Time to Diabetes By Number of Antibodies

This blog posting is not about research into a cure directly.  However, it answers an important question that I often see people asking:

My child (who does not have type-1) was tested for autoantibodies,

 and was found to have X of them. What does that mean?

Will he or she get type-1, and if so, when?

To put it bluntly, the best data I have ever seen on this question comes from TrialNet, and is wonderfully summarized on the slide below (which I did not create!):

I know this slide is a dense with information, so I discuss it below.
Survival Distribution Function is a (very poor) way of saying "% of people who don't have type-1".
Strata refers to the number of autoantibodies detected.

Basically, each line above represents a group of people with a different number of autoantibodies.  The top line represents people with zero autoantibodies when first tested, the second line from the top represents the group of people who had one autoantibody when first tested, and so on.  When first tested, none of the people in the group had been diagnosed with type-1 diabetes, so all the lines start at 1.0, meaning that zero percentage of the people in the group had type-1 diabetes.

However, as time progresses, some of the people in the group are diagnosed with type-1 diabetes, and so the lines drop as that happens.  So 0.8 on the left side means 20% of the group has type-1 diabetes.  As time passes, the lines move from left to right, so you can see on the bottom as one year goes by, two years etec.  Because of the small number of patients, and odd behaviors out at 8 years, I don't think I would use the 8 year data, but you can see how things change from 0 to 7 years.

So what does all this mean?

Let's assume that your child does not have type-1 diabetes, but tested positive for 1 autoantibody.  That is the solid purple line.  If your child is statistically average, then the chance that he or she will be diagnosed with type-1 diabetes after two years is very small.  Eyeballing the purple line at 2 years, it looks like it is maybe at 0.97; so the chance is 3%.   Even if you look out to 7 years, the line looks to me to be around 0.9, so that means a 10% chance after 7 years.

Unfortunately, the news is a little different if your child has four autoantibodies.  Those kids have a 50/50 chance of being diagnosed within the next two years, and by 6 years, the chance looks to be about 80%.  (Using the bottom most broken brown line of data.)

Discussion

First, no one can tell you if your child is going to get type-1 diabetes or not, and no one can tell you when it will happen.  This data is all about the percentage chance that someone will be diagnosed.

Second, in my opinion, this is the best data available, certainly for people who live in the USA. However, it is only one study, and it would be nice to compare it to data from other studies, and especially from other countries.  This slide reports on almost 25,000 people, and the study is constantly enrolling more people, and will report on them in future years.

Third, the numbers shown above are what happens naturally, without any attempt to prevent type-1 diabetes. So therefore, if researchers claim a treatment prevents type-1, they must provide data better than the data shown above. For example, if someone tells you, "My kid had all 4 autoantibodies, but I gave the drug X (or holistic, all natural, superfood Y), for a year, and that prevented their type-1 diabetes".   Then you can look on the table above, and see that only 30% of 4 autoantibody patients were diagnosed in the first year, just by chance.  So drug X or superfood Y should not be getting credit as a prevention.

Fourth, more generally, this data shows the importance of testing preventative treatments on large groups of people.  In one sense, not having type-1 diabetes is the normal situation, even people who have many autoantibodies.  If you follow them for a year or two, most will not get type-1 diabetes.  Statements like "Given drug Z, even people who had 3 and 4 autoantibodies did not come down with type-1 in the year they took it" are meaningless, because even without drug Z, most people would not be diagnosed in that time period.

I consider this a very important posting, because I know that some people are very scared and nervous after they find out how many autoantibodies their children have.  I want to encourage people to repost this blog entry, tweet it's URL, include it in newsletters, and generally to redistribute it (with credit, and in it's entirety) to anyone and in any way.  The question of "what do autoantibody counts actually mean", has been vexing us for years, and I think the slide above is the best answer I've ever seen.

I want to particularly thank the researcher who presented this slide, and also TrialNet, for collecting the data.
 
