In today͛s edition, I will describe a project that I was involved with, in a minor role. It has been an on-going
debate in the field whether immune responses against the insulin molecule itself are the driving force behind beta cell destruction. George Eisenbarth͛s laboratory put this question to the test in a 2005 report which
demonstrated that one key mutation to the insulin protein abrogated autoimmunity in NOD mice. Whether
this paradigm applies to human T1D has remained an open question. Addressing this question in human
samples has been a major challenge, because while determining individual risk of developing T1D is possible,
predicting when an individual will develop T1D is not straight-forward.
In addition, CD4 T cells are relatively rare in the peripheral blood. To address this question in human subjects therefore requires relatively large volumes. As a caveat, obtaining pancreas or pancreatic lymph node biopsies is not feasible in most cases from human patients, limiting our analyses to peripheral blood. These logistic
challenges restrict the number of clinical institutions capable of executing these experiments very short. Such institutions include the Barbara Davis Center in Denver, the Benaroya Institute in Seattle, the Joslin Center in
Boston, and the University of Florida. Perhaps a handful of others.
Lastly, the human HLA DQ8 molecule, which is very unstable chemically, is the HLA molecule most closely
related to the mouse MHC molecule I-Ag7. These MHC alleles are most closely associated with T1D onset –
presumably through their ability to load unique peptide sequences and activate CD4 T cell responses. Since
insulin is the hypothesized key target of CD4 T cells during diabetes pathogenesis, making a peptide-MHC
tetramer reagent for insulin loaded in DQ8 is a key road block. Enter into this conversation the laboratory of Dr. Brian Fife at the University of Minnesota and his post-
doctoral trainee Dr Justin Spanier. Dr Fife has been interested in the peripheral regulation of autoreactive CD4 T cells since his post-doctoral work with Dr Jeff Bluestone at
UCSF and has been on the faculty of the University of Minnesota since 2008. Dr Spanier is a trained biochemist and learned to make peptide-MHC tetramers for this study, including the DQ8-insulin reagent described
above. These authors quantified and characterized autoreactive insulin-specific CD4 T cells in the peripheral
blood of people with T1D or control subjects. To summarize, Dr Fife and Spanier found that shorter disease
duration correlated with the presence of more insulin-specific CD4 T cells in the peripheral blood, which suggests that insulin-specific CD4 T cell number ͞peaks͟ around the time of diabetes onset, and then decreases as beta cells are destroyed and progressively fewer target cells remain. Dr Spanier also found that
insulin-specific CD4 T cells tended to have an ͚effector memory͛ phenotype in these individuals, suggesting
recent antigen experience, or that these CD4 T cells had recently fulfilled their function in promoting beta cell destruction.
Dr͛s Fife and Spanier then collaborated with Dr Aaron Michels at the Barbara Davis Center to determine
insulin-specific autoantibody production in these individuals. Reminder that insulin-specific autoantibody is a marker of disease but does not appear to be directly pathogenic to beta cells. In contrast, the current
understanding in the field is that T cells are the perpetrator of beta cell death (see figure 1 of this recent
review about beta cell destruction from the Fife lab. Regardless, this quantification is key,
because self-tolerance to insulin is lost among both T cells and B cells during the development of T1D. The
question of which response occurs first and ͚helps the other along͛ is key to the future design of therapies to
perturb this disease process and prevent T1D onset. Work with Dr Michels demonstrated a direct correlation between the number of insulin-specific CD4 T cells in the blood and insulin autoantibody titers. This means the more insulin-specific CD4 T cells detected, the higher than anti-insulin antibody levels. This suggests a
functional relationship between autoreactive T cells and B cells. Since CD4 T cells ͞help͟ B cells to mature,
class-switch, and produce antibodies within germinal centers of lymph nodes, these results suggest CD4 T cell responses to insulin are
critical for promoting B cell responses and subsequent beta cell destruction. An important note is that CD4 T
cells are also key for activating CD8 T cell (or ͞killer͟ T cell) responses. As such, due to this critical role for CD4
T cells in activating both cell-mediated and humoral immunity, an interpretation of these results is that
depletion or inhibition of CD4 T cells specific for insulin might delay or prevent T1D in at-risk individuals. In
other words, without insulin-specific CD4 T cell help it is unlikely that human T1D would develop. This report
demonstrates the ability to detect insulin-specific CD4 T cells in the blood of recent-onset T1D patients and
that the number of these cells correlates with insulin autoantibody levels.
Perturbing the development and function of insulin-reactive CD4 T cells in at-risk individuals is the next
frontier of this type of immune-profiling research. There are a number of on-going clinical trials which have
the potential to affect these cells at clinicaltrials.gov. If you or a loved one live with T1D, please contact your
Congressional representatives and express your strong support for the Special Diabetes Program. To continue to move clinical
care forward, as a society we need to make evidence-based decisions, decisions based on scientific research.

