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Diet & Health

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Edexcel IAL Biology • Diet & Health

Diet & Health: Risk Factors, Data & CVD

🧠 Big idea: Cardiovascular disease is driven by a tangle of lifestyle and biological risk factors — and the whole chapter is really about learning to read scientific evidence carefully: spotting genuine patterns, not confusing correlation with causation, and judging whether a study is actually trustworthy.

📋 Summary — What This Chapter Covers

CVD risk factors: lifestyle (diet, blood pressure, smoking) vs uncontrollable factors (genetics, age, biological sex)
Dietary antioxidants: the vitamin C/heart disease story — a classic example of conflicting evidence
Core Practical 2: titrating vitamin C against DCPIP to build a calibration curve
Interpreting data: describing data, drawing conclusions, and judging validity — without falling into the correlation-causation trap
Designing good studies: sample size, randomisation, controls, repetition, avoiding bias
Perceived vs actual risk: why people fear flying more than driving, statistically speaking
Cholesterol & lipoproteins: HDL ("good") vs LDL ("bad") and why the ratio matters more than either alone
Reducing CVD risk: diet, smoking cessation, exercise — and how public health messaging uses this evidence
Treatments for CVD: antihypertensives, statins, anticoagulants, platelet inhibitors — their benefits and risks

1. CVD Risk Factors

Cardiovascular disease (CVD) is an umbrella term for anything going wrong with your heart and blood vessels. It's almost always linked to two underlying processes, and it's worth nailing the difference between them because exam questions love testing this:

Two Core Processes Atherosclerosis — hard fatty plaques (atheromas) build up inside the artery lining, narrowing it and making it stiff.

Thrombosis — a blood clot forms in an artery, often triggered by a ruptured atheroma, which can block blood flow entirely.

Think of atherosclerosis as the slow build-up of gunk narrowing a pipe over years, and thrombosis as the sudden clog that finally blocks it. One sets the scene; the other delivers the crisis (heart attack or stroke).

What exactly is a "risk factor"?

A risk factor is anything statistically linked to a higher chance of developing a disease. Crucially — a risk factor is not a guarantee. A smoker isn't certain to get lung cancer; they just have a much higher probability than a non-smoker. This distinction (risk vs certainty) comes up constantly in exam answers.

Risk factors fall into four broad categories:

Category	Example
Lifestyle	Diet, smoking, alcohol, exercise
Substances in body/environment	Air pollution, asbestos
Genetic predisposition	Inherited alleles affecting cholesterol handling
Other biological factors	Age, biological sex

Many diseases arise from the interaction of several risk factors at once — e.g. someone who smokes, eats a high-cholesterol diet, AND doesn't exercise has a much higher combined risk than any single factor would suggest alone. This is why public health advice targets multiple behaviours at once rather than just one.

Lifestyle factors and their mechanisms

Factor	How it increases CVD risk
Diet high in saturated fat	Raises blood cholesterol → more atheroma formation → more thrombosis risk
Diet high in salt	Raises blood pressure
High blood pressure	Damages artery walls → more atheroma formation
Smoking	3 mechanisms (see below) — carbon monoxide, nicotine, and reduced antioxidants

🚬 Smoking's Triple Attack — Worth Memorising 1. Carbon monoxide binds to haemoglobin, reducing oxygen-carrying capacity → less respiration in brain/heart cells → increases stroke and heart attack risk.
2. Nicotine increases platelet agglutination (stickiness) → higher thrombosis risk.
3. Smoking decreases antioxidant levels in the blood → more damage to the cells lining arteries → more atheroma formation.

Non-lifestyle (uncontrollable) factors

Factor	Associated risk
Genetics	Inherited alleles can predispose someone to high blood pressure or high cholesterol
Age	Risk rises with age — blood vessels become more fragile and plaque accumulates over time
Biological sex	Men are roughly 3× more likely to suffer CVD than premenopausal women, likely due to lower oestrogen (a hormone linked to raising "good" cholesterol)

Practice Question

Explain why having a diet high in saturated fat AND smoking together produces a greater risk of CVD than either factor alone would predict.

2. Dietary Antioxidants & CVD

This section is really a case study in how scientific understanding evolves — and how conflicting evidence should be handled.

Antioxidant vitamins include:

Vitamin	Found in
Vitamin A	Orange vegetables (carrots, sweet potato)
Vitamin C	Citrus fruits
Vitamin E	Leafy vegetables, some nuts and oils

The logic seemed clean: vitamin C helps build connective tissue, so more of it should mean healthier artery linings, less damage, and less atherosclerosis.

