Coronary Heart Disease: Skills (HL IB Biology)

• Occlusion of the arteries can be defined as

The narrowing of the arteries due to a blockage

• The arteries can be blocked by the process of atherosclerosis
  • Atherosclerosis begins when there is damage to the walls of the arteries due to high blood pressure
  • This damage can lead to the build-up of fatty deposits known as atheromas under the endothelium
  • These fatty deposits narrow the lumen of the artery, reducing the space for blood flow
• Atherosclerosis can lead to an increase in blood pressure within the artery, which causes further damage to the artery wall
  • Fibrous tissue is produced to repair the damage to the artery wall
    • This type of tissue is not elastic, so the overall elasticity of the artery wall is reduced
  • The smooth lining of the arteries breaks down and forms lesions called plaques
• This further damage can lead to the rupturing of blood vessel walls, which results in blood clotting
  • Clots formed within a blood vessel are called a thrombus
  • Once it circulates in the blood clots are known as an embolus

Consequences of atherosclerosis of the arteries

• When an embolus blocks a small artery or arteriole, tissues further down from the blockage do not receive the required level of oxygen and nutrients
  • This can inhibit cell functions and cause the cells to die
• If this happens in the coronary arteries then parts of the heart muscle die
  • This may stop the heart from pumping blood and lead to a myocardial infarction, or heart attack
• Blockages in the coronary arteries may be bypassed by undergoing heart bypass surgery
  • Blood vessels from the patient's leg are removed and used to create an alternative route for blood to flow past the blockage

Atherosclerosis & coronary heart disease diagram

Atherosclerosis leads to narrowing of the arteries; this can lead to coronary heart disease

Buildup of plaque in the coronary arteries narrows the lumen, and can lead to a heart attack

Evaluating epidemiological data relating to the incidence of coronary heart disease

• Claims about the importance of different risk factors and coronary heart disease, e.g. a diet high in saturated fats, are based on:
  • Epidemiological studies on human populations
    • The evidence provide correlation data and so do not provide a definite causal link between coronary heart disease and risk factors such as saturated fat intake
  • Clinical studies of individual patients
    • Such studies are small, e.g. they may focus on just a few individuals, so they may not provide representative data
    • Studies will not include a suitable controlled experiment so it is not possible to make a definite causal link from the results
      • A controlled experiment would involve. e.g. one group of participants eating a normal diet while another group eats a diet high in saturated fat
      • Ethical considerations would prevent such controlled experiments from being carried out, due to the risk of harm to a group consuming a high fat diet over a long period
• When evaluating data from studies on coronary heart disease you could consider the following:
  • The sample group used must be representative of the population
    • Larger sample sizes are more likely to be representative as they cover a larger cross-section of the population
    • Samples must not all come from the same demographic group, e.g. not all white men who are over 60 and live in London
    • Samples must be human; results from animal trials do not perfectly represent human physiology
  • Statistical analysis should be used to check that any differences between results are statistically significant
    • E.g. the use of error bars in graphical data or the comparison of mean values from different trial groups
  • Some studies need to have a control with which to compare the results
    • E.g. when testing a drug to treat heart disease, a control group that is not given the drug should be included in the study to ensure that any effect shown is due to the drug and not any other factor
  • Studies should be repeated, or there should be many studies that show the same result, before conclusions can be drawn
  • The study should be designed to control any variable that is not being tested; this increases the validity of the results
    • Controlled factors might include, e.g. prior health of participants, other lifestyle factors of participants such as exercise and stress levels, age of participants, and biological sex of participants
    • Results are considered to be valid if they measure what they set out to measure, i.e. they are not influenced by external variables or poor experimental design, and have been analysed correctly
  • Researchers should not be biased, i.e. looking for a particular outcome
    • This could be a problem if someone is being paid to come up with a particular result
  • Data collection methods must be accurate, e.g. participants may not tell the truth in a questionnaire about diet or exercise

NOS: Correlation coefficients quantify correlations between variables and allow the strength of the relationship to be assessed

• Sometimes correlation between two variables can appear in the data
  • Correlation is an association, or relationship, between variables
  • There is a clear distinction between correlation and causation: a correlation does not necessarily imply a causative relationship
    • Causation occurs when one variable has an influence on, or is influenced by, another
• In order to get a broad overview of the correlation between two variables the data points for both variables can be plotted on a scatter graph
• Correlation can be positive or negative
  • Positive correlation: as variable A increases, variable B increases
  • Negative correlation: as variable A increases, variable B decreases
• The correlation coefficient (r) can be calculated; this indicates the strength of the relationship between variables
  • Perfect correlation occurs when all of the data points lie on a straight line with a correlation coefficient of 1 or -1
    • Remember that even strong correlations do not imply a causal relationship between the variables
  • The closer the correlation coefficient is to 1 or -1, the stronger the relationship
  • If there is no correlation between variables the correlation coefficient will be 0
• Low correlation coefficients or no correlation between variables may provide evidence against a hypothesis

Scatter graphs can be used to show the correlation between variables