Case Study - Type 2 Diabetes

Case Study - Type 2 Diabetes

Diagnosis of Diabetes Mellitus Using the Oral Glucose Tolerance Test and Interventions for Prevention

Author: Benjamin R. Holmes

(NOT PEER-REVIEWED LITERATURE)

 

ORIENTATE IN LANDSCAPE IF VIEWING ON MOBILE DEVICE

 

Introduction

Insulin is a hormone that is released by the pancreas due to signals caused by an elevated level of glucose in the blood, it shuttles glucose from the blood stream into a multitude of cells around the body. Conversely, when blood glucose levels drop below a certain level this triggers the release of another hormone called glucagon that signals to the liver and muscle cells to convert stored glycogen into glucose. The action of both insulin and glucagon together is what allows for optimal and stable levels of blood glucose. Diabetes Mellitus (DM) is a chronic state of hyperglycaemia, which is when a high concentration of glucose remains in the blood for an extended period, this can either be caused by the lack of insulin or other factors negatively affecting the proper function of insulin. DM can cause a multitude of health issues depending on how prolonging it is, symptoms include: increased thirst and hunger, increased urination, dizziness, blurred vision and death. More chronic effects of DM can include: Neuropathy, retinopathy, nephropathy and cardiovascular disease (Hu et al., 1991; Wilson, 1998). There are currently three main types of DM: Type 1 Diabetes (T1D), which is characterised by a lack of insulin production and requires daily administration of insulin injections, it usually occurs earlier in life and the cause of it is unknown. Type 2 Diabetes (T2D) is the bodies failure to use insulin effectively and it is the most common type, over consumption of calories can have a big influence on someone’s risk of T2D. The last type is Gestational Diabetes, which is hyperglycaemia during pregnancy, it is similar to T2D and is usually diagnosed during prenatal screening (www.who.int, 2013). Before an individual is diagnosed with DM, early signs are often experienced of which are an indication that the person could be in a prediabetic state, there are two distinct types of prediabetes. Impaired Fasting Glucose (IFG), which is when fasting blood glucose levels are higher than normal but do not exceed levels of DM (Table 1). The other type is called Impaired Glucose Tolerance (IGT), which is condition where blood glucose levels are higher than normal and higher than IFG but still don’t meet the threshold to be considered DM (Table 1). The prevalence of DM can vary depending on many biological and environmental factors like race, age, hereditary history of DM, BMI, diet and lifestyle. A Fasting Plasma Glucose (FPG) test is the simplest and quickest way to test blood glucose but it is somewhat limited as it does not provide any information on altered post-prandial metabolism (Bartoli, Fra and Schianca, 2011). An Oral Glucose Tolerance Test (OGTT) is a slightly more comprehensive method and it is the most common test in determining a patient’s blood glucose status, Conn (1940) has been noted as the first person to describe the OGTT within a publication. It consists of one plasma glucose test taken after an 8-hour fasting period (FPG) and then four plasma glucose tests over a 2-hour period following the ingestion of 75g of glucose and 100ml of water. The test can either be taken from capillary or venous blood, Table 1 shows the values that are used to diagnose DM and other categories of hyperglycaemia. Both the OGTT and FPG have limitations as the results can be inconsistent from patient to patient as glucose tolerance can vary within the population. This means that a specific blood glucose value for one patient could have different implications when observed in another patient (Bartoli, Fra and Schianca, 2011). There is also a risk of stress related hyperglycaemia which may cause a false positive reading with OGTT and FPG (Ko et al., 1998). Becoming more frequent in determining if a patient has DM is a Glycated haemoglobin (HbA1c) test in plasma, which occurs when haemoglobin, that is a protein that carries oxygen molecules in the blood, joins to a glucose molecule resulting in it being ‘Glycated’. This test can be used to determine a patient’s overall blood glucose status over a period of weeks/months and in some ways, it can be a better method to OGTT as it can test for chronic glycaemia instead of momentary glycaemia.

The objective of this study is to understand the efficacy of the OGTT in diagnosing and classifying DM, and the (non)-environmental factors that influence the risk of DM and comorbidities related to it. Interventions for treatment and prevention of DM were also explored.

