Latest / DOC Updates / Variability in Autoantibody Ordering Patterns & Diabetes Misclassification and Determinants of Cognitive Change Over 1 Year in Older Adults With T1D

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Variability in Autoantibody Ordering Patterns & Diabetes Misclassification and Determinants of Cognitive Change Over 1 Year in Older Adults With T1D · DOC Updates

0:08
Hello, everybody, and welcome back to this October 2026 edition of
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the Points of Care, the official podcast of the journal Diabetes,
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Obesity, and Cardiometabolic Care.
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This is the American Diabetes Association's journal for clinicians,
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educators, and advanced practice professionals.
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I'm your podcast co-host.
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Dr. Richard Bezer, and joining me as usual is my friend and colleague and co-host,
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Dr. Jane Roosh. Welcome, Jane.
0:39
Glad to be here, Rich.
0:42
Now, Jane, I I know you agree that it's been really exciting for us
0:47
to see how diabetes obesity and cardiometabolic care has grown during this
0:52
its inaugural year. The editorial board,
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I know, has been been receiving many really good quality submissions
0:58
for potential publications. And not surprisingly,
1:02
as a result, it's it's been getting more difficult for us to select from among them
1:07
which ones that we want to highlight into interview.
1:11
I could not agree more, Rich. This has been a very exciting journal to even read
1:17
the abstracts of, with so many things that are specifically relevant
1:22
to clinical practice and leveraging real world data in order
1:26
to improve diabetes care.
1:30
So it just
1:31
with having said that, after considerable deliberation between Jane and I,
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it wasn't in a smoke-filled room,
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but it was definitely all over the internet and consultation with editorial board,
1:43
we've come up with our final selections for interviews and other articles
1:48
to highlight. But as I say, the choice was hard.
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And I I do urge our listeners to get a copy of the journal and read through it.
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Maybe you'll disagree with our selections and find there are other articles that
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are of particular interest to you.
2:00
So
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You really can't tell until you look.
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So let's get started talking with the authors of the two key articles that we've
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selected for focus in today's podcast from this month's edition.
2:22
So let's get started talking with the authors of the two key articles that we're
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going to focus on for this month's edition.
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First article, first interview,
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~ I want to introduce two of the authors.
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One is Dr. Clara Klein, and she's assistant professor of medicine
2:38
at the University of North Carolina School of Medicine in Chapel Hill,
2:42
North Carolina. And also Claire Abram.
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She's an attending physician at Maine Health Medical Center in Portland,
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Maine.
2:51
So welcome to the two of you.
2:53
Thanks so much for having us.
2:54
Yes, it's a it's a pleasure. ~ the title of your article
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is Variability in Autoantibody Ordering Patterns suggest potential
3:04
for diabetes misclassification.
3:07
So, Claire, let's let's start with you.
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And you know, the the ability to check autoantibodies is the help with diabetes
3:14
classification has been around for quite a long time.
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But as you note in the title of your paper,
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the variable pattern of ordering these tests has prompted you and your group
3:24
to have a closer look. And specifically,
3:27
what did you want to learn from this?
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Originally, we were reviewing charts for people who had islet autoantibodies ordered
3:36
within our system to better identify those with new onset type 1 diabetes
3:42
so that we could contact those individuals for potential recruitment into other
3:48
research studies or clinical trials.
3:50
~
3:52
And that was how I was originally brought onto the project to do that manual chart
3:56
review. But very quickly we noticed that there were a lot of discrepancies
4:00
and how different providers across from our system were ordering those tests,
4:06
meaning which ones they were ordering and how they were interpreting those labs,
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particularly amongst adult individuals ~
4:14
who were being evaluated for diabetes.
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~
4:18
And we became concerned that there were type 1 diabetes diagnoses that were being
4:22
missed within our system. And that this care gap was leading
4:26
to diabetes misclassification and also serious clinical implications.
4:31
~ As we know, you know, type one and type two diabetes are treated very differently.
4:36
They have different comorbidities that we screen for.
4:40
And then with the advent of ~ disease modifying therapy like diplosimab.
4:45
there could be patients who or individuals who are being missed ~
4:50
who could qualify for such for such treatment without that correct diagnosis.
4:55
Yeah, and one of the really interesting things is that antibodies are
5:00
not ~ equal across the lifespan.
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~ and so can you tell us a little bit about what you learned?
5:07
Yeah, so there are some clinical guidelines that help guide providers
5:12
in terms of determining which antibodies may be most appropriate to order
5:16
for each individual. But these are not very specific guidelines.
