3 ms·

I probably should never have entertained the Dr Davis derail. The whole bringing up of this character was your attempt to answer my question "What’s the evidenc

I probably should never have entertained the Dr Davis derail. The whole bringing up of this character was your attempt to answer my question "What’s the evidence that [atherosclerosis] only takes place in individuals with arterial wall injury or without the ability to repair damage?" Your response was "Some people live a long time with high(er) fat diets and no heart disease". But this is not evidence that atherosclerosis only takes place in individuals with arterial wall injury or without the ability to repair damage. That could equally be evidence that some people are genetically less predisposed to arterial retention, regardless of arterial wall injury, for example. There are plenty of hypotheses that negate your proposed one that are equally likely on the evidence you've provided. So this whole thread is just a tangent that doesn't engage with the core question. That said, here goes: My point on CAC was that your entire argument hinges on being able to look at a CAC score and say "this score is a reliable indicator of whether a person has coronary artery disease". But if you're under 40 and actually have CAD, there's a 58% chance that when you get your test results back, it will show 0 CAC (even though you have CAD!). So demonstrably, CAC is a poor proxy for CAD, and so a lynchpin proposition in your argument is clearly false. Whether the numbers look different when read a different way is irrelevant. The above is true, and demonstrates how CAC is not a reliable metric. > I don't think Dr Davis has said serum cholesterol is irrelevant, the statement I linked is that dietary fat removal doesn't stop CAC score increasing. Literally from the article you linked: "Can you look at your cholesterol and say “Oh, my cholesterol is 240. I must have heart disease.”? No, of course not — cholesterol is virtually worthless when it comes to predicting heart disease" So he says exactly that, unless you're willing to quibble over a semantic difference between "virtually worthless" and "irrelevant". > Why we should believe him is that he's got a feedback loop with a measuring machine It's not just "why should we believe him", it's "why should we believe him over meta-analyses of RCTs, and other high quality data?" Look at where "expert opinion" and "case reports" (which is the evidence you're citing, if we're being generous) sits relative to the MAs of RCTs that I'm bringing to the table on the hierarchy of evidence (https://en.wikipedia.org/wiki/Hierarchy_of_evidence https://en.wikipedia.org/wiki/Hierarchy_of_evidence). It's not clear to me why you're throwing out robust evidence with comparatively low risk of systematic bias and relying on bottom-tier evidence instead. Can you please explain to me why this is a superior epistemic framework? > He's a quack selling a book? Yes. I'd take a quack to be anyone wearing the coat of science while violating the standards of modern science. Whether or not he stands to gain (or cares about) the money his book makes isn't relevant to that claim, it's based entirely on the nature of what he has to say/publish. Indeed, going back to the previous point, dismissing the enormous body of high quality evidence showing LDL cholesterol is a causal agent in atherosclerosis by referring to serum cholesterol measurement as "virtually worthless" merely because of his own personal experience, with the plethora of biases that entails, is textbook quackery. The fact that he's published a pop science book that blames many of the ills of the world on wheat with no evidence to back up such an assertion is just the cherry on top. So I don't need to speculate why he's a quack, and his salary doesn't alter my point. Maybe he does it because being a number one bestseller satisfies his ego in a way nothing else can. Maybe having that accolade means he can charge more for his cardiology services, so there's an indirect financial benefit. Maybe he genuinely believes wheat is the devil's food? I don't really care, my point is that his output suggests he's not a sound practitioner of the scientific method. Your entire argument here seems to boil down to "there's this guy who sees patients and he says they're not leaving his practice and they're getting better, how would he have patients if everything he says about heart disease wasn't true?" This is wild, the kind of arguments I see from homeopathy advocates. We should always aim for the evidence base most likely to be free from bias. That is not one clinician's account of how they do things.

