The effect of lifestyle on some non-communicable diseases — AQA GCSE Biology
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The effect of lifestyle on some non-communicable diseases explained
Non-communicable diseases cannot be passed from person to person.
Read the full explanation
Their human cost includes pain, disability, reduced quality of life, premature death and emotional strain on families and carers. Their financial cost includes treatment, lost income, care costs and reduced productivity. Lifestyle factors affect incidence, which means the number of new cases in a population over a period. A diet high in saturated fat, salt and sugar raises the risk of cardiovascular disease, type 2 diabetes and some cancers. Alcohol misuse raises the risk of liver disease and some cancers. Smoking raises the risk of lung cancer, cardiovascular disease and chronic obstructive pulmonary disease. These effects can be analysed at individual, local, national and global levels.
Risk factors are linked to an increased rate of a disease.
A risk factor is anything associated with a higher chance of developing a disease. Risk factors do not guarantee disease; they increase the rate at which it occurs in a population. Some risk factors are lifestyle choices, such as smoking, poor diet, alcohol use and lack of exercise. Others are not directly chosen, such as age, genetics or exposure to radiation. Scientists identify risk factors by comparing disease rates in groups with and without the factor. For example, if lung cancer is much more common among smokers than non-smokers, smoking is a risk factor. A causal link is stronger when evidence shows a mechanism, a dose response and consistency across studies.
They can be: • aspects of a person’s lifestyle • substances in the person’s body or environment.
Risk factors are influences that raise the chance of developing a non-communicable disease, but they are not guarantees. They fall into two broad groups. Lifestyle aspects are the choices and behaviours a person controls, such as diet, exercise, smoking and alcohol intake. Substances in the body or environment are materials the person encounters, such as carcinogens in tobacco smoke, ionising radiation, or chemicals in the air or workplace. For example, a diet high in saturated fat is a lifestyle factor linked to cardiovascular disease, while exposure to ionising radiation is an environmental substance linked to cancer. Identifying which group a factor belongs to helps students explain how disease risk arises and how it might be reduced.
A causal mechanism has been proven for some risk factors, but not in others.
A causal mechanism is a biological explanation of how a risk factor leads to a disease. For some factors, the mechanism is well established. For example, tobacco smoke contains carcinogens that can mutate DNA in lung cells, and this mutation can lead to uncontrolled cell division and lung cancer. For other factors, scientists observe a statistical association but cannot yet prove the mechanism. For instance, some dietary factors are linked to cardiovascular disease, but the exact biological steps may be complex or uncertain. Students should distinguish between a proven causal mechanism and a correlation, and explain why proving causation is difficult when many factors interact.
The effects of diet, smoking and exercise on cardiovascular disease. • Obesity as a risk factor for Type 2 diabetes. • The effect of alcohol on the liver and brain function. • The effect of smoking on lung disease and lung cancer. • The effects of smoking and alcohol on unborn babies. • Carcinogens, including ionising radiation, as risk factors in cancer.
This statement links risk factors to non-communicable diseases. A diet high in saturated fat raises blood cholesterol, leading to fatty deposits in arteries and cardiovascular disease; smoking damages blood vessels and raises blood pressure; regular exercise lowers cardiovascular disease risk. Obesity increases the risk of Type 2 diabetes because body cells become less responsive to insulin. Alcohol damages liver cells, causing cirrhosis, and impairs brain function. Smoking damages lung tissue, causing chronic obstructive pulmonary disease, and contains carcinogens that cause lung cancer. Chemicals in tobacco smoke (such as carbon monoxide) and alcohol can harm an unborn baby by crossing the placenta. Carcinogens, including ionising radiation, can mutate DNA and cause cancer.
Many diseases are caused by the interaction of a number of factors.
Non-communicable diseases often arise from several interacting factors rather than a single cause. These factors can include genetics, lifestyle choices and environmental exposures. For example, a person may inherit a tendency towards high blood pressure, eat a diet high in saturated fat, smoke and take little exercise; together these raise the risk of cardiovascular disease more than any one factor alone. Similarly, Type 2 diabetes risk can involve obesity, diet, physical activity and family history. Because factors interact, it is difficult to prove that one factor alone causes a disease. Students should explain how multiple factors combine and why this makes disease prevention and causal proof complex.
Students should be able to understand the principles of sampling as applied to scientific data in terms of risk factors.