Joshua Levy -- http://cureresearch4type1diabetes.blogspot.com
publicjoshualevy at gmail dot com
All the views expressed here are those of Joshua Levy, and nothing here is official JDRF, JDCA, or Tidepool news, views, policies or opinions. My daughter has type-1 diabetes and participates in clinical trials, which might be discussed here. My blog contains a more complete non-conflict of interest statement. Thanks to everyone who helps with the blog.

The Value of a few Beta Cells

This blog post is about type-1 diabetes research, but not research aimed at a cure.

The Value of a few Beta Cells

A recently published study, which you can read about here:
http://diabetes.diabetesjournals.org/content/early/2013/09./23/db13-0881.abstract.html?papetoc
found that type-1 diabetics who were long out of the honeymoon phase and generated their own insulin had fewer long term side effects than those who did not.  This was true even if the amount of generated insulin was tiny.  I had always assumed this was true, so it is not a surprising finding.  However it is important, because in the past studies have shown that people over a certain level (0.2 nmol/l) benefited, but only about 10% of type-1 diabetics generated that much insulin.  What was found in this study, was that even people who generated 0.15 nmol/l were better off than people who generated 0.10 nmol/l, and so on.

Specifically, they found the effect was "linear" down to even the smallest amount of insulin they could measure.  This is very good.  It means that no matter how little insulin you generate, if you generate a little more, it is better for you.  The example in the abstract was that going from 0.10 to 0.15 nmol/l, resulted in an 8.2% drop in serious low blood sugar events, and a 25% drop in serious eye-sight issues.  Obviously, more type-1 diabetics have these lower insulin levels, so this study covers more real people.

Why Does This Matter?

Over the last few years there have been several trials, which have raised the amount of insulin a person generates by similar amounts, which have been measured for months or years:

0.15 nmol/l    Zhao's Stem Cell Educator
0.16 nmol/l    Marek-Trzonkowska's Polyclonal Tregs
0.60 nmol/l    Herold's Teplizumab
(Numbers are measured under different circumstances, so should not be compared directly.  But you get the idea.  There are several different treatments which are all giving these kinds of results.)

This study shows the benefit of these treatments as they exist right now.  This can serve four important purposes:

First, it gives these clinical trials a benefit to the people participating, which makes it easier to recruit.  Right now, a researcher can say, we hope that your insulin dose might go down 2%.  And the patient (or their parents) say "who cares".  But if the researcher can say, "if successful you might have a 25% lower chance of blindness, or an 8% lower chance of serious low blood sugar, or something similar for kidney failure" (or all of these things).  That's a clear benefit.

Second, it gives insurance companies a reason to pay for these treatments (if/when the FDA approves them).  Insurance companies know how expensive blindness is, and the same for kidney failure, and trips to the hospital for low blood sugar.  They can do the math for every treatment, and this will encourage them to pay for the treatments.

Third, it gives a quicker, shorter path to a benefit.  Although a cure remains the same distance that it always was, this study suggests intermediate benefits might be seen much earlier in the process.

Fourth, it suggests that curing type-1 diabetes might be a little easier than our worst case worries. The worst case for curing type-1 is that we will need to stop the autoimmune attack and then regenerate all the beta cells that have been lost.  This research suggests that once the autoimmune attack is stopped, that maybe the body's own beta cells will regrow, or maybe they will only need a little prodding to regrow.  (This study does not provide any direct evidence that beta cells will regrow without help, but it does provide a little hope in that area.)

Related News

This study is not the only recent study showing that even long term type-1 diabetics generate some of their own insulin.  The link below is to a different study, which also found some natural production of insulin in established type-1s.  I think the link above is more interesting, because it went a step farther: it connected the small amounts of insulin to fewer long term complications, and fewer low blood sugar episodes.  But the one below also adds support to the idea that tiny amounts of insulin are produced in many type-1 diabetics:

http://www.sciencedaily.com/releases/2013/10/131009213816.htm?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+sciencedaily%2Ftop_news%2Ftop_health+%28ScienceDaily%3A+Top+News+--+Top+Health%29That is a

(If you are reading this on Brave Buddies, you notice this is the study that Lynn asked about a few days ago.)

