Common Words, Phrases, and Ideas in this Blog

The page is still a little "clunky", but better than nothing.

This page describes various words, phrases and ideas that get used over and over again in my blog:

Scientific, Medical, or Research Theories

Majority (or Dominant) Theory. Consensus Belief.

These terms refer to the most commonly held theory.  This is sometimes called "the scientific main stream" or something like that.

Minority Theory

This is a scientific theory which has support from some researchers, but most researchers do not believe it.

Fringe Theory

This is a scientific theory which has very little research support, or maybe no support at all.  It is typically believed by one person, or possibly a very small group of people.

Quack Theory

This is a scientific theory which has been disproven or is obviously wrong, but is still believed in by a small group of people or maybe just one very vocal person.

Trials and Experiments

Completed Enrollment

Why is this important? For two reasons.  First, because it is now possible to predict when they will finish collecting data.  (This study runs for DURATION, so they should have data collected by DATE.)  Second, because much of the uncertainty that surrounds clinical trials, is involved with recruiting participants.  It is often unclear how hard it will be to recruit people, and long it will take.   But that this point, all that uncertainty is behind the researchers.  From now on, it is just gather data, then analyze data, and then publish data.  Researchers have a lot more control over those later stages, then over recruiting people in the first place.

Confidence Bars

You can see the confidence bars in the image above, they are the little horizontal bars above and below the measurements, and the same color.  Confidence bars represent the uncertainty in measurement, and the uncertainty in averaging a small number of data points.  We talk about an average as though it were one, exact number.  But an average is a statistical combination of many numbers.  Each one of those numbers has a measurement error (was it really 0.6 or was it 0.62 or maybe 0.59)?  Hopefully those errors cancel out as we measure more data points, but it is possible they combine to get worse.  Also, the average of a small number of data points might be different than the average of a large number of data points even if they are measuring the same thing.  Researchers summarize these uncertainties on their graphs with confidence bars.  Two data points within each other's confidence bars might be the same, even if the one number point is different.

The Real Publication Requirement

A lot of people seem to think there is some rule that says you must publish the results of clinical trails.  Twenty years ago, the answer was simple: no.  There was no law saying anyone had to publish anything.  Sure, academics built their reputations by publishing, so there was pressure for them to publish.  But for commercial companies, there was no reason to publish anything that would not increase their profits.  Then the USA passed a law saying that any clinical trial that was part of a new drug application must be listed on the FDA's clinical trial registry.  That was enforced as part of drug approvals, so suddenly there was a trial registration requirement, but no requirement to publish results.  (Companies had to submit results for FDA approval, but not publish them in the scientific literature or make them available to anyone else.)  

Years later the law was updated to require results be added to the clinical trial registry.  However, there was no enforcement, and therefore few researchers did.  Even researchers who published results rarely added them to the clinical trial registry.  Years later, a news service published a series of articles describing the legal requirement, the fact that it was widely ignored, that there was no enforcement, and that most of this research was funded by US taxpayers via government agencies.  Finally, there was some movement.  Over the next few years many old clinical trial registries were updated with results, and now more results are posted when the studies are completed.  But the number is still low.

So the summery right now, in the US, is that there is a paper rule that researchers must make results of clinical trials public, but, in fact, this data is not available for many clinical trials, including many focused on T1D.  I think the situation is worse in the rest of the world, but I've never investigated in depth.

For example, in 2015 none of the research results previously funded by JDRF were available in the FDA's clinical trial registry.  The publication of this information by Statnews resulted in several JDRF funded clinical trials posting results to the clinical trial registry, in some cases years after the research finished.  I'm sure JDRF funded researchers are doing better now, but I don't have any more recent data.

As of 2019, the two local (to me) universities who do the most T1D research were University of California San Francisco (reporting less than 50% of results) and Stanford (reporting less than 75%).  These numbers are for all the clinical trials done at those universities, not just their T1D trials.

