1. can you trust the questionnaire? Just imagine, when study is done for a certain substance, that substance is measured in accredited labs, lots and lots of standards are applied, accreditation measures checked. And here? What scrutiny was applied to the study participants? To be honest, you are lucky that they participate with honesty at their side. But honesty and accuracy can still be miles away.
2. confounding variables: in this study, it was the medical community, so even if the conclusion is true, it is valid only for that community. But this niche population in the study is just the tip of the iceberg regarding confounding variables in such studies. There are so many confounding variables in such studies, with their noise signals just adding up. And it works both ways: if you take them into account, they can lead to overadjustment, if you do not take them into account ... well,... you did not take them into account! Even a well-conducted study can produce misleading results if overadjustment introduces excessive noise, masks true effects, or amplifies statistical instability. Sensitivity analyses, careful confounder selection, and alternative causal inference methods (e.g., instrumental variables, directed acyclic graphs) can help mitigate these risks, but who does that?
Finally, a small shopping list of issues with confounding variables that make most epidemiological studies laughable (but unfortunately still taken seriously in popular media):
1. Overadjustment - when a study controls for variables that are intermediates in the causal pathway (leading to attenuation or reversal of a true effect) - a setup in which the (intermediate) variable is correlated with the studied hypothetical cause ("exposure" in epidemiology) or effect ("outcome" in epidemiology) but not true confounder.
For example, if an exposure increases both blood pressure and cardiovascular disease (CVD) severity, and the study adjusts for blood pressure when analyzing cardiovascular risk, it may mask or even inverse the exposures' true effect. Thus we could get that salt is preventing CVD (or of course that it causes it), depending on tinkering with confounders.
2. Residual Confounding and Measurement Noise - If confounders are not measured accurately or completely, statistical adjustments may still leave residual confounding. In studies with high variability in confounders, noise can overwhelm the signal, leading to wide confidence intervals, spurious associations, or null results. But for wide confidence intervals, that would at least indicate that there is a problem with the study.
3. Model Instability and Multicollinearity - Including too many correlated confounders can cause statistical instability (e.g., variance inflation, collinearity), making it difficult to isolate the independent effect of the exposure. This can lead to wide confidence intervals, false negatives (Type II error) and incorrect direction of effect estimates
4. Data-Dredging and Multiple Comparisons - When adjusting for many factors, the risk of spurious findings increases, especially if many subgroup analyses are performed. Even with proper statistical corrections, extreme variability in data can amplify false signals and reverse the result.
5. Effect Modification and Heterogeneous Populations - If intervention and control groups differ drastically in confounding factors, standard adjustment methods (e.g., regression, propensity scores) might fail to balance them adequately. Subgroup interactions might distort findings, making generalization difficult.
Isn't common sense another word for confirmation bias?
The cohort were all health professionals. Wouldn't they down-report bad foods, like meat, eggs, and butter, and up-report grains, while also up-reporting healthy parameters (since being sickly is embarrassing for a health professional)?
Would these distortions be enough to completely reverse supposed good foods with supposed bad foods? 'Cause that's what this study did - wasn't just an onion ring study.
On the other hand, onion rings are a rich source of quercetin.
"Five minutes of frying onions in sunflower oil, butter, and rapeseed oil led to 21%, 24%, and 39% quercetin losses."
Crozier, A.; Lean, M.E.J.; McDonald, M.S.; Black, C. Quantitative Analysis of the Flavonoid Content of Commercial Tomatoes, Onions, Lettuce, and Celery. J. Agric. Food Chem. 1997, 45, 590–595.
