dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Joon Ha, PhD. Associate Professor, Department of Mathematics, Howard University, Washington DC.
Abstract
The most common form of diabetes, type 2 diabetes (T2D) is a failure of insulin-secreting pancreatic beta-cells to increase insulin to the level required to maintain normal blood glucose. Thus, identifying beta-cell function and insulin sensitivity in those who are at high risk is crucial to preventing and delaying the disease. Hyper-glycemic clamp and euglycemic hyper- insulinemic clamp are considered to be gold standard measures for these quantities. However, these two methods demand highly skilled labor and thus are cost-prohibitive. Glucose challenge tests have been used to estimate beta-cell function and insulin sensitivity. The product of beta-cell function and insulin sensitivity, termed the disposition index (DI), is of great value because it measures beta-cell function relative to insulin requirements. However, glucose challenge tests are expensive and time-consuming and therefore impractical to implement in large-scale clinical studies. To address this challenge, we developed a model disposition index (mDI estimated without insulin) that does not require insulin measurements during an oral glucose tolerance test (OGTT) (Ha et al., Diabetes 2021 (70) suppl. 1). mDI outperforms the conventional oral disposition index (oDI) at predicting progression to diabetes.
To further increase access and refine the assessments of beta-cell function, we are adapting our model to calculate a model disposition index using continuous glucose monitoring (CGM). CGM has been in the spotlight of diabetes management and has revolutionized the field of medicine as they are approved for glucose monitoring and clinical decision-making in patients with diabetes. CGM devices are relatively inexpensive compared to oral glucose challenge tests, accessible, and simple to use, especially in remote or free-living environments. The CGM device continuously measures interstitial glucose every 5 minutes and provides glucose profiles for 7-14 days. Thus, there are numerous data points compared to glucose challenge tests, but the abundant data points have not previously been used for estimating metabolic parameters. We compared mDI to two widely used CGM-derived metabolic parameters for assessing metabolic status and risk, mean glucose and glycemic excursion. Both mean glucose and glycemic excursion correlated strongly with mDI. The new approach promises to be cost- effective and easy to perform and therefore implementable in large-scale clinical studies. As for specific clinical applications, estimated model parameters during OGTTs identified ethnic differences in common pathways to T2D between Pima Indians and Koreans.
Upcoming webinars schedule: https://dknet.org/about/webinar
2018 Update in Diabetes Technology: Closed Loop, CGM, and MoreAaron Neinstein
A 2018 update in diabetes technology, including closed loop insulin delivery, continuous glucose monitoring, and more. Presented by Dr. Aaron Neinstein, faculty in Endocrinology at UCSF, at the UCSF Diabetes CME course in San Francisco, in April 2018.
2018 Update in Diabetes Technology: Closed Loop, CGM, and MoreAaron Neinstein
A 2018 update in diabetes technology, including closed loop insulin delivery, continuous glucose monitoring, and more. Presented by Dr. Aaron Neinstein, faculty in Endocrinology at UCSF, at the UCSF Diabetes CME course in San Francisco, in April 2018.
Alicia Wong1
, Wan Chien Han1
, Elsie Low1
,
Chai Xiang Goh1
,
Siew Li Ng1
,
Lee Kuan Kwan1
Abstract: Diabetes-specific formulas have shown to be effective at improving glucose control with additional
nutritional benefits. Furthermore, diabetes-specific formulas are commonly used for diabetic patients with
insufficient oral intake. However, not much diabetes-specific formulas in the market shows the GI of these
formulas, which is clinically useful on glycemic control in patients with diabetes. The aim of this study was to
assess the GI of a newly developed diabetes-specific formula, Contro eazy NOW. The open labelled, single center
study involved 11 individuals from a pool of 18 healthy subjects. After an overnight fast, volunteers were given
Contro eazy NOW containing 50g of carbohydrate or the reference drink (glucolin) on different occasions in
random order. Postprandial blood glucose levels were measured in finger pricked capillary blood for two hours
after intake of the beverages and positive incremental area under the curve (AUC) was calculated for both Contro
eazy NOW and reference drink. The GI of Contro eazy NOW was determined by dividing AUC (Contro eazy
NOW) by the AUC (reference drink). The results show that the diabetes-specific formula has the GI of 38.4, which
is categorized as low GI. Therefore, Contro eazy NOW with low GI can be the preferred option for nutritional
management of diabetic patients in need of nutritional support.
Keywords: diabetes-specific formula, diabetes, low glycemic index, medical nutrition therapy.
This seminar explores the potential connection between two inositol stereoisomers supplements and improvements in insulin sensitivity and various metabolic parameters.
Memorias Conferencia Científica Anual sobre Síndrome Metabólico 2017 - Programa Científico
Futuro en el tratamiento de la DM2
Dr. Guillermo E. Umpierrez
Professor of Medicine in the Division of Endocrinology at Emory University School of Medicine, Section Head, Diabetes and Endocrinology. USA. Editor en Jefe del BJM Open Diabetes Research and Care
Background
No previous studies have compared the DPP-4 inhibitors vildagliptin and sitagliptin in terms of blood glucose levels using continuous glucose monitoring (CGM) and cardiovascular parameters.
Methods
Twenty patients with type 2 diabetes mellitus were randomly allocated to groups who received vildagliptin then sitagliptin, or vice versa. Patients were hospitalized at 1 month after starting each drug, and CGM was used to determine: 1) mean (± standard deviation) 24-hour blood glucose level, 2) mean amplitude of glycemic excursions (MAGE), 3) fasting blood glucose level, 4) highest postprandial blood glucose level and time, 5) increase in blood glucose level after each meal, 6) area under the curve (AUC) for blood glucose level ≥180 mg/dL within 3 hours after each meal, and 7) area over the curve (AOC) for daily blood glucose level <70 mg/dL. Plasma glycosylated hemoglobin (HbA1c), glycoalbumin (GA), 1,5-anhydroglucitol (1,5AG), immunoreactive insulin (IRI), C-peptide immunoreactivity (CPR), brain natriuretic peptide (BNP), and plasminogen activator inhibitor-1 (PAI-1) levels, and urinary CPR levels, were measured.
