DNA and Obesity/Diabetes

Am I Fat Because of My DNA?

There are a small number of people who may be fat due to faulty genetics (Lorenzo DN, Bennett V. Cell-autonomous adiposity through increased cell surface GLUT4 due to ankyrin-B deficiency. Proc Natl Acad Sci U S A. 2017;114(48):12743-12748.).

If not much of the fault can likely be blamed on your genes, but just how much can be? From (Sandholt CH, Vestmar MA, Bille DS, Borglykke A, Almind K, Hansen L, Sandbæk A, Lauritzen T, Witte D, Jørgensen T, Pedersen O, Hansen T. Studies of metabolic phenotypic correlates of 15 obesity associated gene variants. PLoS One. 2011;6(9)).

Five of the 15 gene variants associated with overweight, obesity and/or morbid obesity. Per allele ORs ranged from 1.15-1.20 for overweight, 1.10-1.25 for obesity, and 1.41-1.46 for morbid obesity. Five of the 15 variants moreover associated with increased measures of adiposity.

BDNF rs4923461 displayed a borderline BMI-dependent protective effect on type 2 diabetes (0.87 (0.78-0.96, p = 0.008)), whereas SH2B1 rs7498665 associated with nominally BMI-independent increased risk of type 2 diabetes (1.16 (1.07-1.27, p = 7.8×10(-4))).

Another study on obesity and genetics (Gudmar Thorleifsson, G Bragi Walters[…]Kari Stefansso. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nature Genetics volume 41, pages 18–24 (2009)).

Here’s another study on obesity and genetics (Sungshim Lani Park, Iona Cheng, Sarah A. Pendergrass, Anna M. Kucharska-Newton, Unhee Lim, Jose Luis Ambite, Christian P. Caberto, Kristine R. Monroe, Fredrick Schumacher, Lucia A. Hindorff, Matthew T. Oetjens, Sarah Wilson, Robert J. Goodloe, Shelly-Ann Love, Brian E. Henderson, Laurence N. Kolonel, Christopher A. Haiman, Dana C. Crawford, Kari E. North, Gerardo Heiss, Marylyn D. Ritchie, Lynne R. Wilkens, Loïc Le Marchand; Association of the FTO Obesity Risk Variant rs8050136 With Percentage of Energy Intake From Fat in Multiple Racial/Ethnic Populations: The PAGE Study, American Journal of Epidemiology, Volume 178, Issue 5, 1 September 2013, Pages 780–790).

A similar paper on genetics and Type 2 Diabetes (McCarthy MI1, Zeggini E. Genome-wide association studies in type 2 diabetes. Curr Diab Rep. 2009 Apr;9(2):164-71).

Macronutrient Sensitivity and Genetics

My own AncestryDNA data shows an inconclusive result with one less of a carb seeker, one intermediate and one more of a carb seeker:

#ChromPositionSNP IDReliabilityGenotypePhenotypePopulationReferences
1chr2161894663rs197273AA
AG
GG
More a carbohydrate seeker
Intermediate
Less a carbohydrate seeker
European
2chr1949248730rs838145GG
GA
AA
More a carbohydrate seeker
Intermediate
Less a carbohydrate seeker
European
3chr5167226979rs1549309AA
AG
GG
More a carbohydrate seeker
Intermediate
Less a carbohydrate seeker
European
4chr85764942rs2840445GG
GA
AA
More a carbohydrate seeker
Intermediate
Less a carbohydrate seeker
European
5chr1451323742rs8019546AA
AG
GG
More a carbohydrate seeker
Intermediate
Less a carbohydrate seeker
European

This data is based on this study (Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake).

Signif. log(p) Effect Size / Odds Ratio

Confidence

Interval

rs

Reported

Allele

+ Strand

Allele

Allele

Frequency

Trait Genes
9.4 0.22 (Fat)
[0.14-0.3] % decrease
rs838145 G G 0.46 FGF21 Dietary macronutrient intake
9 0.1 (Protein)
[0.061-0.139] % increase
rs1421085 C C 0.42 FTO Dietary macronutrient intake
6.52 0.23 (Carbohydrate)
[0.15-0.31] % increase
rs838145 G G 0.46 FGF21 Dietary macronutrient intake
6.15 0.22 (Carbohydrate)
[0.14-0.3] % increase
rs838147 A A 0.48 Intergenic Dietary macronutrient intake
5.7 0.27 (Carbohydrate)
[0.15-0.39] % increase
rs1549309 A A 0.17 Intergenic Dietary macronutrient intake
5.3 0.22 (Carbohydrate)
[0.12-0.32] % decrease
rs2840445 A A 0.27 Intergenic Dietary macronutrient intake
5.3 0.22 (Carbohydrate)
[0.12-0.32] % increase
rs8019546 A A 0.3 Intergenic Dietary macronutrient intake