Table 1. Values for diagnosis of Diabetes Mellitus and other categories of hyperglycaemia. (values taken from the report (World Health Organization, 1999))

Glucose concentration mmol-1 (mg/dL)

 

Venous

Capillary

Diabetes Mellitus:

 

 

Fasting or

≥ 6.1 (≥ 110)

≥ 6.1 (≥ 110)

2-h post glucose load

≥ 10.0 (≥ 180)

≥ 11.1 (≥ 200)

Impaired Glucose Tolerance (IGT):

 

 

Fasting (if measured) and

< 6.1 (< 110) and

< 6.1 (< 110) and

2-h post glucose load

≥ 6.7 (≥ 120)

≥ 7.8 (≥ 140)

Impaired Fasting Glycaemia (IFG):

 

 

Fasting (if measured) and

≥ 5.6 (≥ 100) and

≥ 5.6 (≥ 100) and

 

< 6.1 (<110)

< 6.1 (<110)

2-h post glucose load

< 6.7 (< 120)

< 7.8 (<140)

 

Methods

This case study involved one participant that was a 40-year old British male of African-Caribbean ethnicity. Married with 4 children, a smoker, complains of sleep disturbances and is currently on no prescription medication. At the time of the test the participants weight and height were 98.7kg and 169.4cm respectively. A consent form was completed prior to taking the test in the morning, the participant was fasted for 8 hours and the first reading of OGTT was carried out in that fasted state, then 75g of glucose mixed with 100ml of water was consumed by the participant and the test was repeated 4 times at 0.5 hr intervals after. Blood Glucose levels were determined using capillary blood and a single use lancet, these samples were analysed using an Analox GM7 Microstat (Enzymatic oxygen-­‐rate) analyser and was calibrated using an 8mmol/L glucose standard solution. The samples were administered to the analyser using a capillary tube and a 10 μl Gilson pipette. The readings expressed in the results were inputted into Microsoft Excel and a line graph was generated to illustrate how the participants blood glucose levels compared with threshold levels associated with DM.

 

Table 2. A table to show the participants information (BMI calculated using formula in Appendix 1

Participants Information

Age (yrs)

40

Gender

Male

Bodyweight (kg)

98.7

Height (m)

1.694

BMI (kg/m2)

34.4

 

Results

Blood glucose concentration was 8.44 mmol/L (152 mg/dL) after the 8-hour overnight fast, after 0.5 hrs of the glucose consumption the blood glucose concentration was 11.89 mmol/L (214 mg/dL), after 1 hrs – 14.33 mmol/L (258 mg/dL), after 1.5 hrs – 15.22 mmol/L (274 mg/dL) and after 2 hrs -  15.33 mmol/L (276 mg/dL).

Fig 1. A line graph to show the participant’s blood glucose concentration (mmol/L) in a fasted state then over a 2-hour period following the consumption of 75g of glucose in 100ml of water. The dotted red line shows the Diabetes threshold (See Appendix 4 for Raw data). 

 

Discussion

The participants result in Fig 1 show that the blood glucose concentration is more than the threshold for fasting and 2-h post glucose ingestion associated with DM (Table 1). This means that the participant would be considered diabetic and if remained in this hyperglycaemic state for extended periods of time would have an elevated risk of permanent organ damage or even death (Rubin et al., 2012; Han and Susztak, 2014). More specific disorders and complications associated with DM are as follows: Non-Alcoholic Fatty Liver Disease (NAFLD) (Tilg, Moschen and Roden, 2017), stroke (Hyvarinen et al., 2008), Obstructive Sleep Apnea (OSA) (Pamidi and Tasali, 2012; Greenberg and Rajan, 2015) and Cardio Vascular Disease (CVD) (Qazi and Malik, 2013). There has also been some evidence suggesting that T2D is associated with elevating the risk of some types of cancer (liver, pancreas, endometrium, colon and rectum, breast, bladder) and a reduced risk of prostate cancer (Giovannucci et al., 2010).   There could be a multitude of factors that have caused the participants hyperglycaemia and DM as there are many interrelated mechanisms between the diseases and disorders mentioned previously, this has been illustrated in Fig 2. This helps to summarise some of the literature and show the relationships between DM and important comorbidities. Understanding these links will enable the identification of the main underlying cause(s) that contributes to DM risk and that the correct steps are taken to reverse it and prevent any complications from occurring in the future that could cause serious health implications.