A Quick History of Measuring Insulin Production

In order to measure insulin production, researchers actually measure C-peptide, because it is made when your body makes insulin, but is not part of injected insulin, so you can tell the difference between generated insulin and injected insulin, by measuring C-peptide.  In the past, about 10 years ago, the smallest amount of C-peptide that could be measured was 0.2 nmol/l.  Many studies found that about 10% of type-1s generated that much insulin, or slightly more, and 90% did not.  The tests reported no C-peptide, but everyone knew that it meant less than 0.2.

Meanwhile, pathologists looking at the pancreases of long term diabetics who had died, often found tiny, tiny numbers of beta-cells.  So they suggested that even long term diabetics generated a tiny, tiny amount of insulin.  But there was argument about this, and no way to know.  (Many researchers felt that the high BG numbers of a type-1 would prevent those beta cells from working, even if they did exist, for example.)

However several years ago, some researchers (in Europe, I think) created a lab test that could measure C-peptide amounts as low as 0.003 nmol/l.  As is the nature of these things, when it first came out, very few people knew about it, I suspect it was expensive, and no one was sure it actually worked.  (How do you test a test that is so much more sensitive than other common tests?) Anyway, over time the test became more widely known, I suspect it became cheaper, and researchers became more confident that it was actually as sensitive as claimed.

So now, we are starting to see papers that use this much more sensitive test to look at long term type-1 diabetics.  It is becoming pretty clear, by multiple studies, that if you look carefully enough, many more type-1 diabetics are generating a very little of their own insulin.

And now for something completely different.....My Involvement With Tidepool

Tidepool is a non-profit which is developing open source software to help use, manage, store, analyze, and communicate blood glucose data. This would be especially useful between different devices, over wireless networks, and on the web.  You can read more about it here:

http://tidepool.org/  

Howard Look is the prime mover behind it, and Dr. Adi (of UCSF) is also involved.  For those familiar with venture capital,  Bryan Roberts, who is a partner at Venrock is on the board of directors.

It is my hope that Tidepool will do two things:
1. Make it easier for companies (especially start ups and hobbyists) to create useful BG tools for type-1 diabetics.  I hope this will lead to more and different types of tools; things we can not even imagine now.
2. Make it easier for researchers to run experiments on new hardware and software.  Right now, in many cases, before you even start such an experiment, you need to duct tape cell phones to CGM devices, yourself, and write special purpose software, and so on.  I'm hoping the free software that Tidepool produces will help these researchers focus on their research, and not cobbling together software.

So in my professional capacity as a software engineer, I will be doing volunteer work for them.  It is certainly possible that in the future I will report on artificial pancreas clinical trails that use Tidepool software.  At least I hope that I do.

Joshua Levy -- http://cureresearch4type1diabetes.blogspot.com
publicjoshualevy at gmail dot com 
All the views expressed here are those of Joshua Levy, and nothing here is official JDRF, JDCA, or Tidepool news, views, policies or opinions. My daughter has type-1 diabetes and participates in clinical trials, which might be discussed here. My blog contains a more complete non-conflict of interest statement. Thanks to everyone who helps with the blog.

JDRF Funding for a Cure 2013

In the US, we are starting the "Walking Season" when JDRF asks us to walk to raise money for a cure. So I'd like to do my part, by reminding you all how important JDRF is to the human trials of potential cures for type-1 diabetes, which I track.

Let me give you the punch line up front: 65% of the treatments currently in human trials have been funded by JDRF. (And the number is 73% for the later phase trials) This is a strong impact; one that any non-profit should be proud of.  This summary does not include Artificial Pancreas research or stem cell growth trials.