​by Adam Burrack, PhD

Today I have the privilege of describing research I performed. A fundamental problem facing scientists seeking to cure type 1 diabetes (T1D) through beta cell replacement is the immune response to “foreign” tissues following transplantation. Ironically, the genes with the highest diversity in the human genome (“polymorphic”) are related to the activation of T cells of the immune system. Long story short, T cells recognize these genetic differences between proteins very effectively, and these differences promote very strong immune responses. Unfortunately, T cell responses against these differences resist tolerance-promoted therapies and must be suppressed for the lifetime of transplant recipients. These lifelong immune suppressive therapies limit wide application of organ transplantation to cure kidney failure or hypoglycemia unawareness in people with T1D.

Here is where details matter, a lot. The molecule we think T cells are targeting is the Major Histocompatibility Complex – MHC – which shows or presents T cells peptides derived from viruses, bacteria, or other pathogens. MHC is the most polymorphic gene in the human genome, so any transplant from anyone other than your identical twin (if you have one) will be targeted by your immune system. But it gets worse! In theory, if we understood precisely what the immune system is targeting during these responses, scientists would have a chance to develop target-specific therapies to promote specific tolerance, leaving the rest of the immune system intact. We think, as immunology researchers, that MHC is the key target of the immune response following transplantation. But MHC only gets to the surface of cells if it’s loaded with a peptide. So the question for transplant response is this; what are the peptides loaded in donor MHC? Is there a common peptide that loaded in any donor MHC would promote an immune response in transplant recipients?

Enter into this context the laboratory of Dr. Marc Jenkins. For the past 25 years, Dr Jenkins’ laboratory has pioneered and popularized the use of a research tool called peptide-MHC tetramers to study CD4 T cell biology also called helper T cells, in a variety of biological settings including vaccination studies, infectious disease, autoimmunity, and now transplantation. Peptide MHC tetramers are a method to study very specific set of T cells which are specific for particular peptides. For example, a vaccine is intended to expand virus-specific T cells and peptide-MHC tetramers are a great method to track that expansion. Using a machine called a flow cytometer immunologists can quantify both the expansion of peptide-specific T cells (using peptide-MHC tetramers) as well as determine the behavior of the cells by analyzing cell surface proteins characteristic of various types of activation. These tools and techniques give researchers useful information to help determine (a) the presence, (b) the expansion, and (c) the effector type of T cells in various normal biology and pathologic conditions.

So, given this context, I was trying to determine ‘what CD4 T cells see’ which precipitates transplant rejection, in general. For this study we used a skin transplantation model to study organ rejection in general. We were not addressing autoimmunity against the transplant, which would also be in play in a T1D recipient of beta cells. The reagents we used in this study were peptide-MHC tetramers specific for the MHC of the transplant donor – which would be foreign to the recipient – loaded with peptides derived from cells called dendritic cells. Dendritic cells are key antigen-presenting cells which interact with T cells to influence immune responses. A long-standing hypothesis in basic immunology is that “passenger leukocytes” from the transplant – ie, dendritic cells – are a key target promoting recipient T cell responses. We tested this premise in our paper. Following transplantation of skin, donor dendritic cells travel to lymph nodes or the spleen of the recipient and interact with recipient T cells. This promotes a massive immune response leading to transplant rejection. We made four key discoveries. First, several peptides that are expressed on the surface of dendritic cells and loaded in donor MHC promote CD4 T cell responses in transplant recipients; no individual response dominated, it was a lot of separate T cell responses all happening in parallel. Second, both the MHC of the donor and the specific peptides are required as targets for a specific set of T cells. We showed this using MHC knockout or over-expression models, as well as peptide knockout and over-expression molecules. Third, these individual sets of transplant-reactive T cells are much smaller than sets of T cells elicited by immunization protocols; the very large overall immune response to transplants is a consequence of many of these responses happening in parallel. Fourth, transplant-reactive T cells appear to have a characterize effector type, or phenotype, called Th1. This effector type is also associated with autoimmunity and responses against viruses. The other possibility was an effector type called Th17 which are critical for responses against fungal infections. Our results clearly demonstrated transplant-reactive T cells were not of the Th17 type.

