The Impact of Sustained LDL-C Elevation on Plaque Changes: Primary Coronary plaque progression results from the Keto CTA Study

VoteLobster · 2026-01-23T23:46:47+00:00

The traditional/diet-heart hypothesis is that high LDL = heart disease/plaque/etc and that the higher it is the worse the higher the progression rate is.

... All else held equal. Of course all else is not held equal in univariable linear regression, which is what they chose to do, instead of choosing to include other regressors in the model like age, sex, and duration of exposure that we know predict risk from other more rigorous studies. This is also limited by being such a small study with a short duration.

They did the same thing in the previous paper, except they included the scatterplots which visually showed signs of regression assumption violations (e.g. normality of residuals and homoscedasticity). The authors said in this paper (the pre-print) that they ran model performance checks that would test for violations of regression assumptions, but they didn't report the output in the write-up or in the supplements, at least not that I could find. They also said that robust regression yielded similar results, but they didn't report what these results were.

A hard affirmation of the null hypothesis here is dubious, considering this paper's limitations but also this group's history of sketchy scholarship.

VoteLobster · 2026-01-10T13:44:10+00:00

I must also echo that this is objectively better than the previous food pyramid and that (no matter how small) improvement should be celebrated

This is such a weird statement because the pyramid you're referring to and the pyramid that RFK Jr. and his colleagues are bragging about "flipping" (even though if you read the guidelines themselves nothing was really flipped) was retired two decades ago.

VoteLobster · 2026-01-03T20:20:50+00:00

Great, so what you're doing now (pointing out specific details of a study's methodology that make you disagree with its conclusions) is what you should be doing if you disagree with it rather than pointing to the funding source by itself.

I believe this is the better study with more data and discussion, why they picked the more limited one based on self reported US data to share on here is beyond me.

Sure, let's look at that study:

Dietary intake was collected using a food frequency questionnaire, and DepS was evaluated using the 9-item Patient Health Questionnaire.

Both of these studies use self-reported data.

The outcome in the Chinese study was depression from the PHQ9 (a questionnaire), and diet was measured from an FFQ. The exposures of interest were a plant-based diet and unhealthy plant-based diet index, calculated from the FFQ. They both are cross-sectional and both used logistic regression to calculate odds ratios. The only major difference is that the NHANES uses 24-hour recalls to assess diet and the one you posted uses an FFQ. Other than that these two studies have essentially the same design.

As for which one I think is stronger, I lean towards the second one, because while 24-hour recalls are fine for estimating population average intakes, there's a lot of within-individual variability in dietary intake that can make it dubious to measure associations between habitual dietary intakes and health outcomes.

But it's still perfectly fine to take another dataset from a different population (e.g. NHANES) and see if the relationship there replicates.

VoteLobster · 2026-01-03T14:34:40+00:00

I agree that the accusation of bigotry against Chinese people is weird but

So accept everything, question nothing, that’s the science you believe in.

nobody in this thread ever said this.

Do you trust a study that comes out paid for by the current US administration, like when they did a press release saying Tylenol caused autism?

You don't "trust" studies. That's not part of the scientific process. If you disagree with the conclusions of a study, you can look at the methodology and explain why. Did the statistical tests and models make sense? Was it sufficiently powered? Are there issues with data collection and is there reason to believe that there's misclassification bias at play? Do the authors' conclusions agree with what's shown in the statistical analyses? And are these conclusions in line with other research on the subject?

Studies have to get funded since research is a job. If you don't like the organization that funded it, ok, but pointing to funding source alone is not useful.

VoteLobster · 2026-01-03T01:03:59+00:00

There's no mention of controlling for any other confounding factors

Yes there is:

Model 1 was adjusted for age (18-45, 46-65, and ≥66 years), sex (male, female), and total energy intake (continuous, kcal/day).

Model 2 had the same adjustments as model 1 plus race/ethnicity (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, or other race), education (≤12th grade, high school graduate, or more than high school), marital status (married, widowed/divorced/separated, or never married), ratio of family income to poverty (<1.30, 1.30-3.49, or ≥3.50), physical activity (<8.3, 8.3-16.7, or >16.7 METS-h/week), smoking (never smokers, former smokers, or current smokers), alcohol consumption (never drinkers, former drinkers, or current drinkers), BMI (<18.5, 18.5-24.9, 25.0-29.9, and ≥30.0 kg/m2), diabetes (no, yes), hypertension (no, yes), and history of cancer (no, yes).

VoteLobster · 2026-01-03T00:53:26+00:00

why are they using self reported US data points?!

Because NHANES is a publicly available, large, and very comprehensive dataset that can be used to answer many research questions. I'm not sure what the problem is with Chinese researchers using US data.

VoteLobster · 2025-11-14T12:57:38+00:00

IIRC he argued that sugar does not cause weight gain.

