Metabolic Flexibility: A Literature Review

Metabolic Flexibility: A Literature Review

Below is a shorter literature review I did as part of my PhD research. It can be on the dry side, but the take away is that as your body gets closer to a Metabolically INflexible state (e.g. diabetes) you have a much harder time process any food and turning it into a good fuel sources.

If you are very Metabolically Flexible, you can adapt to virtually any fuel source (e.g. various foods). Now this is not an argument for going crazy and eating Ho Hos and Krispy Kremes, there are limits!

The point is that every is different and perhaps there is a way to quantify how metabolically efficient each person’s body is without subjecting them to IVs and sticks in the arm for hours at a time.

Any questions, let me know and I will be happy to discuss. Big thank you to my advisor Dr. Don Dengel and Dr. George Biltz for the ideas, background, and all the support.

This ran awhile back, but since I am doing a presentation on Metabolic Flexibility for Fitcon II and some other projects, I thought I would re-run it here.   Other had asked for literature that supports that theory too, so here you go.  I am more than happy to answer any questions in the comment section too, so post away.  It is a VERY dense post and I will have more break downs that are easy to digest in the future again.

For a simple version, see this post

Metabolic Flexibility: You Need to Burn that Fat Off!

Enjoy

Mike N

METABOLIC FLEXIBILITY (and also INFlexibility)

It is no secret that in the United States, the rate of obesity in children is on the rise. In fact, childhood obesity in the US has tripled over the last 40 years and doubled in the past 15 years

(32). About 40% of adolescents seen in the University of West Virginia pediatric clinic have body mass index (BMI) greater than 85% for gender and age (44). Body fat and its distribution is related to cardiovascular disease, hypertension and type 2 diabetes, all diseases that are considered to have an “incubation period” during childhood and adolescence (51). In 2003-2004 17.1% of US children and adolescents (age 2 to 19) were overweight (defined as at or above the 95th percentile of the sex specific BMI for age growth charts) (29). If the current epidemic of child and adolescent obesity continues at the same rate, life expectancy could be shortened by two to five years in the coming decades(30) and it will be the first time in recent history that life

expectancy has decreased.

LITERATURE REVIEW

Metabolic Flexibility

Due to possible discontinuities in both the supply and demand for energy, humans need a “clear capacity to utilize lipid and carbohydrate fuels and have the ability to transition between them.” (18). This capacity is a healthy state and termed “Metabolic Flexibility”. It is hypothesized that metabolic inflexibility may play a role in various disease processes such as the metabolic syndrome that may even start in childhood (3, 27, 28, 46). Location of body fat may affect

disease risk also and data from prospective studies using waist to hip ratio or waist circumference confirmed that abdominal obesity is more closely associated with disease risk than total body fatness(6, 7, 22).

A key to understanding metabolic flexibility is the vital role of insulin. In humans, insulin is a regulatory hormone synthesized in the pancreas within the beta cells (?-cells) of the islets of Langerhans. Insulin can be characterized by two phases an initial (cephalic phase) driven by the nervous system and a sustained secondary phase (1). Some data indicated that variations in prestimulatory glucose can secondarily affect the magnitude and pattern of subsequent glucose-induced insulin secretions (13). Humans in a healthy state with normal insulin

metabolism have the ability to effectively switch from primarily a fat metabolism to a carbohydrate metabolism. Also, in human subjects that reach a stage in the metabolic syndrome characterized by insulin resistance and glucose intolerance bordering on frank diabetes, there is still considerable beta-cell capacity demonstrating a clear absence of the normal initial peak of insulin secretion (5, 45). Skeletal muscle is a major player in energy balance due to its metabolic activity, storage capacity for both glycogen and lipids, and its effects on insulin sensitivity (9-11). Obesity/visceral fat, transient state of puberty, ethnicity, genetic factors, and physical inactivity all may lead to insulin resistance (2).