Understanding targets of the immune response which leads to transplant rejection gets us, as a field, one step closer to being able to rationally develop therapies to modulate these immune responses. Combined with our ever-increasing knowledge of autoantigens targeted in T1D onset, we now have two separate, parallel immune responses to modulate in future attempts to prevent rejection of replacement beta cells in individuals with long-standing T1D.

These measures create a more complete clinical picture of diabetes management, but there remains work to be done. If these outcomes are to be applied consistently to patient management, their
measurement needs to be standardized by physicians. Further, more research is needed to establish how these ͞new͟ measures affect the health outcomes of the patient.
The reality is that managing type 1 diabetes is challenging and to summarize disease management with a single outcome/number does a disservice to the patients and the doctor. Establishing these measures as appropriate clinical outcomes will serve to broaden the understanding of managing a complex and dynamic disease.
Read the whole consensus statement here.

by Adam Burrack, PhD

Previously in our series, we have described the exercise science of maximal performance in terms of oxygen consumption (basically 2-mile race pace) and how imperfect, artificial, control of insulin levels during exercise may negatively affect exercise performance. Too much insulin in the circulation during intense exercise could lead to low blood sugar levels, which can be dangerous in fast-moving sports. Too little insulin in the circulation can lead to high blood sugars – and potentially diabetic ketoacidosis – in long-duration endurance events.

But what about the other side of the blood sugar level thermostat? Glucagon is the antagonist and mirror image of insulin. Since insulin levels can be so out-of-whack in the exercising T1D, how does this effect glucagon levels and the efficacy of glucagon that is produced? Since the main function of glucagon is to increase blood sugar levels, during prolonged high-intensity exercise (think 5k, 10k, or half marathon running races), it would be useful to have a little boost from our internal glucagon to ‘make sure’ our blood sugar levels don’t crash. That is, in fact, the normal sequence of events in non-diabetic individuals in these situations: levels of insulin in the plasma go way down, and levels of glucagon to up. At running paces approaching 75% of VO2 max (approximate half marathon pace for most of us), or faster, this relationship between insulin and glucagon is key for mobilizing fuel to get us to finish line without bonking. In other words, in this situation glucagon is your friend, big time.

It is known that some people with type 1 diabetes will eventually develop hypoglycemia unawareness, a potentially life-threatening condition. This condition is the current clinical prerequisite to go onto the pancreas transplant waiting list. We also know that at least some of the autonomic nerves going into pancreatic islets are destroyed as part of the disease process of type 1 diabetes. These nerves – which likely carry signals both from the pancreatic islets to the brain stem and back from the brain stem to the islets – do not appear to be destroyed during the development of type 2 diabetes. This is one of several key differences between type 1 and type2 diabetes. Today, we will delve into the biology of glucose metabolism during exercise in the individual with type 1 diabetes through the lens of glucagon, the antagonist of insulin.

The lab of Ananda and Rita at the Mayo Clinic in Rochester MN has been studying glucose metabolism for >10 years. This husband-and-wife team have published on a range of topics, including glucagon dynamics in individuals with type 1 diabetes during exercise. Long story short, type 1 diabetic athletes have more insulin and less glucagon in their circulation during steady-state exercise (60% VO2 max for 60-75 minutes) than do non-diabetic individuals. This represents a sort of “double jeopardy” for the T1D athlete at low-to-moderate exercise intensity. Due to injections of insulin (reminder that normally insulin goes essentially straight from the pancreas to the liver, where it directly promotes glycogen production and energy storage), they have way more insulin in their blood during exercise and also significantly less glucagon, creating a situation where they are at very high risk for hypoglycemia. This mirrors empirical results referenced in the JDRF exercise guidelines for T1D athletes. One method around this situation is to consume 30 grams of carbohydrate (or more) per hour of moderate intensity exercise. Another option is to tend toward higher-intensity exercise – with the attendant risk of low blood sugar levels after high-intensity exercise. Regardless of approach, the athlete with T1D being aware of the fundamental problem – and not blaming themselves for poorly managing their during-exercise blood glucose levels – is the point.