Results
The mean 24-hour blood glucose level was significantly lower in patients taking vildagliptin than sitagliptin (142.1 ± 35.5 vs. 153.2 ± 37.0 mg/dL; p = 0.012). In patients taking vildagliptin, MAGE was significantly lower (110.5 ± 33.5 vs. 129.4 ± 45.1 mg/dL; p = 0.040), the highest blood glucose level after supper was significantly lower (206.1 ± 40.2 vs. 223.2 ± 43.5 mg/dL; p = 0.015), the AUC (≥180 mg/dL) within 3 h was significantly lower after breakfast (484.3 vs. 897.9 mg/min/dL; p = 0.025), and urinary CPR level was significantly higher (97.0 ± 41.6 vs. 85.2 ± 39.9 μg/day; p = 0.008) than in patients taking sitagliptin. There were no significant differences in plasma HbA1c, GA, 1,5AG, IRI, CPR, BNP, or PAI-1 levels between patients taking vildagliptin and sitagliptin.
Conclusions
CGM showed that mean 24-h blood glucose, MAGE, highest blood glucose level after supper, and hyperglycemia after breakfast were significantly lower in patients with type 2 diabetes mellitus taking vildagliptin than those taking sitagliptin. There were no significant differences in BNP and PAI-1 levels between patients taking vildagliptin and sitagliptin
Transplantation of Autologous Bone Marrow- Derived Stromal Cells in Type 2 Di...CrimsonpublishersITERM
Type 2 Diabetes is a debilitating metabolic disorder which is also the seventh leading cause of death worldwide. Current therapeutic regimes to date have failed to achieve significant long-term glycemic control even with intensive insulin therapy as revealed by deregulated Hb1Ac and C-peptides levels. In the current study, we have evaluated the effect of regenerative cellular therapy for functional recovery from Diabetic pathophysiology. 10 patients with a median age of 51 years were selected for the study and subjected to bone marrow isolation. These samples were processed under sterile conditions for the enrichment of mononuclear cells (BM MNCs) from bone marrow. After strict quality control and characterization of cells, 2 x 106 cells/kg of BM MNCs were infused back into the patient through the anterior pancreaticoduodenal artery. We performed an evaluation of clinical parameters like Body Mass Index, Fasting Plasma Glucose, Fasting Plasma Insulin, HbA1c and C-peptide levels, and followed up the patients for 12 months. Our study showed a reduction in insulin dependency by ≥ 50%.
Conferencia Posgrado del Dr. Iván Contreras Fernández - Dávila, Universitat de Girona: "Diabetes: Patrones ocultos en series temporales" impartida el 09 de Abril de 2015
dkNET Webinar: The 4DN Data Portal - Data, Resources and Tools to Help Elucid...dkNET
Presenter: Andrew Schroeder, PhD. Project Manager & Senior Data Curator, 4D Nucleome Data Coordination and Integration Center (4DN-DCIC), Park Lab, Department of Biomedical Informatics, Harvard Medical School
Abstract
The Common Fund 4D Nucleome program, currently in its 9th year, is a consortium of researchers that aims to understand the principles behind the three-dimensional organization of the nucleus and how this organization can change over time to affect a variety of cellular processes. The 4DN Data Portal (data.4dnucleome.org) is an expanding resource hosting data generated by the 4DN Network and other reference nucleomics data sets. The portal provides tools for search, exploration, visualization, and download. An overview of the data portal, highlighting available data, how it can be found, visualized and used for analyses will be presented.
The top 3 key questions that the 4DN data portal can answer:
1. Are there significant sites of long-range chromatin contacts near my gene or region of interest?
2. What omics datasets are available for my tissue of interest?
3. Are there imaging datasets available that are relevant to my tissue of interest?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours: NIH Data Management and Sharing Mandate 05/03/2024dkNET
Presenter: Jeffrey Grethe, PhD, Principal Investigator of NIDDK Information Network (dkNET), Center for Research in Biological Systems, University of California San Diego
For all proposals submitted on/after January 25 2023, NIH requires the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources that could help.
*Previous Office Hours Slides and Recording: https://dknet.org/rin/research-data-management
Upcoming Webinars Schedule: https://dknet.org/about/webinar
More Related Content
Similar to dkNET Webinar: Estimating Relative Beta-Cell Function During Continuous Glucose Monitoring and Its Clinical Applications 03/10/2023
Alicia Wong1
, Wan Chien Han1
, Elsie Low1
,
Chai Xiang Goh1
,
Siew Li Ng1
,
Lee Kuan Kwan1
Abstract: Diabetes-specific formulas have shown to be effective at improving glucose control with additional
nutritional benefits. Furthermore, diabetes-specific formulas are commonly used for diabetic patients with
insufficient oral intake. However, not much diabetes-specific formulas in the market shows the GI of these
formulas, which is clinically useful on glycemic control in patients with diabetes. The aim of this study was to
assess the GI of a newly developed diabetes-specific formula, Contro eazy NOW. The open labelled, single center
study involved 11 individuals from a pool of 18 healthy subjects. After an overnight fast, volunteers were given
Contro eazy NOW containing 50g of carbohydrate or the reference drink (glucolin) on different occasions in
random order. Postprandial blood glucose levels were measured in finger pricked capillary blood for two hours
after intake of the beverages and positive incremental area under the curve (AUC) was calculated for both Contro
eazy NOW and reference drink. The GI of Contro eazy NOW was determined by dividing AUC (Contro eazy
NOW) by the AUC (reference drink). The results show that the diabetes-specific formula has the GI of 38.4, which
is categorized as low GI. Therefore, Contro eazy NOW with low GI can be the preferred option for nutritional
management of diabetic patients in need of nutritional support.
Keywords: diabetes-specific formula, diabetes, low glycemic index, medical nutrition therapy.
This seminar explores the potential connection between two inositol stereoisomers supplements and improvements in insulin sensitivity and various metabolic parameters.
Memorias Conferencia Científica Anual sobre Síndrome Metabólico 2017 - Programa Científico
Futuro en el tratamiento de la DM2
Dr. Guillermo E. Umpierrez
Professor of Medicine in the Division of Endocrinology at Emory University School of Medicine, Section Head, Diabetes and Endocrinology. USA. Editor en Jefe del BJM Open Diabetes Research and Care
Background
No previous studies have compared the DPP-4 inhibitors vildagliptin and sitagliptin in terms of blood glucose levels using continuous glucose monitoring (CGM) and cardiovascular parameters.
