Alcohol

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Alcohol intake and life expectancy

Higher alcohol intake is linked to higher all-cause mortality and shorter life expectancy, with no clearly safe threshold. Heavy and binge drinking can reduce healthy life by around 3–6 years, and raise the risk of dying earlier from cardiovascular disease, cancer, and other causes.

Summary

Large genetic and cohort studies show a mostly linear increase in mortality risk with higher alcohol intake, with no clear protective effect of “light–moderate” drinking once confounders are handled.
Heavy and binge drinking (and alcohol poisoning / problem drinking) are consistently linked to substantially higher mortality and 3–6 fewer disease-free years between ages 40 and 75.
The pattern of drinking (binge vs regular, with meals, wine vs spirits, stable vs changing) matters: binge episodes and unstable, high-risk patterns are more harmful than small amounts spread out over the week.
In KamaLama, alcohol intake is treated as a risk-only factor: there is no benefit from higher intake, and the best outcome is very low or no use, especially without binge drinking or a history of alcohol problems.
Moving from heavy or binge drinking to low-risk or no drinking can improve your KamaLama result by several years, particularly when combined with improvements in blood pressure, BMI, sleep, and smoking.

1. What research shows about alcohol and mortality

1.1 Big picture

Across many decades of research:

Earlier observational cohort studies often reported a J-shaped curve:
- Lowest mortality in “light–moderate” drinkers (e.g. <10–15 g/day).
- Higher mortality in abstainers and heavy drinkers.
More recent Mendelian randomisation and better-controlled cohort studies challenge this picture and find:
- No convincing protective effect of low or moderate alcohol intake.
- A linear association: more alcohol → higher risk of early death, including from cardiovascular disease and cancer.

Key large and/or genetic studies in Europe, China, and the US show that genetically predicted higher alcohol intake is associated with higher all-cause mortality and cause-specific mortality, without a safe threshold.

Many “protective” findings in older studies appear to come from:

Confounding (e.g. moderate drinkers being richer, more educated, more active, less likely to smoke).
“Sick quitter” bias (abstainer groups including ex-drinkers who stopped because of illness or previous heavy use).
Misclassification and selective participation.

When these issues are addressed, the apparent benefit of moderate drinking mostly disappears.

1.2 How much can alcohol shorten life?

Impact estimates vary by study and population, but consistent patterns emerge:

Heavy and binge drinking are linked to:
- 3–6 fewer disease-free years between ages 40–75 vs never-drinkers or low-risk drinkers.
- Higher risk of death from cardiovascular disease, cancers (especially breast, digestive, liver), liver disease, accidents, and injuries.
At a country level, higher per-capita alcohol consumption is associated with lower life expectancy, especially in high-income countries where other causes are better controlled.
Heavy alcohol use also explains part of the life-expectancy gap between higher and lower socioeconomic groups, particularly when combined with smoking and other risk factors.

In many cohorts, those with very high or problematic alcohol use have several years shorter overall life expectancy than low-risk or never-drinkers, even after adjusting for other factors.

2. Patterns of drinking and years of life

2.1 Disease-free life years

A large multicohort study (IPD-Work consortium + UK Biobank) examined alcohol use and years lived without major chronic diseases (type 2 diabetes, stroke, myocardial infarction, cancer, chronic lung disease). From age 40 to 75:

Drinking pattern	Disease-free life years (men)	Disease-free life years (women)
Never-drinkers	29.7	29.8
Moderate, no binge episodes	28.7	29.6
Heavy + binge drinking	26.0	27.5
Alcohol poisoning / problem drinking	23.4	24.0

Key takeaways:

Compared with never-drinkers, men with alcohol poisoning / problem drinking lost about 6.3 disease-free years (29.7 → 23.4).
Compared with moderate, non-binge drinkers, heavy + binge drinkers lost around 2.7–2.8 disease-free years (men: 28.7 → 26.0; women: 29.6 → 27.5).
The pattern (especially poisoning or frequent binges) matters almost as much as the average amount.

2.2 Socioeconomic differences

Alcohol does not act in isolation. Evidence shows:

People in lower socioeconomic groups often experience greater harm from similar or lower levels of alcohol – partly because of more harmful patterns (binge, mixing with smoking) and less access to care.
Heavy alcohol use contributes meaningfully to income and education gaps in life expectancy, especially in Nordic countries with high-quality register data.

For KamaLama, this means alcohol intake should be interpreted together with other cardiovascular risk factors and social context (blood pressure, BMI, smoking, stress, country, etc.), not as a standalone number.

3. How KamaLama uses the alcohol factor

3.1 Question in the test

Alcohol intake
How often do you drink alcohol (beer, wine, or spirits)? Count all drinks per week over the last 12 months. One drink ≈ 10–14 g of pure alcohol (e.g. a small glass of wine, a shot of spirits, or a small beer).

(Exact in-product wording can be adjusted to match UI.)