Elevated lipid content and intramuscular triglyceride (IMTG) are both linked to insulin

resistance (20)and thus compromise efficient lipid utilization. Perseghin et al. (31) used magnetic resonance spectroscopy (MRS) to report that lipids contained within muscle fibers were strongly correlated with the severity of insulin resistance. In metabolically inflexible subject, lipid oxidation may fail to increase with fasting and fail to suppress with hormonal insulin elevation. Lowered post-absorptive fatty acid oxidation leads to excess accumulation of IMTGs and begins a downward spiral. Interestingly, endurance trained athletes also have an increased IMTG level, but remain insulin sensitivity (perhaps from increased turnover rate) (9).

Kelley et al. (17) (as shown in Figure 1 below) showed that under basal fasting conditions glucose uptake and oxidation are normal or even increased in obese subjects compared with lean subjects. Fatty acid uptake is also normal, but fatty acid oxidation is lower and its storage is elevated in the obese group which may explain why they have a higher body fat as they are more apt to store fat.

During a hyperinsulinaemic euglycaemic clamp condition the differences between lean and obese are quite different. In lean subjects, glucose uptake increased 10 fold with both oxidation and storage primarily contributing while fatty acid uptake decreased equally dramatically. In

obese subjects however, glucose uptake, oxidation and storage are reduced; which is quite a different response from the lean group.

Figure 1 (47) shows the contributions of lipid and glucose oxidation to resting energy expenditure of the leg. Obese subjects derived relatively less energy from lipid oxidation during basal conditions; showing a blunted fat burning response. During insulin-stimulated conditions, lean subjects show a greater suppression of lipid oxidation compared to the obese group under

the same conditions.

Figure 1 from Kelley et al. 1999

In summary, Kelley et al. (17) presented data from subjects with type 2 diabetes showing metabolic inflexibility as obese subjects derived relatively less energy from lipid oxidation during basal conditions (P<0.01). Lean subjects showed a greater suppress
ion of lipid oxidation during insulin-stimulated conditions (p<0.01). As shown in Figure 2 below, lean subjects have a different response compared to obese and diabetic’s subjects as carbohydrate oxidation is increased (19).


Figure 2 from Kelley et al. (19)

Assessment of Metabolic Inflexibility

One way to assess metabolic flexibility is by the infusion of drugs (insulin, glucose, etc) to alter the metabolic environment. The downside is that this is more difficult to use in a clinic, requires more specialized training, and is not generally an option for children due to its invasive nature. Metabolic inflexibility is also dynamic in nature and the data collected are normally for acute settings and brief time periods only. An ideal method of assessment would be non invasive and able to collect dynamic data.

HRV

A noninvasive measure of a dynamic system is done currently by the collection of cardiac data via heart rate variability (HRV) (40). HRV analysis has been used extensively to assess autonomic control of the heart under various physiologic conditions. Most often linear analysis is done in both the time and frequency domain.

There are some data to suggest a difference in HRV for obese and non-obese individuals (25). It is well know that the autonomic nervous system ANS) plays an important role in regulating energy expenditure and body fat content, but to what extent is not exactly clear. Nagai, et al. (25) studied 42 non-obese and obese healthy school children where both groups were matched for age, gender, and height. ANS activity was assessed by HRV power spectral analysis. The results showed that the obese children had reduced sympathetic as well as parasympathetic nerve activity which could be a factor in preventing and treating obesity.

Activity is also known to affect HRV (26). Nagai et al. (26) presented data that lean active children demonstrated a lower resting heart rate (HR) as well as higher total power (TP), low frequency (LF), and high frequency (HF). LF reflects mixed sympathetic (SNS) and parasympathetic (PNS) activity, HF reflects PNS activity and TP evaluating the overall ANS activity. In contrast, obese-inactive group showed significantly lower TP, LF and HF. These data suggest obese children have reduced sympathetic and parasympathetic nervous activities as compared to lean children with similar physical activity levels. This autonomic reduction that is associated with the amount of body fat in inactive state may be an important factor for the onset or development of childhood obesity. The good news is that regular physical activity could contribute to enhance the ANS activity in both lean and obese children (26).