Ewald, C.; Fjelkner-Modig, S.; Johansson, K.; Sjöholm, I.; Åkesson, B. Effect of processing on major flavonoids in processed onions, green beans, and peas. Food Chem. 1999, 64, 231–235
● Antal, B. B., van Nieuwenhuizen, H., Chesebro, A. G., Strey, H. H., Jones, D. T., Clarke, K., Weistuch, C., Ratai, E. M., Dill, K. A., & Mujica-Parodi, L. R. (2025) Brain aging shows nonlinear transitions, suggesting a midlife "critical window" for metabolic intervention. Proceedings of the National Academy of Sciences of the United States of America, 122(10), https://www.pnas.org/doi/10.1073/pnas.2416433122
■ Tan, D. X., Reiter, R. J., Zimmerman, S., & Hardeland, R. (2023). Melatonin: Both a Messenger of Darkness and a Participant in the Cellular Actions of Non-Visible Solar Radiation of Near Infrared Light. Biology, 12(1), 89. https://doi.org/10.3390/biology12010089
US Dietary Guidelines specifically, which looking back is a bit of a jump but as was pointed out it is these types of epidemiological data that tend to steer them in their guidelines.
This study definitely lox credibility, seems fishy to me, and doesn't have a ring of truth about it.
+3 pun points
These studies have two major flaws:
1. can you trust the questionnaire? Just imagine, when study is done for a certain substance, that substance is measured in accredited labs, lots and lots of standards are applied, accreditation measures checked. And here? What scrutiny was applied to the study participants? To be honest, you are lucky that they participate with honesty at their side. But honesty and accuracy can still be miles away.
2. confounding variables: in this study, it was the medical community, so even if the conclusion is true, it is valid only for that community. But this niche population in the study is just the tip of the iceberg regarding confounding variables in such studies. There are so many confounding variables in such studies, with their noise signals just adding up. And it works both ways: if you take them into account, they can lead to overadjustment, if you do not take them into account ... well,... you did not take them into account! Even a well-conducted study can produce misleading results if overadjustment introduces excessive noise, masks true effects, or amplifies statistical instability. Sensitivity analyses, careful confounder selection, and alternative causal inference methods (e.g., instrumental variables, directed acyclic graphs) can help mitigate these risks, but who does that?
Finally, a small shopping list of issues with confounding variables that make most epidemiological studies laughable (but unfortunately still taken seriously in popular media):
1. Overadjustment - when a study controls for variables that are intermediates in the causal pathway (leading to attenuation or reversal of a true effect) - a setup in which the (intermediate) variable is correlated with the studied hypothetical cause ("exposure" in epidemiology) or effect ("outcome" in epidemiology) but not true confounder.
For example, if an exposure increases both blood pressure and cardiovascular disease (CVD) severity, and the study adjusts for blood pressure when analyzing cardiovascular risk, it may mask or even inverse the exposures' true effect. Thus we could get that salt is preventing CVD (or of course that it causes it), depending on tinkering with confounders.
2. Residual Confounding and Measurement Noise - If confounders are not measured accurately or completely, statistical adjustments may still leave residual confounding. In studies with high variability in confounders, noise can overwhelm the signal, leading to wide confidence intervals, spurious associations, or null results. But for wide confidence intervals, that would at least indicate that there is a problem with the study.
3. Model Instability and Multicollinearity - Including too many correlated confounders can cause statistical instability (e.g., variance inflation, collinearity), making it difficult to isolate the independent effect of the exposure. This can lead to wide confidence intervals, false negatives (Type II error) and incorrect direction of effect estimates
4. Data-Dredging and Multiple Comparisons - When adjusting for many factors, the risk of spurious findings increases, especially if many subgroup analyses are performed. Even with proper statistical corrections, extreme variability in data can amplify false signals and reverse the result.
5. Effect Modification and Heterogeneous Populations - If intervention and control groups differ drastically in confounding factors, standard adjustment methods (e.g., regression, propensity scores) might fail to balance them adequately. Subgroup interactions might distort findings, making generalization difficult.
Isn't common sense another word for confirmation bias?
The cohort were all health professionals. Wouldn't they down-report bad foods, like meat, eggs, and butter, and up-report grains, while also up-reporting healthy parameters (since being sickly is embarrassing for a health professional)?