AncestryDNA Raw Data Format

AncestryDNA Raw Data Format. From the site:

The information that you’ll receive with your AncestryDNA raw data will include the ‘rs’ ID where possible, chromosome, and the base pair position on the human reference genome (GRCh37). The genotype (the observed alleles at each position) will be provided on the forward strand. The raw DNA data provided has passed the AncestryDNA data quality filters.

An example of raw DNA data looks like:

rsIDchromosomepositionallele1allele2
rs4477212172017AA
rs30943151742429AA
rs31319721742584GG
rs121248191766409GG

It looks like from my data that ancestry.com tests about half of these genes. That’s unfortunate from a health perspective. Maybe the select other genes which are more genetically heritable?

SlimFast Keto Products

Here are another set of products I am going to avoid.

SlimFast Keto Meal Bars

I imagine they could have done worse than they did. But it’s hardly very good.

I have no clue how the label says free from artificial sweeteners but the ingredients list includes Erythritol. I suppose technically it’s considered to be a sugar alcohol and not an artificial sweetener…

Stevia is another “natural” sweetener.

Cultured Dextrose? That’s sugar.

If Ted Naiman and the Protein Leverage Hypothesis is true, we are people in search of protein. Eating products low in protein just makes us eat more.
I get the convenience part, but what’s wrong with real food?

How Metformin Works

Researchers have unlocked more about how Metformin works (
Zydrune Polianskyte-Prause, Tuomas A. Tolvanen, Sonja Lindfors, Vincent Dumont, Mervi Van, Hong Wang, Surjya N. Dash, Mika Berg, Jette-Britt Naams, Laura C. Hautala, Harry Nisen, Tuomas Mirtti, Per-Henrik Groop, Kristiina Wähälä, Jukka Tienari, and Sanna Lehtonen. Metformin increases glucose uptake and acts renoprotectively by reducing SHIP2 activity. The FASEB Journal 0 0:0. 15 Oct 2018).

Metformin inhibits SHIP2 in cultured cells and in skeletal muscle and kidney of db/db mice. In SHIP2-overexpressing myotubes, metformin ameliorates reduced glucose uptake by slowing down glucose transporter 4 endocytosis. SHIP2 overexpression reduces Akt activity and enhances podocyte apoptosis, and both are restored to normal levels by metformin. SHIP2 activity is elevated in glomeruli of patients with T2D receiving nonmetformin medication, but not in patients receiving metformin, compared with people without diabetes. Furthermore, podocyte loss in kidneys of metformin-treated T2D patients is reduced compared with patients receiving nonmetformin medication.

So not only does Metformin reduce the glucose production of the liver by downregulating GNG (Joseph A. Baur and Morris J. Birnbaum, Metformin inhibits gluconeogenesis via a redox-dependent mechanism in vivo.Nature Medicinevolume 24, pages1384–1394 (2018)), it also increases the uptake of glucose in skeletal muscle and the kidneys.

Another paper on the action of Metformin (Baur JA, Birnbaum MJ. Control of gluconeogenesis by metformin: does redox trump energy charge?. Cell Metab. 2014;20(2):197-9.).

Here’s yet another paper on an effect of Metformin (Cheryl A. Collier, Clinton R. Bruce, Angela C. Smith, Gary Lopaschuk, and David J. Dyck. Metformin counters the insulin-induced suppression of fatty acid oxidation and stimulation of triacylglycerol storage in rodent skeletal muscle).

Because increased muscle lipid storage and impaired FA oxidation have been associated with insulin resistance in this tissue, the ability of metformin to reverse these abnormalities in muscle FA metabolism may be a part of the mechanism by which metformin improves glucose clearance and insulin sensitivity.

