studies.  I really hope there’s a good justification for that, but the only one I’ve heard is that the math is easier, which might have made sense when everyone was doing statistics by candle light with a slide rule, but is hard to swallow now given the abundance of software packages that will do it for you.  So I will tentatively accept HAES claim as true, but I don’t see how you can turn the claim into actionable suggestions, except to worry less, which is actually an extremely valuable suggestion in this context. So let’s go with “supported, but of limited usefulness.”

 

*This is your once-per-post reminder that someone who talks about weight or BMI and opposed to body fat percentage is already wrong.

**I spent several very confused minutes googling what “android” meant in context.  I assumed the “android” referred to “women”, but android (when it doesn’t mean “obviously superior phone”) means “man-like”, which would make this an extremely unrepresentative study.  Turns out that “android” modifies “obesity”, and it means fat growth around the waist and upper body (more often seen in men) as opposed to around the thighs, hips, and breasts (more often seen in women).

Fiber: the Mr. Rogers of nutrition

I have consciously decided to let the arguments for high protein diets pass me by, despite how convincing some of the research and underlying mechanistic claims sound.  Humans have been cycling through high -protien, -fat, and -carb diets since we developed enough of a surplus to choose, and I’m pretty sure if any of them were that superior to the others it wouldn’t have been supplanted.  I would say “everything in moderation”, but that is just a zen way of saying “Eat the correct amount.  Idiot.” which I don’t think is very helpful.

HAES brings up a more constructive suggestion: eat fiber.  Fiber* is usually left out of the macronutrient triumvirate, but so is water, and water is extremely useful.  Off the top of my head: Fiber has the advantage of being natural**, because our ancestral foods had much more fiber than our current ones, yet its lack of calories is well suited to our current, couch-based, lifestyle.  It evens out digestion of other nutrients, reducing the destructive sugar/insulin boom and bust cycle.  It simultaneously treats diarrhea and constipation (if you drink enough water, which you should anyway).  Have you ever wished you could eat a good thing and have it cancel out a bad thing?

mitch-hedberg-onion-ring

Well, that might actually be true if the good thing is fiber, and the bad thing is not cocaine.

Here is where I meant to track down all the good things HAES says about fiber, but when I read it more carefully.  I realized most of what it was praising was fruit, with the implication that fiber was the reason fruit is beneficial.  The one actual fiber claim is that high-glyemic diets increase the risk of type 2 diabetes unless the diet is also high in fiber (five sources, all of which say nice things about fiber, one of which is actually about type 2 diabetes.  The paper did conclude that fiber fights type 2 diabetes, but it was a post-hoc survey, which is a weak methodology).  However, those other four sources also say pretty good things about fiber, just not the one the book claims they did.  I think we are all on board with “fruit and whole grains” > “potato chips and soda”, although I’m severely disappointed that this of all books is backing that up with data on weight loss, rather than actual health outcomes.

But Dr. Wikipedia has many very specific nice things to say about fiber, and it cites much more relevant sources.  Fiber increases micronutrient absorption (a little).  Fiber increases nutrient absorption (more).  Fiber fights inflammation.  Basically, it does everything good and nothing bad.  So while HAES didn’t lay out its case properly, fiber is definitely good, and I look forward to finding out who takes this too far and what the negative effects are.

*Definition of fiber varies a little from institution to institution, so for clarity: I am defining fiber as carbohydrates that are not broken down for energy in the human body.  It’s worth noting that there are two contributors to whether an organism can digest food: its own enzymes, and the bacteria in its digestive track.  Termites can’t naturally digest wood, but they house a bacteria that can.

**It’s easy to oversell “naturalness”, but when you have a mission critical system made of complex legacy code no one understands, sticking close to its original environment is a good default strategy.

Bariatric Surgery

I had a pretty poor opinion of weight loss surgery already, but Health At Every Size all but says any doctor recommending it should lose their license for malpractice.  That claim seems worth investigating.  Luckily, she cites her sources.

