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 (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.
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.
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.
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.
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).
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.
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