📊 The Finland Study (1984–1992) 1,605 men were tracked; 70 had a heart attack.
• 13.2% of men with low vitamin C had a heart attack
• 3.8% of men with normal vitamin C had a heart attack
This looked like strong support for "more vitamin C = less heart disease."

But then a 2016 meta-analysis (a study that pools and analyses the results of many existing studies) found no clear relationship between vitamin C intake and reduced heart disease risk — and even suggested antioxidant supplements could be harmful to circulatory health.

The Takeaway Lesson This is a textbook example of conflicting evidence. The correct scientific response isn't to pick whichever study you like — it's to say the evidence is currently inconclusive, and that all available evidence must be weighed together, not cherry-picked.

Practice Question

A single study found a strong correlation between low vitamin C and heart attacks. Explain why this alone is not enough to conclude that low vitamin C causes heart attacks.

3. Core Practical 2 — Vitamin C Content of Food & Drink

Vitamin C (chemical name: ascorbic acid) is a good reducing agent, meaning it's easily oxidised. This chemical property is exactly what the practical exploits.

The Core Principle DCPIP is a blue dye that turns colourless when it reacts with vitamin C. The more vitamin C needed to decolourise a fixed volume of DCPIP, the lower the concentration of vitamin C in that solution — and vice versa.

Method (the exam-relevant steps)

Make a series (e.g. 6) of known vitamin C concentrations by serial dilution.
Measure 1 cm³ of DCPIP solution into a test tube.
Add vitamin C solution drop by drop from a pipette/burette.
Shake for a set time (this shaking time is a control variable).
Record the volume (number of drops) needed to turn the DCPIP colourless.
Repeat 3 times per concentration and average.
Repeat for every known concentration.
Plot a graph: volume of vitamin C needed to decolourise DCPIP (y-axis) vs concentration of vitamin C (x-axis).

📈 Calibration Curve The line of best fit from this experiment is called a calibration curve. Once you have it, you can test an unknown sample (like a fruit juice), see how much of it is needed to decolourise the DCPIP, and read off the estimated vitamin C concentration from the graph.

Shape of the graph: as vitamin C concentration increases, the volume needed to decolourise DCPIP decreases — it's an inverse relationship (a downward curve/line).

⚠️ Risk Assessment DCPIP is an irritant — avoid skin contact and wear eye protection.

Practice Question

Why is it important to keep the shaking time constant for every concentration tested in this experiment?

4. Interpreting Data on Risk Factors

This is arguably the most exam-heavy section of the whole chapter — questions here follow an extremely predictable three-part structure: describe → conclude → evaluate validity. Get comfortable with all three and you'll pick up marks reliably.

Step 1: Describing data

Just state what the data shows — using actual numbers, not vague words. "Risk increases with age" is weak. "Risk increases from 1.00 in the 20–39 age group to 2.4 in the 80+ age group" is strong.

Step 2: Drawing conclusions

The Golden Rule Conclusions must be limited to exactly what the data shows. Never claim causation from one data set — only ever say there is a correlation or association. And never extrapolate beyond the study's actual conditions (e.g. a study on 40–50 year olds cannot be applied to over-70s; a mouse study cannot be directly applied to humans).

Step 3: Evaluating validity

Ask yourself these questions about any study:

Question	Why it matters
Is the sample size large?	Larger samples are more likely to represent the whole population
Is there a control group?	Isolates the effect of the variable being tested from other factors
Has it been repeated?	Repetition (by the same or other researchers) shows reliability
Were other variables controlled?	Otherwise you can't be sure what actually caused the effect
Is there bias?	E.g. funding from a company wanting a particular result
Is the difference statistically significant?	Rules out "it might just be random chance"

🔬 Reading Standard Deviation Bars If error bars (standard deviations) on a bar chart overlap, the difference between the two groups is not statistically significant. If they don't overlap, the difference likely is significant. This comes up again and again in data questions — memorise it cold.

Worked Example (from the specification)

A study of 523 non-smoking partners of smokers found: relative risk of CVD = 1.00 at 0 cigarettes/day exposure, 1.23 at 1–19 cigarettes/day, and 1.31 at ≥20 cigarettes/day.

Describe: Relative risk of CVD increases as exposure to secondhand smoke increases — from 1.00 with no exposure, to 1.23 at 1–19 cigarettes/day, to 1.31 at 20+ cigarettes/day.

Conclude: There is a correlation between increased secondhand smoke exposure and increased relative risk of CVD. (Avoid saying smoking "causes" this from one study alone.)