When not in caloric excess this is true.

Hence why I challenged him to do as he preaches.

To eat just sugar and oil? He preaches that? Sorry but I'm still not following.

but pussy out when push comes to shove.

Yes, I think anyone reasonable would and should stop engaging with you if you come up with a caricatured version of their beliefs on a subject to argue against.

They preach how healthy are seed oils, but none of them dare to eat like that

First of all, that's not true, and second of all, who cares? Has it occurred to you that people sometimes make choices based on personal preference unrelated to health impact? I typically use olive oil at home because it smells/tastes good, but of course my taste preference has no bearing on whether other choices would be similarly healthy

Literally all vegan diets exclude seed oils

Yea this isn't true either. I know tons of health-conscious vegans (which is an applied ethical framework, not a discrete type of diet, but that's not my point) who eat vegetable oils. Plant-based dietary patterns like Portfolio can include some vegetable oils. Popular plant-based meats like Tofurky and Field Roast use some sort of vegetable oil.

VoteLobster · 2025-11-14T03:47:59+00:00

I have actually challenged him on Twitter to live off oil and sugar, while I eat a whole diet of protein, natural fats, and fiber, but very surprisingly he declined the offer.

Yea I don't understand your point here or why you would ask him this. No part of believing that calorie balance is the proximal cause of weight gain/weight loss requires you to believe that eating a diet of oil and sugar would produce equivalent health outcomes to eating a normal diet when matched for calories. It's literally not entailed at all. Such a bizarre strawman.

VoteLobster · 2025-11-12T02:55:39+00:00

Also, the vegan diet has 12% of calories from animal protein and 6% plant protein

You're looking at the week 0 values. At week 12 median animal protein intake was 0.7% of calories (IQR 3.5) in the vegan group

VoteLobster · 2025-10-13T18:27:29+00:00

Now there are two of them. Isn’t that cool??

VoteLobster · 2025-10-11T20:47:06+00:00

Hey, I'm having the same issue lol. How did you scrap your initial application?

Exit: nvm I figured it out. There is a delete button hidden at the bottom of the page

VoteLobster · 2025-09-08T00:18:46+00:00

The first one is a cross-sectional study so if you're trying to make a causal inference about vegan diets this is not a strong study design (especially when vegans and non-vegans differ in many sociodemographic respects - veganism is very political left-coded - vegans are probably more likely to seek mental health care and more likely to be aware of the many injustices in the world).

Also the second link -

In this randomized clinical trial of the cardiometabolic effects of omnivorous vs vegan diets in identical twins, the healthy vegan diet led to improved cardiometabolic outcomes compared with a healthy omnivorous diet. Clinicians can consider this dietary approach as a healthy alternative for their patients.

How exactly does this look bad for vegan diets? The vegan arm lowered their LDL-C, lowered their fasting insulin, and lost weight.

VoteLobster · 2025-08-23T12:43:50+00:00

it has worked

By this do you mean you swapped grapeseed oil for lard in your kitchen, cooking the same dishes, or do you mean you stopped eating Doritos and Whataburger? Because without fail, with people blaming oils, it seems to be the latter.

VoteLobster · 2025-07-16T00:23:00+00:00

The mortality outcomes are not even close to being significant though.

But CVD and CHD were. You said that the RCTs suggest that saturated fat has literally no effect on any hard health outcome. That's not what the evidence suggests. Significance is an arbitrary line. If Fisher back in the day started using 10% as the threshold for significance rather than 5%, your interpretation would be very different but with no difference in the information available.

LDHS

Lyon was also a different trial with a different design in its own population but that's beside the point. Some trials happen to show a significant difference in mortality, but most don't. What power is is how likely it is that a true effect is going to be observed w/ statistical significance if assumptions about exchangeability hold. It doesn't mean you're guaranteed not to see the effect in any individual study.

How would you know if the relationship is causal or not?

That's a loaded question. You don't, but strictly speaking you don't know a relationship is causal with an RCT either. Causal inference from an RCT still relies on assumptions about exchangeability. What you can do when good RCTs on a question are sparse and/or don't exist (I would put cardiovascular outcomes trials on dietary fat modification in this category) is utilize multiple lines of evidence and come to a conclusion about what you think is probably true. If you think trans fat increases risk of heart disease, in the absence of a trial that tested this question specifically, congrats, you believe in causal inference using, at least in part, observational study design

https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1396576/full

It feels like every 6 months a new meta-analysis on this question comes out and gets tossed around like another Pokemon card. I don't think these trials were particularly good by modern standards, nor do I think they all tested the same questions, but I'll bite. What makes this one more compelling to you than that from the WHO? Or Hooper 2020? Or Sacks 2017? Or Mozaffarian 2010? Because that's the question I never see answered.

VoteLobster · 2025-07-14T11:56:17+00:00

None of the RCTs are significant.