Another clinically applicable area the Basu lab is engaged in is development better methods to track glucagon levels and to deliver glucagon in tandem with insulin, in a stable form. To track glucagon this research group is using of labeled-water methods to track metabolism over the course of an in-hospital study. This group is one of several working – in collaboration with industry partners – to determine more stable formulations of glucagon that would be remain useable for up to 5 days in an insulin pump-type device. Finally, the Basu lab is exploring methods of glucagon delivery (ie subcutaneous as insulin is currently delivered via pumps compared to intravenous delivery) that was optimize its physiologic function. Overall, the field has much more experience with making and delivering insulin than glucagon. There is some homework to be done to get glucagon up to speed for a widely applicable dual-hormone replacement system. And there are some clever, hard-working folks working on these challenges.

by Rachel Fenske, BS

I am excited to bring you my second post in a special series for YOUglycemia regarding the complex interactions of type 1 diabetes pathophysiology, diet, and exercise.

Today, I will be reviewing what’s been termed the “Paleo” diet, or “cave-man” diet. The anecdotal indications of the benefits of a paleo diet for T1D management are growing. The paleo diet encourages essentially unlimited consumption of fresh vegetables, lean meats, fish and eggs, and more limited consumption of fruits, nuts, and seeds. Most notably, dairy and grains are prohibited. Prior to the very recent development of commercially available “paleo” snack products, like crackers, tortillas, baked goods, etc., all processed foods were also prohibited on the diet. Thus, a type 1 diabetic following this diet would be consuming a diet high in protein and fat, low in simple starches, while not void of carbohydrates entirely as vegetables would provide a variety of complex starches. These dietary conditions would result in less blood glucose spikes and a general reduction in the volume of daily insulin required, meaning the validity of the anecdotal evidence is somewhat substantiated.

The efficacy of low-carbohydrate diets for individuals with T1D has been evaluated briefly, with a consensus from most studies showing an improvement in blood glucose control and lowering of HbA1c. These improvements are extremely dependent on the level of adherence to the diet. Adherence to the diet is very important due to the dramatic reduction in required insulin. Blood glucose management would become extremely difficult if for one day you decided to switch back to a more standard diet that contained a higher carbohydrate load.

Additional considerations for athletes need to be taken into account as carbohydrates are a crucial source of fuel and are limited on the paleo diet. Primary among these considerations are enhanced insulin sensitivity following exercise and the replenishment of carbohydrate stores on a diet which limits carbohydrate intake. Other studies have attempted to evaluate these concerns for athletes on low carbohydrate diets and have found mixed results dependent on type and duration of exercise. Importantly, results also vary based on how the remaining calories are divided among fats and protein. Low carbohydrate diets themselves are inherently higher in fats and/or protein and this can have a large impact on fuel source availability for an athlete. The complexity of high protein and high fat diets elicits a more thorough evaluation and will be the focus of future posts in this series.

Studies focusing specifically on the “paleo diet” began building in the early 2000s, not all that long ago. One of the earliest proponents and foremost researcher of all things Paleo diet is Dr. Loren Cordain. His interest and research have notably culminated in the popular N.Y. Times Best Seller, “The Paleo Diet”, and more recently “The Paleo Diet – Revised” for athletes. He has worked diligently to synthesize and elaborate on the current body of evidence into a comprehendible, but extensive read. His peer-reviewed work encompasses Paleolithic nutrition and fitness, and has recently focused on the role of milk protein in age-related metabolic diseases.

In addition to the work by Dr. Cordain, some of earliest studies investigating the diet are in healthy volunteers, those with impaired glucose metabolism, or those with type 2 diabetes. Unfortunately, more studies have not strayed from these populations. The data emerging from these studies is mixed (reviewed thoroughly here ); in some cases showing improved blood pressure , insulin sensitivity , and HbA1c , while showing no improvement in others . Markers of inflammation and intestinal permeability, which have been discussed previously in the “Science of Diabetes” series, were shown to be no different, in one study of 24 patients with metabolic syndrome .