Methods
Twenty patients with type 2 diabetes mellitus were randomly allocated to groups who received vildagliptin then sitagliptin, or vice versa. Patients were hospitalized at 1 month after starting each drug, and CGM was used to determine: 1) mean (± standard deviation) 24-hour blood glucose level, 2) mean amplitude of glycemic excursions (MAGE), 3) fasting blood glucose level, 4) highest postprandial blood glucose level and time, 5) increase in blood glucose level after each meal, 6) area under the curve (AUC) for blood glucose level ≥180 mg/dL within 3 hours after each meal, and 7) area over the curve (AOC) for daily blood glucose level <70 mg/dL. Plasma glycosylated hemoglobin (HbA1c), glycoalbumin (GA), 1,5-anhydroglucitol (1,5AG), immunoreactive insulin (IRI), C-peptide immunoreactivity (CPR), brain natriuretic peptide (BNP), and plasminogen activator inhibitor-1 (PAI-1) levels, and urinary CPR levels, were measured.
Results
The mean 24-hour blood glucose level was significantly lower in patients taking vildagliptin than sitagliptin (142.1 ± 35.5 vs. 153.2 ± 37.0 mg/dL; p = 0.012). In patients taking vildagliptin, MAGE was significantly lower (110.5 ± 33.5 vs. 129.4 ± 45.1 mg/dL; p = 0.040), the highest blood glucose level after supper was significantly lower (206.1 ± 40.2 vs. 223.2 ± 43.5 mg/dL; p = 0.015), the AUC (≥180 mg/dL) within 3 h was significantly lower after breakfast (484.3 vs. 897.9 mg/min/dL; p = 0.025), and urinary CPR level was significantly higher (97.0 ± 41.6 vs. 85.2 ± 39.9 μg/day; p = 0.008) than in patients taking sitagliptin. There were no significant differences in plasma HbA1c, GA, 1,5AG, IRI, CPR, BNP, or PAI-1 levels between patients taking vildagliptin and sitagliptin.
Conclusions
CGM showed that mean 24-h blood glucose, MAGE, highest blood glucose level after supper, and hyperglycemia after breakfast were significantly lower in patients with type 2 diabetes mellitus taking vildagliptin than those taking sitagliptin. There were no significant differences in BNP and PAI-1 levels between patients taking vildagliptin and sitagliptin
Transplantation of Autologous Bone Marrow- Derived Stromal Cells in Type 2 Di...CrimsonpublishersITERM
Type 2 Diabetes is a debilitating metabolic disorder which is also the seventh leading cause of death worldwide. Current therapeutic regimes to date have failed to achieve significant long-term glycemic control even with intensive insulin therapy as revealed by deregulated Hb1Ac and C-peptides levels. In the current study, we have evaluated the effect of regenerative cellular therapy for functional recovery from Diabetic pathophysiology. 10 patients with a median age of 51 years were selected for the study and subjected to bone marrow isolation. These samples were processed under sterile conditions for the enrichment of mononuclear cells (BM MNCs) from bone marrow. After strict quality control and characterization of cells, 2 x 106 cells/kg of BM MNCs were infused back into the patient through the anterior pancreaticoduodenal artery. We performed an evaluation of clinical parameters like Body Mass Index, Fasting Plasma Glucose, Fasting Plasma Insulin, HbA1c and C-peptide levels, and followed up the patients for 12 months. Our study showed a reduction in insulin dependency by ≥ 50%.
Conferencia Posgrado del Dr. Iván Contreras Fernández - Dávila, Universitat de Girona: "Diabetes: Patrones ocultos en series temporales" impartida el 09 de Abril de 2015
dkNET Webinar: The 4DN Data Portal - Data, Resources and Tools to Help Elucid...dkNET
Presenter: Andrew Schroeder, PhD. Project Manager & Senior Data Curator, 4D Nucleome Data Coordination and Integration Center (4DN-DCIC), Park Lab, Department of Biomedical Informatics, Harvard Medical School
Abstract
The Common Fund 4D Nucleome program, currently in its 9th year, is a consortium of researchers that aims to understand the principles behind the three-dimensional organization of the nucleus and how this organization can change over time to affect a variety of cellular processes. The 4DN Data Portal (data.4dnucleome.org) is an expanding resource hosting data generated by the 4DN Network and other reference nucleomics data sets. The portal provides tools for search, exploration, visualization, and download. An overview of the data portal, highlighting available data, how it can be found, visualized and used for analyses will be presented.
The top 3 key questions that the 4DN data portal can answer:
1. Are there significant sites of long-range chromatin contacts near my gene or region of interest?
2. What omics datasets are available for my tissue of interest?
3. Are there imaging datasets available that are relevant to my tissue of interest?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours: NIH Data Management and Sharing Mandate 05/03/2024dkNET
Presenter: Jeffrey Grethe, PhD, Principal Investigator of NIDDK Information Network (dkNET), Center for Research in Biological Systems, University of California San Diego
For all proposals submitted on/after January 25 2023, NIH requires the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources that could help.
*Previous Office Hours Slides and Recording: https://dknet.org/rin/research-data-management
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...dkNET
Presenter: Chen Li, PhD. Professor, Department of Computer Science, University of California Irvine
Abstract
Many data analytics projects have collaborators with complementary backgrounds, including biologists, bioinformaticians, computer scientists, and AI/ML experts. Many of them have limited experience to code, set up a computing infrastructure, and use MLmodels. Existing tools and services, such as email attachments, GitHub, and Google Drive are inefficient for sharing data and analyses. In this talk, we present an open source system called Texera that provides a cloud computing platform for collaborators to share data and analyses as workflows. After seven years of development, the system has a rich set of powerful features, such as shared editing, shared execution, version control, commenting, debugging, user-defined functions in multiple languages (e.g., Python, R, Java), and support of state-of-the-art AI/ML techniques. Its backend parallel engine enables scalable computation on large data sets using computing clusters. We will show a demo of the system, and present our vision supported by a recent NIH award, dkNET(NIDDK Information Network, https://dknet.org), to serve the diabetes, endocrinology, and metabolic diseases research communities through the FAIR sharing of data and knowledge.