More to read:

Compassionate Use (Right Now)

In the past, I've often said there were two ways to get access to a drug in the USA.  Either through FDA approval for your illness, or through "off label" use if the drug is approved for a different illness.  However, since 2016, there is a third way, called "Compassionate Use".  This is authorized by the 21st Century Cures Act.  I bring it up here, because Orbus, the company developing DFMO has an official Compassionate Use policy, which you can read here:

It is quite limited, and completely at the discretion of the company to approve.  Patients (and their parents) can not even request Compassionate Use.  Instead, their doctors must request it.  They will need to fill out an FDA form 3926.

I'm sure there are many rules and requirements for this, but the key ones are that the drug is in active clinical development (phase-II or phase-III), and the patient must have a serious or life-threatening disease or condition. 

I expect to see more of this in the future, as companies (and even later, doctors) get more comfortable with this aspect of the 21st Century Cures Act.  I do think it is important for everyone to understand their options before the situation comes up where they might need to use those options.

Outcome Measurements

In this section I describe the common outcome measurements, how they are done, and why they are important. 


C-peptide measures are critical for two reasons.  From a scientific point of view, they are the gold standard that the body is producing it's own insulin.  (Measuring insulin will confuse injected insulin and produced insulin, but C-peptide is a side effect of the production process.  If you see that, you know the body is generating it's own insulin.)  From a regulatory point of view, the US FDA has issued guidance that C-peptide numbers will be used to approve any future cure or treatment for type-1 diabetes.  So these numbers will be central to the approval of any new treatment.

For example the following quote is from the D-Cure workshop of international experts held in Barcelona in April 2007:

"It is now an accepted approach to evaluate endogenous insulin secretion by measuring C-peptide levels (with highly sensitive and normalized measurement methods) in response to a physiologic stimulus (liquid mixed-meal) under standardized conditions."

For C-peptides, higher numbers are good, because they mean the body is generating more of it's own insulin.  In untreated people those numbers dropped about 15%, which is normal for the first year after diagnosis.  

A1C (or HbA1c)

Not written yet.

Insulin Used

Not written yet.

Everything Else

Off Label Use

In the USA once a drug or device is approved, a doctor can prescribe it in situations different than it was originally approved for.  As an example, a doctor may prescribe it for a different disease, at a different dose, or for a different type of person than it has been approved for.  In the world of type-1 treatments, drugs or devices that have been approved for use on adults are often prescribed for children.  This is a classic "off label" use.  Off label use is based on the professional opinion of a doctor, and consent of the patient.  Therefore safety data (such as from this study) can make doctors more willing to prescribe "off label", and patients more interested in trying it.

Case Study

A case study is the presentation of something very unusual that happened to one person.  Occasionally a case study will motivate a researcher to run a clinical trial.  Case studies are part of the scientific literature, but they are not clinical trials; rather they occur before clinical trials in the scheme of things.


This leads into discussion of the symposium's magic word: "platform".  You might think that a platform is a wooden box that you stand on.  I'm a software engineer, and we use "platform" to mean a bunch of software that helps develop new software and can be used over and over again for that purpose.  The pharma guys use the word in much the same way that software guys do.  A pharma "platform" is a way to speed the development of multiple drugs.  Everyone who is working on a drug, talks about their platform.  They hope that their development can be used again and again to develop multiple different cures for different diseases.   The word "platform" represents the unbridled optimism common to researchers; and the funding opportunity that every venture capitalist, pharma company, and non-profit is looking for.  They haven't even started a clinical trial, and are already talking about how they will cure multiple diseases in the future. "Platform" is the pot of gold at the end of the rainbow.

To be a little more specific, two types of platforms were discussed.  The first was a common collection of ingredients that you can customize to cure different diseases.  Consider this bread analogy: you have a recipe for pecan bread.  You try replacing the pecans with almonds, now you have almond bread.  You replace them with blueberries and now you have blueberry bread.  Your bread is what the pharma guys would call a "platform".  You drop in one new ingredient to create a new bread.  If your bread recipe is good at this sort of flexibility, then a commercial bakery would be very interested in it, as well as all the "specialty" bread recipes.