GNG is Context Driven

I encouraged someone in a Facebook group to increase his protein intake. Let’s call him Josh (not his real name). Josh is a Type 2 Diabetic who controls his diabetes with the low carb diet (not intermittent fasting).

Josh was afraid to increase his protein because in the past when he upped his protein his blood sugar went up the next morning.  This discouraged Josh in the past from eating a higher protein amount thinking it was making his blood sugar higher. But Josh bit the bullet and decided to try and drop his fat intake and increase his protein.

True to his past experiences, this time his blood sugar went up again but he stuck with it and saw his blood sugar fall. It took some days but it did improve. His blood sugar numbers (in the morning) on the new higher protein were 126, 147, 152, 186, 155, 160, 155, 134, 128. Those first four days would have scared me but to his credit he stuck it out.

Why the Increase in Blood Sugar?

Cahill explained this effect in his study on starvation. Here’s the chart from that study.

I’ve looked at this before in this BLOG but let’s look at it in this particular instance to try and understand why Josh’s blood sugar went up.
Josh’s numbers look a lot like the GNG values in Cahills’ curve above – they are just delayed a bit. It took at least a day or two for Josh’s glycogen to drop since he is eating three meals a day (unlike Cahill’s paper where the subjects were starved). 

Type 2 Diabetics are also really good at GNG. We produce 2x-3x the glucose from GNG than non-diabetics. That is part of why we get exaggerated responses in our blood sugars.  I think it has to due with insulin resistance in the liver. The Type 2 diabetic’s liver is Insulin Resistant meaning it doesn’t listen to the clue that rising insulin is giving that it needs to drop the production of glucose (ie, GNG). 

GNG is done in the liver until glycogen and TG stores are gone. That only happens with a sustained caloric deficit or alternately a longer fast. Cahill points out that GNG is done later in the kidneys, etc which are apparently signaled more by the presence of increased ketones. Josh reported seeing his ketone production increasing. This is as a response to a caloric deficit and the drop in glycogen and TG stores.  Ketones have to go up when glucose drops because our brains need energy.

Context, Context, Context

We diabetics get concerned about our blood sugar but we sometimes don’t understand the context (reason) that the blood sugar goes up. My goal is to better understand for myself and help explain the context to others.

What Josh has now learned is that the increase in blood sugar follows a pattern where it happens for several days then stops to drop. If he keeps this up long enough he will see it level out at a good number. The event (blood sugar going up) needs be put into the proper context (trends, what the protein is doing in the body, etc).

Increases in blood sugar can be a sign (and are in this context) of the start of a caloric deficit which leads to weight loss. The body has to do GNG to make up for the lost energy from food.

This is where the people who say GNG is demand driven are right and wrong. GNG is context driven as Cahill demonstrated clearly. The demand for GNG changes in the context of glycogen status. 

The first evidence that you are doing the right thing with blood sugar shows exactly the opposite and leads you to think you are doing the wrong thing. If you try something for three or four days and it gets worse every day it confirms your fears that it’s not good. But if it starts to turn a corner it encourages you to press on.

Cortisol is [Partly] to Blame

A part of the reason is that the first few days of a caloric deficit (or fast for that matter) increase cortisol. Your body is telling you to get up and find/eat more food. Your body could care less about whether you’ve got enough energy stores (body fat). It just knows that you need to get up and hunt down or gather in dinner. 

Cortisol and Dawn Phenomenon

Increasing cortisol is what coincidentally happens before you get up in the morning. Your body is giving you the cortisol boost to get you moving. And for a diabetic, even though their last meal was 12 hours before, their blood sugar spikes up. The blood sugar wasn’t spiking up from any particular food. It was spiking up because of the cortisol that was being produced.

Josh’s blood sugar went up because he was not getting enough calories and his body was pumping out cortisol. That’s a completely good thing in this case since it signals good things are happening. It sucks that the blood sugar goes up, but it’s completely expected for several days as Cahill shows. Stay at the caloric deficit and it will begin to drop.

GNG is Context Driven

Just because GNG is demand driven doesn’t explain the demand. The demand for GNG is produced when glycogen stores drop and ketone production hasn’t kicked in enough yet. That happens every morning in the Dawn Phenomenon.