There are some data to suggest alterations in HRV in young patients with diabetes (14). Autonomic neuropathy is a common complication of diabetes mellitus (DM) and the aim of the study was to assess HRV changes during prolonged (40 minute) supine rest in 17 young patients with DM compared to an aged matched healthy control group. HRV analysis consisted of time/frequency domains, Poincare and sequence plots and sample entropy. The study found that HRV was able to distinguish cardiac dysregulation in young patients with DM from a control group. However, it did not find any significant difference in sample entropy between the groups, perhaps due to the subtle nature of the cardiovascular impairment in young DM patients (14). Data from Porta et al. (41) used SampEn and ApEn to analyze HRV during a head-up tilt test and concluded that with short duration data SampEn was significantly more reliable at producing accurate entropy scores.

HRV provides a non invasive method that is able to capture data in a dynamic fashion, but to date it has very limited data regarding its relation to metabolic inflexibility.

Sample Entropy

Entropy, in the original context of thermodynamics is a measure of system disorder and randomness. Approximate entropy was first coined by Pincus et al. (36) in 1991 as a way to quantify the dynamic control of a system (such as HR control) and possibly analyze many other “random” sequences (34). The promise of approximate entropy (ApEn) is that it can classify complex systems with only 100 data values in diverse setting that include both deterministic chaotic and stochastic processes (34). To date, ApEn has been used in the analysis of medical data (37), cardiology (16, 43) and neurohormonal responses (15, 35, 38, 49, 50).

The ApEn algorithm counts each sequence as matching itself to avoid the occurrence of ln (0) in the calculations. ApEn is heavily dependent on the record length and is uniformly lower than expected on short records (42). It is also lacking in relative consistency meaning that if ApEn for one data set is higher than another, it should but does not remain higher for all conditions tested (33).

Sample entropy (SampEn) was developed to reduce the bias of ApEn as it does not count self-matches. Richman et al. (42) defines SampleEn as “precisely the negative natural logarithm of the conditional probability that two sequences similar for m points remain similar at the next point, where self-matches are not included in calculating probability.” So a lower value of SampEn indicates more self-similarity (and thus less variability). SampEn is defined in terms (m,r, N) where m is the length of sequences to be compared, r is the tolerance for accepting matches and N is the length of the time series. Another benefit of SampEn is that it does not use a template-wise approach when estimating conditional probabilities as it is in essence an event-counting statistic (42). In a study by Richman et al. (42) SampEn agreed much better than ApEn statistics with theory for random numbers with known probabilistic character over a broad range of operating conditions and it has successful been used to calculate HRV on very short ECG mV recordings (10 to 60 seconds); so it does not appear to require long periods of data collection (4). HRV calculated by SampEn has been used in studies on recovery post exercise training (12, 24) and alterations due to disease and aging (39). Lake et al. (21)performed a sample entropy analysis of neonatal HRV in an attempt to predict sepsis and found that entropy falls before clinical signs of neonatal sepsis and also that missing data points were well tolerated.

RER

The RER is the ratio of the volume of CO2 to O2 and can be measured with a metabolic cart to collect expired gases. The RER at steady state is displayed as a ratio between 0 .7 to 1.0 where 0.7 corresponds to 100% fat metabolism, 0.85 corresponds to 50% fat and 50% carbohydrate metabol
ism and 1 corresponds to 100% carbohydrate metabolism.

RER has been found to be reproducible during exercise under standardized conditions (23), but factors such as age, gender, dietary substrate intake, insulin, and plasma free fatty can influence the selection of substrates during exercise and hence alter RER(8, 48).