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They do not give a lot of information in terms of the rationale
5:26
for which antibodies would be most helpful in each situation.
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And so it's not a one-size-fits-all type model that we have.
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And specifically outside of the endocrinology specialty,
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there are a lot of providers who are ordering these tests in a way that
5:43
Yeah.
5:44
we found could be higher yield or potentially provide more information regarding
5:50
~ diabetes classification for that individual.
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Specifically, one of our findings that we found most interesting was.
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How many people are ordering insulin autoantibodies or IAA for adults,
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which is not likely at all to be positive amongst the adult population.
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And is it was one of the more shocking findings we found how many providers were
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only ordering in ~ an insulin antibody and interpreting that negative result
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as evidence that an individual did not have type one diabetes.
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We did see several instances where this resulted in in poor outcomes
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for the patient, meaning you know,
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eventually going into DKA or continued weight loss
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and hyperglycemia until eventually being referred to endocrinology.
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Now from these findings, Claire,
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what did you conclude? What w what is the punchline here?
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~ basically, we found that across the board,
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eyelet autoantibody testing is much more uniform within the pediatric population.
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We think that this is for several reasons,
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meaning that most children with diabetes are being treated
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by an endocrinologist with a diet association with a diabetes care team.
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~ but adults, on the other hand,
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are often being diagnosed and cared for by their primary care providers
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and are often requiring or
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Or needing something out of the ordinary with their diabetes diagnosis or
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a type 1 diabetes diagnosis before their referral to
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~ adult endocrinology providers.
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And so we found that compared to children,
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adults are are not having the same autoantibodies ordered from provider
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to provider. It's much more likely to only have one or
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two islet autoantibodies tested.
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~ and then sometimes we are finding that that interpretation is not is not accurate.
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So therefore, we feel that adults are at most risk for misclassification
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of their diabetes. And that also was highlighted by the information that
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we got from the group of adults that had their diabetes reclassified
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to type one diabetes following this auto antibody testing.
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Okay, you know, maybe I'll throw this open for discussion and and Clara
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and you can weigh in also. Where do we go from here?
8:17
You know, I I've always scratched my head with about the antibodies.
8:21
What do I order? Who do I order?
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You know, working in a diabetes center,
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it's a little clearer, but but for the primary care people out there,
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how do we how do we fix this?
8:33
Yeah, thanks. thanks for that question.
8:35
I think this is a it's a real challenge.
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~ and I think one of the conversations that Claire and I have ~ over
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and over again is that, you know,
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we, you know, we live, breathe,
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think about diabetes a hundred percent of the time.
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This is our life. ~ and it's very challenging in the primary care world where you're
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supposed to be experts in every disease state to know.
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exactly what to do for every situation.
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And so I think it's very important to be very clear that this is nobody's fault.
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It's very challenging to diagnose ~ type 1 diabetes in adults.
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And the literature has shown that over and over again,
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so many people are being misclassified and it takes a long time for people
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to get to the right diagnosis.
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And so I don't think there's gonna be a simple solution.
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I think we have to develop new tools to help
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Primary care providers, you know,
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determine who should be ~ tested for islet autoantibodies
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and develop good ways for them ~ to identify which tests to order.
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And I think that's really the future of the research that we'd like to do here
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~ in my group is to identify potentially machine learning algorithms,
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electronic health record-based tools.
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That can really guide primary care doctors and endocrinologists,
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because we miss this as well. You know,
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who who might be? Maybe maybe there's a better way to identify the best people
10:08
who should get eyelid autoantibody testing.
10:10
~ and I'll just add in that that I think some people would argue that with people
10:15
with a new diagnosis of diabetes,
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everyone should get autoantibody testing.
10:18
~ and I I would argue, and and I think this is a conversation,
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a longer conversation that many people can have.
10:24
I would probably argue that that's dangerous too,
10:27
because there are minimally positive antibodies.
10:30
It's hard to interpret. ~ I think there will be a lot of false false positives.
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And so then it could be not only ~ cost prohibitive,
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but also ~ challenging to really interpret who truly has type one diabetes.
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So I think we need to come up with new methods to help people to figure
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out the best people to get autoantibody testing and then
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~ potentially referrals over to endocrinos.
10:52
So one thing I was really struck by in your be very beautiful infographic
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is that is that 81% were using insulin,
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89% had a previous diabetes, ~ misdiagnosis at at in type two,
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they were, they were obese. There weren't a lot of clinical clues.
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And so I I I just want to encourage all of our listeners to read this paper
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and look at it and think about it.