Another way to put it is that the demand can change and it does (as Cahill put it) by the caloric deficit not being met by carbs we eat (Phase I), or glycogen stores (Phase II). Eventually the body will down-regulate the need for glucose and even GNG will drop(Phase II to Phase IV). Phase V is marked by a much higher level of ketone production which takes the place of energy from glucose.

Ketogenic Diet – Fat Adaptation

To my way of thinking, this is what is meant by fat adaptation and the ketogenic diet… Ketones being used primarily as fuel. Doesn’t come from eating a lot of dietary fat. Quite the opposite. If you eat a lot of dietary fat it will get converted to glycogen.

If you are in a caloric deficit and your glycogen stores are lowered it has implications on athletic performance. And this may be the necessary state for diabetics who are keto to be in the majority of the time in order to control their diabetes. It may be the case that we can’t control our diabetes effectively without losing performance in glycogen demanding sports (sports at higher intensities).

Easier Paths?

It would have helped if Josh had gone into this by way of Intermittent Fasting or even a two or three day fast since it would have smoothed the transition. 

The same chart can be used to help people understand why Low Carb PLUS Intermittent Fasting is an effective strategy for Diabetics.
Low Carb eliminates phase I since you are not eating carbohydrates.
Just doing Low Carb without Intermittent Fasting causes you to refill your glycogen stores every day.

An 8-12 hour cycle isn’t enough to deplete glycogen. The person stays in Phase II forever. Note that at 6-8 hours glycogen peaks and at 12 hours it’s starting to drop a bit. Glycogen has dropped quite a bit more between hours 8 and 20. 

Suppose you do a 20:4 intermittent fast (into Phase III). If you do that your glycogen stores are getting more depleted every day. It doesn’t take too many days for your glycogen stores to drop enough that you don’t refill them the next day – especially if you are Low Carb where it’s harder to refill the Glycogen stores.

That is why Intermittent Fasting works so well. But it has to be done long enough to start to downregulate GNG and upregulate ketone production. Starting at OMAD or a 20:4 margin worked for me. It’s what got me to my HbA1C of 5.2 (non-diabetic blood sugar control).

People mistakenly think that the magic of Intermittent Fasting is that you just happen to eat less calories. Maybe that’s true or maybe it isn’t but the real magic is that it depletes your glycogen stores. At some point your body has to be producing ketones. By depleting glycogen (exercise helps and hurts this BTW) you are on your way to ketone production.

Glycogen Shifts

Working on a theory of glycogen and caloric surplus/matching/deficit. My theory is that glycogen stores are somewhat related to carbohydrate consumption (how full the tank gets) but also to caloric status. My theory is that eating at a surplus of calories even on low carb will fill the glycogen stores higher than the “normal” keto level.

This explains to me the wide fluctuations in weight that I and others see when we gain or lose 5-7 lbs in a few days. In fact, I think it’s pretty easy to gain in a day or two and might take some days to lose again what was gained in that day or two. The reason is that glycogen stores can get filled quickly but unless you are at a caloric deficit they won’t get drawn down.

Explains the “LBM gains” people have when they increase their caloric intake. Normal body water amount vary greatly along with the glycogen.

See (Keto Flush – How Body Water and Glycogen Affect Ketogenic Weight Loss).

Competition for Calories

Here is a very new paper which has an interesting way of looking at nutrient partitioning (Archer Edward, Pavela Gregory, McDonald Samantha, Lavie Carl J., Hill James O. Cell-Specific “Competition for Calories” Drives Asymmetric Nutrient-Energy Partitioning, Obesity, and Metabolic Diseases in Human and Non-human Animals. Frontiers in Physiology, v9:2018, 1053):

…we posit that the chronic positive energy balance (i.e., over-nutrition) that leads to obesity and metabolic diseases is engendered by apparent deficits (i.e., false signals) driven by the asymmetric inter-cellular competition for calories and concomitant differential partitioning of nutrient-energy to storage. These frameworks, in concert with our previous theoretic work, the Maternal Resources Hypothesis, provide a parsimonious and rigorous explanation for the rapid rise in the global prevalence of increased body and fat mass, and associated metabolic dysfunctions in humans

Obesity and Diabetes

There’s a common definition of the word “obese”. We think of people who are really fat as being obese. I was one of them. 

What is Obesity?