Resource link: https://github.com/Texera/texera
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Unlocking the Power of FAIR Data Sharing with ImmPort 04/12/2024dkNET
Presenter: Sanchita Bhattacharya, ImmPort Science Program Lead, Bakar Computational Health Sciences Institute UCSF
Abstract
The Immunology Database and Analysis Portal (ImmPort, https://www.immport.org/home) is a domain-specific data repository for immunology-related data which is funded by the National Institutes of Health, National Institute of Allergy and Infectious Diseases, and Division of Allergy, Immunology, and Transplantation. ImmPort has been making scientific data Findable, Accessible, Interoperable, and Reusable (FAIR) for over 20 years. ImmPort data sets encompass over 7 million experimental results across 160 diseases and conditions, including data related to diabetes, kidney and liver transplantation, celiac disease, and many more conditions. In this webinar, participants will learn about data management and sharing through ImmPort, as well as finding and leveraging data sets of interest for research.
The top 3 key questions that the ImmPort can answer:
1. How can researchers share data through ImmPort to comply with the NIH Data Management and Sharing policy?
2. How does ImmPort support FAIR data and why is this powerful for research?
3. What scientific data does ImmPort house that would be of interest to NIDDK researchers?
Upcoming webinars schedule: https://dknet.org/about/webinar
Presenter: Angela Oliveira Pisco , PhD
Abstract
Although the genome is often called the blueprint of an organism, it is perhaps more accurate to describe it as a parts list composed of the various genes that may or may not be used in the different cell types of a multicellular organism. While nearly every cell in the body has essentially the same genome, each cell type makes different use of that genome and expresses a subset of all possible genes. This has motivated efforts to characterize the molecular composition of various cell types within humans and multiple model organisms, both by transcriptional and proteomic approaches. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. One caveat to current approaches to make cell atlases is that individual organs are often collected at different locations, collected from different donors, and processed using different protocols. Controlled comparisons of cell types between different tissues and organs are especially difficult when donors differ in genetic background, age, environmental exposure, and epigenetic effects. To address this, we developed an approach to analyzing large numbers of organs from the same individual. We collected multiple tissues from individual human donors and performed coordinated single-cell transcriptome analyses on live cells. The donors come from a range of ethnicities, are balanced by gender, have a mean age of 51 years, and have a variety of medical backgrounds. Tissue experts used a defined cell ontology terminology to annotate cell types consistently across the different tissues, leading to a total of 475 distinct cell types with reference transcriptome profiles. The Tabula Sapiens also provided an opportunity to densely and directly sample the human microbiome throughout the gastrointestinal tract. The Tabula Sapiens has revealed discoveries relating to shared behavior and subtle, organ-specific differences across cell types. We found T cell clones shared between organs and characterized organ-dependent hypermutation rates among B cells. Endothelial cells and macrophages are shared across tissues, often showing subtle but clear differences in gene expression. We found an unexpectedly large and diverse amount of cell type–specific RNA splice variant usage and discovered and validated many previously undefined splices. The intestinal microbiome was revealed to have nonuniform species distributions down to the 3-inch (7.62-cm) length scale. These are but a few examples of how the Tabula Sapiens represents a broadly useful reference...Full abstract: https://dknet.org/about/blog/2726
Resource link: https://tabula-sapiens-portal.ds.czbiohub.org
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "The Multi-Omic Response to Exercise Training Across Rat Tissue...dkNET
Presenter: Malene Lindholm, PhD, Instructor, Department of Medicine, Stanford University
Abstract
The Molecular Transducers of Physical Activity Consortium (MoTrPAC) aims to map the molecular responses to exercise and training to elucidate how exercise improves health and prevents disease. The first MoTrPAC data provides an extensive temporal map of the dynamic multi-omic response to endurance training across multiple rat tissues. All results can be viewed, interrogated, and downloaded in a user-friendly, publicly accessible data portal (https://motrpac-data.org). The MoTrPAC data compendium includes transcriptomics, proteomics, metabolomics, phosphoproteomics, acetylproteomics, ubiquitylproteomics, DNA methylation, chromatin accessibility, and multiplexed immunoassay data. This compilation constitutes of 211 datasets across 19 tissues, 25 molecular assays, and 4 training time points in adult male and female rats. Over 35,000 analytes were found to be differentially regulated in response to endurance training, with many displaying sexual dimorphism. We observed a male-specific recruitment of immune cells to adipose tissues and an anticorrelated transcriptional response in the adrenal gland related to the stress response. Temporal multi-omic and multi-tissue integration demonstrated similar temporal responses in the heart and skeletal muscle, reflecting a concerted adaptation of mitochondrial biogenesis and metabolism. Integrative multi-omic network analysis revealed connections between the heat shock-mediated stress response and mitochondrial biogenesis. Training increased phospholipids and decreased triacylglycerols in the liver, and there were extensive changes to mitochondrial protein acetylation. Many changes were relevant for human health conditions, such as non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular wellness, and tissue damage and repair. Altogether, this MoTrPAC resource provides an unprecedented view of the effects of exercise across an organism, revealing mechanistic details of how exercise impacts mammalian health. The MoTrPAC data hub is the primary online resource to disseminate this large-scale multi-omics data.