IMPLICATIONS

With the rise in obesity, it will be imperative to have a method to determine which children are on the fast track to further metabolic damage. Current methods such as insulin clamps may be effective, but they require more training on the clinician side, more difficult to obtain IRB approval and many times will not be used children due to their invasive nature. Future studies may be conducted on newer non-invassive methods to determine metabolic inflexibility and potentially investigate the effects of various forms of exercise and nutrition methods to combat obesity in children and target those in high risk groups.

References

1. The Cell Physiology of Biphasic Insulin Secretion — Rorsman et al. 15 (2): 72 — Physiology. 2007(12/12/2007).

2. Amiel S. A., S. Caprio, R. S. Sherwin, G. Plewe, M. W. Haymond, W. V. Tamborlane. Insulin resistance of puberty: a defect restricted to peripheral glucose metabolism. J Clin Endocrinol Metab. 72(2):277-282, 1991.

3. Arslanian S., C. Suprasongsin. Insulin sensitivity, lipids, and body composition in childhood: is “syndrome X” present? J Clin Endocrinol Metab. 81(3):1058-1062, 1996.

4. Bornas X., J. Llabres, M. Noguera, A. Pez. Sample entropy of ECG time series of fearful flyers: preliminary results. Nonlinear Dynamics Psychol Life Sci. 10(3):301-318, 2006.

5. Bruce D. G., D. J. Chisholm, L. H. Storlien, E. W. Kraegen. Physiological importance of deficiency in early prandial insulin secretion in non-insulin-dependent diabetes. Diabetes. 37(6):736-744, 1988.

6. Donahue R. P., R. D. Abbott. Central obesity and coronary heart disease in men. Lancet. 2(8569):1215, 1987.

7. Ducimetiere P., J. Richard, F. Cambien. The pattern of subcutaneous fat distribution in middle-aged men and the risk of coronary heart disease: the Paris Prospective Study. Int J Obes. 10(3):229-240, 1986.

8. Goedecke J. H., A. St Clair Gibson, L. Grobler, M. Collins, T. D. Noakes, E. V. Lambert. Determinants of the variability in respiratory exchange ratio at rest and during exercise in trained athletes. Am J Physiol Endocrinol Metab. 279(6):E1325-34, 2000.

9. Goodpaster B. H., J. He, S. Watkins, D. E. Kelley. Skeletal muscle lipid content and insulin resistance: evidence for a paradox in endurance-trained athletes. J Clin Endocrinol Metab. 86(12):5755-5761, 2001.

10. Goodpaster B. H., D. E. Kelley. Skeletal muscle triglyceride: marker or mediator of obesity-induced insulin resistance in type 2 diabetes mellitus? Curr Diab Rep. 2(3):216-222, 2002.

11. Goodpaster B. H., S. Krishnaswami, H. Resnick, et al. Association between regional adipose tissue distribution and both type 2 diabetes and impaired glucose tolerance in elderly men and women. Diabetes Care. 26(2):372-379, 2003.

12. Heffernan K. S., C. A. Fahs, K. K. Shinsako, S. Y. Jae, B. Fernhall. Heart rate recovery and heart rate complexity following resistance exercise training and detraining in young men. Am J Physiol Heart Circ Physiol. 293(5):H3180-6, 2007.

13. Henquin J. C., M. Nenquin, P. Stiernet, B. Ahren. In vivo and in vitro glucose-induced biphasic insulin secretion in the mouse: pattern and role of cytoplasmic Ca2+ and amplification signals in beta-cells. Diabetes. 55(2):441-451, 2006.

14. Javorka M., J. Javorkova, I. Tonhajzerova, A. Calkovska, K. Javorka. Heart rate variability in young patients with diabetes mellitus and healthy subjects explored by Poincare and sequence plots. Clin Physiol Funct Imaging. 25(2):119-127, 2005.

15. Juhl C. B., O. Schmitz, S. Pincus, J. J. Holst, J. Veldhuis, N. Porksen. Short-term treatment with GLP-1 increases pulsatile insulin secretion in Type II diabetes with no effect on orderliness. Diabetologia. 43(5):583-588, 2000.