Fig 2. The association between DM and important comorbidities

Cigarette smoking is well understood as a risk factor for a multitude of diseases and it has been linked with increasing the risk of DM. It aggravates the micro- and macro-vascular complications associated with DM (Chang, 2012) and can increase the prevalence of hypertension (Halperin, Michael Gaziano and Sesso, 2008) and CVD (Mons et al., 2015) that contribute to the development of DM and the absolute risk of smoking is usually greater in diabetic subjects than in nondiabetic subjects (Fagard, 2009).

Obesity is currently defined by the BMI scale, which is a ratio between a person’s bodyweight and height. It is a level of body fat that increases your risk of obesity related diseases and there are 3 classes that define the severity of obesity in terms of BMI value (Table 3), and the health risks associated with obesity increases as you go up the classes.

 

Table 3. A table to show the different categories of BMI and classes with obesity (Nuttall, 2015).

Underweight

15-19.9

Normal Weight

20-24.9

Overweight

25-29.9

Class I Obesity

30-34.9

Class II Obesity

35-39.9

Class III Obesity

40

 

Studies show that there is a strong link with DM and higher values of BMI, especially within males (Bays, Chapman and Grandy, 2007; Gray et al., 2015). Insulin resistance (IR) is also strongly linked with obesity (Kahn and Flier, 2000) and due to the participants current BMI of 34.4 (Table 2), this may indicate to the classification of the participants type of diabetes. In a study by Ganz et al., (2014) a positive association between BMI and the risk of T2D and the strength of the association increased with higher BMI categories.  

The participants current BMI is within a Class I Obesity status (Table 3), which is considered a ‘low risk’ for health complications related to obesity. There are many comorbid conditions that are linked with obesity that T2D shares, some of them include: Kidney disease, osteoarthritis, cancer, OSA, NAFLD, hypertension, and CVD (Field et al., 2001; Rydén and Torgerson, 2006). The relationship between obesity and these comorbidities can be seen in Fig 3.

Fig 3. The association between obesity and important comorbidities (Pi-Sunyer, 2002).  

T2D and the comorbidities that go along with being diabetic are therefore strongly linked with obesity and if you take an obese participant out of obesity and into normal BMI ranges you could vastly reduce the risk of T2D and the comorbidities that are associated with it. Furthermore, the methods of which you reduce obesity like a hypocaloric diet (Calleja Fernández et al., 2012) and regular exercise (Hu et al., 2007) are also linked at reducing T2D/IR and associated comorbidities (Table 4).

Table 4. A table that aggregates the evidence of the reductive effects of both a hypocaloric diet or exercise on T2D/IR and associated comorbidities.

Disease/Disorder

 Hypocaloric Diet

Exercise

Hypertension

(Sasaki et al., 2002)

(Diaz and Shimbo, 2013)

NAFLD

(Ferolla, 2015)

(Whitsett and Van Wagner, 2015)

OSA

(Johansson et al., 2010)

(Iftikhar, Kline and Youngstedt, 2013)

CVD

(Lefevre et al., 2009)

(Agarwal, 2012)

T2D/IR

(Larson-Meyer et al., 2006)

(Asano, 2014)

 

The main cause of obesity is due to a prolonged state of positive energy balance, which is when energy intake exceeds energy expenditure. For the participant to achieve this level of obesity, a positive energy balance would have been sustained for an extended period, this may not have been the only contribution to the DM, but it could have played a major role in the deterioration of it. The participants Basal Metabolic Rate (BMR) can be estimated using the Mifflin St Joer formula and the Total Daily Energy Expenditure (TDEE) can be estimated by multiplying the BMR by the Physical Activity Level (PAL) (see Appendix 2). The participants PAL is unknown so an average value (1.65) was used based off a normal distribution (Westerterp, 2013).

