wellness – My science, My way

“My name is Khan” phenomenon in medicine:

“My name is Khan, I am not a terrorist”. Was a famous dialogue from a 2010 movie. It has a very clear political message. All (most to be precise) terrorists are Muslims, but all Muslims are not terrorists. Looking at all Muslims with suspect; treating every Muslim as if they are terrorists is ethically, legally, politically wrong and that is very clear.

But the field of medicine does that, I mean a logically equivalent blunder, and nobody says it is wrong there!! I want to point this out only as a logical problem, with no political intentions. Just as some Muslims happen to be terrorists, some of the type 2 diabetics develop heart, kidney, brain related complications; certainly not all. But we still treat diabetics as if all of them are bound to develop these and insist on treating them. This is similar to what China is believed to be doing with Uighur Muslims. The china act came under heavy criticism a few years ago. (I don’t claim to know the reality and wonder why they have suddenly stopped talking about it now!). Are the two logically different? If one is unethical how is the other one ethical?

Perhaps diabetic medicine wants to treat everyone to be on the safer side and that should be good. Not treating them would perhaps be inviting trouble for them. So not treating them should be unethical isn’t it? This is far from reality. Putting together data from dozens of clinical trials and carefully analyzing it shows that glucose lowering treatment of diabetes as being practiced hardly prevents any of the complications (https://www.qeios.com/read/IH7KEP , https://doi.org/10.1002/14651858.CD015849.pub2 ). A number of trials claim so but a look at their raw data is sufficient to know that they have really tortured the data to come at the pre-determined conclusion. In many large scale trials, the treated group had significantly higher mortality than the controls. Many trials did not find any difference at all. If we cherry pick only the most “successful” trials, we find only 1 or a few percent absolute difference in the incidence of complications. There are many clear inferences from this. Even without any treatment, only a small percentage of diabetics develop complications over a span of decades. Treatment at the most makes a marginal difference. So how is this different from the “My name is Khan” (MNIK) phenomenon? There too only a small proportion of the community turn terrorists and huge investment in anti-terrorist squads is unable to prevent it entirely.

Treatment might be justified by saying that, “but we don’t know who is going to get complications. So it is good to treat everyone.” Then how is it different from suspecting every Muslim to be a terrorist? There also you do not have any a priori knowledge.

Moreover, it is not true that we cannot predict who will get complications. Data clearly show that in all classes of HbA1c, those who are physically fit are unlikely to develop complications (https://pmc.ncbi.nlm.nih.gov/articles/PMC6908414/ ). Physical fitness prevents many types of complications independent of weight loss or glucose control. The odds ratios for mortality across HbA1c categories varies between 1.1 to 2 in different studies whereas the odds ratios across fitness categories can even exceed 10 (https://pubmed.ncbi.nlm.nih.gov/40569873/ ). So physical fitness is much more important than glucose control. This means that even among the different glucose classes it is possible to judge who are more likely to develop complications and who are not. Then why treat everyone with high blood sugar?

But what is wrong in treating everyone? The answer depends upon the cost benefits of the treatment. The new generation drugs, mainly GLP-1RA drugs really cost a fortune. Apart from that there are psychological costs. An impression is created in the public in such a way that being irregular in taking medicine gives a guilt complex quite unnecessarily. But even more important and less well known is that under certain contexts the drugs are dangerous. In particular stringent sugar control in some trials resulted in greater mortality than control (https://pubmed.ncbi.nlm.nih.gov/18539917/ ; https://www.nejm.org/doi/full/10.1056/NEJMoa0810625 ; https://pubpeer.com/publications/417DE03905005C28E226F823C2AF63 ). Why do we still insist that everyone needs to be treated?

