Blog 3 - Weight gain and nutritional guidelines

Problem narrative

The World Health Organisation database on Body Mass Index (BMI) refers to a phenomenon known as “globesity”. This term refers to the increase in obesity that is being seen in many parts of the world (WHO, 2012). This has led to governmental efforts to encourage healthy eating habits, such as the food pyramid guide released by the United States Department of Agriculture (USDA, 2012). However, such efforts have been unsuccessful at curbing this problem and obesity is still on the rise. A recent ideology known as the paleolithic diet operates on the hypothesis that the obesity problem is fuelled by the modern day reliance on grain-rich diets that are carbohydrate based (Lever, 2012). This idea has been supported by scientific investigations, however it has not been incorporated into mainstream nutrition practices (Kopp, 2003). Therefore, governmental suggestions of 6-11 servings of grain a day (USDA, 2012) may be aggravating the problem.  This text shall therefore be discussing the problem of obesity due to high-carbohydrate intake from the perspective of a single average person.

Behaviour over time

This study shall be using BMI as an indicator of weight gain since it takes into account a person’s height, is easily calculated and allows comparison between different individuals. The plot below illustrates the increase in the average BMI of males and females between the ages of 20-74 in the United States between 1960 and 2002:

Figure 1: Plot of average BMI against time (data from Ogden et al., 2004)

The data illustrates the fact that the average BMI increased from 25 to 28 (approximately), with the average woman’s BMI surpassing that of the average male. It should be noted that a BMI between 18.5-24.9 is considered “healthy”, 25-29.9 is classed as “overweight”, while anything above is considered “obese” (CDC, 2012). Therefore in a 40 year period, the average person can be said to have changed from “healthy” to “overweight”.

 

Relevance of a systems perspective

The system being discussed exhibits two main feedback dynamics. First of all, nutrition affects BMI which in turn will result in dieting efforts that should (theoretically) limit what is being eaten. Secondly, the body’s own feedback dynamics that regulate appetite and blood sugar. In addition, the problem with weight gain is witnessed over a whole person’s life and therefore exhibits long time horizons and may occur very gradually. Unfortunately, nutritional efforts to curb weight gain have also failed, as seen in Figure 1 above. These factors indicate that the efforts of a person attempting to control weight gain through nutrition should be looked at from a systems perspective to identify which dynamics are leading to this problem.

Study objectives and questions to be addressed

The objective of this study is to build a systems dynamics model of a person attempting to maintain a healthy weight using official nutritional guidelines. Since the objective is a nutrition analysis, the role of other factors such as physical activity shall not be examined and for the purposes of this study the average person shall be considered to be sedentary.  The study shall use this model to attempt to address whether the grain-based food pyramid guidelines are alleviating or aggravating weight gain in individuals.

Dynamic hypothesis

The discussed system contains only one actor: an average individual trying to lose weight through nutritional efforts. The action this individual takes is to follow government issued nutritional guidelines to reduce weight. The intended consequence is that a high carbohydrate meal controls hunger levels. The unintended consequence is that carbohydrate dense foods cause a spike and crash in blood sugar levels, causing the individual to feel hungry again resulting in overeating.

The table below illustrates the variables used in this model and their significance:

Variable name	Significance
BMI value (dashboard variable)	The individual’s current BMI value, which shall be used to indicate weight gain/loss.
Recommended intake of grains	The amount of grain servings recommended by official guidelines. (Note: This was considered to be exogenous since it is not affected by what the individual does and is based on a series of other systems beyond the scope of this study)
Consumption of food	The amount of food eaten.
Consumption of carbohydrates	Represents the size of the portion of the food eaten which is composed of carbohydrates.
Calorie deficit	The calorie deficit signifies the actual amount of calories needed by the individual for bodily function.
Hunger level	The perceived level of hunger felt by the individual.
Amount of fat in body	The amount of fat present in the individual’s body.
Blood sugar concentration	The current concentration of sugar in the individual’s blood (represents the energy obtained from food that is accessible to the individual’s body)
Insulin in blood	The concentration of insulin currently in the individual’s blood (release of insulin by the pancreas results in sugar being stored as fat, making it less accessible to a body’s cells)
Motivation to limit food intake	The individual’s motivation to limit the amount of food eaten to reduce weight gain.

 

Table 1: Variables used in the study

 

At its most basic level the system being discussed deals with hunger being felt when the body needs energy (calorie deficit) which results in food being eaten, thus addressing the body’s calorie deficit. This can be seen in Loop B1, highlighted in Figure 2 below:

 

Figure 2: Loop B1

If the consumption of food is high, the individual gains weight. This should stimulate the individual to limit food intake, thus controlling weight gain. This can be seen in Loop B2, highlighted in Figure 3 below: 

 

 Figure 3: Loop B2

However, reducing food consumption causes a calorie deficit. This increases hunger levels, resulting in even more food being consumed, thus thwarting dieting efforts. This can be seen in Loop B3, highlighted in Figure 4 below: 

 Figure 4: Loop B3

 

