Friday, December 28, 2007

Metabolic Inflexibility 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.

Enjoy

Mike N

METABOLIC 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 (β-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 suppression 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 metabolism 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.

Monday, December 24, 2007

Merry Christmas and Z Health Story


Merry Christmas (Happy Holidays to those that celebrate something else other than Christmas) to everyone! Thanks again for the precious time that you take to read my ramblings, as it is much appreciated. I feel so privileged to be doing something that I truly love and anything that I can do to help provide some good info in the process is great.

I look forward to even more excellent interactions coming up in 2008! Tons of great stuff coming up.

Z Health Story
The athlete that I have been working with for the past year about 2-3 times a week showed up last week with major "upset stomach" and progressively got worse on all movements. His stomach was quite bloated and hurt with mild pressure. We did the Z Health Neuro Warm up 1 and it did not help. I tried some hands on work around the abdominal area in different directions, pressure, etc with no luck. I did a visual test (PREP) and he tested positive with eyes down and closed (lately he has been testing clear), so I had him do an Egyptian (Z Health drill where you move your head side to side like in that old 80s horrible video from a certain nameless band, hehehe) with his eyes closed and down.

After walking around for a bit he started to feel better and about 10 minutes later we were able to do some push ups and lighter body weight movements and his movement dramatically improved from earlier in the session.

The amazing part was that by the time he left he felt relatively good (not great) and the distention in his abdominal area had reduced quite a bit. Pretty amazing and behold the power of the nervous system!

I honestly am not entirely sure of the reason for the result. Maybe it was related to some cranial tension, particular eye movement, perhaps just more neuro input, or opposite joints between the neck and pelvis; but it worked and he left better than when he came in---so I met my goal.

Rock on!
Mike N

Saturday, December 8, 2007

Stress, Holidays, Pain and Chili Peppers?

It is the Holiday season and stress levels are on the rise! Just remember that for all you gym rats, that ANY stress has an effect on the body and it is not just the added stress on your body from the gym. The term eustress and distress defined by Webster’s dictionary as

eustress (noun) : Stress that affects your body in a positive fashion.

Distress is the opposite.

Distress (noun): pain or suffering affecting the body, a bodily part, or the mind

In training, it may not be beneficial to have every session perceived as distress, but without the principal of overload the body has no reason to adapt; so it is a fine balancing act. It was rumored that trees planted in the first Biosphere experiment did not grow straight up since there was no wind!

Take Away
Monitor your training progress based on if it was a eustress or distress session and see how it goes. I would be interested in your feedback. Special thanks to Zachariah Salazar for the "new to me" definition of stress.

I am a Kiteboarding Adict!
I just got back from another kiteboarding session in S Padre TX this past Sunday through Wensday and it was great! Only one day out of 3 with nice wind, but that day of riding made the entire trip worthwhile! I even caught some air for about 3-4 seconds on purpose this time. Wow, that is an amazing feeling of being lifted up off the water as everything goes dead quiet and if you do it right, you can even land softly (or in my case cannonball into the water 6o% of the time). If you have not checked out the book "The Four Hour Workweek" by Tim Ferris, I highly highly recommend it. I have no plans to ever really retire, so I might as well enjoy everything to its fullest extent now and not put it off.

Super Geek Alert: Study on Pain Physiology
I have tons of cool studies coming up, but I need to get through my finals first and only 8 more days to go. Whoo ha. The of this quarter is drawing near.

From some work published in Nautre (1), scientist are working on combining two compounds to elicit a very cool effect on reduction of pain! Most agents used for acute pain reduction like lidocaine (think of the dentist if you have ever had any work done there), result in numbness and a general lack of feeling including a loss of motor control--try drinking that crappy Kool Aid they give you after you get your wisdom teeth pulled. I think the dentist just does it for entertainment as I know I spilled mine all over myself.

In the experiment (1), they combined the effects of capsician (that stuff that turns your mouth to fire from hot chili peppers) with a local anesthetic (QX-314 which is in the lidocaine family). This worked to shut up (technical term) the local pain sensations (nociception) without affecting the motor control qualities (hey, I can sill move).

Capsaicin binds to TRPV1 and causes the protein to open a gate leading to a small channel in the nerve cell's membrane. So researchers scratched their heads and thought maybe injecting capsaicin followed by QX-314 would allow the chili pepper compound to open the doors of pain-sensing neurons, clearing the way for the anesthetic to enter and shut down the cells; and it did! (2)

For all the geeks out there, capsaicin, as a member of the vanilloid family, binds to a receptor called the vanilloid receptor subtype 1 (VR1). When the neuron is stimulated it sends a signal to the brain (remember, that is where pain lives). Anything that binds to the VR1 receptor can produce the same sensation that excessive heat or abrasive damage would cause, thus explaining why the spiciness of capsaicin is described as a burning sensation.

Why You Might Care
Alexander M. Binshtok et al (1), stated “Long-lasting decreases in pain sensitivity were also seen with regional injection of QX-314 and capsaicin near the sciatic nerve; however, in contrast to the effect of lidocaine, the application of QX-314 and capsaicin together was not accompanied by motor or tactile deficits.” Translation—the pain signal was shut down, but there were no other deleterious (bad) effects!

References

1. Binshtok A. M., B. P. Bean, C. J. Woolf. Inhibition of nociceptors by TRPV1-mediated entry of impermeant sodium channel blockers. Nature. 449(7162):607-610, 2007.

2. www.sciam.com