First, I feel it’s important to note that bariatric is medical Greek for “obesity related medicine.”  I’m already not thrilled with that because I think excess fat is a symptom of health problems, but rarely a health problem in and of itself.  “Bariatric surgery” is often sold as something that is fixing a problem, the way an appendectomy fixes appendicitis, but it is at best undoing the damage of something else that is making you fat.

That said, let’s start with the immediate death rate.  HAES quotes a study as reporting a 4.6% death rate within the year: what it doesn’t say is that that study was done on Medicare recipients, meaning they were older than 65 or disabled.    Moreover, the 4.6% number is based on death from any cause, not what would be expected above and beyond what is normal for patients’ age and health.  Controlling for age, sex, and likelihood they would have died anyway* the researchers found that surgery increased your risk of death in the 90 days after surgery by somewhere between 90% and 200% (=3 times as likely to die), depending on which demographic you were in.  Inexperienced surgeons make this worse (which they do not back out of their model).  This is not just the stress of surgery: that’s twice the death rate following coronary revascularization or hip replacement, neither of which are minor.

HAES cites another study, published in JAMA as reporting a 6.4% four-year death rate.  This study has a number of problems.  Its only control was matched for age- and sex- but not health status.  A lot of the deaths stem from heart disease, which could plausibly be caused by being fat or having been fat, which is not a case against weight loss surgery.  Worse, that was the death rate only among people considered “at risk” enough to justify four years of follow ups.  The article doesn’t explain what qualified someone as “at risk”, but rarely does that risk mean “at risk of living too long”.  HAES cites a blogger who cites the study as demonstrating a 250%-360% increase in mortality over four years, relative to age- and BMI- matched controls, but I don’t see that anywhere in the original paper.

Meanwhile, the American Society for Metabolic and Bariatric Surgery aka “the people doing the surgeries” is happy to report a mere 0.2%-0.5% mortality rate after the first month of gastric bypass surgery.

That’s everything the book cites on mortality, which I found unsatisfying, so I turned to Dr. Google.  This Swedish study actually bothered to match controls (although surgery was not assigned at random, introducing the possibility that the surgical patients varied on a factor they didn’t think of) and found a 30% reduction in death over 10 years.

But I hate it when people act like death is the only bad thing that could ever happen to you.  What about people who don’t die, but do suffer for the surgery?  HAES cites six studies showing long term nutritional deficiency.  Of the five I was able to find online, all showed serious deficencies and none had a control.  Interestingly they all found a vitamin D deficiency, when vitamin D is primarily produced by your skin in response to sunlight, unless you live in Seattle, in which case you mostly get it through supplements.  Either way, food is not a major source of it, and if bariatric surgery effects vitamin D levels (which these studies have not demonstrated) I am extremely curious as to why.  Given the current controversy as to the efficacy of vitamins even in people with normal stomachs, it’s not clear how much this issue could be fixed with supplementation.

Every study I’ve read agrees that people lose substantial weight after surgery and then gain some of it back, I’m not even bothering looking for citations for this.

CONCLUSION: bariatric surgery has severe risks.  These may be partially compensated for by a skilled surgeon and good nutritional technique.  For extremely obese patients the benefits may outweigh the risks.  We don’t know where the cut off is for “fat enough to benefit.”  The strongest piece of evidence against bariatric surgery is that no one has done the fairly obvious studies that would conclusively demonstrate their effectiveness.

Some of the benefits probably stem from societal approval rather than genuine health issues, and the long term fix for that is for society to stop shaming people for their weight.  Another part of the benefit may be a forcing function, i.e. if patients ate like they’d had the surgery they’d lose weight whether or not they actually had it.  For an individual living in the society they live in and who has already tried dietary changes, this is sad but irrelevant to the decision.

I’m really uncomfortable with this conclusion.  It doesn’t fit my prior model, and I prefer the tribal affiliation of strong weight loss surgery opponents to strong weight loss surgery advocates.  I consider the evidence I’m basing this on somewhat iffy,  but in all honesty if it had come out the way I expected I would be fine with it.  I’m also pretty disappointed in HAES for so blatantly misrepresenting the evidence.