Bayhills has exactly this kind of platform:  it is a ring structure of several chemicals, one of which is specific to type-1 diabetes.  That drug is called BHT-3021 and is targeted at type-1 diabetes.  But if you take the same basic ring, and replace the type-1 chemical with a different one aimed at multiple sclerosis, then the drug is called BHT-3009 and is aimed at MS.  And so on....

Another kind of platform is a method to find drugs, which you can use again and again on different diseases.  Again, to use a cooking analogy, let's say you are looking for a dinner recipe and so you grab a can of chicken soup and pour it over chicken meat and bake it.  A week later you grab mushroom soup, pour it over beef and bake that.  You now have a "platform" for making recipes.  The platform is this: pour a can of soup over a meat and bake it.  None of the ingredients are reused (as they are above), but the basic technique is reused.    There were a couple of different researchers with this kind of platform at the meeting.  Including Apitope and Dr. Mannie (neither in clinical trials as yet).


The second most important word at the symposium was "bio-marker".  A bio-marker is a way to do an experiment for cheap.  For example, lets say you have a drug and you think it cures type-1 diabetes.  Running an experiment to see if it does will take years: you need to make sure type-1 doesn't come back.  However, if you had some blood test that told you the person no longer had type-1, then you would not need to follow them for years.  You would just need to do the blood test.  That blood test would be for a "bio-marker".  Something that showed you the drug had worked, but was cheaper, quicker, and easier than seeing if it had really worked.  Finding a bio-marker for a disease speeds up ALL research aimed at curing that disease, and it attracts research money to that disease, since research there is less risky.

C-peptide is such a bio-marker for type-1 diabetes, but the pharma guys are always wishing that there were more.  It makes research, cheaper, quicker, less risky, and less unknown.  It is especially important that the government regulators agree to the use of the bio-marker.  If they do, then you can get government approval that the drug is useful, based on the bio-marker: quicker, cheaper, less risky, and you know approval will be granted.

So here is a quick introduction to treating inflammation as a cure for type-1 diabetes:  Everyone knows that type-1 diabetics have a lot of inflammation in their pancreas and especially around their beta cells. Most researchers believe that inflammation is a result of the body's immune attack on it's own cells. That is, the underlying immune problem causes inflammation and also causes beta cells to die (which causes the symptoms of type-1 diabetes):

            /---˃ causes --˃ beta cells to die    
            \---˃ causes --˃ inflammation

However, some researchers believe that the underlying immune problem causes inflammation, and that this inflammation kills the beta cells, which then causes the symptoms of type-1:

Autoimmunity -causes-> inflammation -causes-> beta cells to die

The difference is that, in the second model, if you stop the inflammation you can stop the symptoms of type-1 diabetes (the high BG numbers and the low numbers). And that is a big difference. But this second model is still a minority opinion.

Monoclonal Antibody
A monoclonal antibody is an artificially created antibody which targets one very specific cell in the body.  Different monoclonal antibodies target different cells.  So if a disease is caused by a bad cell, then using a monoclonal antibody to target that cell is a promising treatment.  Because several have been successful in other autoimmune diseases, they are an active area of research for curing type-1 diabetes.

Because monoclonal antibodies are usually named with names ending in "-mab", they are sometimes called "mabs".  Several have been tested in clinical trails as possible cures for type-1 diabetes.    At one time, approximately 1/3 of new drug approvals were for different kinds of monoclonal antibodies.  They are most commonly used as anti-cancer and anti-inflammatory treatments.  The most well known is probably Rituximab.

General info on mabs:
List of mab drugs:

Black Box Warning

A "black box warning" is a serious warning placed inside a black box in the FDA-mandated drug labeling.  The text will be printed on the drugs "package insert", on the FDA's web pages and on many other web pages and reference books that doctors commonly use to get information on he drugs.  (They are not printed on the label of the pill bottle, however.)  About 9% of prescription drugs have "black box warnings", and the etanercept used in this trial is one of those that has this warning.  More details are here:

CD numbers (such as CD34 or CD52?)