The answer is very clear to me. The difference between why we don’t treat every Muslim as terrorist is that so many of them are intricate part of the social economic machine. They are at responsible positions, often doing good jobs and not easily replaceable. Wherever communities are intricately linked and networked in daily functions and economics, it is beneficial for the society and for the state not to isolate any community for any reason. Perhaps Israel thinks they can do without the Palestinian community and so its behaviour is different. Ultimately what is beneficial to a state or a society in a given context at a given time decides what it politically considers ethical. Similarly in medicine the benefit matters. Treating everyone with ineffective drugs for the lifetime is beneficial for the pharma companies, so it is recommended and considered ethical. Ultimately cost benefit calculations matter. Everyone cares about selfish benefits, but sometime it is possible to fool others and that is the main use of ethics as commonly practiced. Both doctors and patients are fooled into believing that not treating a diabetic is unethical. This does not mean that selflessness or truly ethical behaviour does not or cannot exist. It does, but always in a minority. More commonly the rules of ethics are decided by the benefit of someone who is successful in fooling others to a large extent. As long as people including the practicing physicians are fools, the MNIK phenomenon will continue to exist in medicine.

Twenty five years of interest in type 2 diabetes

My curious interest in type 2 diabetes was triggered by some discussions in Katta by mid 2000 if I remember correctly. I looked at it as an evolutionary biologist and tried to interpret it afresh. Over the years my own entirely novel interpretation developed which I published in a series of over 25 research papers and two books. This journey changed my life, my science as well as my perspective and relationship with academia. But did it change the mainstream research in the field? The answer is a big no. It remains equally confused, laden with serious anomalies in the underlying theories, unscientific beliefs, data manipulations and purposeful misleading of doctors and patients. The entire field is pseudoscience and nobody cares.

As soon as I started browsing through literature, I was struck by the horrendous anomalies in the field, reproducible experiments having proved beyond doubt that the foundational beliefs were wrong, simple mathematical models showing the impossibility of the prevalent theory and on top of it the complete failure to cure diabetes and/or prevent diabetic complications effectively and consistently.

The anomalies that were already there were the following. Inducing insulin resistance by gene knock outs does not result into consequences that the theory expects. Neither insulin nor glucose levels go out of range by the induced insulin resistance. Suppressing insulin production or release in an insulin resistance state does not increase fasting glucose in rodent as well as human experiments. During development of T2D, hyperinsulinemia does not seem to be a response to insulin resistance as the theory says. Hyperinsulinemia precedes insulin resistance and experimentally suppressing hyperinsulinemia brings down insulin resistance and glucose remains normal. All these experiments have been reproducible and are more than sufficient to conclusively show that the prevalent theory is absolutely wrong. All this has been very much there is mainstream literature, high impact journals.

Then there are anomalies that I pointed out. The clinical definition of insulin resistance is circular and non-falsifiable. The compensatory fasting hyperinsulinemia with normoglycemia is illogical and mathematically impossible. If you put together all experimentally demonstrated links to and from glucose and insulin, a network of known causal links can be constructed. Neither glucose nor insulin are central to this network and normalizing these two are unable to cure diabetes even in a theoretical model. The older evolutionary thinking that a “thrifty” tendency developed as an evolutionary adaptation to feast and famine is neither ecologically nor physiologically sound. Humans do not show any physiological characteristics of being thrifty. Fat cells do not induce insulin resistance. In fact the most abundant signal molecule secreted by fat cells is actually insulin sensitizing by the popular definition. The diet theories including high fat, high carb, intermittent fasting, time of eating and all are full of mutually contradicting data.

On top of it no clinical trial has conclusively shown that normalizing glucose can arrest diabetic complications. Clinical trials are full of bad experimental designs and all signs of data twisting, conclusion spinning, unscientific data handling and purposeful misleading. Glucose is not central to T2D and therefore normalizing glucose is not even theoretically expected to avoid diabetic complications and reduce mortality. But this rhetoric is repeated as a religious text and all treatment focuses on reducing glucose which is not going to help anyway.