jodrellblank 2y ago
> "The above is true, and demonstrates how CAC is not a reliable metric." From your study link: "Introduction .. Coronary artery calcium (CAC) that is assessed by computed tomography is reliable in evaluating the future risk in both asymptomatic and symptomatic patients with stable coronary artery disease (CAD)". They call it "unmatched". OK it isn't perfect, that doesn't make it poor. Still instead of "can it miss something", the other way around - if the CAC score measures something is it always definitely plaque buildup? Because then repeated CAC scores have the potential to be good for telling if plaque buildup is changing over time in response to dietary changes in people with non-zero scores and CAD. Fine with him saying cholesterol is worthless, yes. > ""why should we believe him over meta-analyses of RCTs, and other high quality data?"" Because when someone you don't like cites multiple meta-analyses of RCTs you handwave it away with a Twitter thread where he "runs away from an argument chef's kiss". I know of Ben Goldacre's Bad Science, I see plenty of HN links about the reproducibility crises in science about the ways of p-hacking and manipulating data, about the broken incentives to publish and get funding, about the complexities of statistical analysis, things I can't argue through. Medical experts are incentivised to push treatments, academic experts are incentivised to push dodgy results, it's not easy for people who like and care about this stuff to say what's "high quality data" or isn't, let alone me. One can't handwave away measuring the amount of plaque buildup in the heart and say "that's not a measure of the amount of plaque buildup in the heart" as easily. If people change X and their heart health measurably improves, and X isn't reducing dietary fat, that should mean something. > "Your entire argument here seems to boil down to" My argument was less about Dr Davis himself and more as a way to bring the CAC score in and to say Dr Davis says the CAC score isn't affected by removing dietary fat. That's a testable claim. That's much nicer than "this expert says that expert is wrong" or "this paper says that paper is wrong". You don't believe Dr Davis, well, presumably somewhere is a researcher or practitioner who has done many CAC measurements and has studied whether cutting dietary fat changes the score or not, at this point I am not going to look for it. I am interested in the things you've said and linked, and my opinions have changed, but I think I'm well past what I am able to follow or interested to continue.
- KempyKolibri 2y ago
  > I am interested in the things you've said and linked, and my opinions have changed, but I think I'm well past what I am able to follow or interested to continue. Ok, that's unfortunate but obviously your prerogative! I will try to post some more lay-friendly links in this response that may convey my points more straightforwardly. > if the CAC score measures something is it always definitely plaque buildup? Because then repeated CAC scores have the potential to be good for telling if plaque buildup is changing over time in response to dietary changes in people with non-zero scores and CAD. No, it can measure plaque density. Additionally, since calcified plaques are more stable than soft plaques, it can actually go up while risk goes down. We see this occur on some statin regimes, where we have boatloads of data showing statins reduce risk, yet we can see CAC scores go up as soft plaque is stabilised into calcified plaque. I'm not saying CAC is without its merits, but you are stretching credulity beyond breaking point to suggest that because one cardiologist claims that reducing dietary fats doesn't show a reduction in CAC scores, it therefore disproves the lipid hypothesis. CAC has too many caveats to use it to make such bold inferences, even if we get to the wild epistemic standards required to think one cardiologist's claims are sufficient to overturn decades of well-conducted research. Additionally, as I've stated elsewhere, it still wouldn't be evidence that arterial wall damage or inability to repair damage is required for ASCVD, which, let's remember, is the reason we're even talking about this in the first place. There's quite an interesting discussion about the pros and cons of the CAC here. Suffice to say, it's not suitable to stand up the kind of arguments you're making: https://www.youtube.com/watch?v=gxIeRUbHauw https://www.youtube.com/watch?v=gxIeRUbHauw > Because when someone you don't like cites multiple meta-analyses of RCTs you handwave it away with a Twitter thread where he "runs away from an argument chef's kiss" No, that's not why I dismiss Mason's claims. I dismiss Mason's claims because he says "this meta analysis says this" and then when you look at the MA it says nothing of the sort. It's false to claim that I'm handwaving away the evidence itself. > I know of Ben Goldacre's Bad Science, I see plenty of HN links about the reproducibility crises in science about the ways of p-hacking and manipulating data, about the broken incentives to publish and get funding, about the complexities of statistical analysis, things I can't argue through. Medical experts are incentivised to push treatments, academic experts are incentivised to push dodgy results, it's not easy for people who like and care about this stuff to say what's "high quality data" or isn't, let alone me. One can't handwave away measuring the amount of plaque buildup in the heart and say "that's not a measure of the amount of plaque buildup in the heart" as easily. If people change X and their heart health measurably improves, and X isn't reducing dietary fat, that should mean something. I think it's really worth taking a second to think about what you're saying here. Some points of order first: - The reproducibility crisis is about some fields of science