The top 3 questions that the MoTrPAC resource can answer:
1. What is the multi-omic response to endurance exercise across different tissues?
2. What are the top signaling pathways affected in response to exercise and do they differ between males and females?
3. How can the MoTrPAC data hub be utilized to interrogate all the MoTrPAC findings?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...dkNET
Presenter: Pieter Dorrestein, PhD, Professor, Skaggs School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology and Pediatrics, University of California San Diego
Abstract
In the analysis of organs, volatilome, or biofluids, the microbiome influences 15-70% of detectable mass spectrometry molecules. Typically, only 10% of human untargeted metabolomics data can be assigned a molecular structure, with merely 1-2% traceable to microbial origins. Human microbiomes contribute metabolites through the microbial metabolism of host-derived substances, digestion of food and beverage molecules, and de novo assembly using proteins encoded by genetic elements. Despite the significance of microbiome-derived metabolites to human health, there is no centralized knowledge base for community access. To address this, the "Collaborative Microbial Metabolite Center" (CMMC) leverages expertise in mass spectrometry, microbiome innovation, and the GNPS ecosystem to built a knowledgebase. It aims to create a user-accessible microbiome resource, enrich bioactivity knowledge, and facilitate data deposition. The CMMC includes the construction of a knowledge base, MicrobeMASST tool, and health phenotype enrichment workflows, the construction and use will be discussed in this presentation. The use of this ecosystem will be exemplified by the discovery of 20,000 bile acids, many of which were shown to be of microbial origin and linked to diet and IBD.
The top 3 key questions that this resource can answer:
1. How can we leverage the 1000’s of public metabolomics studies to discover microbial metabolites and their organ distributions as well as their phenotypic, including health, associations?
2. If one has an unknown molecule, how can one assess what microbes make a molecule without known structure?
3. How can one contribute to the expansion of the knowledgebase on microbial metabolites?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: An Encyclopedia of the Adipose Tissue Secretome to Identify Me...dkNET
Presenter: Paul Cohen, MD, PhD, Albert Resnick, M.D. Associate Professor, Rockefeller University
Abstract
White and brown adipocytes not only play a central role in energy storage and combustion but are also dynamic secretory cells that secrete signaling molecules linking levels of energy stores to vital physiological systems. Disruption of the signaling properties of adipocytes, as occurs in obesity, contributes to insulin resistance, type 2 diabetes, and other metabolic disorders. Fat cells have been estimated to secrete over 1,000 polypeptides and microproteins and an even larger number of small molecule metabolites. The great majority of the adipocyte secretome has not been defined or characterized. A major obstacle has been the lack of suitable technologies to quantitatively identify circulating proteins and metabolites, determine their cellular origin, and elucidate their function. Building on key innovations in chemical biology and mass spectrometry, our team is generating an encyclopedia of the white and brown adipocyte secretome in mouse models and humans. Our work has the potential to identify new secreted mediators with roles in obesity, type 2 diabetes, and metabolic diseases, provide a crucial resource for researchers and clinicians, and lead to new biomarkers and therapies.
The top 3 key questions that this resource can answer:
1. What techniques can be used to characterize the secretome of a cell type in vitro and in vivo?
2. What is the full complement of proteins and metabolites secreted by different kinds of adipocytes?
3. How should one prioritize uncharacterized secreted mediators for functional study?
Resource link: https://secrepedia.org/
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: A Single Cell Atlas of Human and Mouse White Adipose Tissue 11...dkNET
Presenter: Margo Emont, PhD. Instructor, Beth Israel Deaconess Medical Center/Harvard Medical School
Abstract
White adipose tissue, once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic and heterogenous, and is involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control and host defense. High-fat feeding and other metabolic stressors cause marked changes in adipose morphology, physiology and cellular composition, and alterations in adiposity are associated with insulin resistance, dyslipidemia and type 2 diabetes. Here we provide detailed cellular atlases of human and mouse subcutaneous and visceral white fat at single-cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells, vascular and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease and provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits and cell types in the function of white adipose tissue across species, depots and nutritional conditions.
The top 3 key questions that this resource can answer:
1. How specific is my gene of interest to a particular cell type in adipose tissue?
2. Is the gene/pathway that I am studying in mouse adipose tissue also present in human adipose tissue (and is it regulated similarly in low vs high body weight)?
3. What are the changes in gene expression in a specific cell type at low vs high body weight?
Resource link:
https://singlecell.broadinstitute.org/single_cell/study/SCP1376/a-single-cell-atlas-of-human-and-mouse-white-adipose-tissue#study-summary
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "The National Sleep Research Resource (NSRR) - Opportunities fo...dkNET
Presenter: Susan Redline, MD, MPH, Peter C. Farrell Professor of Sleep Medicine, Professor of Epidemiology, Harvard T.H. Chan School of Public Health
Abstract
Experimental, clinical and epidemiological studies have identified multiple inter-relationships of sleep with glucose regulation and metabolic disease. In one meta-analysis, after overweight and family history of diabetes, the next 7 top risk factors for incident diabetes were measures of sleep health. These included poor sleep quality, insomnia, short or extremely long sleep duration, and sleep apnea; each sleep problem was associated with incident diabetes with relative risks ranging from 1.38 to 1.74. A mechanism linking sleep apnea with diabetes is through the effects of intermittent hypoxemia on insulin sensitivity. However, studies using neurophysiological markers of sleep in healthy adults showed that selective reduction of slow wave sleep reduced glucose tolerance by 23%, thus additionally suggesting the importance neurophysiological mechanisms during sleep in glucose regulation. In support of this, longitudinal epidemiological studies demonstrated that higher proportions of slow wave sleep (N3) were protective for the development of type 2 diabetes. Recent animal and human studies also point to the effects of sleep micro-architecture—specifically the coupling of slow waves and spindles- on short-term and long-term glucose regulation, possibly through the effects on signaling between the hippocampus and hypothalamus, and changes in autonomic nervous system output. Experimental data also demonstrate a prominent role of the circadian system in regulating glucose and lipid levels. In support of those studies, epidemiological associations have identified significant associations between actigraphy-based measures of sleep irregularity (a marker of circadian disruption) with incident metabolic dysfunction and hypertension. This rich data implicating sleep disturbances as drivers of metabolic disease, coupled with data indicating a high prevalence of sleep and circadian disorders in the population, suggest novel opportunities to target sleep and circadian pathways for preventing or treating metabolic dysfunction, as well as key knowledge gaps.
The National Sleep Research Resource (NSRR; sleepdata.org) provides a large and growing repository of well-annotated polysomnograms (PSGs), actigraphy studies, and questionnaires, some associated with clinical and biochemical data relevant to understanding the links between sleep and circadian disorders with metabolic disease. Notably, the NSRR includes over 50,000 PSGs, which concurrently include multiple physiological signals with high temporal resolution, allowing generation of thousands of variables summarizing dynamic physiological changes and “cross-talk” between physiological systems...(Please see https://dknet.org/about/blog/2674 for full abstract)
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...dkNET
For all proposals submitted on/after January 25 2023, NIH will require the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources that could help.