Obesity has a technical definition which is somewhat arbitrary. It is a function of weight and height and is known as BMI (Body Mass Index). The US government definition is (NCHS Data Brief ■ No. 288 ■ October 2017):

Obesity: BMI was calculated as weight in kilograms divided by height in meters squared, rounded to one decimal place.

Obesity in adults was defined as a BMI of greater than or equal to 30.

BMI Weaknesses as a Metric

BMI (and obesity) does not take into account body composition such as body fat or lean body mass.  Two people can have the same BMI and be technically obese and one be solid muscle with little body fat and the other have significantly more body fat.

However, for the “average” person BMI is a decent measurement of fatness.

Obesity and Health

Generally, obesity and health are inversely related but there are people who are obese (by BMI) but are healthy. There are also people who are not obese but have poor health. This observation has led to the concept of personal fat threshold (PFT). This is described in (Taylor R, Holman RR.  Normal weight individuals who develop type 2 diabetes: the personal fat threshold. Clin Sci (Lond). 2015 Apr;128(7):405-10) (PDF).

Personal Fat Threshold (PFT)

The Personal Fat Threshold concept is that there’s a level of fatness which the individual can tolerate before their health is impacted. This concept is tempting but has some problems.

PFT is not all that useful in the a-priori sense. There is no objective test to see if someone is at or near their PFT. Obesity isn’t useful as a metric. Neither is body fat level.

The only use of PFT is to support the medical advice to patients of weight loss as a tool for management of Type 2 diabetes. The PFT concept doesn’t actually contribute much since it has been believed (before the PFT concept was developed) that weight loss of about 15% resolves diabetes (Reversing Diabetes with Weight Loss: Stronger Evidence, Bigger Payoff).

Until there’s an a-priori means of measuring PFT the approach seems to be not all that useful. No medical doctor can tell you that you are 10 lbs away from your PFT. The point is completely hidden until it manifests. All it says that is if you are not technically considered to be obese and you are diabetic it is because you have gone over your personal fat threshold. 

PFT – My Own Experience

There are three lines of reasoning from my own experience that call into question the PFT theory.

One was from my own experience with Insulin as a Type 2 Diabetic. I put on 40 lbs in a short time when I was put on Insulin. Conversely, when I got off Insulin my weight dropped quickly. Teenage females who are Type 1 diabetics and want to lose weight are well aware of this relationship. Weight increases followed Insulin increases (Skovsø S, Damgaard J, Fels JJ, Olsen GS, Wolf XA, Rolin B, Holst JJ. Effects of insulin therapy on weight gain and fat distribution in the HF/HS-STZ rat model of type 2 diabetes. Int J Obes (Lond). 2015 Oct;39(10):1531-8). not Insulin followed weight. Eventually, stasis is reached in weight and Insulin amount – at least in the short term.

Increasing dietary carbohydrates requires pumping more Insulin. When you stop eating dietary carbohydrates you don’t have to inject extra insulin for the meal. 

The second reason was the increase in Insulin that is required over time to maintain blood sugar levels. I started at about 40g of Insulin and had good blood sugar controls. By four later my weight was stable but the amount of Insulin to keep blood sugar stable kept increasing to about 120 units. More particularly, the amount of insulin to cover carbohydrate loads increased. In my own case 1 unit of Insulin could cover 15 grams of carbs when I started Insulin and by four years later 1 unit wasn’t enough to cover 8 grams. All of this was at a stable weight (after the initial gain) and the same level of carbohydrates.

A third reason is my own weight history. I was at 285 lbs and non-diabetic for years. Then I mysteriously lost 50 lbs down to 235 lbs over the course of about six months. This is a common occurrence with Type 2 diabetics (Unexplained Weight Loss and Diabetes). After six months of this unexplained weight loss, I was then diagnosed with diabetes.

Perhaps this is the body pushing back from the PFT but it does call the concept into question – or at least indicate the real issue is much more complicated. After being put on Metformin my weight stabilized at around 10 lbs higher (although Metformin is said to lower weight). As my diabetes got worse my doctor tried different medications some of which added weight and some (like Byetta) caused small weight loss. Finally, the addition of Insulin added 40 lbs to my weight.

I did low carb while on Insulin but it only took my HbA1C down to 6.4. It wasn’t until I did low carb plus Intermittent Fasting that I was able to get off Insulin and my weight fell very quickly. My last HbA1C was 5.2 which is a normal non-diabetic number.