Validity: 523 people is a fairly small sample; this is only one study; there's no control for other risk factors (age, diet, exercise) that might differ between groups; and no statistical test is mentioned, so we can't confirm significance.

Recognising conflicting evidence

One study is never enough. Studies of similar design should be pooled in a meta-analysis. If different studies point in different directions, that's conflicting evidence — usually a sign that an unmeasured third variable is influencing the results. More research is then needed to figure out which pattern is actually correct.

Practice Question

A newspaper headline reads: "Study proves eating chocolate causes heart disease." Explain two reasons why this headline is scientifically problematic, based on how you should interpret data.

5. Designing Studies into the Effects of Risk Factors

If Section 4 is about judging a study, this section is about what makes a study good in the first place. Think of it as a checklist for the "perfect" experiment.

Feature	Why it matters
Representative sample	Large and randomly selected — avoids bias from picking people with similar lifestyles (e.g. friends, gym members)
Control variables	The more variables controlled, the more reliable and valid the results
Avoiding bias	No manipulation of sampling or data to favour a particular outcome (watch for conflicts of interest, e.g. company-funded research)
Experimental control / placebo	A group identical in every way except the variable being tested is absent — isolates cause and effect
Repetition	Repeats within a study should give similar (reliable) results
Reproducibility	The entire study should be repeatable by other scientists — hence why methods are always published in full

💊 The Placebo Concept In a drug trial, the control group receives a sugar pill (placebo) instead of the real drug, but everything else about their treatment is identical. This way, any difference in outcome between the groups can be attributed to the drug itself — not to factors like attention from doctors or the psychological effect of "receiving treatment."

Practice Question

A researcher wants to test whether a new drug lowers blood pressure. They recruit volunteers only from people already attending a gym. Explain why this is a weak sampling method.

6. Perception of Risk vs Actual Risk

Risk = the statistical chance or probability of a harmful event occurring, backed by scientific evidence. But how risky something feels to a person often has very little to do with the actual numbers.

Risk can be OVERESTIMATED because of...	Risk can be UNDERESTIMATED because of...
Misleading media coverage	Lack of information
Overexposure to information	Misunderstanding of the risk factors involved
Personal experience of the risk	Lack of personal experience
Unfamiliarity with the event	Unfamiliarity with the event (yes — cuts both ways!)
The event causing severe harm	Harm being non-immediate/delayed
Lack of enjoyment of the activity	Enjoyment of the activity

✈️ The Classic Example Annual risk of dying in a road accident: about 1 in 1,547.
Annual risk of dying in a plane crash: about 1 in 4.5–5.5 million.
Despite driving being thousands of times riskier, far more people fear flying than driving — largely because plane crashes are rare, dramatic, unfamiliar, and heavily covered by media, while car accidents are common and familiar (so they feel "normal" and safe).

Notice how this connects directly to smoking and diet: people often underestimate the risk of a poor diet because the harm is slow and delayed (non-immediate), while they might overestimate rarer, more dramatic causes of death.

Practice Question

Suggest why someone might underestimate the health risk posed by a diet high in saturated fat.

7. Cholesterol & Lipoproteins

Cholesterol itself is a type of lipid the body needs — for cell membranes, sex hormones, and bile. The problem isn't cholesterol existing; it's how it's transported and in what balance.

Since cholesterol doesn't dissolve in water (blood plasma is water-based), it has to be packaged into lipoproteins — spherical particles made of lipid + protein — to travel around the bloodstream.

	High Density Lipoproteins (HDL)	Low Density Lipoproteins (LDL)
Contains	Unsaturated fat, cholesterol, protein	Saturated fat, cholesterol, protein
Direction of transport	Body tissues → liver (for recycling/excretion)	Liver → bloodstream → cells
Effect on blood cholesterol	Reduces it when too high	Increases it when too low
Nickname	"Good" cholesterol	"Bad" cholesterol
Extra role	Helps remove cholesterol from atheroma plaques	Excess LDL blocks cell receptors → cholesterol builds up in blood → contributes to plaque formation

The Nuance Examiners Want It's tempting to call LDL simply "bad" and HDL simply "good" — but the real driver of risk is the ratio of LDL to HDL. A healthy ratio is roughly 3:1 (LDL:HDL). A ratio above 5:1 is thought to significantly increase CVD risk.

Evaluating lipoprotein/CVD data

Exactly the same checklist from Section 4 applies here: sample size, which individuals were sampled (can results be generalised?), presence of a control group, and statistical significance (check those error bars again!).