16. Kaplan D. T., M. I. Furman, S. M. Pincus, S. M. Ryan, L. A. Lipsitz, A. L. Goldberger. Aging and the complexity of cardiovascular dynamics. Biophys J. 59(4):945-949, 1991.

17. Kelley D. E., B. H. Goodpaster. Skeletal muscle triglyceride. An aspect of regional adiposity and insulin resistance. Diabetes Care. 24(5):933-941, 2001.

18. Kelley D. E., J. He, E. V. Menshikova, V. B. Ritov. Dysfunction of mitochondria in human skeletal muscle in type 2 diabetes. Diabetes. 51(10):2944-2950, 2002.

19. Kelley D. E., L. J. Mandarino. Fuel selection in human skeletal muscle in insulin resistance: a reexamination. Diabetes. 49(5):677-683, 2000.

20. Kelley D. E., F. L. Thaete, F. Troost, T. Huwe, B. H. Goodpaster. Subdivisions of subcutaneous abdominal adipose tissue and insulin resistance. Am J Physiol Endocrinol Metab. 278(5):E941-8, 2000.

21. Lake D. E., J. S. Richman, M. P. Griffin, J. R. Moorman. Sample entropy analysis of neonatal heart rate variability. Am J Physiol Regul Integr Comp Physiol. 283(3):R789-97, 2002.

22. Lapidus L., C. Bengtsson, B. Larsson, K. Pennert, E. Rybo, L. Sjostrom. Distribution of adipose tissue and risk of cardiovascular disease and death: a 12 year follow up of participants in the population study of women in Gothenburg, Sweden. Br Med J (Clin Res Ed). 289(6454):1257-1261, 1984.

23. Laplaud D., R. Menier. Reproducibility of the instant of equality of pulmonary gas exchange and its physiological significance. J Sports Med Phys Fitness. 43(4):437-443, 2003.

24. Lewis M. J., A. L. Short. Sample entropy of electrocardiographic RR and QT time-series data during rest and exercise. Physiol Meas. 28(6):731-744, 2007.

25. Nagai N., T. Matsumoto, H. Kita, T. Moritani. Autonomic nervous system activity and the state and development of obesity in Japanese school children. Obes Res. 11(1):25-32, 2003.

26. Nagai N., T. Moritani. Effect of physical activity on autonomic nervous system function in lean and obese children. Int J Obes Relat Metab Disord. 28(1):27-33, 2004.

27. Nistala R., C. S. Stump. Skeletal muscle insulin resistance is fundamental to the cardiometabolic syndrome. J Cardiometab Syndr. 1(1):47-52, 2006
.

28. Oakes N. D., P. Thalen, E. Aasum, et al. Cardiac metabolism in mice: tracer method developments and in vivo application revealing profound metabolic inflexibility in diabetes. Am J Physiol Endocrinol Metab. 290(5):E870-81, 2006.

29. Ogden C. L., M. D. Carroll, L. R. Curtin, M. A. McDowell, C. J. Tabak, K. M. Flegal. Prevalence of overweight and obesity in the United States, 1999-2004. JAMA. 295(13):1549-1555, 2006.

30. Olshansky S. J., D. J. Passaro, R. C. Hershow, et al. A potential decline in life expectancy in the United States in the 21st century. N Engl J Med. 352(11):1138-1145, 2005.

31. Perseghin G., P. Scifo, F. De Cobelli, et al. Intramyocellular triglyceride content is a determinant of in vivo insulin resistance in humans: a 1H-13C nuclear magnetic resonance spectroscopy assessment in offspring of type 2 diabetic parents. Diabetes. 48(8):1600-1606, 1999.

32. Pietrobelli A., M. S. Faith, D. B. Allison, D. Gallagher, G. Chiumello, S. B. Heymsfield. Body mass index as a measure of adiposity among children and adolescents: a validation study. J Pediatr. 132(2):204-210, 1998.