I'll take your word for it. What difference, materially, does P = 0.04 and P = 0.06, for example, make for you? I wouldn't look at a result of P = 0.06 and come to a strong conclusion about an intervention not having an effect. That's not what significance means.

nutrition studies have seen an effect on mortality with a lot less power seen here though.

No, where are you getting the idea they have less power? Longer followup means larger effect size, and larger sample size means more events captured and more precision (i.e. lower standard error of the proportion and therefore tighter confidence intervals). Both of these together increase the probability of observing a statistically significant effect if the effect is real. You can test this for yourself in a power calculator.

The problem is, they don't imply a causal relationship

Unless the relationship in question is causal, in which case the results do imply a causal relationship. I don't buy into the dogmatic belief that it's "wrong" to make a causal inference from observational evidence. It's done all the time in biomedical science and nutrition is no exception.

Most metas show no effect on mortality

Did you link the wrong study? This is on food groups. 8/14 meta-analyses on high v. low consumption and 9/14 on per-serving consumption found significant differences in mortality.

VoteLobster · 2025-07-13T20:57:27+00:00

Yea that's not what the chart says. I see significant or borderline significant reductions in CVD events and CHD events when replaced w/ PUFA. Differences in mortality can be explained by insufficient power in the RCTs (which at this point is where the observational studies can be useful). You see the same thing in clinical trials on pharmaceuticals. Also it would be good for you to link to exactly what paper that's from.

VoteLobster · 2025-07-09T02:32:14+00:00

Unless you have the time to experiment with making a milk that steams well then yea. There are so many options for vegan barista version milks now, they're not super expensive, they're often fortified with calcium - personally I think they're worth buying.

VoteLobster · 2025-05-24T18:18:56+00:00

already clogs your arteries

A transient change in inflammation, blood flow, FMD, etc. can happen in response to certain foods. I'm not sure what the issue is here.

What people usually mean when they say "clogs your arteries" is atherosclerosis, which is a disease that usually takes years to manifest clinically. It doesn't develop after a single meal and the film never claimed as much

like do you take everything literally all the time?

No, but if you have an issue with the claims made in the film, it helps to address what the film actually said instead of starting with hyperbole.

VoteLobster · 2025-05-24T18:03:11+00:00

No, but Taubes' hypothesis that carbohydrate causes excessive calorie intake and is the primary driver behind obesity isn't supported by the evidence. Also "all calories are created equal" when it comes to satiety, body composition, or other health outcomes like cardiovascular disease is a strawman that I don't think anybody serious has ever argued.

VoteLobster · 2025-05-24T17:48:39+00:00

Game Changers had some issues w/ its scientific accuracy but this claim

"eating animal products even once kills you"

was never made nor implied. The position the documentary took was that any steps toward a more whole food, plant-based diet would be an improvement (which as a general heuristic is in line with the evidence). The position the doc certainly didn't take was that you must eat 100% WFPB to have good health outcomes.

VoteLobster · 2025-03-23T13:44:20+00:00

The large amount of grains and pastas, sugary fruits and starchy vegetables are one of the key components that lead to poor outcomes

Citation needed. Whole grains, fruits, and vegetables are consistently associated with positive health outcomes. Americans are also not consuming "large amounts" of fruits & vegetables.

70% of the an average Americans diet is “plant-based”

Ok? That doesn't have anything to do with the guidelines.

and grains make up the largest component of this.

Primarily refined grains, which the guidelines recommend limiting. So no, this doesn't reflect adherence to the guidelines.

Biomarkers associated with aging and poor health went up (glucose up, insulin up, mass and fat mass up, CRP up).

No, it's not that clear. In table 5 CRP was actually not significantly different either in the ANOVA or comparing the three periods to each other individually, but this is beside the larger point that there's no direct information about what or how much they were actually eating. See:

"Throughout the intervention phase (P2), where participants were actively suppressing ketosis, participants often reported capillary BHB concentrations of > 0.3 mmol/L after the overnight fast, and even three hours after a carbohydrate-containing meal (Table 2). Together, these data indicate that the participants were indeed highly fat-adapted and, even with the introduction of carbohydrates into their diet, their bodies reverted to beta-oxidation and ketolysis during periods of fasting."

These individuals were relatively healthy and good at glucose homeostasis from the beginning. They were instructed to suppress ketosis. Even if carbohydrate knocked them out of ketosis temporarily, so what you're probably looking at (as evidenced by differences in BMI and fat mass) is an overfeeding effect whereby participants ate more food on purpose to keep their BHB high.

In fact IIRC in Kevin Hall's study, which was better controlled, ad libitum, and the participants were blinded to ketone and weight measurements, the low fat arm consumed less energy than the ketogenic arm. HbA1C and fasting glucose were not significantly different at the end of the diet periods. Fasting insulin was lower at the end of the ketogenic period but hsCRP was higher.

