Hypothesis Pitfalls on Excess Coffee Effects Research in University of South Australia

I came across various news posts about the manifest of proof on high coffee consumption’s impact on the brain, heart. Then I traced back to the citing source, and it was referenced in a research article: Kitty Pham, Anwar Mulugeta, Ang Zhou, John T. O’Brien, David J. Llewellyn & Elina Hyppönen (2021) High coffee consumption, brain volume and risk of dementia and stroke, Nutritional Neuroscience, DOI: 10.1080/1028415X.2021.1945858

The approach and declaration remain questionable for me as I consider it potentially falls to several hypothesis pitfalls.

Let’s lay out the critical context in that research article:


Summary: People who drink six or more cups of coffee a day have a 53% increased risk of developing dementia and a higher risk of stroke.

Methods: We conducted prospective analyses of habitual coffee consumption on 398,646 UK Biobank participants (age 37–73 years), …. We examined the associations with brain volume using covariate adjusted linear regression, and with odds of dementia (4,333 incident cases) and stroke (6,181 incident cases) using logistic regression.

Results: … After full covariate adjustment, consumption of >6 cups/day was associated with 53% higher odds of dementia compared to consumption of 1–2 cups/day (fully adjusted OR 1.53, 95% CI 1.28, 1.83) ….

Conclusion: High coffee consumption was associated with smaller total brain volumes and increased odds of dementia.

Coffee is a worldwide drink. The conclusion inference represents the entire population. However, the population sampling is from UK Biobank participants. So we don’t know how random the sampling distribution is. e.g., the sample demographics, genders? Even ethnicity / genes (For example, western people are more allergen in peanut than Asian | Netflix Documentary, Rotten — The Peanut Problem https://www.imdb.com/title/tt7830586/). Properties of the sample? Is it generalizable, uncluster enough to produce a linear regression?

The sampling process potentially biases the results and systematically inflates the regression model from the start.

Cups of coffee are not Amounts of Caffeine.

For example, a 6-ounce cup of coffee may contain twice the caffeine content than a 12-ounce cup of coffee.

It depends on the coffee varietal, coffee blend, etc.

(Think about robusta contains almost twice the caffeine as in arabica)

That implies an inadequate set of data variables on coffee and caffeine in the research.

It analyzes a multivariate study area that should acquire adequate variables on coffee and variables on caffeine, before concluding ‘cups’ of coffee. It is ambiguous.

Confounding Bias.

A common IceCream-Shark correlation pitfall.

Do sharks attack you more if you eat more ice cream?

Psychologically, Our minds are wired to believe such a type of causation from a storytelling perspective.

Hold on a second, what kind of people regularly drink 6+ cups of coffee? That is an insane amount of coffee consumption individually. But, 6+ cups of every type of drink is risky. Beers, cokes, red bulls, you name it. (Even coffee tasters in cupping sessions won’t swallow coffee liquid. They split out after slurp. )

Possibly, people with a high intensive workload, beyond-limit attention, and focus are more likely to drink 6+ cups of coffee/day. As a result, they could lack sleep, rest, exercise, appropriate diet, and mental stress.

There is a latent Confronting variable: lifestyle. Pertinent factors are potentially ignored in this research experiment.

“Blind respect for authority is the greatest enemy of truth.”

Albert Einstein

I don’t think the alternative hypothesis from this research article can reject the null. However, the way the research approach is not convincing, at least for me. To lay such declaration, the research article needs more persuasive evidence to draw association. The effect association may exist, but the approach is questionable.

The bottom line of that research paper, reminds that getting rest, hydrated when necessary, listen to your body, your internal biometrical algorithm won’t lie to you.

Does the research article draw a truth-worthy conclusion? Perhaps no. Even, it sent out an irresponsible impact. “Great power requires great responsibilities.” Especially true in scientific methodology. To extend, think about the illusion, misinterpretation and spread with social media (coffee: brain shrinker, heart attacker) will have a distinct impact on coffee farmer / producer wellbeing in Africa and/or other coffee farms producing regions.

(aka gilzero). I occasionally write about Software Development, Web Development, Machine Learning.