re-running individual studies and finding that when they do so, they cannot reproduce the original findings. - p-hacking is about altering the methodology of a study post-hoc to generate statistically significant findings when such findings were not present under the original methodology. - the objection of the complexities of statistical analyses, I presume, is an objection based on the fact that when using statistical analyses to try to account for confounders and systematic bias, sometimes these analyses are poorly conducted and thus fail to mitigate these biases. - "Medical experts are incentivised to push treatments" is presumably an objection that individuals publishing papers may have an implicit or explicit bias that causes them to over or understate the significance of a given finding, or record data incorrectly. So your argument appears to be: "Ok, so you've provided evidence that LDL is a risk for CVD based on over 200 prospective cohort studies (that is, the results have been successfully produced literally hundreds of times), mendelian randomisation studies, and randomised controlled trials including more than 2 million participants with over 20 million person-years of follow-up and over 150 000 cardiovascular events. However, I believe it's likely that if we were to re-run those studies again, we would not be able to reproduce those results. “I'm also concerned that p-hacking and systematic bias is so consistently prevalent within all of these studies that it affects the summated findings so profoundly, and consistently in the same direction, that we cannot trust the findings to be accurate. Additionally, the individuals conducting these studies are all incentivised to push treatments and therefore we should believe it to be more likely than not, without any evidence to actually demonstrate this, that these individuals are biasing the results because of these incentives. “It's not easy for lay people like me to ascertain whether these data from the top of the evidence hierarchy (that is, the evidence least likely to be affected by bias) are high quality, so we have to find a source of evidence that is less likely to be affected by such bias. To achieve this, I propose that we throw all the above data out, and instead trust the claims of a single cardiologist, who's written a blog post on his website about how serum cholesterol is irrelevant when it comes to cardiovascular disease risk. “After all, p-hacking can't take place when the evidence is so poor that there are no p-values, calculations of statistical significance, or even recorded data from which one could calculate p-values. “After all, you can't make errors in your statistical analyses if you don't perform any statistical analyses. “After all, your attempts to control for bias can't go wrong if you don't attempt to control for bias. “After all, 'One can't handwave away measuring the amount of plaque buildup in the heart and say "that's not a measure of the amount of plaque buildup in the heart" as easily. If people change X and their heart health measurably improves, and X isn't reducing dietary fat, that should mean something,' despite the fact that there are studies that do measure plaque buildup in the heart and show that it is affected by serum cholesterol. I believe that we can handwave it away when it's published in a peer reviewed journal, but we can't do so when it's a blog post on a cardiologist's website. This is the most reliable way to make sound scientific inferences.” I don't even understand how one can reach these conclusions. I've tried my absolute best to interpret what you've said generously, but there's only so much I can do given material like this. This whole line of reasoning is also far closer to what you’re accusing me of - instead of discussing what the data show, we should just “trust the experts”. We can’t analyse Dr Davis’ data because he doesn’t even provide any, but we should just assume he’s telling the truth because he’s a cardiologist and therefore so well paid that he has no motivation to be wrong. We should ignore the evidence I’ve provided because it’s harder for laypeople to understand than this supposed expert just saying “trust me, I ran some CAC tests”. > presumably somewhere is a researcher or practitioner who has done many CAC measurements and has studied whether cutting dietary fat changes the score or not, at this point I am not going to look for it. I've already provided you even better evidence than that: a pooled analysis of studies where SFA is replaced with PUFA and cardiovascular events and coronary deaths are reduced. You don't need to go looking, I gave it to you: https://pubmed.ncbi.nlm.nih.gov/19211817/ https://pubmed.ncbi.nlm.nih.gov/19211817/. If you want a more consumable version, here's an excellent podcast on it (all their stuff is good, you should see if there's anything else there of interest to you: https://sigmanutrition.com/episode481/ https://sigmanutrition.com/episode481/). It's a shame, but not entirely surprising that you no longer desire to continue the discussion. Fundamentally, I think you will always run into issues until you re-assess your underlying epistemic framework when it comes to assessing evidence. Until you do so, reading studies will be largely pointless as you have no reliable tools to interpret those studies. You're running before you can walk. If you're interested, the Cut Through Nutrition podcast is quite a nice primer on how to think about nutrition science research, I'd highly recommend working through the episodes. Once you've internalised the concepts, it becomes a lot easier to see how quacks like Mason and Davis are operating, even without a great deal of domain-specific knowledge. Podcast is here: https://podcasts.apple.com/gb/podcast/cut-through-nutrition/id1467679266 https://podcasts.apple.com/gb/podcast/cut-through-nutrition/... Have a good week!