Carbohydrate Insulin Relationship

At the very least, if the PFT concept is salvageable, it needs to be modified for increasing Insulin Resistance levels. If the best treatment for diabetes is weight loss the best way for Type 2 Diabetics to lose weight is to reduce insulin levels. The best way to reduce insulin levels is to the insulin load of the diet. For a Type 2 Diabetic who is on Insulin this results in a loss of a lot of weight in a very short period of time.

The recommendation that losing 15% of body weight does not seem plausible to a diabetic like myself. I’ve lost more than 15% from my peak weight and not been able to control my diabetes. I lost weight with Low Carb by itself but not enough to get off Insulin. At it was more than 15% of weight loss.  If I was told that losing 15% of my body weight would control my diabetes I would have told my doctor that I tried it and it didn’t work.

I lost much less than 15% of my weight in the beginning of Low Carb plus Intermittent Fasting and was able to get off Insulin completely. It was getting off Insulin which allowed me to lose weight. And it was reducing my body’s Insulin needs by the Low Carb diet and Intermittent Fasting which worked for me.

See (Obesity and Insulin Resistance).

The Right Goal

Weight loss alone should never be your goal. Fat loss should be your goal. This can be demonstrated from the numbers. If you have 25% body fat then the weight you want to lose should come out of that 25% of body fat and not from the 75% of lean body mass. If you lose weight and most of the weight comes from your lean body mass you have not done yourself any favors.

Maximum Fat Loss

The fastest way to lose fat is to greatly reduce your carbohydrates and fat intake. Protein should never be reduced. For most people protein should be increased.

Macros for Fat Loss

There is a pretty simple set of macros for maximum body fat loss.

  • Protein at 1 gram per lb of goal weight. 
  • Carbs at less than 30 grams net. 
  • Fat at less than half the grams of protein. 

Macros Calculator

I made a calculator for maximum fat loss. The calculator estimates your current body fat and asks you to say what percentage body fat you want to reach.

Protein

The recommended daily protein minimums are pretty low. I suggest much more. If you have normal kidney function that is no problem.

You need enough protein in your diet to replace the protein your body will eat up during the diet. You also need some for gluconeogenesis. Since you will be eating at a caloric deficit any extra protein won’t be a problem – it won’t turn into chocolate cake.

Protein has essential nutrients. Eating 3 grams of Leucine (found in about 30g of protein) is a good goal to hit with every protein meal. That’s around 5 ozs of skinless chicken breast.

Carbs

Eat the carbs as green leafy veggies. Broccoli is a great choice for micronutrients. You don’t like the taste? Get over it. It’s good for you. And you will eventually grow to like the taste.

Fat

If you want to lose fat faster, eat less fat. If you are losing too quickly, eat more fat. The fat you eat doesn’t come off your body. The fat you don’t eat in your diet comes off your body. Any fat you eat is stored on your body very efficiently. Fat has few essential nutrients.

Even a low fat diet is still relatively high fat. The fat is just coming off your body. You can’t stay on a low fat diet forever. You have to increase your fat over time as you reach your goals.

It’s a good idea to take a couple of fish oil capsules every day to get more of the good fats.

Studies on this Diet

This is also known as a variant on the Protein Sparing Modified Fast. It is well studied and effective. The PSMF is often done at very low (20g) of fat.

Ketogenic Infants

From (Settergren G, Lindblad BS, Persson B. Cerebral blood flow and exchange of oxygen, glucose, ketone bodies, lactate, pyruvate and amino acids in infants. Acta Paediatr Scand. 1976 May;65(3):343-53):

Mean values from 12 infants (age 11 days-12 months) were: CBF 69 ml/100 g0min-1; cerebral uptake (in mumoles/100 g-min-1): oxygen 104, glucose 27, acetoacetate 0.9, D-beta-hydroxybutyrate 2.3; cerebral release: lactate 2.4 and pyruvate 0.8. Significant uptake of amino acids was found only for histidine 0.95 and arginine 0.7. Significant correlations between arterial concentration and cerebral exchange were found for: ornithine, arginine, phenylalanine, aspartic acid, serine, glutamine and acetoacetate. CBF and substrate exchange were unrelated to age within the group.

Infants had higher mean CBF and greater uptake of ketone bodies than has been reported in adults.