Browse evidence-based analysis of health-related claims and assertions
People who eat more trans fats—especially from processed foods—are more likely to die from any cause, including heart disease and cancer.
Correlational
A specific type of fat found in milk and cheese is linked to a much lower chance of getting type 2 diabetes, possibly because it helps the body use insulin better.
Trans fats from cows and other animals (like butter) don’t seem to raise heart disease risk, but trans fats made in factories (like in margarine) do—so where the fat comes from matters.
Eating more saturated fat from meat, butter, or cheese doesn’t clearly make people more likely to die early or get heart disease or diabetes, but the science isn’t strong enough to be sure.
People who eat more artificial trans fats—like those in fried and baked goods—have a higher chance of having a heart attack or dying from heart disease.
Over the years from 2000 to 2006, both 'bad' and 'good' cholesterol levels in heart disease patients admitted to the hospital got lower on average.
Descriptive
Women with heart disease tend to have higher 'bad' cholesterol levels than men when they are first admitted to the hospital.
Very few heart disease patients—less than 1 in 70—have both their 'bad' cholesterol low enough and their 'good' cholesterol high enough to meet ideal health targets.
More than half of heart disease patients admitted to the hospital have low levels of 'good' cholesterol, which is considered unhealthy.
When people with heart disease are first admitted to the hospital, their 'bad' cholesterol is usually around 105, which is higher than what doctors now recommend.
The model suggests that giving a population a little shock now and then might help it recover better than keeping the stress on all the time—but this is just math, not proven in real life.
The model can tell the difference between when the whole group bounces back after stress, versus just one part of it (like healthy individuals surviving while sick ones die).
You don’t need to believe that low doses of poison help you—this math model can make the same 'helpful low dose' curve just by using how fast populations grow.
Mechanistic
The math model can find the exact point where a little stress helps a population, but too much hurts it—like a tipping point between recovery and collapse.
Quantitative
For a population to bounce back stronger after a small stress, it needs to be really good at healing or reproducing fast—otherwise, it just stays down.
If there aren’t enough individuals left after a shock, the population can’t recover—even if the stress was small. The model says starting size matters a lot.
This math model gives scientists one way to talk about different kinds of 'bounce-back' in nature—whether it’s fish, diseases, or poison responses—using the same equations.
The model says whether a population bounces back too much depends on how fast it naturally grows and how strong the stress is—too much stress kills it, too little does nothing.
In the math model, the way a population grows after being killed off looks the same as when it grows after a small stress—so the model can’t tell them apart without extra info.
By splitting a population into different groups (like sick and healthy), the math model can show how the mix between them might cause the whole group to bounce back after stress.
The math model used for fish populations might also be used to guess how tumors react to treatment—like if a little chemo makes them grow more—but this is just a guess, not proven.
The math model can make the same curvy graphs that scientists see when low doses of poison seem to help organisms—so maybe those graphs aren’t proof of benefit, just math patterns.
For a population to bounce back stronger after a small shock, the model says it needs to grow fast, the shock must be weak, and there must be enough individuals left to start recovering.
A math model shows that how contagious a disease is (R₀) might decide whether a population grows more after being infected—or not—but this is just a theory, not proven in real outbreaks.