Sampling means studying a subset of a population to estimate a characteristic of the whole population, such as the proportion of people exposed to a risk factor. A sample must be large enough and selected without bias, for example by random selection, so that the estimate is reliable. If a sample is small or biased, the estimated risk may be inaccurate. In risk-factor studies, scientists compare disease rates in groups with different exposures, such as smokers and non-smokers, and use samples to estimate how strongly a factor such as smoking is linked to a non-communicable disease. The sample should represent the whole population, so findings can be generalised.
Students should be able to translate information between graphical and numerical forms; and extract and interpret information from charts, graphs and tables in terms of risk factors.
Risk-factor data are often presented as tables, bar charts, line graphs or scatter diagrams. Translating between forms means reading a value from a graph and writing it as a number, or plotting numerical data as a graph. Extracting information means locating the required value, such as the death rate for a given exposure. Interpreting means explaining what the pattern shows about risk, for example that as the number of cigarettes smoked per day increases, the risk of lung cancer increases. You should read axes carefully, including units and scales, and describe trends using comparative terms such as higher, lower, increases or decreases.
Students should be able to use a scatter diagram to identify a correlation between two variables in terms of risk factors.
A scatter diagram plots one variable on the x-axis and another on the y-axis, with each point representing one individual or one group. If the points lie close to a straight line rising from left to right, there is positive correlation: as one variable increases, the other tends to increase. A falling pattern shows negative correlation, and a random scatter shows no correlation. In risk-factor studies, a positive correlation might link the number of cigarettes smoked per day with the risk of lung cancer. Correlation shows an association, not proof that one variable causes the other, because other factors may be involved.
Your focus
- Describe human and financial costs of non-communicable diseases at individual, local, national and global levels.
- Explain how diet, alcohol and smoking affect the incidence of named non-communicable diseases.
- Use the term incidence correctly when comparing populations or time periods.
Show all 27 objectives
- Define risk factor and relate it to increased disease rate.
- Give lifestyle and non-lifestyle examples of risk factors.
- Explain how comparisons of disease rates identify risk factors.
- Identify lifestyle aspects and substances in the body or environment as two categories of risk factor.
- Give a named example of each category and link it to a non-communicable disease.
- Explain that risk factors increase the chance of disease rather than causing it directly.
- Define a causal mechanism and explain why it matters for identifying disease causes.
- Give one example of a proven causal mechanism and one example where causation is not proven.
- Explain why correlation alone does not establish causation.
- Describe how diet, smoking and exercise affect cardiovascular disease risk.
- Explain how obesity, alcohol, smoking and carcinogens contribute to specific non-communicable diseases.
- Describe how chemicals in tobacco smoke and alcohol can affect an unborn baby.
- Explain that many non-communicable diseases result from multiple interacting factors.
- Give a named example of a disease with several contributing factors.
- Explain why multiple factors make it difficult to prove a single causal mechanism.
- Describe why a sample is used instead of studying an entire population.
- Explain how sample size and random selection affect the reliability of conclusions about risk factors.
- Apply sampling principles to interpret a comparison of disease rates between exposed and unexposed groups.
- Convert data between numerical and graphical forms accurately.
- Extract relevant values from charts, graphs and tables about risk factors.
- Interpret trends in risk-factor data and relate them to non-communicable disease.
- Read and describe the pattern of points on a scatter diagram.
- Distinguish positive, negative and no correlation in risk-factor data.
- Explain why correlation between a risk factor and a disease does not prove causation.
The effect of lifestyle on some non-communicable diseases exam tips
Marking Points
- Non-communicable diseases are not infectious and cannot be transmitted between people.
- Human costs include pain, disability, reduced quality of life, premature death and emotional effects on families and carers.
- Financial costs include treatment, care, lost income and reduced productivity for individuals, communities, nations and globally.
- A diet high in saturated fat, salt and sugar increases the risk of cardiovascular disease, type 2 diabetes and some cancers.
- Alcohol misuse increases the risk of liver disease and some cancers; smoking increases the risk of lung cancer, cardiovascular disease and chronic obstructive pulmonary disease.
- Incidence means the number of new cases in a population over a period, and lifestyle factors can change incidence at local, national and global levels.
- Defines a risk factor as something linked to an increased rate or chance of disease.