Although not directly relevant to type 1 diabetics, these studies of healthy subjects are necessary to establish a baseline effect of the diet and studies of individuals with other metabolic conditions are therefore important to developing a breadth of understanding. From this baseline, researchers could then layer-in studies with T1D subjects and note key differences from healthy controls. To further the research, studies which specifically look at metabolic measures and inflammatory markers in type 1 diabetic patients, as well as subjects with other autoimmune conditions, are necessary to provide a more direct implication of paleo diet on T1D glucose control and other outcomes.

Based on the current body of evidence, it cannot be determined if the paleo diet would be an appropriate therapeutic dietary intervention for individuals with T1D. Far more research is warranted to determine the potential influence such an extreme dietary shift would have on blood glucose management for those with T1D and even more work is required to discern if a paleo diet strategy would be appropriate for an athlete with T1D.

by Adam Burrack, PhD

In this edition of our “science of diabetes” blog series, I will delve a little more deeply into the genetics of type 1 diabetes (T1D) HLA risk alleles. HLA is immunology short-hand for ‘human leukocyte antigen’ and is the molecule through which T cells of the immune system interact with the outside world. There are two different types (molecules) of HLA, one of them interacts with “killer” T cells (which express the surface molecule CD8), while the other type of HLA molecule interacts with “helper” T cells (which as an immunologist I call CD4 T cells). This division of labor is common between our research mice and the human immune system. Nearly every cell of the body has molecules of the type of HLA with which CD8 T cells interact: this is critical for clearing viral infections. In contrast, CD4 T cells interact almost exclusively with other cells of the immune system, directing the activity of other cell types, and through those other cells, the outcome for the infection and the organism as a whole. The critical take-home point for today is that the type of HLA molecule that interacts with CD4 T cells is the highest genetic risk factor for T1D, by far.

Most studies of risk factors focus on Caucasian populations, in particular studies in the US and Scandinavia. These are the largest, by number, groups of individuals with T1D in these countries. However, A T1D diagnosis can be a death sentence to children in sub-Saharan Africa, with 5-year mortality rate at 41% in Tanzania. Other studies, suggesting African subjects with T1D have similar 20-year mortality rates to African-Americans with T1D. Long story short, long-term health outcomes are not great with T1D in this patient population. This may be partly due to insufficient clinical care (eg, rural Africa). However, similar 20-year mortality rates among Africans and African-Americans suggests we have a limited understanding of the disease process in non-Caucasian populations (eg, African Americans), raising the hope that improving our knowledge might greatly enhance public health.

In particular, understanding the genetic factors at play in developing autoimmune disease in non-Caucasian populations is critical for developing more effective diagnostic tools. As a field, we do not understand the interplay of genetics and environment leading to the development of autoimmune diabetes in non-Caucasian populations. This deficit in knowledge makes prediction of T1D very challenging in these populations. More specifically, the role of environment (ie, diet and hygiene hypothesis) begs the question of how genetic risk factors would play out in immigrant populations coming from relatively low-risk locations (eg sub-Saharan Africa) to countries with higher risk (eg the United States or Scandinavia) for reasons which we think are driven primarily by genetics.

Enter into this vacuum of knowledge a new study from the University of Minnesota Hospital investigating the HLA alleles of new-onset T1D Somali immigrant children. Long story short, the Twin Cities in Minnesota have the largest group of Somalian immigrants in the United States. Researchers from the University of Minnesota’s Endocrinology department conducted HLA genotyping on a group of 30 new-onset T1D children who were first generation immigrants. It turns out that >80% of these children had insulin autoantibodies but most of these children expressed a different DR allele than that which is most associated with T1D onset in Caucasian populations. Specifically, a majority of these children expressed a DR3 allele, rather than a DR4 allele. It is unclear from this study how this structural change in HLA type would influence what portions of the insulin molecule are ‘displayed’ to T cells. Meaning, we don’t know if autoreactive T cells are targeting the same peptides in these children as in studies of DR4+ subjects.

An intriguing – untested – possibility is that distinct HLA alleles might present the exact same peptides to T cells. An important point to remember in terms of developing an immune response is that it’s not just the peptide, the T cells have to see any peptide in the context of both “co-stimulation” signals as well as inflammation in order to receive robust signals to seek and destroy the source of that peptide. In my opinion, these data suggest that the environmental signals resulting from the Western diet are strong enough to promote autoimmunity in the presence of the DR3 allele and absence of the DR4 allele in first-generation Somalian immigrant children in the US.