To address this problem, nutritional guidelines suggest a grain based diet. Consuming foods rich in carbohydrates keeps blood sugar levels high, so that less hunger is felt. This leads to food feeling more satisfying, reducing the amount of food consumed. This can be seen in Loop B4, highlighted in Figure 5 below: 

 Figure 5: Loop B4

However, blood sugar levels are controlled in the body by insulin. Once blood sugar levels in the body surpass a certain level, insulin is released in the blood to reduce blood sugar concentration. This can be seen in Loop B5, highlighted in Figure 6 below: 

 

  Figure 6: Loop B5

The insulin cycle means that consumption of food rich in carbohydrate, increases blood concentration causing sugar to be stored as fat. This increases the individual’s BMI value, requiring a reduction in food intake. One of the unintended consequences of a high carbohydrate diet is therefore an oscillation of the individual’s weight, which is often referred to as yo-yo dieting. This dynamic is highlighted as loop B6 in Figure 7 below:

 Figure 7: Loop B6

While the dynamics examined all show balancing feedback it should be noted that they have different and opposing goals. Therefore, the individual dynamics do not give a clear picture of the dominant dynamics as proposed by the paleolithic diet ideology. An increase in the average individual’s BMI can be explained through the fact that consumption of high carbohydrate food items results in a spike and crash in blood sugar. This results in an individual feeling hungry despite there not being a calorie deficit, resulting in even more food being consumed. This dynamic would essentially mean that a person would display weight gain and would find it hard to reach his/her ideal weight, eventually reaching a higher equilibrium weight where all the dynamics become balanced. Therefore the hypothesis of this study supports the suggestion that the government guidelines for a grain based diet are aggravating the obesity problem, highlighting the need for revision of these guidelines to address the dynamics listed in this text.

References

CDC. Body Mass Index. http://www.cdc.gov/healthyweight/assessing/bmi/index.html (accessed January 21, 2012). 

Lever, R. Against the grain, 'caveman' diet gains traction. http://medicalxpress.com/news/2011-09-grain-caveman-diet-gains-traction.html (accessed January 21, 2012).

 Kopp, W.  High-insulinogenic nutrition - an etiologic factor for obesity and the metabolic syndrome? Metabolism 2003, 52(7), 840-844. 

Ogden, C. L.; Fryar, C. D.; Carroll, M. D.; Flegal, K. M. Mean Body Weight, Height, and Body Mass Index, United States 1960-2002. Advance Data from Vital and Health Statistics; No. 347; National Center for Health Statistics: Hyattsville, Maryland, 2004.

USDA. The Food Guide Pyramid. http://www.nal.usda.gov/fnic/Fpyr/pmap.htm (accessed January 21, 2012).

WHO. Global Database on Body Mass Index. http://apps.who.int/bmi/index.jsp (accessed January 21, 2012).

Blog 2 – The case of Thalidomide and the unforseen side effects

Large organic molecules such as drugs often have complex spatial arrangements. This leads to a phenomenon known as stereoisomerism, which refers to molecules having the same atoms but a different arrangement in space. A subset of stereoisomers is known as enantiomers, which means that the molecules are non-superimposable mirror images of each other. This blog deals with the case of the drug Thalidomide, with both enantiomers shown below:

Figure 1: Therapeutic Thalidomide (on the left) and its enantiomer (Pasieka, 2012)

Thalidomide was developed and marketed as a tranquilizer and to treat morning sickness by the German pharmaceutical company Chemie Grunenthal in 1957 (Moghe et al., 2008). The therapeutic effects were attributable to one of the enantiomers. However, it was common practice at the time to produce both enantiomers since producing only one enantiomer or separating the mixture is expensive and would translate into a higher market price (Sheldon, 1993).

The problem in this case was that the pharmaceutical company did not know that the enantiomer of Thalidomide was teratogenic i.e. it caused birth defects. Limb malformations in infants started to appear within the first year of sales of the drug (Lenz, 2012). However, it was not until November 1961 that it was recognised that the increasing cases of limb malformations in infants was due to Thalidomide (Smithells and Newman, 2012). The rationale of Chemie Grunenthal in this case was to reduce the production cost, to make it more affordable and increase market share. However, the company did not realise that there was a potential for side effects. The company’s rationale is represented in loop R1 in the diagram below, while the unintended consequence can be seen in loop B1.

Figure 2: Causal Loop Diagram in the case of the Thalidomide Scandal

The problem in the mental model of Chemie Grunenthal (and most of the pharmaceutical industry at the time) was the unproven assumption that enantiomers of a therapeutic drug did not cause any effects. This led to the B1 dynamic seen in the diagram above which eventually led to the withdrawal of the drug from the market in several countries and the company having to pay a 100 million German marks fine to the victims (Lenz, 2012). This case led to significant media coverage resulting in to an increase in pharmaceutical industry regulations, with the FDA creating strict requirements for the testing of both enantiomers during the drug development stage (FDA, 2012). In fact, nowadays most drugs are marketed as pure enantiomers and usually accompanied by claims of greater effectiveness and reduced side effects (Nguyen et al., 2006). Hence, this case highlights the fact that sometimes unintended consequences of a policy can be so significant that a re-evaluation of the mental model of a whole industry may be altered.