*Risk of death was calculated using the  Charlson Comorbidity Index.   I have no idea if that is a good model, but it appears to be standard.  This doesn’t prevent the researchers from being wrong but it does mean they’re probably not being deliberately manipulative.

Nature tricks us again

Nature, the pinnacle of prestige in biology, is going free access, meaning anyone can read their articles for free.  This is definitely an improvement over me not being able to read their articles  (mostly produced and paid for with tax dollars) for free, but it’s not the victory it might look like.  Michael Eisen details how they’re still protecting their subscription model, but the more insidious problem is that they still charge for “reprints”, in which they print out the article and mail it to you like some sort of medieval peasant.

“Why would that matter?”, you say quietly to yourself “I mean, I could see how that was an issue back when articles had to be chiseled onto dinosaur skins, but doesn’t everyone just read them online now?”  First, reprint costs were an issue slightly more recently than that.  When I started college in 2002 a professor loaded me up on articles, and then asked me to please bring back the ones I wasn’t interested in because they were expensive to produce.  These were for articles he wrote, but he still had to pay the publishing journal to hand out copies to freshmen.

Admittedly, that particular problem had already been solved by the time I graduated.  Anyone on campus could read almost anything they wanted, for free.  But the more insidious issue is the people who want to pay for reprints.  Specifically, companies wishing to demonstrate how awesome their product (often but not exclusively a new pharmaceutical drug) is will buy reprints of articles supporting their case.  I admire their uprightness in paying for each copy, rather than buying one and giving an intern some alone time with the copier, but what I don’t understand is why they buy so many copies.  More than they could possibly distribute unless they started throwing them out of helicopters.  It’s like they enjoy giving the publishers money for some reason.

What this means is that people with money don’t just influence science by funding the studies they want done and then writing the articles about them, or by buying advertising in scientific journals (which we can at least detect is happening).  They reward journals for publishing articles favorable to them by buying reprints.  [Source: Bad Pharma, by Ben Goldacre].  And Nature has left that revenue stream wide open.

Insulin and Glucagon: Mockingsugar

There’s one more big food hormone everyone talks about: insulin. The visiting doctor on local news explanation of insulin is that it is produced by your pancreas in response to sugar in order to signal all your other cells that sugar is available in the blood stream and they should eat it.  The truth is, of course, more complicated.

First, your pancreas is producing insulin all the time*.  It then stores in the insulin until triggered.  This makes sense: demand for insulin is very spikey, and producing it all on demand would require an enormous number of cells that would be idle much of the time.  The sugar thing is a simplification as well.  Chemicals other than sugar stimulate insulin release, and not all types of sugar stimulate release.  Insulin is released by glucose in the blood (and by mannose, a sugar that looks very similar to insulin), but not fructose (which has fewer carbon atoms), but also amino acids**.  How much each amino acid stimulates production appears to be an open question.   This paper suggests all essential amino acids stimulate production greatly , this one says leucine, phenylalanine, and arginine (not considered essential in health adults) are the strongest, this one says arginine, lysine, alanine, proline, leucine and glutamine.  We’ll cover why this may matter in a minute.

Just like ghrelin and leptin act as semi-antagonists, insulin has its own nemesis: glucagon.  Where insulin stimulates your cells to take in sugar (and protein), glucagon stimulates your liver to release stored sugar.  This is necessary for keeping energy levels up when there is no immediate dietary source of sugar.  Glucagon also stimulates your body to break down protein (dietary or, in a pinch, your own muscles) for energy.  Remember how we said fasting could lead to muscle growth by stimulating growth hormone production?  Well it also leads to breaking down your muscles for energy, via glucagon.