Moving beyond from T1D onset in immigrant populations, there are important steps we – as a society – can take to help families better manage following a T1D diagnosis. A recent study in the journal Pediatric Diabetes found that an overall measure termed “neighborhood disadvantage” correlates with higher levels of inflammation and poor metabolic control in children with T1D. To connect these two articles, one can speculate the immigrant neighborhoods within major metropolitan areas would score relatively high on a measure of ‘neighborhood disadvantage’ (but would by no means be the only neighborhoods that scored highly on this measure). From a public health perspective, there are steps that can be taken to improve care of children with T1D in disadvantaged neighborhoods.

Results such as these suggest we can serve our disadvantaged and immigrant populations more effectively. Education across cultural boundaries may be a major road-block for management of a chronic disease like T1D. Due to insufficient communication between care-giver and patient, the ‘point’ of keeping track of diabetes management details may become lost in translation. Suffice to say in this space that we are a nation of immigrants (by and large). But as Americans we have a similar dream: hope for a better tomorrow for our children. A big part of those of us with T1D living out our dreams is to understand how we can best manage our diabetes.

                  by Adam Burrack, PhD

Sally Kent, member of nPOD and HIRN, has recently published a couple of studies investigating T cells within pancreatic islets. Dr Kent’s laboratory is located at the University of Massachusetts medical school. In addition, Dr Kent was recently interview for a profile feature on the nPOD website. Dr Kent has been working on determining the T cell receptor sequences found within pancreatic islets. In English, that means that Dr Kent’s work is part of determining the beta cell targets that T cells attack. Understanding the targets and how the T cells “see” them is a critical step in understanding how autoimmunity occurs and beta cells are destroyed. Only through understanding this process can researchers and clinicians develop methods to stop (or remove) autoreactive T cells. Only through stopping/removing autoreactive T cells can we either prevent diabetes (in at-risk individuals) or reverse diabetes in diabetic individuals (in combination with some type of beta cell replacement approach).

One of Dr Kent’s recent reports was published in the journal Nature Medicine, another was recently published in the journal Diabetologia. I’ve previously described in our series the concept of tissue resident memory T cells following infections in mice (to over-simplify: as opposed to be previous paradigm in which memory T cells were thought to remain in the spleen and lymph nodes) and how researchers have described a similar phenomenon within the pancreatic islets of T1D subjects. In today’s post I will extend this concept to CD8 T cells, type 1 diabetes, and how T cells within pancreatic islets may play a role in accelerating the autoimmune disease process.

Firstly, the Nature Medicine paper establishes several observations key for our evolving understanding of how T1D occurs. The UMass press release about this article gives a good overview. The take-home point from this huge body of work is that Dr Kent’s laboratory was able to isolate more than 200 unique T cell lines from islets harvested from people with T1D. Some of these T cells respond to known ‘autoantigen’ targets, some respond to recently described ‘hybrid peptides’, and some T cell lines may further inform our understanding of the targets that are ‘in play’ during development of autoimmunity.

Next, the Diabetologia paper lays out some evidence that – while not definitive – suggest T cell/B cell collaboration within pancreatic islets. T cells and B cells develop close physical interactions and “educate” each other within lymph nodes in structures called germinal centers. Areas of T cell/B cell interactions are called germinal centers and they are critical to promoting antibody production. As our readers will recall, antibodies against insulin and other beta cell-derived targets are required for T1D diagnosis. Data from this manuscript suggests that pancreas-draining lymph nodes (PLN) have a ‘disorganized’ structure around the time of diabetes onset, but that PLN samples from longer duration T1D subjects do not demonstrate these abnormalities. This suggests some perturbations in normal lymph node architecture during T1D pathogenesis. The significance of this finding is not immediately clear, but again points toward the action in the pancreas itself as a critical location for future study.

Together, as Sally Kent argued in a recent review article these papers suggest that looking “where the action is”, within the pancreatic lymph node and within pancreatic islets themselves, is a very useful strategy when trying to develop a better understanding of how T cells of the immune system kill insulin-producing beta cells. Peripheral blood samples tell us some broad information, but looking where the immune response is primed (lymph node) and where it occurs (pancreatic islets), tells us much more.