Worked Example

A study of 300 men (aged 40–80) tracked LDL/HDL levels against CVD event rate over 30 weeks. Result: LDL group ≈ 28% CVD event rate (with wide error bars overlapping the HDL group); HDL group ≈ 18% (error bars also overlapping).

Describe: CVD event rate is higher with LDL (≈28%) than with HDL (≈18%).

Conclude: Because the standard deviation bars overlap, there is no statistically significant difference between the two groups — despite the apparent difference in the bar heights!

Validity: 300 men is a small sample for a whole population; only men aged 40–80 were studied (can't generalise to women or younger/older people); other CVD risk factors (smoking, diet, exercise) weren't measured or controlled.

⚠️ Common Trap Students often see two different-looking bars on a graph and instantly conclude "there's a difference!" — but if the error bars overlap, that conclusion is not supported by the data. Always check the error bars before concluding significance.

Correlation vs Causation — Cholesterol Edition

There IS good evidence linking high blood cholesterol to CVD: statins (which lower cholesterol) reduce CVD risk; diets high in saturated fat (which raise LDL) are linked to more CVD events; lowering total cholesterol is linked to fewer major coronary events.

But even with this much converging evidence, remember: a correlation between a high-saturated-fat diet and raised LDL cholesterol doesn't automatically prove causation on its own — there could be a third variable, such as genetic differences in how efficiently someone's body metabolises dietary fats.

Practice Question

Explain why LDL cholesterol is often called "bad" cholesterol, and why this label is a slight oversimplification.

8. Measures to Reduce CVD Risk

Diet

Diets high in saturated fat have been linked (though not conclusively, as covered above) to increased CVD risk. Food labelling — including traffic light labels (red = high, orange = medium, green = low for sugar, saturated fat, salt) — helps consumers make informed choices.

Measuring obesity

Measure	How it's calculated	Healthy range
Waist-to-hip ratio	Waist circumference ÷ hip circumference	Women: <0.86 \| Men: <0.9
BMI	Mass (kg) ÷ height² (m²)	18.5–24.9 = normal; 25–29.9 = overweight; 30+ = obese; <18.5 = underweight

Smoking & Exercise

Public health responses to the CVD-smoking link include health warnings on packets, media portrayal of smoking as unhealthy, and free stop-smoking support materials/prescriptions. For exercise, initiatives include more PE hours in schools and targeted campaigns for specific groups (e.g. teenagers).

🔗 The Bigger Picture Notice the pattern across this whole chapter: scientific research on risk factors doesn't just stay in a lab — it directly shapes food labelling laws, cigarette packaging regulations, school PE requirements, and public health campaigns. Exam questions sometimes ask you to link evidence to real-world policy, so keep this connection in mind.

9. Treatments for CVD: Benefits & Risks

Prevention (reducing risk factors) is always the best strategy, but CVD is common enough that treatment is often necessary. There are four main drug categories to know — and for each, you should know how it works mechanistically, not just its name.

Antihypertensives (lower blood pressure)

Beta blockers — prevent increases in heart rate
Vasodilators (incl. ACE inhibitors) — increase blood vessel diameter; ACE inhibitors specifically block production of angiotensin (a hormone that constricts vessels), keeping arteries dilated
Diuretics — reduce blood volume by decreasing sodium (and therefore water) reabsorption in the kidneys

Lower blood pressure → less arterial endothelial damage → less atheroma/thrombosis risk.

Statins (lower blood cholesterol)

Statins block a liver enzyme needed to make cholesterol. This lowers LDL concentration in the blood (reducing atheroma formation) and also reduces inflammation in the arterial lining.

Anticoagulants (reduce blood clotting)

E.g. warfarin, which decreases the level of prothrombin in the blood — reducing new clot formation and lowering thrombosis risk.

Platelet inhibitors (a type of anticoagulant)

E.g. aspirin and clopidogrel — prevent platelets clumping together, stopping clots from forming in the first place.

Drug type	Key benefit	Key risk
Antihypertensives	Reduce blood pressure; effects can be monitored at home	Headaches, drowsiness, palpitations, swelling, persistent cough — side effects can cause patients to stop taking them
Statins	Lower "bad" LDL; raise "good" HDL	Slow to become effective; need long-term use; muscle/joint pain, liver damage; may cause false sense of security
Anticoagulants	Reduce new clot formation; shrink existing clots	Excessive bleeding risk (incl. internal); fainting, osteoporosis; can harm a foetus
Platelet inhibitors	Reduce new clot formation	Excessive bleeding; rashes, liver dysfunction, stomach lining damage; combining types raises risk

💭 A False Sense of Security A subtle but exam-worthy point: taking statins can give patients a false sense of security, leading them to return to an unhealthy lifestyle (poor diet, no exercise) because they feel "protected" by the medication — even though the drug doesn't eliminate all their risk.