CD stands for "cluster of differentiation", which is a unique chemical (often a protein or peptide) on the surface of a cell which is part of the immune system.  Different types of cells can have different CDs on them, and one cell can have more than one.  These CDs control how the cell interacts with other cells and are also markers which identify the cell.  For example, the CD34 marker is found only on stem cells, while the CD52 marker is found only on mature cells, and CD4 marker is found only on a specific immune cell called a "helper T-cell".


Each person has to decide for themselves which results excite them, and which don't.  

C-Peptide Changes: Dropping More Slowly vs. Not Dropping vs. Growing

Measuring changes in C-Peptide levels is the best evidence we have that a potential cure is improving a person's ability to make their own insulin.  C-Peptides are created as a side effect of the body's method of making insulin.  More C-peptides mean the body is making more insulin itself.   Obviously, a straight up cure will be easy to see.  However, smaller incremental progress towards a cure can be see in C-Peptide numbers even if we don't have the full cure yet.

So, in the world of research aimed at curing T1D, treatments that raise a person's C-Peptide levels are clearly making progress.  However, a lot of testing is done during the honeymoon period and at that time, people are still generating some C-Peptides, but the amount they can generate is dropping.  In a sense, the honeymoon ends when they level out, generating either no C-Peptides or a very low level.

During the honeymoon phase, clinical trials often report the difference in C-Peptide generation between the treated group and the control (untreated) group.  Since the control group is dropping they can see a statistically significant difference in three different ways:  

  1. The treated group is dropping, but the untreated group is dropping even faster.
  2. The treated group is holding steady, while the untreated group is dropping.  This is sometimes called "preserving beta cells" or "stabilizing C-Peptide production".
  3. The treated group is generating more C-Peptides, while the untreated group is dropping.

Researchers often report all three of these results as success.  After all, from a scientific point of view, they are successes.  They are all statistically significant, good results, in the primary outcome.

But for my part, only results where the C-peptide numbers go up excite me.  Results where they stay constant are unexciting, and, results where they go down are disappointing, even if they go down more slowly than in untreated people.   

I do think that treatments were C-Peptides drop, but more slowly than in untreated people might lead to treatments that delay the onset of T1D.  And I also think that studies where the treated group holds steady might lead to prevention, if given to people at-risk or even a cure if paired with another treatment that generates more beta cells.  

But the studies that excite me are those that show growth in C-Peptide levels.  For me, those are the ones closest to a cure.

Transplants Requiring Immune Suppression Are Not Cures

This trial is currently running, but I'm not covering it as a potential cure for T1D.  This trial requires each person to take a full suite of immune suppression drugs to support the transplant.  They will need to do this for their whole lives, even if the transplant only works for a few years.  Furthermore, the drug combinations used for transplants have significant bad long term side effects, causing health problems in their own right.

Therefore, I view transplants that require life long immune suppression to be trading one drug treatment (T1D) for another (Immune Suppression), and not a cure.  At this point, I'm not even sure which is more dangerous, more hassle, and has more side effects.

Of course, in medicine, things change over time.  In the future immune suppression may become easier and safer and I might revisit this decision, but until then: Transplants Requiring Immune Suppression Are Not Cures.

A Note About Naming Drugs

In the medical world, it is common to capitalize the name brand of drugs, like Olumiant, but not capitalize the generic names for drugs, like baricitinib.  I have never understood this.  To me, they are both proper names of specific things, and both should be capitalized.  Therefore, I do capitalize both.

Organizations of Researchers


This study is part of the INNODIA and T1DUK networks.  INNODIA is a European collection of research universities, commercial companies, and patient organizations aimed at fighting T1D.  Their research goal is "to advance in a decisive way how to predict, stage, evaluate and prevent the onset and progression of type 1 diabetes (T1D)."  T1DUK is a UK collection of research universities focused on Immunotherapy research.  Its principal aim is "To help get immune therapy into the market as part of the management for type 1 diabetes".  Both networks are funded by JDRF.  T1DUK is also funded by Diabetes UK (and others) while INNODIA is funded also funded by the Helmsley Trust (and others).