To learn more about organ donation to nPOD, see their website

by Adam Burrack, PhD

A fundamental problem with islet replacement therapy as a cure for type 1 diabetes is the presence of immune cells that will specifically target and destroy insulin-producing beta cells. These cells are descendants of the T cells that destroyed beta cells in the first place, causing disease, and are present in all individuals with T1D.

So, how would you avoid these immune memory T cells in a transplant scenario? As I’ve previously described, the current clinical approach is a bit of a blunt tool: broad immune suppression, which must be continued for the life of the patient and also leaves one more vulnerable to viral infections and cancer. This is not an ideal solution and a significant amount of thought and effort has been poured into developing better solutions for specifically targeting autoreactive T cells. Unfortunately these efforts have so far been largely unsuccessful. Therefore, immune suppression remains the standard-of-care and islet replacement therapy is limited to patients with very poor blood sugar level control and/or additional debilitating complications such as kidney failure.

Due to the lack of success of the “un-subtle” approach of broad immune suppression, let’s take a journey into the thought process of trying to improve immune tolerance-promoting therapies. One way to begin to address this question is by determining where in the body immune memory is located – and then to consider how those memory T cells would respond to their antigen on secondary exposure. Traditionally, memory T cells have been thought to be located primarily within the spleen. In addition, as I’ve described previously, it has been known for about 2 decades that the initial “priming” events triggering beta cell-specific autoimmunity occur in the pancreas-draining lymph node. So, where else might autoreactive T cells hang out?

Models of immune responses to viral infections indicate that memory T cells also accumulate in the tissues of the body, in particular including the liver and kidneys. These organs serve general de-toxification (liver) and filtering (kidneys) functions, in addition to immune-specific roles that I will not delve into. As one might also suspect, memory T cells can accumulate in the tissues/organs that are infected by a virus. Dr. Dave Masopust at the University of Minnesota has done some very interesting, recent work in this area of virology and immunology research. This “resident memory” or “seeding with memory” approach facilitates a rapid T cell response upon re-infection. Taking this a step further and moving back into the autoimmunity field, one might suspect that autoreactive T cells reside not only within the spleen following onset of T1D, but also possibly within the pancreas itself. Evidence for this concept had been lacking, until two very recent papers.

Two concurrent papers published in March issues of Clinical and Experimental Immunology and the American Journal of Pathology demonstrate that autoreactive T cells accumulate and establish residence within the pancreas of subjects with T1D. This work was conducted in Sweden and Norway, using a collection methodology similar to the JDRF-sponsored network for pancreatic organ donation. In the Scandinavian countries, researchers are able to keep a little “closer tracking” of subjects with T1D due to more centralized record-keeping and other differences between the infrastructure of health care systems. As such, their health care systems facilitate these types of detailed epidemiological analysis.

Interestingly, the resident T cells in the Scandinavian studies were predominantly CD8⁺ T cells, corroborating work from Matthias von Herrath’s group. In addition, Dr von Herrath and colleagues have characterized T cell infiltration within the exocrine pancreas, which when interpreted in light of the “resident memory” observations, may constitute a set of memory T cells prepared to respond to nascent insulin production by producing effector molecules and mediating death of newly-developing insulin producing cell types.

In summary, resident memory T cells within the pancreas of a long-term autoimmune diabetic may represent an important population of autoreactive T cells that need to be inhibited or deleted in order for an islet-replacement strategy to be successful in the clinic. In English, these T cells are clinically relevant and must be dealt with therapeutically.

by Josh Boyer, MS

In this piece I will go over what in my opinion is one of the greatest recent developments in diabetes therapy, closed-loop systems.  With the release of the Minimed 670G system, I would guess that many people are aware of these devices and curious to learn more about what it offers.  Side-note, be prepared for a lot of new devices on the market in the next two years as there are seven additional clinical trials either underway or recruiting.  To me this is great news as I always like to have choices in what I buy. Today I will go over the general concepts behind the operation of most of the current closed-loop systems and any differences between the systems, results from the clinical trials and what in my perspective this means for diabetes management.