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too, you know, a strategy. And and when you're talking about minimally positive
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antibodies, GAD antibodies, so many people will have minimally positive
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GAD antibodies. And maybe consider strategies to deal with that
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in your machine learning. It's it's really,
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really an interesting paper. Sorry,
11:46
Rich, to interrupt.
11:47
No, that's that's fine. And you know,
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I I I've got enough gray hair to remember back in the days when we
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we were testing antibodies because we couldn't,
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and ~ we would wing it, you know,
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we just guess what the diagnosis was,
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but we'd followed people, and if the sugars looked like they needed insulin
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and they're looking like a type one,
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we'd act accordingly. But I think now the importance is that there's more things
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we can do earlier on, and that's why this has become increasingly important
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to clarify.
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The the only last thing that I would add is I think the,
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you know, the circling back to why we did this in the first place
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is it's so important to identify nowadays people early in their natural history
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of their type one diabetes diagnosis,
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so that as more disease modifying therapies are available,
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we can offer them and there are more clinical trials that are available that
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we can offer them to people to potentially change the course of their disease.
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And so
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it is becoming increasingly important to get the diagnosis right early.
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And so figuring out, you know,
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tools to help people identify those people is really,
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I think, a a critical area of research.
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So ~ I think exciting and hopefully ~ more to come.
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And that's a good last word. Thank you,
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Clara and Claire, for joining us today for this podcast.
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Thank guys so much. Thank you.
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Well, everybody, it is with great pleasure that I introduce the author
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of our second article, Dr. Miranda Zudiga Kennedy from Washington
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University. ~ This article is entitled The Determinants of Cognitive Change Over
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One Year in Older Adults with Type 1 Diabetes.
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So, Miranda, are there any other researchers on your team that you would like
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to highlight at the beginning of this podcast?
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Yeah, absolutely. The first would be my mentor,
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Dr. Naomi Chater. She has been just instrumental in this work,
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and I've learned so much from her and hope to continue to learn from
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her throughout my career. And we also had four at the time second-year medical
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students on this paper who really put in a lot of great work.
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And it was really fun seeing their research skills kind of grow.
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And of course, our co-authors at our collaborating institutions.
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Yeah, and and as we do with these articles,
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we want to get a little bit about the perspective of what gaps you were hoping
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to address with this project.
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Basically, over the years, advances in diabetes management,
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continuous glucose monitors, automated insulin delivery have really meant that
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people with type 1 diabetes are living much longer,
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as we know. As a result, we have a rapidly growing population of older adults with
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type 1. And these older adults are known to be at increased risk
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for cognitive impairment and dementia.
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And the changes that we typically see are in the domains of psychomotor speed,
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information processing, and more of those executive functions.
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And we've learned a lot about what risk factors contribute to this increased risk
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for cognitive decline from landmark studies like the DCCT edict trial that kind
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of span over those decades, or those cross-sectional studies that kind
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of provide that snapshot in time.
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What we don't know is how this type of change happens within
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the timeframe that clinicians see older adults,
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like during annual wellness visits.
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So our study was really motivated by this practical clinical question:
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if ADA guidelines ~ recommend annual cognitive screening beginning at age 65,
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can brief screening tools like the recommended Montreal Cognitive Assessment
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or the MOCA actually detect this meaningful cognitive change over one year?
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And which patients are most likely to show that change on these brief measures.
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We also wanted to identify modifiable risk factors that might help clinicians
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determine who needs closer cognitive monitoring.
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So we examined demographic factors,
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vascular risk factors, diabetes complications,
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glycemic measures, and severe hypoglycemia.
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So really the gap wasn't whether cognition matters in older adults with type
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1 diabetes. We know that it does.
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the gap was whether we can detect that meaningful change during the time frame
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of routine annual care.
16:16
That sounds very important, Miranda.
16:17
~ perhaps you could just summarize what your findings are.
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Our our listeners can get the details and by reading the article.
16:25
I will do my best to be brief.
16:27
~ So this was a secondary analysis of the AT1D trial.
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So that was a multi-center randomized crossover trial.
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We had 74 adults aged 65 to 85 complete the MOCA,
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but we know that the MOCA may not be as sensitive to the changes that
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we might see in diabetes, like that processing speed that I mentioned earlier.
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So we included the waste for processing speed index as well.
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Then we had them complete the same measures 12 months later
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and examine their changes in scores.
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And we wanted to look at clinically meaningful change,
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which we defined as two or more points on the MOCA over the year.
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We also looked at how these risk factors established by studies like EDIC
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are related to changes in the shorter interval.
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So we found that greater decline on the MOCA was associated with neuropathy,
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hypertension, and being partnered.
