I see thousands of clients improve on a meat-based diet, whatever the studies say
The improvements are probably real, but a practice doesn't show what caused them. People who change diet under guidance also cut processed food, lose weight and pay closer attention to their health, and the clinic rarely hears from those who quit. Whether a diet prevents heart disease or cancer over decades can't be seen in a few months of client results.
I work with real people, not datasets, and I watch them get better: weight comes off, blood markers improve, energy and mood return, often after years of failing on conventional advice. Nutrition studies are mostly observational, riddled with confounders and often contradict each other. When thousands of people improve in front of me, that is evidence too, and it shouldn't be waved away because it doesn't fit the epidemiology.
The improvements may well be real. What they can't show is what caused them, or how things turn out in twenty years.
Someone who starts a structured diet with a practitioner usually changes many things at once. They stop eating most ultra-processed food, drink less, eat fewer calories, lose weight and get regular attention and accountability. Any of these can improve blood sugar, energy and mood within weeks. A practice sees the whole bundle and credits the meat. People also tend to seek help when they feel worst, so some improvement is likely whatever they do. And the clients who didn't improve mostly stop coming back, so they drop out of the count.
The outcomes that matter most are slow: heart disease, cancer, mortality. No practitioner follows thousands of clients for forty years. Large cohorts do. Some, like the Adventist Health Studies, include lifelong vegetarians and vegans tracked for decades. The point raised in that same exchange applies here: a smoker can lose weight and feel less anxious in the short term, and that tells you nothing about lung cancer.
Studies are imperfect, and some are poor. The answer is to compare their methods and weigh the better ones, not to set them all aside in favour of the cases one person happens to see. Substitution analyses are a good example. They compare people whose diets differ mainly in swapping some red meat for plant protein, and they consistently find lower risk. That is hard to square with a claim that moving further toward meat is what makes people healthier.
If cutting processed food, losing excess weight and having support is what helps, a whole-food plant-based diet delivers all of that. It does not require any animal to be killed.
- Clinicians see hundreds of patients doing the opposite of what the studies predict, so the studies may be wrong
- I see patients leave whole-food plant-based diets and get better when they add meat
- People are improving dramatically on meat-heavy diets, and you can't just dismiss that
- People get better when they switch to an all-meat diet
Different wording, same argument. The response above answers all of them.
Treating a personal story or an isolated case as though it outweighed systematic evidence. Individual cases are real, but they do not establish general patterns.
How it applies here: The speaker sets client outcomes seen in their own practice against the population studies and treats those outcomes as the better evidence. These are uncontrolled, short-term and self-selected cases, and they cannot outweigh decades-long cohorts and controlled comparisons.
Inferring that one thing caused another from the fact that they occurred together or one after the other. Correlation and sequence are not causation.
How it applies here: Clients get better after starting the diet, and the speaker credits the meat. The same clients also cut processed food, lost weight and got regular support, and any of those changes could explain the improvement.