Showing all this with reproducible experiments, rigorous data analysis and sound theory and mathematical models, publishing this in any form has absolutely no effect on the religion of type 2 diabetes. There was no counterargument on what we showed and published. I gave talks in places like Joslin Diabetes Centre and OCDEM arguing that the prevalent theory has been proved wrong with multiple lines of evidence. The Joslin talk was well attended by everyone including the Director. There was no cross questioning or counterargument after my talk. More than one personal responses later were that the argument is not new. We all know the theory is wrong. Just that you openly said it, others don’t.

Not only my group pointed out the glaring anomalies, we also proposed an alternative theory which goes like this. We evolved as hunter gatherers and our physiology evolved to support the necessary behaviours of that lifestyle. Now many of the behaviours are simply missing in the modern lifestyle. These behaviours have been experimentally demonstrated to be linked with many growth factors, angiogenic factors, neurotransmitters and other signal molecules. The deficiency of these behaviours has multiple well demonstrated physiological effects. For example, altered expression of angiogenic factors because of altered behaviours leads to reduced capillary density and endothelial dysfunction. This reduced the glucose supply to the brain (again experimentally demonstrated). When the brain receives subnormal glucose it instructs the liver to synthesize more of it. That is why there is fasting hyperglycemia. This has nothing to do with insulin resistance. Vascular dysfunction is primary and glucose change is only consequential. Therefore bringing blood glucose to normal doesn’t do any good. Getting the growth factor and other signals back to normal is the solution. This is easy to do through sports because all sports is an attempt to get back the hunting fighting behaviours. Exercise is useful not because it burns calories but because it brings back some critical missing behaviours and their neuroendocrine correlated. This theory logically and mathematically explains 19 major anomalies which the classical theory was muddled with. Getting funded for a new idea that too from a person away from the power centers of the field is impossible. We did try and failed to get funded to work further on the hypotheses. But there already existed substantial support to these ideas in literature.

I would have welcomed any criticism of my arguments. I would have taken back my statements if they were shown to be wrong. But nobody did this. They knew any counterarguments would put them in deeper trouble. It is better to pretend that they are just not aware of any such arguments. The multiple serious anomalies just don’t exist and everyone is happy with the ongoing pseudoscience. And the field continues with the theory disproven decades ago, creating new waves by beating drums of a new drug, whose clinical trials actually show no effect. Currently with volunteer researchers I am examining the raw data of clinical trials and feel astonished at how commonly they trample all well known principles of statistics to claim support to their already decided conclusions.

In short, the entire field of type 2 diabetes is pseudoscience and all people supporting it call themselves scientists and enjoy the fat salaries, perks, prestige and positions. With this my perspective of academia changed completely. I no more look at people in academia with respect. They have sold out their souls to funders. Publication metric and funding prospects have completely overtaken basic curiosity and research integrity. I personally gained a lot from the experience so I am grateful to everyone. Understanding of science is complete only when you learn how not to do it as well. These people helped me expanding my understanding of science. I realized that just the principles of science are not enough, human behaviour is an intrinsic part of it and this behaviour is not at all different than that in wars, politics, power and business. Power is more important than fairness and truth even in the field of science. But once in a while, someone follows truth, it may be ignored for a long time. May be at times someone rediscovers it later. The history of science is full of such examples and nothing has changed.

Yes, it is intentional misleading. The authors and editors seem to agree.

When one finds problems in the statistical analysis of a paper, serious enough to invalidate the conclusions drawn, what should the reader do?

It is in the right spirit of science that you assume this might be because of oversight. To err is human and researchers are humans to begin with. If a reader notes serious problems in a paper, it is necessary to point it out with an expectation that the authors and/or editors respond. Science would welcome two types of responses. (i) They disagree with you. What you perceive as serious mistake is not really a mistake and they have a sound justification for what they did. If this is the case they need to justify sufficiently elaborately. Often the differences of opinion may not resolve. In that case both the sides need to be made transparent for the reader. That is the responsibility of the editor. (ii) In case the authors realize that there is some problem with their analysis and inferential logic, they need to correct the analysis or clearly state the limitations of the inference by publishing a correction to the paper. Both these responses are completely in the spirit of science and should be welcome.