Insulin and glucagon are in a very delicate balance.  When you eat a high-protein meal and have adequate energy levels, your body would like to use that protein to build enzymes and muscles and things.  To do this it must release insulin, which triggers your cells to take in amino acids from the blood.  But insulin also triggers them to take in sugar.  If the meal did not contain enough sugar***, this will send your blood sugar level dangerously low.  So your body preemptively releases glucagon, which triggers the release of stored sugar, thus maintaining blood sugar levels at optimal.  But! what if you’re on a chronically protein-rich, sugar-deficient diet?  This pattern could cause you to starve to death.  So glucagon also causes your liver to take in amino acids and use them for energy (which it can then distribute throughout the body).

What I’m wondering is if glucagon and insulin respond to differently to different amino acids. Specifically, if essential amino acids cause a larger [insulin – glucagon] than non-essential ones.  That would allow your cells to preferentially take in the most necessary amino acids, while leaving the less necessary ones for glucogenesis, which would certainly be handy.  Alas, I cannot find any studies on individual amino acids and glucagon.  This may be moot, since my impression is that the modern amino acid mix is pretty much closer to the paleolithic amino acid than sugar or fat are, so our system probably handles it better.

Your pancreas is a real go getter that wants to be prepared for extra sugar, because excess blood sugar is actually pretty damaging.  To do this it looks releases not just enough insulin to cover current blood sugar levels, but what it anticipates those levels will be in the future.  The problem is that it’s prediction algorithm is woefully out of date.  It may not even have caught up to farming (whoohoo, wheat and rice!), much less modern hyperprocessed snack foods.  If you eat a small piece of candy, your body doesn’t release enough insulin to handle the candy.  It looks at the sugar level and assumes you just ate something enormous and releases an appropriate level of insulin.

allthe nutrients

When the rest of the sugar and protein fail to appear, your blood sugar level drops precipitously. I don’t even know what it does to your blood or cellular protein levels, but it can’t be good.  Over time, your muscle cells may get tired of your pancreas’s false promises and stop listening to its pleas.  But the fat cells never stop listening.  This means that when you do eat protein or sugar, your fat cells take in a disproportionate amount of both.  If this progresses too far you get type II diabetes.****  Meanwhile, your glucagon production is completely unchanged, so your liver is happily taking in protein for energy synthesis (which frees up sugar to make fat).  This means it is perfectly possible to get fat as the rest of your body starves.

Oh, and one more thing affects insulin and glucagon production: stimulation of the vagus nerve.  Saying “vagus nerve” is only slightly more helpful than just saying “the body”, because the vagus nerve goes everywhere.  This article suggests that it’s carrying a signal from the liver to reduce insulin production.  This study stimulated the vagus nerve below the heart and found it raised both glucagon and insuline levels.  This study found removing the (hepatic?) vagus nerve in rats reversed type 2 diabetes.  I’m going to put this down as “the digestive system is actually much smarter and more communicative than we realize.”  I’m also curious about the fact that the vagus nerve also extends into the face, where it can detect chewing, but can’t find any studies on it.

Insulin probably doesn’t make you feel or full in the classic sense, but it can drop your blood sugar, which will make you slow and sleepy until you eat more food (“hey, a candy bar would wake me up…”), but it can stimulate leptin production and release which will make you feel full (and is another strike against leptin as The Ultimate Fat Barometer).  Glucagon probably is an appetite suppressant, which I find counterintuitive, and probably means it plays a bigger role in protein digestion than weathering long term calorie deficits.

What I find most amazing is that I have a biology degree, and didn’t realize insulin had anything to do with protein until just now.  We talk about insulin/sugar/diabetes/fat so much, we miss the protein/strength/cellular activity level.  I do not like what this implies at all

*And the brain.  Like leptin, insulin appears to make your brain feel safe to expend energy.  But at least not in the lungs this time.

**Reminder: The human body builds proteins out of 21 different amino acids.  Some of these it can produce itself, some must be taken in from the environment (essential amino acids).  Amino acids can also be used to generate energy, although this produces more ugly byproducts than sugar or fat, to the point it’s actively unsafe at higher levels.

***Admittedly less of a problem now than it was in the evolutionary relevant time period.

****Type 1 is a simple problem of insufficient insulin production.  Injecting insulin isn’t a perfect cure because you can’t perfectly replicate the sensitivity of the pancreas, but it is pretty close.