Below is a list of all the potential cures, grouped by phase of trial that they are currently in, and separated into potential cures that JDRF has funded, and potential cures that JDRF has never funded.

The list is a list of treatments, and many of these are being tested in more than one clinical trial.  For example, the "ATG and autotransplant" treatment is actually running three trials, but since they are all testing the same treatment, it is only one item in the list.  The list below uses the following marks to show the nature of the treatments:
    (Established) One or more trials are open to people who have had type-1 diabetes for over a year.
    (Prevention) This treatment is aimed at preventing type-1 diabetes, not curing it.

Also remember that I give an organization credit for funding a treatment if they funded it at any point in development; I don't limit it to the current trial.  For example, JDRF is not funding the current trials for AAT, but they did fund earlier research into it, which helped it grow into human trials.  I include indirect funding of various kinds.  For example, the JDRF funds nPOD and helps to fund ITN and several other organizations, so I include research done by these other groups as well, as being indirectly JDRF funded.

Cures in Phase-III Human Trials
Summary: there are no treatments aimed at curing type-1 diabetes which are in phase-III trials   (under the definition of cure that I use).

In the past, I have listed DiaPep277 here, but their most recent results make it pretty clear that they are seeing a treatment result, not a cure result.  So I removed it from the list this year.  Also there are two studies being done on Cyclosporine and Lansoprazole ("Prevacid") as a combination treatment.  Those trials are filed with the FDA, but have not started recruiting patients, so they are not listed here, either.  Maybe next year.

Cures in Phase-II Human Trials
Summary: there are a total of 15: 11 of them have been funded by JDRF, and 4 have not. Here are the treatments that have been funded by JDRF:

• Abatacept by Orban at Joslin Diabetes Center
• Aldesleukin (Proleukin) at Addenbrooke’s Hospital, Cambridge, UK
• Diabecell by Living Cell Technologies  (Established)
• Diamyd, Ibuprofen ("Advil") and Vitamin D by Ludvigsson at Linköping University
• Oral Insulin (Preventative)  
• Rituximab by Pescovitz at Indiana
• Sitagliptin and Lansoprazole at Sanford Health
• Stem Cell Educator by Zhao (Established)
• Teplizumab (AbATE study team)
• Umbilical Cord Blood Infusion by Haller at University of Florida
• Xoma 52 by Xoma Corp  (Established)

Not funded by JDRF:

• ATG and autotransplant by Burt, and also Snarski, and also Li
• Atorvastatin (Lipitor) by Willi at Children's Hospital of Philadelphia
• Brod at University of Texas-Health Science Center
• Vitamin D by Stephens at Nationwide Children's Hospital  (Prevention)

Cures in Phase-I Human Trials
Summary: there are a total of 22: 13 of them are funded by JDRF and 9 are not. Here is the list funded by JDRF:

• Alefacept by TrialNet
• AAT (Alpha-1 Antitrypsin) by OmniBio and also Kamada 
• ATG and GCSF by Haller at University of Florida  (Established)
• TOL-3021 by Bayhill Theraputics   (Established)
• CGSF by Haller at University of Florida
• Trucco at Children’s Hospital of Pittsburgh   (Established)
• IBC-VS01 by Orban at Joslin Diabetes Center
• Leptin by Garg at University of Texas
• Nasal insulin by Harrison at Melbourne Health
• Polyclonal Tregs by both Trzonkowski and Gitelman 
• Pro insulin peptide by Dayan at Cardiff University
• Proleukin and Rapamune by Greenbaum at Benaroya Research Institute   (Established)
• Lisofylline by DiaKine

Not funded by JDRF:

• BCG by Faustman at MGH  (Established)
• CGSF and autotransplant by Esmatjes at Hospital Clinic of Barcelona  (Established)
• Encapsulated Islets at University clinical Hospital Saint-Luc   (Established)
• Etanercept (ENBREL) by Quattrin at University at Buffalo School of Medicine
• GABA by Lunsford at the University of Alabama at Birmingham.
• Monolayer Cellular Device  (Established)
• Rilonacept by White at University of Texas
• The Sydney Project, Encapsulated Stem Cells  (Established) 
• Pioglitazone by Wilson at Stony Brook 

Summary of all Trials
37 in total
24 funded by JDRF
So 65% of the human trials currently underway are funded (either directly or indirectly) by JDRF. Everyone who donates to JDRF should be proud of this huge impact; and everyone who works for JDRF or volunteers for it, should be doubly proud.