As Adam mentioned in previous blogs the closed-loop system uses algorithms to dose insulin based on input from a glucose sensor. The goal is to have a system that adjusts the insulin basal rate supplied by the pump in response to glucose data transmitted by the sensor.  As many pump users are likely aware, fine tuning a basal rate takes a lot of practice and any alterations of food, activity, stress, etc. in your day can have profound effects.  Using one flat rate throughout the day isn’t ideal to address all of these variations. Neither is always changing your basal rate based on your best guess as it is difficult to then determine what the impact on your BG is due to your basal and what is due to boluses (personal note; early on I was micromanaging my basal rates and ended up running 10 different ones during 24 hrs; this didn’t work at all).  This is the main advantage to the closed-loop systems.  The basal rate is not a set unit/hour rate but subject to adjustment by the sensor input.  The amount of insulin to be delivered is determined by the algorithm developed for the pump/sensor combination.

These algorithms fall into three main groups; Proportional-integral-derivative control (PID), model predictive control (MPC), and fuzzy logic (FL). Each algorithm involves complex systems of detection (BG reading), correction (insulin delivery) and feedback on the effect of the correction. I will revisit these in a future post to go more in depth on their strengths and weaknesses.

The advantage to the sensor feedback control of basal rates becomes apparent in the data from the multiple clinical trials that have been performed on various closed-loop systems. I will focus on two studies, the Minimed 670G and Insulet iLet, to highlight the functionality of close-loop systems.  However, all recent trials using close-loop systems have shown promise in improving the time spent by patients in the euglycemic BG range.  The Minimed and iLet trials use similar test methods by having the same participants on both normal glucose management techniques and closed-loop system management so that each person can be their own internal control. The Minimed trial ran everyone on the normal management first followed by the closed-loop system. The iLet trial randomized participants into two groups; 1) Normal pump + CGM treatment followed by close-loop system or 2) close-loop system followed by normal pump + CGM treatment.  Both groups have a rest period (sometimes called wash-out period in clinical trials of drugs) of 3+ days between treatment strategies. Both studies then compared Data within each patient between their time on normal therapy and time on the closed-loop system. Closed-loop system trials generally involve two phases; a Hotel study where participants were closely monitored and had to follow a set schedule, diet and exercise regimen and a Home study where they were allowed to follow their daily routines. Recruited subjects in both studies had a median age around 35 and been diagnosed with diabetes for at least one year. I will go over the results for the Home phase of the studies because they are more relevant to day-to-day diabetes management.

The results for the Minimed 670G FDA approval trial at first glance do not appear to be much to get excited about. Using normal management techniques the mean sensor glucose reading is 150.2+/-22.7mg/dl and using the closed-loop system it is 150.8+/-13.7mg/dL. If anything the closed-loop would seem to run a little higher. However, the more important number here is the standard deviation because as most of us know, it’s far better to have a stable BG than to be bouncing from high to low continuously. This also becomes apparent when you look at the time spent within the range of 70-180mg/dL. This increases from 66.7% of the time under normal management techniques to 72.2% of the time under the closed-loop system.

Results for the iLet Home study were published in The Lancet recently. They show a similar promise in the reduction of the variance; 162.16+/-28.83 on standard management versus 140.54+/-10.8 on the closed-loop system. The percentage of time within the target range (70-180mg/dL) was 61.9% on normal management and 78.4% on the closed-loop system.

While these two studies delivered fairly similar results there are a number of key differences between the two systems. The Minimed 670G is comprised of their Guardian Sensor and Transmitter and a 670G insulin pump (plus optional Contour NextLink Meter) that communicate via Bluetooth. The iLet currently consists of two Tandem t:slim pumps connected via an iPhone 4S to a Dexcom G4 sensor. The two pumps are because the iLet is a bihormonal system in that it will deliver both insulin and glucagon. The glucagon dosing is an issue that iLet is still working to straighten out. Currently available glucagon is not very stable at room temperature and the study participants needed to change that site every 24hrs which makes that in my opinion very impractical – especially considering that you would have a total of three subcutaneous patches using this system. However, more stable formulations of glucagon are being developed such as the Zealand Pharma Dasiglucagon as well as sensor/insulin delivery combination systems that allow the sensor to be inserted in tandem with the insulin delivery site. These developments, which I will explore further in future posts, could make the iLet system even more promising in the long-term.

Closed-loop systems hold a lot of promise for the diabetes community. It will be important to follow the development of these tools as they enter the marketplace. To paraphrase the lead investigator behind the iLet system, Edward Damiano, PhD, these devices are a refinement of the current blunt tools we have available that will help diabetics manage their BG until a cure is developed.