33. Pincus S. Approximate entropy (ApEn) as a complexity measure. Chaos. 5(1):110-117, 1995.

34. Pincus S., R. E. Kalman. Not all (possibly) “random” sequences are created equal. Proc Natl Acad Sci U S A. 94(8):3513-3518, 1997.

35. Pincus S. M. Orderliness of hormone release. Novartis Found Symp. 227:82-96; discussion 96-104, 2000.

36. Pincus S. M. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A. 88(6):2297-2301, 1991.

37. Pincus S. M., I. M. Gladstone, R. A. Ehrenkranz. A regularity statistic for medical data analysis. J Clin Monit. 7(4):335-345, 1991.

38. Pincus S. M., J. D. Veldhuis, A. D. Rogol. Longitudinal changes in growth hormone secretory process irregularity assessed transpubertally in healthy boys. Am J Physiol Endocrinol Metab. 279(2):E417-24, 2000.

39. Platisa M. M., V. Gal. Dependence of heart rate variability on heart period in disease and aging. Physiol Meas. 27(10):989-998, 2006.

40. Platisa M. M., V. Gal. Reflection of heart rate regulation on linear and nonlinear heart rate variability measures. Physiol Meas. 27(2):145-154, 2006.

41. Porta A., T. Gnecchi-Ruscone, E. Tobaldini, S. Guzzetti, R. Furlan, N. Montano. Progressive decrease of heart period variability entropy-based complexity during graded head-up tilt. J Appl Physiol. 103(4):1143-1149, 2007.

42. Richman J. S., J. R. Moorman. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 278(6):H2039-49, 2000.

43. Ryan S. M., A. L. Goldberger, S. M. Pincus, J. Mietus, L. A. Lipsitz. Gender- and age-related differences in heart rate dynamics: are women more complex than men? J Am Coll Cardiol. 24(7):1700-1707, 1994.

44. Someshwar J., S. Someshwar, K. C. Perkins. The obese adolescent. Pediatr Ann. 35(3):180-186, 2006.

45. Storlien L., N. D. Oakes, D. E. Kelley. Metabolic flexibility. Proc Nutr Soc. 63(2):363-368, 2004.

46. Stump C. S., E. J. Henriksen, Y. Wei, J. R. Sowers. The metabolic syndrome: role of skeletal muscle metabolism. Ann Med. 38(6):389-402, 2006.

47. Takarada Y., H. Takazawa, N. Ishii. Applications of vascular occlusion diminish disuse atrophy of knee extensor muscles. Med Sci Sports Exerc. 32(12):2035-2039, 2000.

48. Toubro S., T. I. Sorensen, C. Hindsberger, N. J. Christensen, A. Astrup. Twenty-four-hour respiratory quotient: the role of diet and familial resemblance. J Clin Endocrinol Metab. 83(8):2758-2764, 1998.

49. Veldhuis J. D., M. L. Johnson, O. L. Veldhuis, M. Straume, S. M. Pincus. Impact of pulsatility on the ensemble orderliness (approximate entropy) of neurohormone secretion. Am J Physiol Regul Integr Comp Physiol. 281(6):R1975-85, 2001.

50. Veldman R. G., M. Frolich, S. M. Pincus, J. D. Veldhuis, F. Roelfsema. Growth hormone and prolactin are secreted more irregularly in patients with Cushing’s disease. Clin Endocrinol (Oxf). 52(5):625-632, 2000.

51. Wells J. C., M. S. Fewtrell. Is body composition important for paediatricians? Arch Dis Child. , 2007.

Thanks!

Rock on

Mike T Nelson

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Metabolic Flexibility: You need to burn that fat off!

Metabolic Flexibility: You Need to Burn That Fat Off!

fat mouse

Large and In Charge Mouse

I hope all of you got to watch the Vikings win this past Sunday!  Whoo ha!!

Onward to today’s topic of Metabolic Flexibility.

What is that and why do I care?