There are two more possibilities that are not really in the spirit of science. (iii) The third possibility is that the authors neither have a sound justification, nor the readiness to correct themselves, but the editors realize the gravity of the problem and decide to retract the paper. This happens quite often but not so easily. Retraction is treated as serious damage to the prestige and reputation by the authors, their institutes, editors and the journals. Therefore they try to avoid, postpone or cover up the problem. (iv) The forth possibility is that the conclusions drawn from the flawed analysis were published with a deliberate intention to mislead the readers. If this is so, they will certainly not publish any correction because it goes against the very purpose of publishing. They will not have any justification to what they did because it was misleading anyway.

Either for the third or the fourth reason, editors are reluctant to take any action or just keep on delaying the action until the paper is old enough and readers have lost interest in reading any correction, even if published. By the time popular science perception has accepted the misled direction and then it is tough to change it. The publication of correction is a low key event, the purpose of deliberate misleading is served until then. If the editors do not take any action the sleuths still have an option of posting the cross questions in platforms such as PubPeer. If the authors think they are not wrong, they can publish a rebuttal on PubPeer. If they accept the problem they can publish a correction. If nothing of this happens we are left with the conclusion that the authors as well as editors deliberately intend to mislead the readers.

In certain fields of science misleading has a great benefit. The field of medicine, in particular, is prone to this because of the millions of dollars of possible profits involved. After spending huge amounts on developing a drug, if a clinical trial does not show it to be sufficiently effective, accepting the result leads to huge losses for the pharma industry. In that case making an impression that the drug is effective is necessary for the business. And it is not very difficult to mislead the medicine community, because either they do not understand the scientific method and inferential logic, or they simply do not have the time to waste in being careful about what they accept as science. People in academia certainly have the expertise but have no motivation. Their career progress depends upon how many high impact papers they themselves publish. Hardly any credit goes to exposing frauds in the field. The peril of being busy in making a successful career is that a lot of pervert science gets published and nobody cares.

What are the most common ways employed in misleading people in the field of clinical trials? Particularly clinical trials for life-style related disorders. There seem to be a few common tricks repeatedly used.

Multiple statistical comparisons without correction: Statistical inferences are probabilistic. There is always a small chance that your inference can be wrong. When you do hundreds of statistical tests in a single study, at least a few of them turn out to show “significant” results. But this could be just a result of having done a large number of tests each one with a small chance of being wrong. This is a well recognized problem in statistics. Solutions have been suggested, which are not free of problems. But not having a good solution is not a justification for hiding or disowning the problem itself. Most clinical trials in the field of lifestyle related disorders typically use two strategies to take advantage of doing multiple tests and then hide or disown the problem.
- a. Register a large number of clinical trials. Not every trial gives results you want. Publish the ones in which you get favorable results, don’t publish those that give inconvenient results. In the field of type 2 diabetes clinical trials registered on https://clinicaltrials.gov/ , only about one third of the completed trial results have been made public. Two thirds remain silent about what they found.
- b. A given trial looks at a large number of outcomes, then categorize by sex, age groups, BMI groups and other possible subgroups. So the total number of tests typically performed are in hundreds and sometimes even in thousands. Then they prominently report the ones that are significant in the direction that they expect. It is almost guaranteed that a few will turn out to be significant by chance alone. This is enough to start beating drums that the drug is effective.
Selective reporting and different format of reporting: Just as some of the tests turn out to be significant in the expected direction, a few are significant in the other direction. They do one of the two things when this happens. They either just don’t report the inconvenient ones or report them in a way that makes a misleading impression. For example they would use indices of relative risk reduction (such as odds ratio, hazards ratio) while reporting desirable effects of the drug; in contrast they use absolute risk reduction indices while reporting undesirable effects of the drug. Absolute risk reduction generally turns to have smaller looking numbers than relative risk reduction. So the reader is made to think that the good effects of the drug are large and bad effects small.
Convenient subgroups and subtotaling: The subgroups are made by convenience and their results are selectively reported. For example they may make totals of many subcategories when that gives more expected picture, but report the subcategories separately when that gives a more convenient picture. The adverse events where they get expected results, are called “serious adverse events”, the ones in which results go in the other direction are called non-serious adverse events. This classification is not accompanied by any clear definition of the word “serious”. There are no standard guidelines on how to report statistics and in effect they take a systematically misleading path.