 

Table 5. The participants current and target bodyweight, the energy requirements and deficit intervention to achieve a healthy BMI in 59.6 weeks (see Appendix 2 for calculations).

Current Bodyweight

98.7 kg

Target Bodyweight (BMI: 24)

68.9 kg

Rate of weight loss

0.5 kg a week

Time Frame to Target Bodyweight

59.6 weeks

Deficit per week

3850 kcal

Basal Metabolic Rate (BMR)

1851 kcal

Physical Activity Level (PAL)

1.65

TDEE

3054 kcal

Intervention Kcal

2504 kcal

 

The participant can achieve 0.5kg of fat loss per week with a starting daily energy deficit of 550 kcal. BMR will decrease as bodyweight reduces, so the Intervention Kcal’s should be readjusted during changes in bodyweight, especially if weight loss stalls. A negative energy balance can be achieved by reducing calorie intake or increasing energy expenditure, although best results will be obtained by doing a combination of the two. If the participant achieves this negative energy balance consistently for 59.6 weeks there will be a reduction of bodyweight by 29.8 kg resulting in a new bodyweight of 68.9kg which will give the participant a BMI of 24 at a height of 1.694 m, this would achieve a normal BMI weight category (Table 3) and improve or even reverse DM status and many health markers associated with obesity. There are many industry experts that suggest altering macronutrient ratios independent of total calories will reduce DM status to a greater degree than creating a calorie deficit. Often a ketogenic diet is prescribed, which is a diet that is formulated with a total net carbohydrate of less than 25g. This does appear to be viable theoretically, as carbohydrates with a high Glycaemic Index (GI) will spike insulin greater than any other macromolecule but this is not reflected in the literature (Bradley et al., 2009; Gardner et al., 2015). Eliminating one macronutrient entirely would be unnecessary and could present adherence issues to someone that has been prescribed a hypocaloric diet along with regular exercise or negatively affect blood lipids. Further recommendations to contribute to the improvement or reversal of the participants DM could include reducing cigarette use and increasing sleep duration and quality.

There are some predictors of DM risk that are non-environmental and therefore cannot be influenced by lifestyle or behaviour change. The prevalence of T2D specifically increases with age, studies have shown this in Asian populations (the DECODA Study Group, 2003; Ebrahimi et al., 2016) and the mortality rates associated with DM also increase with age (Huang et al., 2014). The prevalence of DM has been shown to increase with age in western countries as well, Fig 4. shows the prevalence of DM in England for the year 2006. It also shows that males suffer a higher risk of DM than females over the age 34.

 

Fig 4. A graph to show the prevalence of diabetes in men and women across different ages groups (Diabetes.org.uk, 2010).

The participant is male and 40 years old, according to the statistics shown in Fig 4, this would contribute to DM risk and could highlight the contributing factors that may have caused the participant DM status. Along with the participants age and gender, populations that share a similar ethnic background (African-Caribbean) have an association with DM prevalence. In Fig 5, the prevalence of DM in 2011 was over 10% in North Africa and the Caribbean, which is higher than all other world regions.

 

*Comparative prevalence adjusts for differences in the age distributions of various countries and regions and allows regional comparisons

 

Fig 5.  Prevalence of Diabetes and Impaired Glucose Tolerance (IGT) in Different Regions of the World in 2011 (Spanakis and Golden, 2013).

Even though there is a clear difference in the prevalence of DM between world regions, which may suggest that race may influence DM risk, some studies suggest that this is not the case and can be explained by differences within socio-economic status that negatively affects other factors that increase DM risk (Signorello et al., 2007; Kupelian, Link and McKinlay, 2008).