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And greater decline in the processing speed was associated with hypertension
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and severe hypoglycemia that occurred during the study,
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but not with a history of severe hypoglycemia overall.
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So, regarding clinically meaningful change,
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we found that at baseline, a third of people scored in the mild cognitive impairment
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range that cut off of less than 26 points.
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But there was a lot of individual variation,
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with some people improving, some declining,
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and then some remaining stable over the year.
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So that was essentially what what we found overall.
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And so what did you take away from this?
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What are your conclusions from this study?
17:59
I think we have a few points that we can expand on for sure.
18:02
So the first is that this is evidence that meaningful changes
18:07
in cognition can really be detected by these ADA recommended,
18:11
very brief measures that can be implemented in various care settings,
18:15
which is really important. And this work also identified several established
18:20
and emerging risk factors, including hypertension,
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that ~ recent severe hypoglycemia.
18:25
And neuropathy that's linked with this meaningful change in scores over this
18:29
one-year interval. And it suggests that people with these risk factors warrant
18:33
pretty close cognitive monitoring.
18:35
And interestingly, recent severe hypoglycemia,
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so the episodes that happened during that study interval,
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predicted a decline in processing speed,
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but not a general history of severe hypoglycemic episodes.
18:48
So that tells us that these events may have a more immediate effect on cognition.
18:55
I would also highlight that there is a fluctuation in the nature
19:01
of cognition in people with type one,
19:03
which is exactly why this annual screening kind of matters.
19:06
One of the most interesting findings was that 45% of the participants
19:11
who scored in that mild cognitive impairment range at baseline no longer
19:15
met that threshold one year later.
19:17
And then at the same time, some participants moved from normal to impaired.
19:22
And other participants remain relatively stable where they were at baseline.
19:26
So that's consistent with the broader MCI literature that over a year or two,
19:30
some people tend to progress towards dementia,
19:33
some remain relatively stable,
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and others revert to this normal cognition,
19:39
quote unquote. So a single screening is really just a snapshot,
19:43
and repeated screening gives us a picture of someone's cognitive trajectory.
19:48
So if I'm in my clinic with my I I tend to accumulate a lot of 80 and
19:55
90 year olds living with type one,
19:57
and I am trying to figure out how to help them through a day with,
20:03
you know, mild cognitive decline or moderate cognitive decline.
20:07
So what you know, and and I'm trying to figure out triggers,
20:11
etc. How might I help them?
20:16
You know, as not not related to this study,
20:18
but in the large you know, trying to look at the implications of which domains they
20:24
have deficits in and how I may be able to help them function in their
20:29
day to day life.
20:30
What I would probably say is that we have a specific recommendation
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is to assess cognition after maybe a recent episode of severe hypoglycemia,
20:40
right? In our data, those effects didn't really show up on the Mocha.
20:44
So if you have someone in your clinic who's had a recent severe episode
20:48
of hypoglycemia, then maybe something like the MOCA won't be able
20:53
to detect those changes, and we need something more.
20:58
Guided towards the processing speed index.
21:00
~ And as we know, diabetes management is complex,
21:05
it's cognitively demanding, and impairments in cognition can really have
21:10
a significant impact on people's ability to manage their condition.
21:13
So I think that ~ updating screenings
21:20
as necessary, sort of regularly checking in if any of these new risk factors
21:25
are emerging.
21:27
Getting a sense of people have been experiencing some difficulties.
21:30
~ We know that initiating an AIDS system or a continuous glucose monitoring system
21:37
could also really be helpful in ~ kind of easing the burden
21:41
of diabetes management in people with this mild cognitive impairment
21:45
or cognitive decline. ~ Kind of tailoring those typical compensatory strategies that
21:52
we suggest to people with cognitive decline to
21:55
Diabetes management. So my favorite one is that we can't use a pillbox for insulin,
22:01
right?
22:01
So
22:02
What could be helpful there is maybe a refrigerator magnet of someone
22:06
who is on multiple daily injections.
22:08
You know, let's flip it every time that we use it.
22:10
So we're remembering that we took our dose for the day if we're taking more
22:14
of that long-acting insulin. But I think another important part too is that
22:20
we don't really know the direction,
22:23
right? So
22:25
Impaired cognition can impact diabetes management,
22:28
but also what we're seeing is that these acute glycemic events also impact
22:33
our cognition. So learning to optimize both and understand that these changes
22:39
do happen and kind of assess when they are happening or if these
22:43
new risk factors pop up could also be really important.
22:47
And I hope that answered your question.
22:49
Well what I think is that everybody needs to read this article.
