Here is a Twitter thread summarizing poster 1056-P at #ADA2019 in category 12-D Clinical Therapeutics/New Technology–Insulin Delivery Systems, Preliminary Characterization of Rhythmic Glucose Variability In Individuals With Type 1 Diabetes, by @azure_dominique & @danamlewis
Background:
Human physiology, including blood glucose, exhibits rhythms at multiple timescales, including hours (ultradian, UR), the day (circadian, CR), and the ~28-day female ovulatory cycle (OR). #ADA2019
Individuals with T1D may suffer rhythmic disruption due not only to the loss of insulin, but to injection of insulin that does not mimic natural insulin rhythms, the presence of endocrine-timing disruptive medications, and sleep disruption. #ADA2019
However, rhythms at multiple timescales in glucose have not been mapped in a large population of T1D, and the extent to which glucose rhythms differ in temporal structure between T1D and non-T1D individuals is not known. #ADA2019
Data & Methods:
The initial data set used for this work leverages the OpenAPS Data Commons. (This data set is available for all researchers – see OpenAPS.org/data-commons)
All data was processed in Matlab 2018b with code written by Azure Grant. #ADA2019
Frequency decompositions using the continuous morlet wavelet transformation were created to assess change in rhythmic composition of normalized blood glucose data from 5 non-T1D, and anonymized, retrospective CGM data from 19 T1D individuals using a DIY closed loop APS. #ADA2019
Wavelet algorithms were modified from code made available by Dr. Tanya Leise at Amherst College (see bit.ly/LeiseWaveletAn…) #ADA2019
Results: Inter and Intra-Individual Variability of Glucose Ultradian and Circadian Rhythms is Greater in T1D #ADA2019
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Figure 1. Single individual blood glucose over ~ 1 year with A.) High daily rhythm stability and B.) Low daily rhythm stability. Low glucose is shown in blue, high glucose in orange. #ADA2019
^^ Figure 2. T1D individuals (N=19) showed a wide range of rhythmic power at the circadian and long-period ultradian timescales compared to individuals without T1D (N=5). #ADA2019
A). Individuals’ CR & UR power, reflecting amplitude & stability of CRs, varies widely in T1D individuals compared to non-T1D. UR power was of longer periodicity (>= 6 h) in T1D, likely due to DIA effects, whereas UR power was most commonly in 1-3 hr range in non-T1D #ADA2019
B.) On average, both CR and UR power were significantly higher in T1D (p<.05, Kruskal Wallis). This is most likely due to the higher amplitude of glucose oscillation, shown in two individuals in C. #ADA2019
Conclusions:
This is the first longitudinal analysis of the structure and variability of multi-timescale biological rhythms in T1D, compared to non-T1D individuals. #ADA2019
Individuals with T1D show a wide range of circadian and ultradian rhythmic amplitudes and stabilities, resulting in higher average and more variable wavelet power than in a smaller sample of non-T1D. #ADA2019
Ultradian rhythms of people with T1D are of longer periodicity than individuals without T1D. These analyses constitute the first pass of a subset of these data sets, and will be continued over the next year. #ADA2019
Future work: JDRF has recently funded our exploration of the Tidepool Big Data Donation Project, the OpenAPS Data Commons, and a set of non-T1D control data in order to map biological rhythms of glucose/insulin. #ADA2019
We will use characterize URs, CRs, ORs in glucose/insulin for T1D; evaluate if stably rhythmic timing of glucose is associated with improved outcomes; and evaluate if modulation of insulin based on time of day or time of ovulatory cycle could lead to improved outcomes. #ADA2019
Mapping population heterogeneity of these rhythms in people with and without T1D will improve understanding of real-world rhythmicity, and may lead to non-linear algorithms for optimizing glucose in T1D. #ADA2019 (/end)
(You can find a longer form copy of this poster content, and all other posters co-authored by @danamlewis at bit.ly/DanaMLewisADA2…)
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1/ What if there was a tool to help identify who might have exocrine pancreatic insufficiency (EPI/PEI)?
EPI is a significant issue for many people with diabetes (likely more common than gastroparesis or celiac).
Here's how such a tool can help PWD👇🏼🧵
#ADASciSessions #ADA2024
2/ The Exocrine Pancreatic Insufficiency Symptom Score (EPI/PEI-SS) has 15 symptoms, rated by how frequent they are and how bothersome they are (aka severity).
n=324 ppl participated in a real-world survey.
n=118 were people with diabetes (PWD)!
#ADASciSessions #ADA2024
3/ Methods:
EPI/PEI-SS scores were analyzed and compared between PWD (n=118), with EPI (T1D: n=14; T2D: n=20) or without EPI (T1D: n=78; T2D: n=6), and people without diabetes (n=206) with and without EPI.
📣 Presentation of the primary outcome results from the CREATE Trial, which assessed open source automated insulin delivery (AID) compared to sensor-augmented pump therapy (SAPT) in adults & kids with T1D, at #ADA2022!
The CREATE trial aimed to study the efficacy and safety of an open source automated insulin delivery system, with a large scale, long term randomized controlled trial.
I just realized it's been 3 (!) years since I published my book on automated insulin delivery, with the goal of helping increased conversation and understanding of AID technology for people with diabetes, their loved ones, and healthcare providers!
I'm still very proud that it is available to read for free online, free to download a PDF (both of which have been done thousands of times each: ArtificialPancreasBook.com), or as an e-book, paperback, and now hardback copy. Proceeds from the purchased copies go to Life For A Child.
And, more recently, it has also been translated into French by the wonderful Dr. Mihaela Muresan and Olivier Legendre!
The French translation is available in Kindle, paperback, hardback, or free PDF download formats as well.
1/THREAD - my presentation is kicking off at #EASD2020 about open source automated insulin delivery.
(You can see a full version of my presentation here: bit.ly/DanaMLewisEASD…, or read the summary below!)
Note we should differentiate between open source (where the source of something is open), and DIY (do-it-yourself) implementations of open source code. Open source means it can be reviewed and used by individuals (thus, DIY or #DIYAPS) or by companies.
Poster 988-P at #ADA2020 by Jennifer Zabinsky, Haley Howell, Alireza Ghezavati, @DanaMLewis Andrew Nguyen, and Jenise Wong: “Do-It-Yourself Artificial Pancreas Systems Reduce Hyperglycemia Without Increasing Hypoglycemia”
This was a retrospective double cohort study that evaluated data from the @OpenAPS Data Commons (data ranged from 2017-2019) and compared it to conventional sensor-augmented pump (SAP) therapy from the @Tidepool_org Big Data Donation Project. #ADA2020
One month of CGM data (with more than 70% of the month spent using CGM), as long as they were >1 year of living with T1D, was used from the @OpenAPS Data Commons. People could be using any type of DIYAPS (OpenAPS, Loop, or AndroidAPS) and there were no age restrictions. #ADA2020
Poster 99-LB at #ADA2020 by @danamlewis, @azure_dominique, and Lance Kriegsfeld, “Multi-Timescale Interactions of Glucose and Insulin in Type 1 Diabetes Reveal Benefits of Hybrid Closed Loop Systems“
Background - Blood glucose and insulin exhibit coupled biological rhythms at multiple timescales, including hours (ultradian, UR) and the day (circadian, CR) in individuals without diabetes. But, biological rhythms in longitudinal data have not been mapped in T1D. #ADA2020
It is not known exactly how glucose and insulin rhythms compare between T1D and non-T1D, and whether rhythms are affected by type of therapy (Sensor Augmented Pump (SAP) or Hybrid Closed Loop (HCL)). #ADA2020