jodrellblank 2y ago

> "The above is true, and demonstrates how CAC is not a reliable metric." From your study link: "Introduction .. Coronary artery calcium (CAC) that is assessed by computed tomography is reliable in evaluating the future risk in both asymptomatic and symptomatic patients with stable coronary artery disease (CAD)". They call it "unmatched". OK it isn't perfect, that doesn't make it poor. Still instead of "can it miss something", the other way around - if the CAC score measures something is it always definitely plaque buildup? Because then repeated CAC scores have the potential to be good for telling if plaque buildup is changing over time in response to dietary changes in people with non-zero scores and CAD. Fine with him saying cholesterol is worthless, yes. > ""why should we believe him over meta-analyses of RCTs, and other high quality data?"" Because when someone you don't like cites multiple meta-analyses of RCTs you handwave it away with a Twitter thread where he "runs away from an argument chef's kiss". I know of Ben Goldacre's Bad Science, I see plenty of HN links about the reproducibility crises in science about the ways of p-hacking and manipulating data, about the broken incentives to publish and get funding, about the complexities of statistical analysis, things I can't argue through. Medical experts are incentivised to push treatments, academic experts are incentivised to push dodgy results, it's not easy for people who like and care about this stuff to say what's "high quality data" or isn't, let alone me. One can't handwave away measuring the amount of plaque buildup in the heart and say "that's not a measure of the amount of plaque buildup in the heart" as easily. If people change X and their heart health measurably improves, and X isn't reducing dietary fat, that should mean something. > "Your entire argument here seems to boil down to" My argument was less about Dr Davis himself and more as a way to bring the CAC score in and to say Dr Davis says the CAC score isn't affected by removing dietary fat. That's a testable claim. That's much nicer than "this expert says that expert is wrong" or "this paper says that paper is wrong". You don't believe Dr Davis, well, presumably somewhere is a researcher or practitioner who has done many CAC measurements and has studied whether cutting dietary fat changes the score or not, at this point I am not going to look for it. I am interested in the things you've said and linked, and my opinions have changed, but I think I'm well past what I am able to follow or interested to continue.

KempyKolibri 2y ago
> I am interested in the things you've said and linked, and my opinions have changed, but I think I'm well past what I am able to follow or interested to continue. Ok, that's unfortunate but obviously your prerogative! I will try to post some more lay-friendly links in this response that may convey my points more straightforwardly. > if the CAC score measures something is it always definitely plaque buildup? Because then repeated CAC scores have the potential to be good for telling if plaque buildup is changing over time in response to dietary changes in people with non-zero scores and CAD. No, it can measure plaque density. Additionally, since calcified plaques are more stable than soft plaques, it can actually go up while risk goes down. We see this occur on some statin regimes, where we have boatloads of data showing statins reduce risk, yet we can see CAC scores go up as soft plaque is stabilised into calcified plaque. I'm not saying CAC is without its merits, but you are stretching credulity beyond breaking point to suggest that because one cardiologist claims that reducing dietary fats doesn't show a reduction in CAC scores, it therefore disproves the lipid hypothesis. CAC has too many caveats to use it to make such bold inferences, even if we get to the wild epistemic standards required to think one cardiologist's claims are sufficient to overturn decades of well-conducted research. Additionally, as I've stated elsewhere, it still wouldn't be evidence that arterial wall damage or inability to repair damage is required for ASCVD, which, let's remember, is the reason we're even talking about this in the first place. There's quite an interesting discussion about the pros and cons of the CAC here. Suffice to say, it's not suitable to stand up the kind of arguments you're making: https://www.youtube.com/watch?v=gxIeRUbHauw https://www.youtube.com/watch?v=gxIeRUbHauw > Because when someone you don't like cites multiple meta-analyses of RCTs you handwave it away with a Twitter thread where he "runs away from an argument chef's kiss" No, that's not why I dismiss Mason's claims. I dismiss Mason's claims because he says "this meta analysis says this" and then when you look at the MA it says nothing of the sort. It's false to claim that I'm handwaving away the evidence itself. > I know of Ben Goldacre's Bad Science, I see plenty of HN links about the reproducibility crises in science about the ways of p-hacking and manipulating data, about the broken incentives to publish and get funding, about the complexities of statistical analysis, things I can't argue through. Medical experts are incentivised to push treatments, academic experts are incentivised to push dodgy results, it's not easy for people who like and care about this stuff to say what's "high quality data" or isn't, let alone me. One can't handwave away measuring the amount of plaque buildup in the heart and say "that's not a measure of the amount of plaque buildup in the heart" as easily. If people change X and their heart health measurably improves, and X isn't reducing dietary fat, that should mean something. I think it's really worth taking a second to think about what you're saying here. Some points of order first: - The reproducibility crisis is about some fields of science