22:53
And if my patients recently had a severe ~ hypoglycemic event,
23:00
I may want to inquire further about how they're doing with their decision making.
23:05
Rich, your final thoughts?
23:08
Yeah, I I guess I wonder how this population might differ from
23:13
the general population. ~ you know,
23:16
because there's a lot of ~ general decline in cognition in in older people as well.
23:24
Yeah, absolutely. So as we know,
23:27
you know, older adults are a general risk for cognitive decline.
23:30
~ what really stands out, and I can't remember the exact study,
23:36
but there was a recent ~ paper from the Alzheimer's Association,
23:41
all of us data that showed that individuals with type one are at higher risk
23:47
for particularly Alzheimer's type dementia and vascular dementia.
23:52
So vascular dementia really makes sense when we're thinking of all
23:55
of these added cardiovascular,
23:58
microvascular risk factors in type one ~ on top of the impact that we
24:05
see in the general population with polypharmacy,
24:08
multimorbid multimorbidity. ~ So it's really just compounding a bunch
24:13
of factors on top of already existing factors.
24:15
So I think that's kind of what warrants that close cognitive monitoring.
24:19
And if we can
24:21
kind of zero in on people who have the risk factors that maybe we found
24:25
in our study and can hopefully replicate,
24:27
~ that it kind of guides a more targeted treatment.
24:31
And then those people can maybe be referred for
24:34
a more comprehensive neuropsychological assessment.
24:37
Well, thank you so much, Miranda,
24:40
for this beautiful work that you're doing and best to luck in your career that
24:46
you're just launching here. Stay in diabetes research,
24:51
and I would welcome our viewers to read this paper and to learn more about this
24:57
important topic.
24:58
Thank you so much. It's been a pleasure talking to you both.
25:08
Well, Rich, I'm so excited to tell you about a few other articles.
25:12
And
25:12
And
25:12
the first one I would like to tell you about is entitled Physical Activity Patterns
25:17
in Adults: Taking GLP1 Receptor Agonists,
25:21
an all of us cross-sectional study,
25:24
by Jones et al. The objective of this article was to
25:29
see if in real-world data they could.
25:34
Analyze whether people using GLP1s are being physically active.
25:38
And what they did was they leveraged the All of Us research program from 2018
25:45
to 2022. And what they found was in adults greater than 18 years taking these
25:52
medications, they were able to look at a set of
25:57
all who had validated actigraphy of one sort or another,
26:02
be it watch, etc.
26:04
And what they were able to do was to look at total daily was moderate
26:09
to vigorous physical activity,
26:10
step counts, and sedentary time,
26:13
all things that specifically change outcomes for people with diabetes.
26:19
Interestingly, in this cohort,
26:22
they found 298 participants that they were able to analyze.
26:27
And what they found in those people was that they were
26:32
About 200 minutes a day, but only 24 of those minutes,
26:36
not the 30 that we would like to see,
26:39
were moderate to vigorous physical activity.
26:41
And over half of them did not meet the physical activity guidelines for Americans.
26:46
Their step counts were about 5,900.
26:50
Their sedentary time was 948 minutes.
26:55
I checked to see. That's 15 hours of sedentary time.
26:59
And
27:01
In people who were older, women,
27:04
and people with diabetes, the physical activity was even less.
27:09
So the conclusions that they made were that GLP1s are phenomenal
27:14
for improving weight and diabetes status,
27:18
but we really still need to be focusing on the basics to increase physical activity
27:24
to meet the guidelines and maintain cardiovascular health.
27:28
Love this. Please go read it.
27:32
Yeah, it sounds like a fascinating article.
27:34
And and you know, the challenges of lifestyle ~ changes that go along with
27:39
GLP one use are are things I think that people didn't really appreciate until we've
27:44
started using them in a more widespread manner.
27:47
~ so I think this is an interesting,
27:49
interesting article. My article,
27:52
next article to talk about.
27:54
School meal participation, overall dietary intake and pre-diabetes prevalence among
28:00
U.S. adolescents. Insight from the National Health and Nutrition Examination Survey
28:06
2017 through 23 by Alexandra ~ Dis Carpentry
28:12
~ et al. I hope I didn't mispronounce the name.
28:16
I'm sorry if I did. The author's premise:
28:19
while diabetes
28:21
Pre-diabetes, rather, is an emerging public health concern among U.S.
28:24
adolescents. Data on contributory dietary factors on a national level is limited.
28:31
And the authors specifically focus on overall dietary intake and then
28:35
the role of school meal participation,
28:38
which provides a substantial portion of daily calories and
28:41
has not been comprehensively evaluated.