Physiology is Messy

Physiology is complex and messy.  Most theories just don’t hold up since they are too simple.  Note, this does NOT mean that the actions you need to take have to be complex, but the theory to explain all the inter-workings gets complicated at times.

Metabolic Flexibility (Met Flex) is the term to describe the ability of the body to burn both fats AND carbs efficiently.

Enter the Diabetic Stage Left

The basic definition of a diabetic is someone who does not handle carbs (carbohydrates) very well.  Their glucose management has gone awry and is messed up.  Don’t get me started on why the popular recommendation is then for diabetics is truck loads of CARBS!  Ok, just a short rant since it is my blog and I read research studies for fun.

Now to be entirely fair, the American Diabetes Association (ADA) has cracked the door a bit to  low-carb diets for people with diabetes and pre-diabetes (and insulin resistant) but for weight control ONLY and it doesn’t recommend low-carb diets for blood glucose control, even though the new guidelines say,

“dietary carbohydrate is the major determinant of postprandial [after meal] glucose levels”.

If you are a diabetic or borderline diabetic, dumping a crap ton (technical term) into your system of the very thing that you have a hard time processing, is a bad idea.

Toxic Sugar

Keep in mind that HIGH level of glucose (sugar) in the blood is TOXIC.   Low levels are also bad, so the body has tons of controls to keep you at a happy medium (homeostasis for my fellow geeks).    The downside is that some of these short term controls (read, so you don’t die) come at a very high cost long term (read: destruction of other tissues).

Fat Metabolism: Torch the Muffin Top


In the USA, Even the Wild Animals Are Now Fat!

source: James Marvin Phelps (mandj98)


On the other end of the spectrum, although not as common, are people who can’t handle high amounts of dietary fats.  The machinery that processes fat has gone off the tracks and this too results in lots of collateral damage.

Summary So Far

So those are the bad circumstances

1) poor handling of carbs

or

2) poor handling of fats

For all practical purposes, the burning of protein as a fuel does not happen that much; despite all the fear mongering of bro-scientists everywhere in the bodybuilding circles.

Now, some poor bastards can have BOTH (fat and carb metabolism) gone awry and are an unfortunate metabolic wreck.

The Good Side

So if that is the bad side, people who are very metabolically INflexible to fats and carbs; there is a good side -  people are who very metabolically flexible to carbs and fats.  This is where you want to be.

You want the ability to handle fats AND carbs without any collateral damage and increase your health and performance.

How?

The most profound effector of this is ………EXERICSE!   Any surprise there?  A high levels of exercise, your body becomes very efficient at handling fats AND carbs (2).  There is accumulating evidence (1) that lower levels of body fat are also correlated to metabolic flexibility.    We used to think that fat cell sat around on their collective fat butts all day, but we now know they run a host of chemical messengers throughout the body.  Fat as it turns out is very metabolically active (think busy fat cells not lazy ones).

Metabolically Flexible Robots?  What?

KITT

KITT from Knight Rider: A Smart Robot

Now I don’t believe much of anything I read on Fox news, but there was a story about the military making new robots that can eat anything.  Sweet!  A metabolically flexible robot!

from Fox News (yeah I know, I am quoting fox news, eeek)

“Robotic Technology Inc.’s Energetically Autonomous Tactical Robot — that’s right, “EATR” — “can find, ingest, and extract energy from biomass in the environment (and other organically-based energy sources), as well as use conventional and alternative fuels (such as gasoline, heavy fuel, kerosene, diesel, propane, coal, cooking oil, and solar) when suitable,” reads the company’s Web site.”


You gotta love the name too EATR.  ha!  For those that want to see the whole presentation on EATR, I tracked it down and you can get it HERE.

Are the Robots Ahead of Us?

It is time to add some more exercise and get more metabolically flexible soon, before a robot comes looking to eat you for lunch.    This also has a great side effect of decreasing that spare tire and muffin top too.

Sprints anyone?  Catch me if you can you lazy robot!

