Correlations & Causal Relationships - The Heart (AQA A Level Biology)

Correlations & Causal Relationships - The Heart

• Correlation is an association or relationship between variables

• Causation occurs when one variable has an influence or is influenced by, another

• There is a clear distinction between correlation and causation: a correlation does not necessarily imply a causative relationship

Recognising Correlations and Causal Relationships

• Scientists present their findings from experiments in graphical and numerical forms to identify if there are relationships between risk factors and certain disease

• Scatter diagrams are used to identify correlations between two variables to determine if a factor (such as obesity) does increase the risk of developing a disease (such as type 2 diabetes)

• Correlation can be positive or negative

  • Positive correlation: as variable A increases, variable B increases

  • Negative correlation: as variable A increases, variable B decreases

• If there is no correlation between variables the correlation coefficient will be 0

Image showing different types of correlation in scatter graphs

Risk factors & causal relationships for coronary heart diseases

• Coronary heart disease (CHD) includes any condition that interferes with the coronary arteries which supply blood to the heart muscle

• Many factors can increase the risk of developing CHD. Some factors are controllable while some factors can not be controlled

• The main risk factors for CHD include:

  • Genetic factors, age and sex, high blood pressure, smoking, obesity and high concentrations of low-density lipoproteins (LDLs)

• All of the risk factors for CHD can interact and affect one another

• The causal relationship for some risk factors can be very clear

  • A diet high in LDLs will cause the lumens of blood vessels to narrow and the increase the likelihood of an atheroma developing in the coronary arteries

• The interaction between risk factors in studies and investigations can make it hard to determine some causal relationships

• For example, it would seem illogical that an overweight smoker would not suffer from CHD but that overweight, non-smoker would

  • The latter individual could have a strong genetic predisposition to CHD (that wasn’t picked up in the study) which when combined with high blood pressure from being overweight results in CHD

• Therefore, it is very important when evaluating data on risk factors that you state that a factor increases or descreases the risk or that there is a correlation between a factor and an outcome but that this one factor is not necessarily the (only) cause