28:44
Therefore, they undertook this study to examine cross-sectional associations.
28:49
Between schoolmeal participation,
28:51
overall dietary intake, and prediabetes,
28:54
as defined by an A1, C and or fasting glucose values within the prediabetes range,
29:00
assessing differences by obesity status.
29:03
They use the National Health and Nutritional Examination Survey and Haynes data from
29:10
2017 through 2020 and 2021 through 23,
29:14
focusing on a nationally representative samples.
29:19
Of US adolescents ages 12 to 18 with complete fasting,
29:24
glucose, and A1C data. And the number was 802.
29:27
They looked at associations between school breakfast and/or lunch participation,
29:32
dietary intake consisting of food groups and nutrients,
29:36
et cetera, and pre-diabetes. Details of the methodology can be found
29:40
in the paper itself.
29:42
Now the results showed that a prevalence of prediabetes was 33.8%,
29:47
prevalence of obesity 20.96%.
29:51
Interestingly, school breakfast participation at least once a week
29:55
was associated with a higher prediabetes prevalence.
29:59
However, school lunch participation was not.
30:01
They also found that higher vitamin E and B6 intake was linked to lower prediabetes.
30:08
Higher fruit intake
30:11
Was associated with a lower prevalence of prediabetes amongst adolescents with
30:16
obesity. So the authors concluded,
30:20
first off, they acknowledge limitations of self-reported observational study data
30:25
such as this, establishing associations,
30:27
but not causality. They also recognized that information on
30:32
all dietary variables that might impact their findings was not available.
30:37
Nevertheless, they concluded in particular.
30:41
School breakfast participation and specific dietary components were associated with
30:46
pre-diabetes risk in the U.S. adolescence.
30:50
They acknowledge that confirmation is needed,
30:52
but suggest, and this is this is I think a key point,
30:56
that the nutritional quality of school breakfasts may represent
31:00
an important intervention target.
31:02
So I think this is an important point for future study and intervention.
31:09
Thank you, Rich. Another article,
31:11
not to miss, is entitled Comparative Effectiveness
31:16
of Continuous Glucose Monitoring versus Blood Glucose Monitoring
31:21
in Pregnancy with Diabetes. This is a systematic review and meta-analysis with trial
31:27
sequential analysis. This ~ this paper was led by Ben Nuru et al.
31:34
and
31:35
In their overall data synthesis,
31:37
they were able to identify 17 randomized clinical trials
31:42
and noted that macrosomia and LGA were reduced
31:47
in individuals using CGM, but the strength of evidence was not high
31:53
for either of these findings. Small for gestational
31:58
age was also elevated with CGM,
32:01
but here again
32:03
significantly and yet somehow the strength of the finding was moderate.
32:10
The trial sequential analysis found
32:14
no neonate size in outcome met the required information size
32:21
to draw firm conclusions for this important question.
32:25
Their conclusion was that the CGM benefits for mothers and neonates with
32:31
Pre-existing diabetes and gestational diabetes varied for outcomes,
32:36
but really gave us the idea that you were going to reduce large size
32:43
at birth. But probably most importantly,
32:46
it pointed out that we really need to be diving into this question more thoroughly
32:53
in either prospective research or in very careful literature
33:00
searches.
33:01
So that we can change these outcomes.
33:04
My next article is Implementation of Diabetes Care Coordination
33:09
in Schools Across Pediatric Centers in the T1D Exchange Quality Improvement
33:14
Collaborative by Christine March et al.
33:16
Now, the authors sought to characterize both barriers
33:21
and facilitators to implementing evidence-based care coordination
33:26
for type 1 diabetes between schools with grades K through 12 and diabetes centers
33:32
in the United States.
33:33
With a recognition that such coordination should be optimized.
33:37
They described their work as being a mixed method study with diabetes centers
33:41
in the T1D Exchange Quality Improvement Collaborative,
33:45
who deliver pediatric care. A representative from each center participated
33:50
in a survey and an optional semi-structured interview.
33:54
Questions focused on processes for communication,
33:58
education, and clinical care related to in-school diabetes support.
34:04
They also probed deeper into barriers and solutions.
34:07
Surveys were analyzed descriptively.
34:10
Interviews were transcribed and coded by two reviewers using a thematic analysis.
34:17
Now the authors received return surveys from 38 predominantly academic pediatric
34:23
centers representing different U.S.
34:26
regions and patient volumes. Analysis showed that most respondents,
34:30
58%,
34:32
We're somewhat satisfied with current processes for communicating with schools
34:36
and providing medical orders, but less than 25% were extremely satisfied.
