Lung Disease and Risk Factors Revision Notes | A Level AQA Biology

1. Why Lung Disease Matters for Gas Exchange

Your lungs are engineered for one purpose: maximising the rate of gas exchange between air and blood. When disease disrupts that engineering, the consequences cascade through every tissue in the body. Understanding how specific diseases impair gas exchange — and why certain risk factors cause them — is exactly what AQA examiners test at A Level.

This lesson builds on your knowledge of the alveolar structure and Fick's Law. You already know that the rate of diffusion depends on surface area, diffusion distance, and the concentration gradient. Every lung disease you study here attacks at least one of those three variables.

2. How Lung Diseases Disrupt Gas Exchange and Ventilation

Picture a healthy lung: millions of alveoli with walls just one cell thick, a vast surface area, and a rich capillary network maintaining a steep concentration gradient. Now consider what happens when disease remodels that architecture. Each condition below attacks the system differently, and the exam expects you to link the structural change to the functional consequence.

In fibrosis, the diffusion pathway can increase from the normal ~0.5 µm to several micrometres. Because the rate of diffusion is inversely proportional to distance (Fick's Law), even a modest increase in wall thickness causes a disproportionate fall in gas exchange rate. Patients typically show a reduced forced vital capacity (FVC) and a normal or elevated FEV₁/FVC ratio — a restrictive pattern rather than an obstructive one.

The loss of surface area in emphysema is the critical point. A healthy adult lung has a surface area of roughly 70 m² — comparable to a tennis court. In severe emphysema, this can fall to a fraction of that value. Less surface area means fewer sites for diffusion, so blood leaving the lungs carries less oxygen per unit time. Patients also trap air because elastic recoil is lost, leading to a barrel-shaped chest and chronically elevated residual volume.

Unlike fibrosis and emphysema, asthma primarily affects ventilation rather than the alveolar membrane itself. During an attack, the lumen of the bronchioles narrows, so less fresh air reaches the alveoli per breath. The concentration gradient for oxygen falls (less O₂ entering blood) and CO₂ accumulates in alveoli (reducing the gradient driving CO₂ removal from blood), both impairing gas exchange. Between attacks, lung structure can be essentially normal, which is why asthma is described as reversible obstruction.

That retrieval question models exactly the kind of 'explain' response AQA rewards: structural change → Fick's Law link → functional consequence. Keep that three-beat structure in mind as we move to the causes of these diseases.

3. Risk Factors: Smoking and Pollution

Knowing that emphysema destroys alveolar walls is only half the story. The exam also requires you to interpret data linking specific risk factors — particularly cigarette smoking and air pollution — to the incidence of these diseases. This is where epidemiology meets biology.

Cigarette smoke contains over 4,000 chemical compounds. For gas exchange, the most damaging are tar (a carcinogen that paralyses cilia and stimulates mucus production), carbon monoxide (which binds haemoglobin with very high affinity, forming carboxyhaemoglobin and reducing oxygen transport), and particulates (which trigger chronic inflammation). The inflammatory response in the alveoli activates macrophages that release elastase — an enzyme that digests elastin in alveolar walls, directly causing emphysema.

Air pollution — particularly fine particulate matter (PM2.5) and nitrogen dioxide from vehicle exhaust — follows a similar inflammatory pathway. Inhaled particles that reach the alveoli trigger macrophage activity and sustained inflammation, increasing the risk of both COPD and lung cancer. Occupational exposure to asbestos fibres causes a specific form of fibrosis called asbestosis, where fibres lodge permanently in lung tissue and provoke continuous scarring.

Notice the lag in the chart above. Lung cancer deaths peak roughly 20–30 years after peak cigarette consumption. This time lag is itself important evidence: it is consistent with a causal mechanism (cumulative DNA damage requiring decades to produce malignancy) rather than a coincidental association. When interpreting such data in the exam, always comment on the lag and what it implies about the biological mechanism.

4. Correlation vs Causal Relationships

This is one of the most frequently examined analytical skills in A Level Biology, and it is worth being precise about the distinction. A correlation means two variables change together — as one increases, the other tends to increase (positive correlation) or decrease (negative correlation). A causal relationship means one variable directly produces the change in the other.

Correlation is necessary but not sufficient for causation. To move from correlation to a justified causal claim, you need three things: a consistent statistical association across multiple studies, a plausible biological mechanism, and the elimination of confounding variables. In the context of smoking and lung disease, all three criteria are met — which is why the scientific and medical consensus is that smoking causes lung cancer and COPD, not merely correlates with them.

• Consistent association: Multiple independent epidemiological studies across different countries and time periods all show the same positive correlation between smoking and lung cancer incidence.
• Biological mechanism: Carcinogens in tar cause DNA mutations in proto-oncogenes and tumour suppressor genes; particulates trigger chronic inflammation and elastase release.
• Dose-response relationship: Heavier smokers have higher incidence rates — a dose-response relationship strongly supports causation.
• Reversibility: Incidence rates fall in populations that reduce smoking, consistent with removing the causal agent.
• Confounders addressed: Studies control for age, sex, occupation, and other pollutants to isolate the effect of smoking.

A confounding variable is one that is associated with both the risk factor and the outcome, potentially creating a spurious correlation. For example, people who smoke may also drink more alcohol — if alcohol independently increases cancer risk, then a naive analysis might overestimate the effect of smoking alone. Good epidemiological studies use statistical techniques to control for known confounders.

5. From Evidence to Legislation: Evaluating the Research Process

One of the most important higher-order skills in this topic is evaluating how experimental and epidemiological data led to statutory restrictions — legal limits — on risk factors. The history of tobacco regulation is the clearest case study available.

