Harnessing the power of AI to advance knowledge of Typediabetes


Date:
March 21, 2022
Source:
University of Missouri-Columbia
Summary:
An interdisciplinary team of researchers has used a new data-driven
approach to learn more about persons with Type 1 diabetes,
who account for about 5-10% of all diabetes diagnoses. The team
gathered its information through health informatics and applied
artificial intelligence (AI) to better understand the disease.



FULL STORY ==========================================================================
An interdisciplinary team of researchers from the University of Missouri, Children's Mercy Kansas City and Texas Children's Hospital has used
a new data- driven approach to learn more about persons with Type 1
diabetes, who account for about 5-10% of all diabetes diagnoses. The
team gathered its information through health informatics and applied
artificial intelligence (AI) to better understand the disease.


==========================================================================
In the study, the team analyzed publicly available, real-world data from
about 16,000 participants enrolled in the T1D Exchange Clinic Registry.By applying a contrast pattern mining algorithm developed at the MU College
of Engineering, the team was able to identify major differences in health outcomes among people living with Type 1 diabetes who do or do not have
an immediate family history of the disease.

Chi-Ren Shyu, the director of the MU Institute for Data Science and
Informatics (MUIDSI), led the AI approach used in the study, and said
the technique is exploratory in nature.

"Here we let the computer do the work of connecting millions of dots in
the data to identify only major contrasting patterns between individuals
with and without a family history of Type 1 diabetes, and to do the
statistical testing to make sure we are confident in our results,"
said Shyu, the Paul K. and Dianne Shumaker Professor in the MU College
of Engineering.

Erin Tallon, a graduate student in the MUIDSI and the lead author on
the study, said the team's analysis resulted in some unfamiliar findings.

"For instance, we found individuals in the registry who had an immediate
family member with Type 1 diabetes were more frequently diagnosed with hypertension, as well as diabetes-related nerve disease, eye disease and
kidney disease," Tallon said. "We also found a more frequent co-occurrence
of these conditions in individuals who had an immediate family history
of Type 1 diabetes.

Additionally, individuals who had an immediate family history of Type 1 diabetes also more frequently had certain demographic characteristics." Tallon's passion for this project began with a personal connection,
and quickly grew as a result of her experience working as a nurse in an intensive critical care unit (ICU). She would often see patients with Type
1 diabetes who were also dealing with other co-existing conditions such
as kidney disease and high blood pressure. Knowing that a person's Type
1 diabetes diagnosis often occurs only when the disease is already very advanced, she wanted to find better ways for prevention and diagnosis,
starting with finding a way to analyze the large amounts of publicly
available data already collected about the disease.



==========================================================================
In 2019, Mark Clements, who is a pediatric endocrinologist at
Children's Mercy Kansas City, professor of pediatrics at University
of Missouri-Kansas City and corresponding author on the study, was
invited to speak at the Midwest Bioinformatics Conference hosted by
BioNexus KC. While Tallon wasn't able to attend Clements' presentation,
she followed up with a phone call to share her proposal for helping
people better understand Type 1 diabetes. He was intrigued. Eventually,
Tallon introduced Clements to Shyu, and an ongoing research collaboration
was born.

Tallon said the results of the collaboration speak to the power and
value of using real-world data.

"Type 1 diabetes is not a single disease that looks the same for everybody
- - it looks different for different people -- and we're working on
the cutting- edge to address that issue," Tallon said. "By analyzing
real-world data, we can better understand risk factors that may cause
someone to be at higher risk for developing poor health outcomes."
While the results are promising, Tallon said researchers were limited
by not having a population-based data set to work with.

"It is important to note here that our findings do have a limitation that
we hope to address in the future by using larger, population-based data
sets," Tallon said. "We're looking to build larger patient cohorts,
analyze more data and use these algorithms to help us do that."
Personalizing medicine


========================================================================== Clements hopes the approach can be adopted as a way to help develop personalized treatment options for people with diabetes.

"In order to get the right treatment to the right patient at the right
time, we first need to understand how to identify the patients who are
at a higher risk for the disease and its complications -- by asking
questions such as if there are characteristics early in someone's life
that can help identify an individual with high risk for an outcome years
down the road," Clements said.

"Having all of this information could one day help us establish a more
complete picture of a person's risk, and we can use that information to
develop a more personalized approach for both prevention and treatment." "Contrast pattern mining with the T1D Exchange Clinic Registry reveals
complex phenotypic factors and comorbidity patterns associated with
familial versus sporadic Type 1 diabetes," was published in Diabetes Care,
a journal of the American Diabetes Association. MU graduate students Danlu
Liu and Katrina Boles, and Maria Redondo at Texas Children's Hospital,
also contributed to the study.

The study's authors would like to thank the funding agency of the T1D
Exchange Clinic Registry, the Helmsley Charitable Trust, the investigators located across the country who drove the data collection for the registry,
as well as all of the registry's participants and their families who
were willing to share their medical information.

The researchers would also like to acknowledge the support provided by
grants from the National Institutes of Health (5T32LM012410) and the
National Science Foundation (CNS-1429294). The content is solely the responsibility of the authors and does not necessarily represent the
official views of the funding agencies.

Potential conflicts of interest are also noted by two of the study's
authors - - Clements and Shyu. Clements is the chief medical officer at
Glooko, and receives support from Dexcom and Abbot Diabetes Care. Shyu
is a consultant for Curant Health.


========================================================================== Story Source: Materials provided by University_of_Missouri-Columbia. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Erin M. Tallon, Maria J. Redondo, Chi-Ren Shyu, Danlu Liu,
Katrina Boles,
Mark A. Clements. Contrast Pattern Mining With the T1D Exchange
Clinic Registry Reveals Complex Phenotypic Factors and Comorbidity
Patterns Associated With Familial Versus Sporadic Type 1
Diabetes. Diabetes Care, 2022; 45 (3): e56 DOI: 10.2337/dc21-2239 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220321150425.htm

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