34:42
So clearly there's room for improvement here.
34:44
Certain problematic barriers were identified,
34:47
and those included insufficient clinician time,
34:51
74%, school call volume, 78%,
34:55
and inefficient communication methods,
34:58
71%.
35:01
Less than 5% of centers bill for care coordination activities.
35:04
Other qualitative themes were also assessed and provide further context
35:10
to barriers and challenges. And they have listed in discussed in the article itself,
35:14
so do have a look. Desired solutions focused on technology or intermediary roles,
35:21
such as nurse navigators, for coordination.
35:23
And the respondents viewed school-partnered interventions favorably.
35:28
But scalability and sustainability were concerned.
35:31
Concerns. So the authors concluded that diabetes care
35:38
coordination between K-12 schools and diabetes centers
35:43
is variably implemented due to challenges within both institutions,
35:48
both sides of the fence, so to speak.
35:51
Further research is, in fact, needed to test implementation strategies.
35:56
which can overcome barriers and augment evidence-based care.
36:00
So I think this article serves as a a challenge to everybody to look at some
36:05
of the issues and see if they can improve the systems and maybe then take another
36:09
look at it sometime down the road.
36:12
Next article comes from my home base,
36:16
the VA, and it is entitled Virtual Diabetes Optimization Program
36:23
for Veterans with Uncontrolled Diabetes.
36:25
The first author is Dr.
36:28
Adel. But what I wanted to start with before I tell you this is a little
36:35
bit of history that comes into this article,
36:37
which is the VA.
36:40
Championed a remote diabetes program for people who hadn't been seen in clinic.
36:47
And what they found was that seeing these people remotely
36:52
and virtually had impressive outcomes.
36:55
So Dr. Mendez and his group are responding currently to the fact that there
37:01
are 25% of veterans that live with diabetes.
37:05
78% of veterans have a BMI greater than 25.
37:10
Despite access to lifestyle interventions that are available in
37:16
the VA and specialty endocrine clinics.
37:19
So many veterans are living with uncontrolled diabetes and obesity
37:25
and are not achieving their desired outcomes.
37:29
So this intervention was designed for people
37:35
not meeting goals despite access to medical care.
37:40
So this VDOP used a multidisciplinary team,
37:45
which included a registered dietitian,
37:48
an occupational therapist, a clinical pharmacist,
37:51
who were all delivering this virtual care under the auspices of a diabetologist.
37:58
The outcomes were compared to veterans who did not,
38:03
who declined to be part of this intervention and were seen in their primary care.
38:10
The primary outcomes were hemoglobin A1C and body weight at 6 and 12 months.
38:15
Now, this is a small study, which included 39 veterans in the VDOP,
38:22
the virtual program, and 62 that were in the standard of care.
38:26
Through this intervention,
38:29
very intriguingly, the VDOP participants had a greater reduction
38:36
in A1C from 9.8% to 7.1%.
38:40
Compared to from 10.6% to 8.2%.
38:44
And importantly, they also had a greater weight loss,
38:50
~ 6.8% compared to 0.7%.
38:55
These improvements were sustained at 12 months,
38:59
and they remained significant after adjustment
39:04
for baseline medications, including GLP1 agonists.
39:10
So they concluded that there is promise to this virtual intervention
39:15
for both glycemic control and weight loss,
39:19
and hope to leverage the VA system and the VA Cooperative Studies program
39:25
in order to test this in a larger population.
39:28
Now, to me, this is very important because we all know that patients will come
39:35
to our clinic and not meet their goals.
39:39
And they are working very hard.
39:40
So we need to be really industrious in novel strategies
39:46
to meet them where they are and come up with an intervention that will help them
39:50
to meet their goals. So read this article.
39:54
Thank you, Jane. The next article that I have is titled Culinary Medicine
40:01
and Medical Nutrition Therapy Interventions to Improve Glycemic Control
40:05
in Type 2 Diabetes, A Pragmatic Trial During the COVID Pandemic
40:10
by Carolyn Smith Morris et al.
40:13
And being one who likes to cook,
40:15
I was very intrigued by this particular article.
40:19
And I have some thoughts about it,
40:21
which I'll express at the end.
40:23
In this study, the authors sought to conduct a pragmatic randomized control trial
40:28
comparing the effectiveness of six registered dietitian-led medical nutrition
40:33
therapy or MNT sessions to six virtual hands-on culinary medicines
40:39
CM sessions for improving glycemic control among people with uncontrolled diabetes
40:46
in a safety net healthcare system.