Would these distortions be enough to completely reverse supposed good foods with supposed bad foods? 'Cause that's what this study did - wasn't just an onion ring study.
On the other hand, onion rings are a rich source of quercetin.
What happens to quercetin in the deep fryer?
"Five minutes of frying onions in sunflower oil, butter, and rapeseed oil led to 21%, 24%, and 39% quercetin losses."
Crozier, A.; Lean, M.E.J.; McDonald, M.S.; Black, C. Quantitative Analysis of the Flavonoid Content of Commercial Tomatoes, Onions, Lettuce, and Celery. J. Agric. Food Chem. 1997, 45, 590–595.
Ewald, C.; Fjelkner-Modig, S.; Johansson, K.; Sjöholm, I.; Åkesson, B. Effect of processing on major flavonoids in processed onions, green beans, and peas. Food Chem. 1999, 64, 231–235
Wild caught or farm-raised salmon? Big difference w.r.t. toxic burden.
And perhaps wrt omega-3 content too. They have very different diets.
Hi Nic
I’m 6 months off 73 years and used to love onions but haven’t had any for 3 years, preferring red meat 🥩 and not dead yet
My wife has just tested positive for Covid and doesn’t look at all like dying either
Harry the dog is fine also
🐕⛓️🐂👍
Ribeye > Rings it is. Agree.
I’m so disappointed in the scientific and medical communities. Such a shame.
N•E•W○ST•A•R•T (the non-Adventist "8 Health Guidelines")
• 𝐍•𝙪𝙩𝙧𝙞𝙩𝙞𝙤𝙣. - M•E•D•S - animal protein & fats. Nutritional ketosis (βHOB). Protect brain & liver.
• 𝐄•𝙭𝙚𝙧𝙘𝙞𝙨𝙚. - Movement (body) & learning (mind)
• 𝐖•𝙖𝙩𝙚𝙧. - Inner (metabolic) & external. Fluoride & deuterium avoidance.
• 𝐒•𝙪𝙣𝙨𝙝𝙞𝙣𝙚. - Full-spectrum & IR (melatonin). Circadian rhythms.
• 𝐓•𝙚𝙢𝙥𝙚𝙧𝙖𝙩𝙪𝙧𝙚. - (HSP) & metabolism (mitochondria). Sauna & cold.
• 𝐀•𝙞𝙧. - Nose breathing. Time in woods & ocean.
• 𝐑•𝙚𝙨𝙩. - Sleep (glymphatic system) & fasting (autophagy & βHOB)
• 𝐓•𝙧𝙪𝙨𝙩. - Love, attachment, —family, community.
● Antal, B. B., van Nieuwenhuizen, H., Chesebro, A. G., Strey, H. H., Jones, D. T., Clarke, K., Weistuch, C., Ratai, E. M., Dill, K. A., & Mujica-Parodi, L. R. (2025) Brain aging shows nonlinear transitions, suggesting a midlife "critical window" for metabolic intervention. Proceedings of the National Academy of Sciences of the United States of America, 122(10), https://www.pnas.org/doi/10.1073/pnas.2416433122
■ Tan, D. X., Reiter, R. J., Zimmerman, S., & Hardeland, R. (2023). Melatonin: Both a Messenger of Darkness and a Participant in the Cellular Actions of Non-Visible Solar Radiation of Near Infrared Light. Biology, 12(1), 89. https://doi.org/10.3390/biology12010089
so... mmm ....
this is non good information....
it risks to make people more confused
so sad
Confusion isn't bad per se... can be step 1 in learning... but...
You are right but we have to give open access to sharing debating
This is great news. It's a source I quit paying attention to about 13 years ago and this just confirms that I still don't need to! Thanks Nick!
It being what... Nature Medicine?
US Dietary Guidelines specifically, which looking back is a bit of a jump but as was pointed out it is these types of epidemiological data that tend to steer them in their guidelines.
Studies and especially lobbyists.
They are criminals!!
Harsh...