*Previous Office Hours Slides and Recording: https://dknet.org/rin/research-data-management
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch 06/02/2023dkNET
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch
Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego
Abstract
The dkNET (NIDDK Information Network) team is announcing an exciting new service - Biomed Resource Watch (BRW, https://scicrunch.org/ResourceWatch), a knowledge base for aggregating and disseminating known problems and performance information about research resources such as antibodies, cell lines, and tools. We aggregate trustworthy information from authorized sources such as Cellosaurus, Antibody Registry, Human Protein Atlas, ENCODE, and many more. In addition, BRW includes antibody specificity text mining information extracted from the literature via natural language processing. BRW provides researchers and curators an easy-to-use interface to report their claims about a specific resource. Researchers can check information about a resource before planning their experiments via BRW-enhanced Resource Reports. This new service aims to help improve efficiency in selecting appropriate resources, enhancing scientific rigor and reproducibility, and promoting a FAIR (Findable, Accessible, Interoperable, Reusable) research resource ecosystem in the biomedical research community.
Join us for a webinar to introduce the following resources & topics:
1. An overview of dkNET
2. How Resource Reports benefit you
3. Biomed Resource Watch
3.1 Navigating Biomed Resource Watch
3.2 How to Submit a Claim
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Leveraging Computational Strategies to Identify Type 1 Diabete...dkNET
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Wenting Wu, PhD. Research Assistant Professor, Center for Diabetes and Metabolic Diseases, Department of Medical and Molecular Genetics, Associate Director of Data and Analytics Core for Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine
Abstract
Type 1 diabetes (T1D) is an immune-mediated disease that results in insulin insufficiency and affects 0.3% of the population, including both children and adults. To support clinical trial efforts, there is an urgent need to develop reliable biomarkers capable of predicting T1D risk and guiding therapeutic interventions. Recently, whole blood bulk RNA sequencing has been used to guide T1D clinical trial design and assess response to disease modifying interventions. While the use of bulk RNA sequencing is cost-effective, these datasets provide limited information about cell specific gene expression changes. Here, we aimed to apply computational strategies to deconvolute cell type composition using cell specific gene expression references. Single-cell RNA sequencing (scRNA-seq) was conducted to profile peripheral blood mononuclear cells obtained from youth within recent T1D onset and age- and sex-matched controls and identified 31 distinct cell clusters. Using this pre-defined reference dataset, we ran computational algorithms CIBERSORTx and other deconvolution methods simultaneously to deconvolute cell proportions using public clinical trial data. We focused our initial analysis on data from the TN-20 Rituximab trial, which tested the anti-CD20 monoclonal antibody rituximab vs placebo in recent onset T1D. This talk will introduce recent advances of scRNA-seq techniques and computational deconvolution methods and demonstrate that how we apply different deconvolution approaches for secondary analysis of existing clinical trial data, in the purpose of linking cell specific immune signatures associated with drug responder status.
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...dkNET
For all proposals submitted on/after January 25 2023, NIH requires data sharing from all NIH-funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and available resources that could help.
In our upcoming session on March 3, 2023, we are pleased to invite Dr. Jeffrey Grethe, dkNET co-PI and expert on Data Management and Sharing, Dr. Rebecca Rodriguez, Repository Program Director at NIDDK, Ms. Reaya Reuss, Chief of Staff to the Deputy Director at NIDDK, and the support team members from the NIDDK Central Repository. They will be available to answer any questions you may have.
*Previous Office Hours Slides and Recording: https://dknet.org/about/blog/2535
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Webinar: Postpartum Glucose Screening Among Homeless Women with Gestati...dkNET
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Rie Sakai-Bizmark, PhD. Assistant Professor, The Lundquist Institute at Harbor-UCLA Medical Center, David Geffen School of Medicine at UCLA
Abstract
Women with gestational diabetes mellitus (GDM) are at high risk of developing glucose intolerance after delivery. In the long term, women with GDM have a nearly 10-fold higher risk of developing type 2 diabetes mellitus (T2D) than women without GDM. The American Diabetes Association (ADA) and the American College of Obstetrics and Gynecology (ACOG) recommend that women with GDM undergo a 75-g oral glucose tolerance test (OGTT) between four and 12 weeks postpartum, and periodically thereafter. However, postpartum glucose screening (PGS) rate is historically low despite of various interventions to improve such rate. We hypothesized that PGS rate is lower among postpartum homeless women than their housed counterparts, and that interventions to improve PGS rate among postpartum homeless women with GDM should be tailored to their unique circumstances. The Japanese Society of Diabetes and Pregnancy (JSDP) modified the method to perform PGS with random plasma glucose (RPG) and glycated hemoglobin (HbA1c), which are simple and less invasive, to reduce the risk of COVID-19 infection by shortening the time spent in the hospital. RPG or HbA1c test do not require fasting. Therefore, homeless women who utilized care for other reasons could have the test as PGS. Given the barriers faced by homeless individuals, we hypothesize that RPG and HbA1c at healthcare utilizations during the postpartum period could be one of the strategies to identify high-risk individuals early because 1] healthcare utilizations are an opportunity for healthcare providers and social workers to educate homeless patients on GDM and their insurance eligibility and coverage for the screening, and 2] the physical barriers to health care access, which are often cited as a reason for the low PGS rate, are removed.
This proposed study will use administrative data from five states (AZ, CO, NC, NJ, and OR), which collectively include 9.3% of the US female homeless population. Each state will provide detailed, linked, multi-level, anonymized data for postpartum homeless women from four sources: 1] Medicaid claims; 2] Homeless Management Information System (HMIS); 3] birth records; and 4] the American Hospital Association (AHA) database to obtain hospital characteristics. With data from 2013 to 2020, an estimated sample size of 24,000 homeless women who delivered babies and 3,290 postpartum homeless women with GDM will be included.