Rock on

Mike T Nelson

PS

For those that are interested in this topic, hold on to your hats as I have a whole product coming out soon called “The Truth about Protein, Fats, and Carbs: Implications for Metabolic Flexibility”   I am also in the process of writing up some studies for peer review on metabolic flexibility (silly dissertation).

REFERENCES

1) MITOCHONDRIAL RESPIRATION IS INCREASED FOLLOWING
LIPID EXPOSURE IN CULTURED MYOTUBES FROM LEAN BUT
NOT OBESE DONORS

Appl. Physiol. Nutr. Metab. Vol. 34, 2009
Boyle KE, Zheng D, Anderson ET, Neufer PD, & Houmard JA. Dept.
of Exercise & Sport Science & The Metabolic Institute, East Carolina
University, Greenville, NC

The skeletal muscle of obese humans oxidizes less lipid compared to
leans and is unable to respond to a lipid challenge. We utilized satellite
cells derived from vastus lateralis tissue of 7 lean (BMI=22) and 8 obese
(BMI=38) human males to determine the mechanisms involved with
the inability to utilize lipid with obesity. On day 6 of differentiation,
myotubes were incubated in differentiation media supplemented with
either 100?M oleate/palmitate + 0.05% BSA or 0.05% BSA for 24h. Cells
were then permeabilized and state 4, state 3, and uncoupled respiration
was measured in the presence of palmitoyl carnitine + malate (+succinate
for uncoupled). State 3 and uncoupled respiration increased in leans with
the lipid incubation (50% & 35%, respectively; P<0.05). There was no
corresponding change in the cells from obese donors. Mitochondrial
DNA copy number increased in leans but decreased in obese with lipid
incubation (16% & -13%, respectively; P<0.05) and COX-IV protein
content showed a significant lipid incubation x body size interaction (38%
increase in leans and -13% decrease in obese; P<0.05). These data suggest
that the skeletal muscle of obese individuals does not respond to lipid
exposure by increasing lipid oxidation; this metabolic inflexibility may be
a mechanism involved in the reduced ability to oxidize lipid evident in the
muscle of obese subjects.
Funded by NIH DK561112 & DK073488.

2) ADAPTATIONS IN NR4A3 ISOFORMS FOLLOWING EXERCISE
TRAINING IN OBESE HUMANS
Appl. Physiol. Nutr. Metab. Vol. 34, 2009

Haus, J.M., Solomon, T.P., Kirwan, J.P. Cleveland Clinic, Cleveland, OH

The orphan nuclear receptor NR4A3 responds to acute exercise and
has been implicated in the regulation of genes that mediate glucose
and lipid metabolism in skeletal muscle. Data on the effects of exercise
training on NR4A3 gene expression are lacking. We examined mRNA
expression of the known NR4A3 isoforms (A,B,C) from muscle biopsy
samples obtained at basal and under insulin stimulated conditions (INS)
during a 40 uU/m2/min hyperinsulinemic-euglycemic clamp before and
after 12 weeks of aerobic exercise training. Subjects included obese
men and women. At baseline, NR4A3 isoform C was most abundant
(2.7±1.1, 3.5±1.4, 5.6±1.7 AU), and INS increased expression of all three
isoforms (3.4, 1.6, 4.7 fold; P<0.05). Exercise training increased basal fat
oxidation, glucose disposal rates (GDR) and basal mRNA expression of
NR4A3 isoforms B and C (1.4 and 2.1 fold; P<0.05 vs. pre). In addition,
the expression of NR4A3 isoforms A, B and C were decreased during INS
(-55, -29, -61% vs. Pre INS). Following exercise training, increased basal
expression of NR4A3 isoforms B and C may reflect the increase in basal
whole body fat oxidation. The exercise-induced attenuation of NR4A3
gene expression during INS is consistent with the observed improvements
in metabolic flexibility following exercise training. These novel data
provide evidence that NR4A3 may regulate glucose and lipid metabolism
following exercise training in obese, insulin resistant adults

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