Practice Question

Explain how ACE inhibitors reduce the risk of CVD, linking your answer to blood pressure and arterial damage.

🧷 What to Memorise

Atherosclerosis

Formation of hard fatty plaques (atheromas) inside the artery lining, narrowing and stiffening it.

Thrombosis

Formation of a blood clot inside an artery, often at the site of a ruptured atheroma.

Risk factor

Anything statistically linked to an increased chance (not certainty) of developing a disease.

Ascorbic acid

The chemical name for vitamin C; a strong reducing agent, easily oxidised.

DCPIP

A blue dye that turns colourless in the presence of vitamin C — used to measure vitamin C concentration by titration.

Calibration curve

A line of best fit from known concentrations, used to estimate the concentration of an unknown sample.

Correlation vs Causation

A correlation just means two variables change together; it does NOT prove one causes the other — a third variable could be responsible.

Meta-analysis

A study that combines and analyses the results of many existing studies to reach a stronger overall conclusion.

HDL (High Density Lipoprotein)

Transports cholesterol from tissues to the liver; "good cholesterol"; helps remove cholesterol from atheromas.

LDL (Low Density Lipoprotein)

Transports cholesterol from the liver to cells; "bad cholesterol" in excess; healthy LDL:HDL ratio ≈ 3:1, risky above 5:1.

BMI

Mass (kg) ÷ height² (m²). 18.5–24.9 normal; 25–29.9 overweight; 30+ obese.

Statins

Block a liver enzyme needed to make cholesterol, lowering LDL and reducing arterial inflammation.

Anticoagulants / Platelet inhibitors

Drugs that reduce blood clotting (e.g. warfarin, aspirin) to reduce thrombosis risk — but increase bleeding risk.

Perceived vs Actual Risk

How risky something feels (media exposure, familiarity, dread) often doesn't match its true statistical probability.

✅ Concepts Checklist

I can explain the difference between atherosclerosis and thrombosis
I can list lifestyle and non-lifestyle risk factors for CVD, with mechanisms
I understand why smoking increases CVD risk via three separate mechanisms
I can explain why vitamin C/CVD evidence is conflicting and what a meta-analysis is
I can describe the Core Practical 2 method for testing vitamin C content using DCPIP
I can explain what a calibration curve is and how it's used
I can describe data, draw a limited conclusion, and evaluate validity from a data set
I know that overlapping error bars mean no statistically significant difference
I never confuse correlation with causation in my answers
I can list the features of a well-designed scientific study
I understand why perceived risk often doesn't match actual/statistical risk
I can distinguish HDL and LDL, including their roles and healthy ratio
I can calculate and interpret BMI and waist-to-hip ratio
I can name the four CVD drug categories and explain how each works
I can weigh up the benefits vs risks of CVD medications

🎯 Exam Tips & Common Mistakes

❌ Mistake #1 Writing "X causes Y" from a single study or correlation. Examiners specifically look for the words "correlation" or "association" — using "causes" without strong, repeated evidence will cost you marks.

❌ Mistake #2 Describing data without using actual numbers. "Risk goes up with age" is a weak description — always quote the specific figures from the graph/table given.

❌ Mistake #3 Ignoring error bars. If a question gives you standard deviation bars and they overlap, you cannot conclude a significant difference — even if the bars themselves look visually different in height.

❌ Mistake #4 Calling LDL simply "bad" and HDL simply "good" without mentioning the ratio. Top-band answers mention that it's the LDL:HDL ratio (ideally ~3:1) that determines risk, not either lipoprotein type in isolation.

❌ Mistake #5 Forgetting to comment on sample representativeness. When asked to evaluate validity, always check: Was the sample large? Was it random? Does it represent the whole population being discussed, or just a subgroup (e.g. only men, only one age range)?

Exam Question Patterns to Expect • "Describe the data shown in Figure X" (use numbers!)
• "Draw a conclusion from this data" (correlation, not causation)
• "Evaluate the validity of this study" (sample size, controls, bias, statistical significance)
• "Explain how [drug type] reduces the risk of CVD" (mechanism-based answer)
• "Suggest why perceived risk might differ from actual risk in this scenario"
• "Explain the roles of HDL and LDL in cholesterol transport"

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