References:

FDA. Development of New Stereoisomeric Drugs. http://www.fda.gov/Drugs/ GuidanceComplianceRegulatoryInformation/Guidances/ucm122883.htm (accessed January 14, 2012).

Lenz, W. The History of Thalidomide. http://www.thalidomideuk.com/proflenz.htm (accessed January 14, 2012).

Moghe, V. V.; Kulkarni, U.; Parmar, U. Thalidomide. Bombay Hospital Journal 2008, 50 (3), 472-476.

Nguyen, L. A.; He, H.; Pham-Huy, C. Chiral Drugs: An Overview. International Journal of Biomedical Science 2006, 2 (2), 85-100.

Pasieka, A. S and R Enantiomers of Thalidomide. http://www.visualphotos.com/image/ 1x6039114/s_and_r_enantiomers_of_thalidomide_molecules (accessed January 14, 2012).

Sheldon, R. A. Chirotechnology: Industrial Synthesis of Optically Active Compounds; Marcel Dekker: New York, 1993.

Smithells, R. W.; Newman, C. G. H. Recognition of Thalidomide Defects. http://www.thalidomideuk.com/smithellsnewman.htm (accessed January 14, 2012).

Blog 1 – Rising incidence of Asthma

Asthma is a respiratory disease which affects airflow in the lungs. It is a disease caused through a combination of genetic and environmental factors and hence, tends to exhibit a diverse combination of symptoms in sufferers. The issue with this disease is that it is on the rise worldwide, with statistics showing that incidence increases by 50% every decade (Braman, 2006). Asthma tends to affect children, who then grow out of it, and the elderly. Hence, it would be useful to monitor the incidence of asthma in these age ranges since they are indicative of the prevalence of the disease. In fact a study in Malta has shown that between 1994 and 2001, has shown that childhood asthma increased from 7.5% to 14.8% (Montefort et al., 2009).

The development and the increase of the disease is a very complex issue. Causes of the disease originally were attributed to genetics, poor air quality, exposure to allergens and stress. However, recent studies also indicate the existence of the “hygiene hypothesis”. This states that the high level of hygiene in developed countries has made humans far more sensitive to allergens, thus increasing the prevalence of asthma (WHO, 2007). Hence, the original approach of removing allergens from the environment to reduce incidence of asthma may have been wrong. That said, air quality, especially relating to substances such as sulphuric acid and combustion products, cannot be excluded from the equation. In addition, under diagnosis tends to exacerbate the problem since it allows the disease to progress without treatment (Pace Asciak et al., 2002).

Tracking of the problem would require regular monitoring of the number of asthma cases worldwide. This would also need to be accompanied by air quality measurements and possibly analysis of dust deposits in the most affected areas. This would allow for both the “hygiene hypothesis” and the more conventional causes of asthma to be monitored. It might also be beneficial to track specific emissions from industries emitting known lung irritants such as sulphuric acid. Using Braman’s (2006) statistics of a 50% increase every decade, such a monitoring program would result in an exponential-type plot similar to the one below (using arbitrary values):

 

The problem seen is therefore an increasing one, showing a reinforcing trend. This clearly shows the complex nature of the problem, which keeps getting worse despite efforts to control it. The system being viewed would contain both hard and soft elements to it. Hard elements would be the measurable variables mentioned above, such as incidence of cases, air quality, emissions and dust composition. Soft elements of the system might include public awareness of the problem, willingness to act, doctor’s awareness of the problem, undiagnosed cases and political importance of the problem.

A combination of all these elements would be required to build a causal loop diagram of this system. However, further research into the nature of asthma and whether the “hygiene hypothesis” dominates over the air quality explanation would be required for the system to be understood completely.

References:

 Braman, S. S. The Global Burden of Asthma. Chest 2006, 130, 4S-12S.

 Montefort, S.; Ellul, P.; Montefort, M.; Caruana, S.; Agius Muscat, H. Increasin prevalence of asthma, allergic rhinitis but not eczema in 5- to 8-yr-old Maltese children (ISAAC). Pediatric Allergy and Immunology 2009, 201 67-71.

 Pace Asciak, R.; Camilleri, M.; Azzopardi Muscat, M. Public Health Report Malta 2002. Department of Health Information Report 2002.

 WHO. Prevalence of asthma and allergies in children. Fact Sheet No. 31, 2007.

About my hobbies

Apart from my academic endeavors, I like to read fictional stories, running, martial arts and gaming. I'm a firm believer in doing active stuff for both the mind and the body, with martial arts I find that you get both in a nice package.

I do WingTsun, which is a type of chinese kung fu specifically created for close-quarter self defence fighting. Although not many people have heard of it, it's actually very popular in the movies, with famous practitioners such as Bruce Lee and Robert Downey Jr. (who used some of the techniques in the Sherlock Holmes movies).

I also love Playstation 3 games. I'm a big fan of military style games and also role-playing games. A recent study has shown that game playing improves hand-eye coordination and it was found to even enhance the performance of surgeons... so I guess I'm not wasting all of my time on nothing.