Just Looking at Trials on Established Type-1 Diabetics
11 of these treatments (29%) are being tested on established type-1 diabetics.
Of these, 6 are funded by JDRF
So 55% of the trials recruiting established type-1 diabetics are funded by JDRF.

Compared to Last Year
In 2012 there were 38 treatments in clinical trials, in 2013 there are 37 (drop of 3%)
In 2012 there was 1 treatment in Phase-III trials, in 2013 there are none (drop of 100%).
In 2012 there were 14 treatments in Phase-II trials, in 2013 there are 15  (growth of 7%).
In 2012 there were 23 treatments in Phase-I trials, in 2013 there are 22 (drop of 4%).

How I Count Trials for This Comparison

• I give an organization credit for funding a cure if it funded that cure at any point in it's development cycle.
• I mark the start of a research trial when the researchers start recruiting patients (and if there is any uncertainty, when the first patient is dosed).  Some researchers talk about starting a trial when they submit the paper work, which is usually months earlier.
• For trials which use combinations of two or more different treatments, I give funding credit, if the organization in the past funded any component of a combination treatment, or if they are funding the current combined treatment. Also, I list experiments separately if they use at least one different drug.
• The ITN (Immune Tolerance Network) has JDRF as a major funder, so I count ITN as indirect JDRF funding.
• I have made no attempt to find out how much funding different organizations gave to different research. This would be next to impossible for long research programs, anyway.
• Funding of research is not my primary interest, so I don't spend a lot of time tracking down details in this area. I might be wrong on details.
• I use the term "US Gov" for all the different branches and organizations within the United States of America's federal government (so includes NIDDK, NIAID, NICHD, etc.)
• I don't work for the US Gov, JDRF, or any of the other organizations discussed here.  I have a more complete non-conflict of interest statement on my web site.

This is an update and extension to blog postings that I've made for the previous five years:
http://cureresearch4type1diabetes.blogspot.com/2012/09/jdrf-funding-for-cure-2012.html
http://cureresearch4type1diabetes.blogspot.com/2011/10/jdrf-funding-research-for-cure-2011.html
http://cureresearch4type1diabetes.blogspot.com/2010/09/jdrf-funding-research-for-cure-2010.html
http://cureresearch4type1diabetes.blogspot.com/2009/09/jdrf-funding-research-for-cure.html
http://cureresearch4type1diabetes.blogspot.com/2008/10/jdrf-funding-of-cure-research-phases-ii.html

Finally, please remember that my blog (and therefore this posting) covers research aimed a curing or preventing type-1 diabetes that is currently being tested in humans.  There is a lot more research going on, not covered here.

Please think of this posting as being my personal  "thank you" note to all the JDRF staff, volunteers, and everyone who donates money to research a cure for type-1 diabetes:

Thank You!

Finally, if you see any mistakes or oversights in this posting, please tell me!  There is a lot of information packed into this small posting, and I've made mistakes in the past.

Joshua Levy -- http://cureresearch4type1diabetes.blogspot.com
publicjoshualevy at gmail dot com 
All the views expressed here are those of Joshua Levy, and nothing here is official JDRF or JDCA news, views, policies or opinions. My daughter has type-1 diabetes and participates in clinical trials, which might be discussed here. My blog contains a more complete non-conflict of interest statement. Thanks to everyone who helps with the blog.