40:49
Adults 18 years of age or greater with type 2 diabetes and an A1C 7%
40:55
or greater were eligible, and they assessed within and between group changes
40:59
in the A1C and Mediterranean diet score using difference
41:05
in differences analysis adjusted for the number of sessions completed.
41:09
The results showed 79 participants were randomized to either culinary medicine
41:15
classes, 36, or health system.
41:18
medical nutrition therapy visits,
41:20
43. Baseline characteristics were similar between groups.
41:25
Now you can find details of the study design ~ and data in the article,
41:30
but some of the highlights showed that at six months there were clinically
41:34
meaningful improvements in A1C in the medical nutrition therapy group,
41:38
but not in the culinary medicine group.
41:41
However, the authors found no significant within or between group differences in the
41:48
Mediterranean diet score. ~ They do acknowledge that the study
41:53
was underpowered due to the impact of the COVID-19 epidemic
41:57
on recruitment engagement, with sessions being virtual and in group settings.
42:02
Now they concluded: despite the impact of COVID-19,
42:06
participants in the diverse format MNT,
42:09
Medical Nutrition Therapy Intervention,
42:11
had clinically meaningful improvements in A1C at six months.
42:16
Unmet recruitment and retention goals contributed to underpowered results.
42:20
The authors comment that these results highlight the importance
42:24
of nutrition interventions that create supportive environments across multi-level
42:29
systems, such as individual, family,
42:32
community, etc., to improve the dietary health.
42:36
In this case, COVID was a barrier,
42:40
but there are likely to be other barriers that would need to
42:43
be addressed regardless of the intervention.
42:46
~ and and they did encourage additional randomized control trials
42:51
to evaluate the impact of culinary medicine on glycemic control
42:55
and dietary measures. And you know,
42:58
my my comment about this is that intuitively one would think that imparting
43:03
knowledge, the medical nutrition therapy,
43:06
while important, can be carried forward with culinary medicine training,
43:11
actually teaching people how to prepare food and
43:15
cook food in an appropriate way.
43:18
And just because this study, which may have been interfered with by COVID and so on,
43:24
~ was not showing positive results,
43:27
~ I would think that further studies,
43:31
~ maybe overcoming some of those barriers that focus on really hands-on training,
43:36
~ would be valuable. And I would urge people to continue trying ~ to cook
43:42
up some better results, pun intended.
43:45
Well, that brings us to about ~ the end of our time for this edition
43:50
of the Points of Care podcast.
43:52
But I again I do want to emphasize that this edition of diabetes,
43:57
obesity, and cardiometabolic care really has a lot of other interesting articles
44:02
that we don't have time to highlight,
44:04
but I I certainly would encourage you all to have a good look at.
44:07
Jane, I noted some other important topics in there.
44:12
just to highlight diabetes screening,
44:14
exercise and diabetes, diabetes distress,
44:18
care delivery programs, eating behaviors,
44:21
foot infections, hypoglycemia prevention.
44:24
what are your thoughts about the the the array of things in this edition?
44:30
Just say that each each with each edition,
44:34
we're getting more and more practical insights into how to optimize care
44:39
for people living with and at risk for diabetes.
44:41
This this is really evolving into a sort of
44:46
a practical guidebook for people caring for those individuals.
44:52
I I agree. And and as I said a moment ago with regard to the
44:56
the nutrition ~ comment, there's a lot of data out there showing
45:02
the the academic parts of things,
45:04
which are of course the backbone and important part of of what we're doing.
45:08
But the how-to I think is is very important.
45:11
That's where this journal is, I think has a strength.
45:13
~ so there are many other interesting topics,
45:17
and I urge you all to have a
45:18
A good look at this full edition,
45:20
and you'll probably find certainly some of the articles,
45:23
if not all of them, to be quite interesting.
45:25
So that brings us to the end of this edition of Points of Care,
45:30
the podcast of the journal Diabetes,
45:33
Obesity, and Cardiometabolic Care.
45:35
And just a reminder, the full contents of this journal,
45:38
Diabetes, Obesity, and Cardiometabolic Care,
45:41
can be found at Diabetes Journals,
45:44
one word, diabetesjournals.org.
45:47
Forward slash D O C ~ care C A R E with a link in the episode notes.
45:54
And do subscribe to the Points of Care on your favorite podcast app to keep
45:58
up with diabetes, obesity, and cardiometabolic care.
46:02
Points of care is made possible by our dedicated donors,
46:06
listeners, and partners. Thanks to your support,
46:10
we're changing the future of diabetes.
46:12
I'm Dr. Richard Beezer.
46:15
On behalf of my co-host, Dr. Jane Roosh,
46:18
thank you for joining us.