First, we will estimate rates of GDM and PGS among homeless women. Second, we will estimate the cost-effectiveness of performing RPG and HbA1c tests...[Full abstract: https://dknet.org/about/blog/2581]
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Choosing Sample Sizes for Multilevel and Longitudinal Studies ...dkNET
dkNET Webinar: Choosing Sample Sizes for Multilevel and Longitudinal Studies Analyzed with Linear Mixed Models
Presenter:
Kylie K. Harrall, MS, Research Instructor, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus
Abstract
Planning a reproducible study requires selecting a sample size which will ensure appropriate statistical power. Free point-and-click software (Kreidler et al., Journal of Statistical Software, 2013, 10.18637/jss.v054.i10) makes it easy to select a sample size for clustered and longitudinal designs with linear mixed models. The software, a suite of training modules, and reference materials are freely available online (www.SampleSizeShop.org ). The software interface and training materials are aimed at biomedical scientists, included those funded by NIDDK. We give examples of study designs for which the software will compute power and sample size, including a study with clustering, a study with longitudinal repeated measures, and a study with multiple outcomes, where heterogeneity of response among subgroups is of interest.
The top 3 key questions that the Sample Size Shop can answer:
1. What free, online, point-and-click, wizard-style, NIH-funded, validated, published power and sample size software provides calculations for studies with clusters, longitudinal studies, and longitudinal studies with clusters?
2. Can GLIMMPSE (www.SampleSizeShop.org) compute power and sample size for randomized controlled clinical trials and observational studies funded by NIDDK?
3. Why use validated power and sample size software instead of writing simulations?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: : FAIR Data Curation of Antibody/B-cell and T-cell Receptor Se...dkNET
Abstract
AIRR-seq data (antibody/B-cell and T-cell receptor sequences from Adaptive Immune Receptor Repertoires) can describe the adaptive immune response in exquisite detail, and comparison and analysis of these data across studies and institutions can greatly contribute to the development of diagnostics and therapeutics, including the discovery of monoclonal antibodies for treatment of autoimmune diseases.
The AIRR community has developed protocols and standards for curating, analyzing and sharing AIRR-seq data (www.airr-community.org), and supports the AIRR Data Commons, a set of geographically distributed repositories that follows the AIRR Community’s metadata standards and the FAIR principles. The ADC currently comprises > 5 Bn receptor sequences from over 86 studies and ~9000 repertoires. The data model of the ADC has recently been expanded to include gene expression and cell phenotype data from single immune receptor cells, as well as MHC/HLA genotyping.
The iReceptor Gateway (ireceptor.org) queries this AIRR Data Commons for specific “metadata”, e.g. “find all repertoires from T1D studies” or for specific CDR3 sequences (e.g., find all repertoires from healthy individuals expressing this CDR3 sequence). Data from these federated repositories can then be analyzed through the Gateway by several sophisticated analysis tools, or downloaded for further analysis offline. The iReceptor Team at Simon Fraser University has recently initiated a collaboration to greatly expand the amount of bulk and single-cell immune profiling data from T1D studies in the AIRR Data Commons. For more information on obtaining or sharing AIRR-seq data contact support@ireceptor.org.
The top 3 key questions that the Adaptive Immune Receptor Repertoire (AIRR) can answer:
1. A researcher observes that many individuals with Type 1 Diabetes express a specific B-cell or T-cell receptor compared to controls (i.e., a “public” clonotype). To what degree is this receptor observed to be public across other T1D studies or other autoimmune disease populations?
2. Can Machine Learning be used to identify individuals who will respond well to a new cancer immunotherapy based on differences in their antibody/B-cell or T-cell receptor repertoires as curated in the AIRR Data Commons?
3. Is there an association between particular HLA, immunoglobulin (IG), or T-cell receptor (TR) germline gene polymorphisms and propensity toward specific infectious or autoimmune diseases?
Presenters:
Dr. Felix Breden, Scientific Director, iReceptor
Dr. Brian Corrie, Technical Director, iReceptor
Dr. Kira Neller, Bioinformatics Director, iReceptor
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours - "Are You Ready for 2023? New NIH Data Management and Sha...dkNET
For all proposals submitted on/after January 25 2023, NIH will require the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources (https://dknet.org/rin/research-data-management) that could help.
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Webinar "The Mission and Progress of the(sugar)science: Helping Scienti...dkNET
Abstract
The(sugar)science was launched two years ago with the aim of helping scientists who study type 1 diabetes (T1D) and related interdisciplinary fields connect globally. We also wanted to create a digital space where trainees in the field can be supported, celebrated and connected to future positions. As part of our mission, our all volunteer team created the State of the Science series (2021. 2022), connecting global thought leaders around T1D research topics for discussion with a larger scientific audience. The second State of Science series was led by women scientists following the ADA publication which highlighted the paucity of women scientists in the leadership positions in the field.
To encourage the scientific community at large to dive into pre-existing data and pull out novel hypotheses that pertain to T1D, we created and together with dkNET, hosted D-Challenge 2021 and 2022. These competitions awarded $40K and $50K respectively to those who mined data and developed the most creative and testable hypothesis as judged by scientific experts in the field. These teams were also able to have an audience with the JDRFT1D Fund as part of a "pitch polish" which facilitated their interaction with venture capital.
To date, we have hosted over 200 interviews with T1D focused scientists in academia and industry and have an audience of 35K. Our reach on social media continues to grow and our metrics indicate a robust following. We share opportunities for positions in the field, engage and support trainees and together, our young scientific team published a paper, Similarities between bacterial GAD and human GAD65: Implications in gut mediated autoimmune type 1 diabetes, PLOS, February 2022.
We are currently engaged in the build of a T1D TCR Repository. We connected the AIRR data commons community with top TCR scientists in the field to begin this community based venture. It has the possibility to be incredibly instructive in defining the prodrome , which will further inform the field as it pertains to understanding the etiology of T1D.
Current team members that will join the discussion today will be Neha Mejety, Johns Hopkins University undergraduate and Tiffany Richardson, doctoral degree candidate at VUMC Diabetes.
The top 3 key questions that the(sugar)science can answer:
1. How can I find scientists to collaborate with in Type 1 diabetes research?
2. Where can I learn about Type 1 diabetes trending topics?
3. Where can I find forums to discuss novel ideas with scientists or key opinion leaders and find opportunities for Type 1 diabetes research ?