- Distinguishes risk factors from causes: they raise probability, not certainty.
- Gives examples including lifestyle factors such as smoking, diet and alcohol, and non-lifestyle factors such as age or genetics.
- Explains how risk factors are identified by comparing disease rates between groups.
- Uses the term rate correctly, referring to occurrence in a population over time.
- State that a risk factor increases the probability of developing a non-communicable disease rather than causing it directly.
- Classify lifestyle factors as aspects of behaviour or choice, giving examples such as poor diet, lack of exercise, smoking or excess alcohol.
- Classify substances in the body or environment as materials encountered, giving examples such as carcinogens, ionising radiation or harmful chemicals.
- Distinguish between factors a person can change and factors present in surroundings or taken into the body.
- Link each named factor to a specific non-communicable disease, such as smoking to lung cancer or obesity to Type 2 diabetes.
- Define a causal mechanism as an explanation of how a risk factor produces a disease.
- Give an example where a causal mechanism is proven, such as carcinogens in tobacco smoke causing DNA mutations linked to lung cancer.
- Give an example where a causal mechanism is not proven, such as an uncertain dietary link to cardiovascular disease.
- Explain that correlation alone does not prove causation because other factors may be involved.
- Recognise that multiple interacting factors can make it difficult to isolate one cause.
- Link a diet high in saturated fat to raised cholesterol, artery narrowing and cardiovascular disease.
- Explain that smoking damages blood vessels and increases blood pressure, contributing to cardiovascular disease.
- Explain that obesity makes body cells less responsive to insulin, increasing Type 2 diabetes risk.
- Describe how alcohol damages liver cells and impairs brain function.
- Describe how smoking damages lung tissue and how carcinogens in smoke cause lung cancer.
- Explain that chemicals in tobacco smoke (such as carbon monoxide) and alcohol can cross the placenta and harm an unborn baby.
- State that carcinogens, including ionising radiation, can mutate DNA and cause cancer.
- State that many non-communicable diseases have multiple contributing factors rather than one single cause.
- Identify categories of interacting factors, such as genetic, lifestyle and environmental influences.
- Give a named example, such as cardiovascular disease, where diet, smoking, exercise and genetics interact.
- Explain that interacting factors can increase risk more than any single factor alone.
- Explain why multiple factors make it difficult to prove a single causal mechanism.
- Defines sampling as selecting and studying part of a population to draw conclusions about the whole population.
- Explains that sample size affects reliability: larger samples usually give more reliable estimates than very small samples.
- Explains that sampling must avoid bias, for example by random selection, so the sample represents the population.
- Applies sampling ideas to risk factors by comparing disease occurrence in groups with different levels of exposure.
- Recognises that a sample gives an estimate, not a certain value for the whole population.
- Uses a specific example, such as comparing lung cancer rates in a sample of smokers and a sample of non-smokers.
- Reads values accurately from axes, tables or chart labels, including correct units.
- Translates a value from a graph into numerical form, or plots given numerical data correctly.
- Describes the overall trend shown, such as an increase, decrease or no clear change.
- Interprets the trend in terms of risk factors, linking exposure to the likelihood of a non-communicable disease.
- Compares data for different groups, for example smokers and non-smokers, using values from the graph or table.
- Uses appropriate comparative vocabulary such as higher, lower, greater risk or reduced risk.
- Identifies the variables on the x-axis and y-axis and states what each represents.
- Recognises positive correlation when both variables increase together.
- Recognises negative correlation when one variable increases as the other decreases.
- Recognises no correlation when points are randomly scattered.
- Describes the strength of correlation as strong or weak based on how closely points fit a line.
- Explains that correlation does not prove causation and that other risk factors may contribute.
Examiner Tips
- 💡Structure extended answers by scale: individual, local community, nation and global, using a named disease example for each lifestyle factor.
- 💡Use data or graphs if provided to compare incidence between populations or over time, and quote figures accurately.
- 💡Distinguish human costs from financial costs clearly, and avoid repeating the same point in both categories.
- 💡Use the phrase increased rate or increased chance when defining risk factors.
- 💡Support each risk factor with a named disease and a brief comparison of rates.
- 💡Mention that other factors may contribute, showing balanced scientific reasoning.
- 💡Use the wording of the specification to sort factors into the two groups before writing your answer.
- 💡Give one clear example for each group and name the disease it is linked to.