Richard Doll and Austin Bradford Hill published a retrospective study in 1950 and launched their prospective British Doctors Study in 1951, with first results published in 1954, following approximately 40,000 British doctors. They found that doctors who smoked had significantly higher rates of lung cancer than non-smoking doctors, and that the risk increased with the number of cigarettes smoked per day. This dose-response relationship was a pivotal piece of evidence because it was difficult to explain by confounding alone.

Despite this evidence, statutory restrictions took decades to implement. The UK government banned tobacco advertising on television in 1965, introduced health warnings on cigarette packets in 1971, and eventually passed the Health Act 2006, which prohibited smoking in enclosed public spaces. Each legislative step was preceded by accumulating evidence — from animal studies confirming carcinogenicity of tar, to passive smoking research showing risk to non-smokers, to economic analyses of NHS costs attributable to smoking-related disease.

Worked example: Evaluating How Evidence Led to Statutory Restrictions on Smoking

Evaluate the process by which experimental data on smoking led to statutory restrictions. [6 marks]

1. Doll and Hill (1950, 1954): retrospective and prospective cohort studies showed a strong positive correlation between cigarette smoking and lung cancer in British doctors. — Name the specific study and its design — examiners reward precise reference to evidence type.
2. Heavier smokers had proportionally higher lung cancer rates. This dose-response relationship strengthened the causal inference beyond correlation alone. — Dose-response is a key criterion for causation — include it explicitly.
3. Laboratory studies identified specific carcinogens in tar (e.g., benzopyrene) that cause DNA mutations in cell lines and animal models, providing a biological mechanism. — Mechanism evidence moves the argument from statistical association to causal explanation.
4. Tobacco industry-funded research disputed findings; long latency periods made causation harder to demonstrate; confounders such as occupation and diet required controlling. — A balanced evaluation must acknowledge why restrictions were delayed — this is the 'counter-argument' beat.
5. Accumulating evidence led to: TV advertising ban (1965), health warnings on packets (1971), ban on smoking in public places (Health Act 2006), and plain packaging legislation (2016). — Specific legislative examples demonstrate that you can trace evidence to statutory outcome.
6. The weight of consistent epidemiological, mechanistic, and dose-response evidence eventually overcame commercial and political resistance, demonstrating that robust, replicated data can drive public health legislation. — A conclusion that weighs evidence against limitations and reaches a judgement secures the top mark band.

Answer: A full 6-mark response would include: named evidence (Doll and Hill), dose-response relationship, biological mechanism, acknowledgement of confounders and delays, specific legislative examples, and a justified evaluative conclusion.

Notice that the step-by-step above models the 6-mark 'evaluate' structure: evidence → mechanism → counter-argument → conclusion. That four-beat pattern applies to any evaluate question in this topic, whether the risk factor is smoking, pollution, or occupational exposure.

6. Interpreting Data: Worked Examples

AQA questions on this topic frequently present unfamiliar data — graphs, tables, or spirometry traces — and ask you to interpret them. The skill is not memorising specific numbers but applying your understanding of lung disease mechanisms to whatever data appears. Here is a structured approach.

Worked example: Interpreting Spirometry Data for a Patient with Emphysema

A spirometry trace shows: tidal volume = 0.4 dm³ (normal), FVC = 2.1 dm³ (normal reference: 4.0 dm³), FEV₁ = 1.0 dm³ (normal reference: 3.2 dm³), FEV₁/FVC ratio = 0.48 (normal: ~0.80). Interpret this data and explain what it suggests about the patient's lung condition.

1. FEV₁/FVC = 1.0 ÷ 2.1 = 0.476, approximately 0.48 as stated. This is well below the normal value of ~0.80. — Always verify the ratio given in the question by dividing the stated values — this checks for internal consistency in the data.
2. Both FVC and FEV₁ are reduced. The FEV₁/FVC ratio is also reduced (0.48 < 0.80). This is an obstructive pattern. — In restrictive disease (e.g. fibrosis), FVC falls but FEV₁/FVC is normal or high. In obstructive disease (e.g. emphysema, COPD), both fall but FEV₁ falls proportionally more.
3. Emphysema destroys alveolar walls and reduces elastic recoil. Without elastic recoil, the patient cannot force air out rapidly — FEV₁ (the volume expelled in the first second) falls more than FVC. — This is the mechanistic explanation the examiner wants: structural change → functional consequence.
4. Reduced surface area (from alveolar wall breakdown) and air trapping (from loss of elastic recoil) both reduce the concentration gradient across the alveolar membrane, impairing oxygen uptake and CO₂ removal. — Always close the loop back to gas exchange — that is the core spec requirement.

Answer: The data shows an obstructive pattern consistent with emphysema: reduced FVC, disproportionately reduced FEV₁, and a low FEV₁/FVC ratio (~0.48). This reflects loss of elastic recoil and alveolar wall destruction, impairing both ventilation and gas exchange.

7. Exam Technique and Common Mistakes

The final section consolidates the exam skills this topic demands. Questions here tend to be data-heavy and command-word specific. Getting the command word right is the difference between a mark-winning response and a frustrating near-miss.

When a question says 'interpret', it wants you to go beyond describing the numbers — it wants you to explain what the data means in terms of the underlying biology. When it says 'evaluate', it expects a balanced argument with a conclusion. When it says 'suggest', there may be more than one acceptable answer, so apply your biological knowledge to the novel context rather than recalling a memorised fact.

Across this lesson, every spec requirement connects back to the same core principle: lung diseases impair gas exchange by disrupting surface area, diffusion distance, or the concentration gradient, and the risk factors that cause these diseases are supported by epidemiological and mechanistic evidence strong enough to justify statutory action. Keeping that thread visible in your exam answers is what separates a competent response from an excellent one.