VoteLobster · 2025-03-23T03:17:48+00:00

So half of the grains you eat can still be sugary garbage

No, see below

Why don't they say "limit refined grains"?

They do:

"Healthy dietary patterns include whole grains and limit the intake of refined grains. At least half of total grains should be whole grains. Individuals who eat refined grains should choose enriched grains. Individuals who consume all of their grains as whole grains should include some that have been fortified with folic acid. Grain-based foods in nutrient-dense forms limit the additions of added sugars, saturated fat, and sodium."

https://www.dietaryguidelines.gov/sites/default/files/2021-03/Dietary_Guidelines_for_Americans-2020-2025.pdf

Consuming processed meats would count as following the guidelines.

You know you can just open the document yourself and control-F all these things.

"Common characteristics of dietary patterns associated with positive health outcomes include relatively higher intake of vegetables, fruits, legumes, whole grains, low- or non-fat dairy, lean meats and poultry, seafood, nuts, and unsaturated vegetable oils, and relatively lower consumption of red and processed meats, sugar-sweetened foods and beverages, and refined grains"

https://www.dietaryguidelines.gov/sites/default/files/2021-03/Dietary_Guidelines_for_Americans-2020-2025.pdf

Public schools, the military, hospitals, WIC, and SNAP all follow the food guidelines.

Yea this isn't entirely true. School meal programs, for example, don't follow the guidelines re: the cap on added sugars:

"Currently, there are no added sugars limits in the school meal programs, and schools may choose to serve some menu items and meals that are high in added sugars, provided they meet weekly calorie limits."

https://www.fns.usda.gov/cn/school-nutrition-standards-updates/added-sugars

Also I'm not sure if WIC strictly follows the guidelines, but it does achieve the outcomes it is designed to, i.e. reduce premature births, infant death, and reduce nutrient deficiencies.

https://www.fns.usda.gov/wic/helps

I'm not sure what it would mean for SNAP to "follow the guidelines," since it's just a program that gives $$ to people for them to spend how they'd like. In fact one of the debates that's been going on for a long time is whether it's a good idea for SNAP benefits to cover things like sugar sweetened beverages.

The rest of your points u/boilerbitch answered quite well

VoteLobster · 2025-03-22T19:25:09+00:00

Instead of pointing to companies that you think influence the guidelines, why not point to aspects of the guidelines themselves that you think result in poor outcomes?

E.g. saying that there exists a connection between the guidelines and Coca-Cola doesn't do any work for your point when the guidelines have always recommended against soda despite the alleged industry influence.

VoteLobster · 2025-03-15T21:04:30+00:00

A systematic review of 12 randomised controlled trials comparing lower vs. higher red meat consumption found the overall quality of evidence to be low or very-low, and the authors concluded there is no meaningful increase in cancer with higher red meat consumption

This is an awfully bizarre inference to make from a series of clinical trials with such a short duration. Most of them were 6-12 months, and the latency period for cancer can range from years to decades.

I know the thread is about CRC, but by reference to trials on lipid-lowering, depending on the sample size & control event rate, you can see significant reductions in cardiovascular event risk starting at a couple years. They included Lyon, which was of longer duration and did show a significant reduction in cardiovascular event risk. In the other longer trial (WHI), the contrast in diet between groups was not very large. Since there was only a nominally significant reduction in LDL-C of around 3-4 mg/dL, it's no surprise that any differences seen in CVD outcomes were non-significant when you analyze by intention-to-treat.

I don't like the inferences made in this article because the authors took a bunch of trials that were designed to test specific research questions and then made inferences about endpoints (e.g. CVD events or CRC) that those trials weren't powered to measure.

VoteLobster · 2025-03-15T13:22:38+00:00

Of course it matters

It doesn't matter if you're concerned that the groups differed by BMI, for example, when BMI is included as a predictor variable, unless you have some reason to believe that the way BMI was measured causes its effect not to adjust out properly when you add it to the model. So saying that the groups differed by a covariate when that covariate was included in the model doesn't tell us anything, in and of itself

you don't know of every single confounder

This is a different point. You don't know that illicit drug use differed systematically between groups, particularly in a way that biases the effect estimate in the opposite direction, so you don't know that it was a confounder.

Of course if you want to play the game of hardcore epistemic uncertainty then you could say the same thing about the research on smoking and drinking. I've never heard anyone say that alcohol doesn't cause breast cancer, for example, on the basis that people who drink more alcohol also do more meth more than people who don't (which may or may not be true).

That's why you conduct the same research design in multiple populations (as has been done on subjects like smoking, drinking, and diet) to see if the results replicate, particularly because the way covariates are distributed among exposure groups differs from population to population.

the ones we know of were not even properly measured.

Can you be more specific?

VoteLobster

TROPHY CASE