re-running individual studies and finding that when they do so, they cannot reproduce the original findings. - p-hacking is about altering the methodology of a study post-hoc to generate statistically significant findings when such findings were not present under the original methodology. - the objection of the complexities of statistical analyses, I presume, is an objection based on the fact that when using statistical analyses to try to account for confounders and systematic bias, sometimes these analyses are poorly conducted and thus fail to mitigate these biases. - "Medical experts are incentivised to push treatments" is presumably an objection that individuals publishing papers may have an implicit or explicit bias that causes them to over or understate the significance of a given finding, or record data incorrectly. So your argument appears to be: "Ok, so you've provided evidence that LDL is a risk for CVD based on over 200 prospective cohort studies (that is, the results have been successfully produced literally hundreds of times), mendelian randomisation studies, and randomised controlled trials including more than 2 million participants with over 20 million person-years of follow-up and over 150 000 cardiovascular events. However, I believe it's likely that if we were to re-run those studies again, we would not be able to reproduce those results. “I'm also concerned that p-hacking and systematic bias is so consistently prevalent within all of these studies that it affects the summated findings so profoundly, and consistently in the same direction, that we cannot trust the findings to be accurate. Additionally, the individuals conducting these studies are all incentivised to push treatments and therefore we should believe it to be more likely than not, without any evidence to actually demonstrate this, that these individuals are biasing the results because of these incentives. “It's not easy for lay people like me to ascertain whether these data from the top of the evidence hierarchy (that is, the evidence least likely to be affected by bias) are high quality, so we have to find a source of evidence that is less likely to be affected by such bias. To achieve this, I propose that we throw all the above data out, and instead trust the claims of a single cardiologist, who's written a blog post on his website about how serum cholesterol is irrelevant when it comes to cardiovascular disease risk. “After all, p-hacking can't take place when the evidence is so poor that there are no p-values, calculations of statistical significance, or even recorded data from which one could calculate p-values. “After all, you can't make errors in your statistical analyses if you don't perform any statistical analyses. “After all, your attempts to control for bias can't go wrong if you don't attempt to control for bias. “After all, 'One can't handwave away measuring the amount of plaque buildup in the heart and say "that's not a measure of the amount of plaque buildup in the heart" as easily. If people change X and their heart health measurably improves, and X isn't reducing dietary fat, that should mean something,' despite the fact that there are studies that do measure plaque buildup in the heart and show that it is affected by serum cholesterol. I believe that we can handwave it away when it's published in a peer reviewed journal, but we can't do so when it's a blog post on a cardiologist's website. This is the most reliable way to make sound scientific inferences.” I don't even understand how one can reach these conclusions. I've tried my absolute best to interpret what you've said generously, but there's only so much I can do given material like this. This whole line of reasoning is also far closer to what you’re accusing me of - instead of discussing what the data show, we should just “trust the experts”. We can’t analyse Dr Davis’ data because he doesn’t even provide any, but we should just assume he’s telling the truth because he’s a cardiologist and therefore so well paid that he has no motivation to be wrong. We should ignore the evidence I’ve provided because it’s harder for laypeople to understand than this supposed expert just saying “trust me, I ran some CAC tests”. > presumably somewhere is a researcher or practitioner who has done many CAC measurements and has studied whether cutting dietary fat changes the score or not, at this point I am not going to look for it. I've already provided you even better evidence than that: a pooled analysis of studies where SFA is replaced with PUFA and cardiovascular events and coronary deaths are reduced. You don't need to go looking, I gave it to you: https://pubmed.ncbi.nlm.nih.gov/19211817/ https://pubmed.ncbi.nlm.nih.gov/19211817/. If you want a more consumable version, here's an excellent podcast on it (all their stuff is good, you should see if there's anything else there of interest to you: https://sigmanutrition.com/episode481/ https://sigmanutrition.com/episode481/). It's a shame, but not entirely surprising that you no longer desire to continue the discussion. Fundamentally, I think you will always run into issues until you re-assess your underlying epistemic framework when it comes to assessing evidence. Until you do so, reading studies will be largely pointless as you have no reliable tools to interpret those studies. You're running before you can walk. If you're interested, the Cut Through Nutrition podcast is quite a nice primer on how to think about nutrition science research, I'd highly recommend working through the episodes. Once you've internalised the concepts, it becomes a lot easier to see how quacks like Mason and Davis are operating, even without a great deal of domain-specific knowledge. Podcast is here: https://podcasts.apple.com/gb/podcast/cut-through-nutrition/id1467679266 https://podcasts.apple.com/gb/podcast/cut-through-nutrition/... Have a good week!