Diabetes (AQA A Level Biology): Revision Note
Exam code: 7402
Type I and type II diabetes
Diabetes is a condition in which the homeostatic control of blood glucose has failed or deteriorated; this can result in:
high blood glucose
symptoms such as glucose in the urine, dehydration and fatigue
There are two different types of diabetes:
type I
type II
Type I diabetes
In type I diabetes the pancreas fails to produce insulin
Onset often occurs in childhood, and is caused by an autoimmune attack on the β cells
Type 1 diabetes is normally treated with insulin injections that are calculated on the basis of carbohydrate intake and exercise
Type II diabetes
In type II diabetes insulin receptors no longer respond to insulin
Development usually occurs in adults, and is linked to risk factors such as obesity, a high carbohydrate diet, age and family history
Treatments for type II diabetes include:
a low carbohydrate diet
exercise
medications that help the cells to take up glucose from the blood
Examiner Tips and Tricks
Remember that individuals with type II diabetes still have insulin-producing cells, and are still able to produce insulin, they just have insulin receptors that are unable to sense insulin.
Evaluating approaches to type II diabetes
Incidence of type II diabetes is increasing, so the responses of public bodies are important in determining health outcomes
Approaches from health advisers include:
promoting public health campaigns that encourage healthy eating and regular physical activity
making recommendations that people reduce intake of processed foods, saturated fats, and sugary drinks
asking the government to improve laws around nutrition labelling on food
Approaches from the food industry include:
reformulating products to reduce sugar, salt, and fat content
attempting to maintain profits by continuing to produce and advertise unhealthy foods
Worked Example
Type II diabetes is a growing public health concern in the UK. Over the past 10 years, various public health campaigns and food industry changes have aimed to reduce risk factors associated with the disease.
The table below shows data collected from two UK regions over 10 years.
Region A - with strong public health campaigns + food reformulation
Region B - limited public health campaigns, minimal food industry reformulation
Year | Region A (cases per 1,000 people) | Region B (cases per 1,000 people) |
|---|---|---|
2013 | 5.2 | 5.0 |
2015 | 5.4 | 5.7 |
2017 | 5.6 | 6.5 |
2019 | 5.8 | 7.2 |
2021 | 5.9 | 7.9 |
2023 | 6.0 | 8.6 |
Using the data, evaluate the potential impact of public health campaigns and food reformulation in Region A.
Step 1: summarise the data trend for Region A
Look at the numbers over time: Did incidence go up, down, or stay the same? How quickly?
E.g. In Region A, the number of cases increased slowly from 5.2 to 6.0 over 10 years
Step 2: compare it with Region B (the control group)
What would have happened without intervention?
E.g. In Region B, where no intervention took place, incidence rose more steeply from 5.0 to 8.6
Step 3: interpret the meaning of the data
What does this difference suggest about the effect of the interventions in Region A?
E.g. This suggests that public health campaigns and food reformulation may have helped slow the rise in type II diabetes
Step 4: acknowledge limitations or alternative explanations
Are there other reasons the difference might have occurred?
For example:
the continued increase in Region A shows that these measures alone may not be fully effective
other lifestyle or environmental factors may still contribute
the data alone does not confirm causation
the sample size or population demographics are not provided, so it’s unclear how representative the results are
no statistical analysis has been carried out, it's uncertain whether the differences are significant.
Examiner Tips and Tricks
Exam questions may ask you to evaluate data that links diet with diabetes, and to consider the implications of findings for the health and food industries. Factors to consider when evaluating data include:
whether or not the sample is representative, e.g. is the sample size big enough? Are the test subjects human?
do the data suggest causation, or just correlation?
has statistical analysis been carried out, and what does it show?
could the research be biased, e.g. has it been funded by the food industry?
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