These are common tricks used for painting a picture that the drug is effective. Then they concoct more ways to mislead in a context specific manner. Very intelligently twisting data they reach conclusions that have been already pre-decided. Sometimes, raw data are available or are made available on request. I analyzed the raw data in many such cases and my analysis did not support their conclusions at all. The published conclusions could not be reached without cherry picking or twisting the data in some way or the other, and there are so many ways in which data can be twisted.

I am giving below the details of three clinical trials published in very reputed journals where we detected serious flaws in the published analysis, we wrote to authors and editors and ultimately made our concerns public on PubPeer. To begin with we were open to all the four possibilities. The authors could have counter-argued to show that they were not wrong; they could have published the corrected versions; if authors did neither, editors could have retracted the papers. But none of these things happened. Both authors and editors kept mum and did nothing except some promises that they will consider the cross questions seriously. From the response (or lack of it) of authors and editors we could differentiate between the above four possibilities. After careful scrutiny in all the three cases we are compelled draw a conclusion that both authors and editors in all these cases clearly intend to mislead people. Perhaps this is quite representative of clinical trials. We have more reasons to believe that a significant proportion of clinical trials have been systematically fooling people all the time.

The first case is that of a paper in Lancet Diabetes and Endocrinology. Our pubpeer comment on it and the prior correspondence with the editors is at these two links.

https://pubpeer.com/publications/BB3FA543038FF3DF3F83B449F8E5AA

https://drive.google.com/file/d/1XNzxif4ybJdgAQ4YmiKg_6mSqZGh2Mn1/view

The Pubpeer comments were published in August 2024. The authors had ample time to respond, but they didn’t.

The second case is about a paper in PLOS Medicine. This paper was clearly hiding inconvenient results although the raw data could reveal them. We wrote to the editors in Aug 2024 who initially responded positively and asked clarifications from the authors. We have no idea whether the authors responded because the editors suddenly stopped all correspondence. Ultimately we published our comments on pubPeer in February 2025 to which the authors have not responded. Links to the correspondence with PLOS medicine editors and the pubpeer comments respectively are here.

https://drive.google.com/file/d/1XWyxiadnS_DxffbwJUNeQKw3FzsTfSUx/view?usp=sharing

https://pubpeer.com/publications/9D3DFEAFFCF1CD343B4E4F71A6F747

Recently with some colleagues we started looking at the GLP-1RA trials about which there is much brouhaha. They are being projected as wonder drugs effective against anything that you name on earth. Looking at raw data shows that these clinical trials suffer the same set of problems which make their inferences invalid. On one of the papers published in Nature medicine and another on NEJM both from the SELECT trial we wrote comments on PubPeer in February 2025 and the authors haven’t responded as yet.

https://pubpeer.com/publications/C900B991EA977183BA7FA4170A6796

Interestingly to the letter in response to PLOS Medicine paper and the PubPeer comments on the Nature medicine paper after requesting correction we wrote, “However, corrections need not be made if the misleading is intentional since the purpose is served.” Both the authors did not make any correction, nor counter-argued in defense of their analysis in any form. This is a clear admission that the misleading was intentional. Since the editors also did nothing, it is clear that even editors of such prestigious journals are interested in deliberately misleading people.

Perhaps intentional misleading is common across clinical trials. Earlier I published my analysis of multiple clinical trials related to type 2 diabetes. There were many responses to this article, all in the public domain, mostly supporting our arguments. Not a single response was from authors of the original papers (https://www.qeios.com/read/IH7KEP ).

If anyone has any doubt about intentional misleading, please contact the authors of these papers or editors of the respective journals for confirmation.

April 11, 2025

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