Poverty, Medicine, and Research

John: http://gap.hks.harvard.edu/women%E2%80%99s-empowerment-action-evidence-randomized-control-trial-africa
[Women’s Empowerment in Action: Evidence from a Randomized Control Trial in Africa]
Me: That’s awesome. Wait, why are they jumping between percentage points and absolute percentages? And they don’t give the absolute numbers at all.*
John: http://www.ucl.ac.uk/~uctpimr/research/ELA.pdf
Me: Sweet. Wait, so they plopped some afterschool clubs down and then measured outcomes for girls that attended them? That’s a hell of a confound.**
Paper: Nope, this is an RCT, and we compared both attendees and non-attendees (will overestimate impact due to confounds, but miss any spillover affects on non-attendees) and treatment communities with control communities (will underestimate impact because only 20% of girls attended the club, will catch spillover effects).
Me: But mobility is high, what if girls leave the area?
Paper: we track them. Plus attendees, members of treatment communities, and members of control communities had similar attrition rates.
Me: I’m still distraught you’re only giving rates of change, not absolute numbers.
Paper: Jesus Christ, not everyone loves numbers as much as you. The numbers are in the appendix.
Me: This looks like you made it worse.
Paper: Maybe it would help if you read the part that explains how to read the numbers.
Me: Your sexual health knowledge test includes questions like “A woman cannot catch HIV while on her period. T/F”. That’s the opposite of true.
Paper: You see why we’re concerned.
Me: HA! You said you calculated based on living in a treatment area, not participation, but table 2 is contingent on participation.
Paper: Table 2 describes duration and intensity of club attendance.
Me: Fine. Your study was perfect and its results are amazing. But you said Africa and the study takes place entirely in Uganda and treating Africa as a uniform mass is racist.  Why don’t you just talk about your tiger prevention efficacy?

africa

The paper graciously conceded my last point, but it knew my heart wasn’t in it. There is no end to the number of follow up studies one can suggest, but this is as good as a single study can be, and I accepted their conclusions. Founding afterschool clubs for girls in Uganda, with a mix of social activities and vocational, and health education, has pretty amazing results. $17.90 US (I know the exact number because the paper specifies it, which I love) spent on a girl translates to an additional $1.70 in monthly spending, almost a 50% increase (they tracked spending rather than earnings because self-employment earnings tend to be feast or famine. Employment also went up significantly), and a decrease in rape and child bearing. That means the program pays for itself in less than a year, and they get some additional benefits on the side. And to the researchers’ credit, the abstract trumpeted the less impressive community-wide numbers, when they could just as easily have used the confounded but shiny attendee numbers.

I mention this for two reasons. One: someone found a way to improve the bodily autonomy and earnings of African young women, basically for free. That’s neat. Two, I read this paper the morning after spending hours on a HAES post (which you may or may not ever read because wordpress ate it, thank you very much. WordPress ate this one halfway through too, so what you read is a cliff’s notes version of my original Socratic dialogue). The HAES post was enormously frustrating, because of the two claims I investigated, I found one (that cyclic dieting, rather than current weight, increases blood pressure) to be pretty misprepresentative of the data, and the other (high blood pressure hurts thin people more than fat people) pretty well supported…for a medical claim. By which I meant the evidence came from either retrospective studies (too many confounds to contemplate) or rats specifically bred to have the physical fitness of an aging Tony Soprano. That is genuinely good for medical research, and that fact is really frightening given how much is riding on getting the correct answer.

So when I read this paper, and see the study is well designed, they explain their modeling in a way an educated non-expert can understand, and they refuted every one of my criticisms, I felt a kind of relief. I’m not quite ready to say “trust the experts”, but at least I didn’t spend two hours tracking down reasons to not trust them

*If something goes from 10% to 20%, that’s an increase of 100% but only 10 percentage points. Switching between the two and failing to give the absolute percentages is a common trick for making data look more impressive than it is.