3.2 Ranges and scores in KamaLama

In the KamaLama model, alcohol intake is treated as a risk-only factor. There is no health benefit from drinking more, and the best outcome is very low or no alcohol, without binge episodes or a history of alcohol problems.

The factor currently spans –7 to +1 years in the model, interacting with other risks (e.g. blood pressure, BMI, lipids, smoking).

Note: “1 drink” ≈ 10–14 g ethanol. Weekly amounts are approximate ranges for guidance.

Answer in KamaLama (example wording)	Typical pattern (approx.)	KamaLama score*	How this maps to research
Never or almost never drink (no past problem drinking)	0 drinks/week	+1 year	Avoids alcohol-related risks entirely; small positive in model vs population baseline.
A couple of drinks per month	1–3 drinks/month	0 years	Very low exposure; risk close to never-drinkers in most cohorts.
1–3 drinks per week, no binge episodes	1–3 drinks/week	0 years	Slightly higher risk in genetic studies, but much lower than heavy/binge use.
4–7 drinks per week or occasional binges	~4–7 drinks/week or ≥1 binge/month	–2 years	Higher all-cause and CVD/cancer mortality; especially when combined with binges.
8–14 drinks per week or regular binge drinking	~8–14 drinks/week or ≥1 binge/week	–4 years	Clearly higher mortality and 2–3 fewer disease-free years in many cohorts.
15+ drinks per week or alcohol abuse/poisoning history	≥15 drinks/week and/or alcohol poisoning / abuse	–7 years	Strongly elevated mortality and 3–6 fewer disease-free years; high risk of many causes.

*Scores are model estimates, not guaranteed years of life gained or lost. They are calibrated against all-cause mortality evidence and interact with other risk factors inside KamaLama.

In the current UI, a typical low-risk answer like “A couple of drinks per month” is shown as 0 years (neutral) in the report, consistent with the table above.

4. Practical ways to improve this factor

For most people, the biggest health gains come from:

4.1 Move towards low or no alcohol

If you drink heavily or binge, aim first to cut down frequency and amount, especially on your heaviest days.
Set a clear weekly limit (for example, “no more than 2 drinks on any day, and at least 3–4 alcohol-free days per week”) and track it for 4–6 weeks.

4.2 Eliminate binge drinking

Binge drinking (e.g. ≥4–5 drinks in a short time) drives a large share of the risk – including accidents, injuries, and acute cardiovascular events.
Focus on avoiding these episodes even if your total weekly amount is not extremely high.

4.3 Change situations, not just willpower

Identify your “high-risk situations”: certain friends, Friday nights, stress, specific venues.
Swap some of these with alcohol-free routines: walks, cinema, sports, or dinners with non-alcoholic options.
Keep alcohol out of the home or harder to access, and pre-decide what (and how much) you’ll drink before you arrive.

4.4 Combine with other heart-protective changes

Alcohol’s effect is stronger when combined with:

High blood pressure
Smoking
High LDL cholesterol and triglycerides
Obesity and abdominal fat
Poor sleep and high stress

Improving these at the same time (for example, using the KamaLama factors on blood pressure, BMI, steps, sleep, stress, and smoking) can amplify your overall gain in calculated life expectancy.

4.5 Get support if cutting down is hard

If you notice:

You can’t stick to limits,
You drink to manage stress or emotions, or
People close to you are worried,

then it’s worth speaking with a healthcare professional or counsellor experienced in alcohol use. Evidence-based support (brief counselling, digital tools, support groups) can substantially reduce intake and related health risks.

5. Methods and evidence quality

For the KamaLama alcohol factor, we prioritised:

Large prospective cohort studies with many participants and deaths, long follow-up, and detailed drinking data.
Mendelian randomisation and genetic studies, which use genetic variants as instruments to approximate causal effects of alcohol on disease and mortality.
Studies reporting absolute differences in life expectancy or disease-free years, or data that can be reasonably converted into those terms.

Key steps:

Screening hundreds of papers on alcohol and all-cause mortality, cardiovascular disease, cancer, and life expectancy.
Prioritising analyses with:
- Clear dose–response curves.
- Stratification by sex, age, and smoking.
- Adjustment for major confounders (socioeconomic status, physical activity, BMI, diet, comorbidities).
Cross-checking results from different regions (US, Europe, China, Thailand, etc.) and different methods (cohorts, twin studies, MR).

Main limitations

Self-reported alcohol intake is imperfect; people often under-report or misremember their drinking.
Many cohorts are from high-income settings; evidence for low- and middle-income countries is still limited.
“Moderate drinkers” often differ from abstainers in many ways (income, education, social networks), which is hard to fully adjust for.
Genetic approaches assume specific statistical conditions; while they reduce some biases, they are not perfect.

Despite these limitations, the overall pattern is remarkably consistent:
more alcohol → higher risk of earlier death, especially with heavy and binge drinking.

6. References (all in one block)

All-cause mortality, dose–response, and patterns

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