- 💡Avoid saying a risk factor 'gives you' a disease; say it increases the risk or chance.
- 💡Use the phrase 'causal mechanism' accurately and explain what it means in your own words.
- 💡When giving an example, state the factor, the disease and the biological step that links them.
- 💡For an unproven link, say that the evidence shows an association but the mechanism is not established.
- 💡For each factor, name the disease and describe the biological effect in one clear sentence.
- 💡Use comparative language such as 'increases the risk of' rather than 'causes'.
- 💡When discussing unborn babies, mention that specific chemicals cross the placenta and can affect development.
- 💡Use the word 'interaction' and give a specific disease with at least two factors.
- 💡Structure your answer by naming the disease, then the factors, then how they combine.
- 💡Explain why proving one cause is difficult when several factors are involved.
- 💡Link sampling directly to risk factors by naming a factor such as smoking, diet or exercise and the disease it affects.
- 💡Use comparative language such as higher, lower, greater risk or reduced risk when describing sample results.
- 💡If asked to improve a study, suggest increasing sample size or using random selection rather than repeating the same method.
- 💡Quote figures from the graph or table to support your description, including units where given.
- 💡Use the command word: describe means state the pattern, while explain means give a reason linked to risk.
- 💡Check whether the graph shows risk increasing or decreasing before writing your answer.
- 💡State the direction of the correlation first, then its strength, then link it to the risk factor.
- 💡Use the phrase positive correlation or negative correlation rather than saying the line goes up or down.
- 💡If asked about cause, mention that other lifestyle or genetic factors could also affect the disease.
Common Mistakes
- Confusing incidence with prevalence; correct by defining incidence as new cases over a period and prevalence as all existing cases at a point in time.
- Claiming that a lifestyle factor guarantees disease; correct by saying it increases the risk or is associated with a higher incidence.
- Describing non-communicable diseases as infectious; correct by stating that they are not passed from person to person.
- Saying a risk factor always causes the disease; correction: state that it increases the chance or rate.
- Treating correlation as proof of causation; correction: require evidence such as a mechanism or dose response.
- Listing only lifestyle factors; correction: include non-lifestyle factors such as age and genetics.
- Treating every risk factor as a direct cause of disease; correct this by saying risk factors increase the chance of disease and may act alongside other factors.
- Assuming all risk factors are lifestyle choices; correct this by recognising that substances in the body or environment, such as ionising radiation, are not chosen behaviours.
- Confusing correlation with causation; correct this by stating that an association does not prove that the factor causes the disease.
- Claiming that any statistical link proves causation; correct this by stating that a mechanism must be demonstrated.
- Assuming all risk factors have a known mechanism; correct this by noting that some links remain uncertain.
- Confusing a risk factor with a causal mechanism; correct this by explaining that the mechanism describes the biological process, not just the association.
- Saying that obesity directly causes Type 2 diabetes; correct this by explaining that it increases the risk by reducing insulin sensitivity.
- Stating that 'smoking' crosses the placenta; correct this by specifying that chemicals in tobacco smoke, such as carbon monoxide, cross the placenta.
- Stating that alcohol only affects the liver; correct this by including its effects on brain function and on unborn babies.
- Attributing a disease to one factor only; correct this by describing at least two interacting factors.
- Assuming that factors simply add up; correct this by explaining that they can interact and multiply risk.
- Ignoring genetic factors; correct this by including inherited tendencies alongside lifestyle and environment.
- Thinking a sample must include the whole population; correct this by stating that a sample is a subset used to estimate population values.
- Assuming any sample is valid regardless of size; correct this by explaining that small samples can give unreliable estimates.
- Ignoring bias, for example selecting only volunteers; correct this by explaining that random selection helps the sample represent the population.
- Misreading the scale on an axis, for example treating each grid line as one unit when it represents ten; correct this by checking the labelled intervals before reading values.
- Describing only one data point instead of the overall trend; correct this by summarising the pattern across the range of data.
- Confusing correlation with cause; correct this by stating that the data show an association, while other factors may also contribute.
- Calling a scatter diagram a line graph and joining the points; correct this by leaving the points unjoined and describing the pattern.
- Claiming that correlation proves cause; correct this by stating that correlation shows an association only.
- Mixing up the axes; correct this by checking axis labels before describing the relationship.