**Confounding variable, i.e. something that varies between your control and treatment group that is not the thing you are studying, and affects outcomes. The most popular confounding variable is time, e.g.

Pirate_Global_Warming_Graph
But here I’m worried about motivation: girls who show up to a club to learn entrepreneurial and life skills are probably more likely to start businesses and delay marriage than those that don’t attend,

Leptin: Catching Chemicals

Leptn is often considered the anti-ghrelin.    It is produced by fat cells to say “I exist and am full, you do not need to feed me.”  Animals with their leptin gene knocked out grow enormously fat.  This is a perfectly lovely story that can be conclusively proven by a picture of a fat rat.

Figure2diabetes
Bring me Solo and the wookie

If you do not find this story compelling, please consider that I also have a photo of a fat mouse.

Well, if it isn't Lone Star. And his sidekick, Puke
Well, if it isn’t Lone Star. And his sidekick, Puke

Are you convinced yet?  Look, I know last week I said all hormones are almost fractally complicated and anyone who says they completely understand one is lying, but that entry forever to write (thanks for publishing that a week early, wordpress), and this entry has pictures of obese rodents.  Surely you believe the rodents?

Original source: http://commons.wikimedia.org/wiki/File:Big_Fat_Red_Cat.jpg
If no, would a cat be sufficient?

*sigh* I’ve created a monster.

Like ghrelin, leptin is important to fetal lung development because, and I quote, “I don’t know stop asking me.”  Leptin is also produced by the ovaries, skeletal muscle, stomach (some cells produce both ghrelin and leptin), mammary epithelial cells, bone marrow, pituitary, liver, and of course adipose tissue.

Leptin stimulates ovulation and sperm production, which makes some evolutionary sense: getting pregnant when you don’t have the resources to carry it to term in a healthy way is extremely costly (men have to be nearly dying before they stop producing sperm entirely, but levels can drop incompletely before then).  This doesn’t explain why the ovaries (but not testicles) produce leptin, since they don’t have any independent information about fat stores.  This may be an example of an override (in which the ovaries decide they want a baby even though the rest of the body doesn’t believe it has enough fat), but the fact that I can come up with a clever anthropomorphization does not make an explanation legitimate.  You can sort of see why leptin facilitates the onset of puberty, since puberty takes a lot of energy.

What you can’t see is why, despite everything we know about pregnancy and eating, the placenta produces leptin. Excess amounts appear to cause hyperemesis gravidarum (extreme morning sickness aka Kate Middleton’s one weakness).

katemiddleton

High amounts of leptin appear to be good for your brain.  Just so story: brains are extraordinarily expensive, so if you don’t have sufficient savings your body turns on the dimmer switch.  They also have a long term protective effect against Alzheimers.  On the other hand, high levels of leptin alter the immune system in a way that encourages artery hardening.  I am way more afraid of living with Alzheimers than I am of dying of a heart attack, so I will count this as one point for fat.

Leptin’s overall effect on the immune system is complicated.  Leptin is an inflammatory agent, possibly to prevent damage from overreating as your body suddenly tries to shove extra calories that won’t fit in the white adipose tissue under the bed and in the coat closet (the organs).  Which may explain why ghrelin is an anti-inflammatory.  Leptin and ghrelin chose opposite powers and color schemes, like an early 90s superhero cartoon.

Or an early 90s cartoon
The safe represents the hypothalamus

Fatness in humans does not appear to be a problem of inadequate leptin production, and more leptin does not make people thinner.  Instead, it appears that the brains of obese individuals are less sensitive to leptin.  No one knows exactly why, but “crash dieting” is high on the list of suspects.  Two people with identical body compositions but different genes or life history may produce very different amounts of leptin, which means they may require very different behavior to stay the same weight, in ways we do not understand at all.  Which I could have told you before we went on this magical photographic tour of my childhood.  But now we know for sure, plus I learned that fetal lung development is creepily intertwined with food in a way no other organ is.  Let us go forth and use this new knowledge

The Rescue Rangers also want me to play video games.
The Rescue Rangers also want me to play video games.