The OGTT in the instance of the participant, is an adequate method of determining hyperglycaemia due to the high concentrations shown in the results. Although, the OGTT could have limitations when the values get closer to borderline ranges as it can be sensitive to intra-individual variability which is accounted for by differences in glucose tolerance. Glucose tolerance can change with lots of factors like: Age (O'Dowd and Stocker, 2013), circadian rhythm (La Fleur et al., 2001), caffeine consumption (Pizziol et al., 1998; Bidel et al., 2006) and the dysfunction of the pancreatic B-cells (Peter et al., 2009). This could mean a blood glucose concentration value may have different health implications from person to person due to individual tolerances to glucose present in serum. A test like the HbA1c, which has less intra-individual variability (Jorde and Sundsfjord, 2000) and provides an indication of a patient’s blood glucose over time, would be a favourable adjunct to the OGTT as this could illuminate more nuanced cases of hyperglycaemia like pre- or gestational DM that require deeper analysis (Claesson et al., 2017; Nam et al., 2018). Although, the HbA1c falls short in terms of variability across different age and ethnic groups, so this further supports the combination of the two tests to eliminate intra- and inter-individual variability that is inherent with the OGTT and HbA1c respectively. The HbA1c test can also be a more expensive, so the availability of it in lower income countries can be limited (Florkowski, 2013).

Information detailing the participants current diet and previous consumption history could provide further insight into the specific factors that have caused the results shown by the participants OGTT. Alongside diet, exercise history and current activity level would be a helpful indication to where the participant needs to improve his lifestyle to control and reduce his obesity status. The OGTT is adequate in the diagnosis of the participants DM, but its limitation lies with highlighting the severity of DM and the damage it has done to the body thus far. Further blood and urine tests could be carried out to determine how much damage has been done whilst elevated levels of hyperglycaemia have been present. A microalbumin urine test would screen for kidney damage that may have been caused by the prolonged hyperglycaemia, as the protein albumin is the first protein to leak into the urine during kidney disease. A creatinine urine test would also be a good test for screening kidney damage, it is used to test the kidneys glomerular filtration rate (GFR), low levels of creatinine is the urine is an indication of build-up in the blood and thus kidney damage.

 

Conclusion

The capillary OGTT is a simple and non-invasive test that can quickly determine a patients DM status with a proficient level of accuracy providing the patient follows the correct instructions that are required prior to the test. The participants age, gender, ethnicity, BMI and OGTT results all point to T2D as the classification of DM and even though factors like age, gender and ethnicity have been associated with increasing T2D prevalence, these factors cannot be influenced and just provide statistical information on which caution can be practiced within the participants lifestyle and diet. A reduction in the participants energy intake within a well formulated, healthy, balanced diet and the addition of regular exercise will work towards treating T2D, furthermore reducing the use of cigarettes or quitting entirely as well as improving sleep duration and quality will be very advantageous to not only the present DM status but also overall health.

 

Appendices

 

Appendix 1 – Calculations for the participants current BMI

BMI Calculation: kg/m2 = BMI

98.7/1.6942 = 34.4

 

Appendix 2 – Calculations for the participants energy requirements and dietary intervention to achieve target BMI (24).

Equation used to determine: Mifflin St Jeor

Variables used in the equation: Bodyweight in kilograms, Height in centimetres and Age in years.

BMR calculation: Male: (10 * Bodyweight) + (6.25 * Height) - (5 * Age) + 5

(10 *98.7) + (6.25 *169.4) - (5 *40) + 5 = 1851

TDEE calculation:  PAL*BMR:

1.65*1851 = 3054

Kcal of 1kg of bodyfat:

7700kcals

Daily Deficit calculation (at 0.5kg per week): (kcal of 1kg of bodyfat/2)/7

(7700/2)/7 = 550

Intervention Kcal calculation: TDEE – Daily Deficit:

3054 – 550 = 2504

Target weight based off a BMI of 24: BMI*m2 = kg

24*1.6942 = 68.9

Time Frame to Target Bodyweight (weeks): (Current Weight – Target weight)/0.5

(98.7 – 68.9)/0.5 = 59.6

 

Appendix 3 – Unit calculations - mg/dL to mmol-1

mg/dL to mmol-1

mg/dL / 18 = mmol-1

mmol-1 to mg/dL

mmol-1 * 18 = mg/dL

 

Appendix 4 – RAW data from the results

Raw data used to generate Fig 1. values are in mmol-1

Time (hrs)

Participant

Diabetes Threshold

0

8.44

6.1

0.5

11.89

6.1

1

14.33

6.1

1.5

15.22

6.1

2

15.33

11.1

 

 

 

 

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