Presenters:
Monica Westley, PhD, Founder, the(sugar)science
Tiffany Richardson
Neha Majety
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Discovering and Evaluating Antibodies, Cell Lines, Software To...dkNET
Abstract
dkNET’s Resource Reports (https://dknet.org/rin/rrids) enable researchers to discover research resources that would be useful for their research. The resource report integrated data set and analytics platform combines Research Resource Identifiers (RRIDs), text mining and data aggregation to help you identify key biomedical resources, track these resources, and compare their performance. Resource Reports offer a detailed overview of each resource along with citation metrics from the biomedical literature and even information about what resources have been used together. You'll gain insights about who is using particular resources and how the community views those resources, including usage in published protocols.
The dkNET Co-PI, Dr Jeffrey Grethe, will give you live demos during this webinar, including:
- How to find and select a research resource such as an antibody or a cell line
- How to find Research Resource Identifiers (RRIDs) and proper citation of your resources
- How to register resources to obtain RRIDs if the resources do not exist in the system
We hope this short webinar will provide an opportunity to use this tool to shape your research activities.
Presenter: Jeffrey Grethe, PhD, dkNET Co-Principal Investigator, University of California San Diego
Upcoming webinars schedule: https://dknet.org/about/webinar
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
dkNET Webinar: Estimating Relative Beta-Cell Function During Continuous Glucose Monitoring and Its Clinical Applications 03/10/2023
1. Joon Ha
Department of Mathematics, Howard University, Washington DC.
1
Estimating Relative Beta-Cell Function During Continuous
Glucose Monitoring and Its Clinical Applications
2. Glucose Pattern Leading to Type 2 Diabetes (T2D)
Glucose does not increase much until reaching the threshold and sharply rises
2
Mason et al., Diabetes 2007;56:2054-2061
mean of 50 Pima Indians
Good Biomarker?
3. What is a good biomarker?
• Robustness to detect progression to the disease; already
substantially changed before onset of the disease (counter
example; blood glucose), leading to an early biomarker
• Prediction of the disease, typically assessed by Receiver Operating
Characteristic Curve (ROC) or Survival Analysis (longitudinal),
leading to a good predictor
• Identifying metabolic characteristics, leading to personalized
therapies
5. Contents
1. Summary of current metabolic parameter surrogates during various glucose
challenge tests
2. Novel marker during standard oral glucose tolerance tests (OGTTs)
3. Novel marker during Continuous Glucose Monitoring (CGM)
4. Clinical applications:
A) Personalized intervention
B) Detecting subjects at high risk on a plane of disposition index
C) Heterogeneity and Homogeneity across Ethnics
6. Gold standard measurements of metabolic parameters
• Insulin resistance and β-cell dysfunction are key pathophysiological factors
for onset of type 2 diabetes (T2D)1
• Two clamp experiments are generally accepted as the “gold standard” to
measure the two risk factors2,3
• β-cell function relative to insulin sensitivity (i.e., Disposition Index [cDI] =
insulin sensitivity x insulin secretion) is considered the strongest metabolic
predictor for T2D4,5
• However, not practical for large-scale studies; up to 4 hours experiment time
and highly skilled labor and thus are cost-prohibitive
1Hannon., Ann. N.Y. Acad. Sci, 2015; 2Arslanian S., Horm Res, 2005; 3Sjaarda L., Diabetes Care, 2013
4Bergman N. R. et al., Diabetes, 2002; 5Utzschneider M.K., Diabetes Care, 2013
Hyperinsulinemic-Euglycemic clamp: Peripheral insulin sensitivity
Hyperglycemic clamp: Beta-cell function
7. Frequently sampled intravenous glucose tolerance tests (FSIGT)
• Blood samples collected every 2 min to 30 minutes during two hours: 22
Glucose and Insulin measurements
• Beta-cell function: Acute insulin response to Glucose (AIRg), AUC I of the first
10 minutes
• Insulin sensitivity: fitting the minimal math to G, MINMOD, R. Bergman;
Banting Medalist
IGT
G
DI
High Risk
Insulin Sensitivity (10-4/(µU/ml)*min)
Insulin
Secretion
(
µ
U/ml)
G constant Normal G
SI =2.0
AIRG =400
DI=800
SI =0.4
AIRG =2000
DI=800
AIR
G
• DI originally derived
from Intravenous
Glucose Tolerance Test
(IVGTT)
8. Current Metabolic Estimates during OGTTs
• Insulin Resistance or Sensitivity: HOMA-IR, QUICKI, Matsuda Index
• Beta-cell Function: HOMA-B, Insulinogenic Index(IGI)
• Relative Beta-Cell Function: fDI(HOMA-B*(1/HOMA-IR), oDI (IGI*Matsuda)
• All are built by algebraic formulae using single and average measurements
rather than glucose and insulin profiles
• Current mathematical model-derived estimates require frequently
sampled OGTTs up to 6 hours (11 G, 11 I, 11 C-peptide points), Cobelli’s
model and Mari’s model; not applicable for large-scale epidemiological
studies.
• Standard OGTTs (G, I at 0,30,60,90,120) that underwent under clinical
settings. However, the current math models (Cobelli and Mari) are not
applicable to estimate metabolic parameters with standard OGTTs
9. Pancreatic b-cells
(Insulin Secretion)
s
(b-cell
function)
Liver
(Hepatic Glucose
Production)
HGP
Plasma Insulin
I(t)
k
Glucose Space
G(t)
EG0
(G Effectiveness)
OGTT
(75g)
Muscle
(Glucose Uptake)
SII
Novel metabolic parameters During Standard OGTTs
𝒅𝑮
𝒅𝒕
= 𝑶𝑮𝑻𝑻(𝒕) + 𝑯𝑮𝑷(𝑰, 𝑺𝑰) − 𝑬𝑮𝑶 + 𝑺𝑰𝑰 𝑮
𝒅𝑰
𝒅𝒕
=
𝜷
𝑽
𝑰𝑺𝑹(𝝈, 𝑮) − 𝒌𝑰 6,7
mDI = SI*s
• Estimation: SI and s
• Data: G and I, at 5 time points:
t=0, 30, 60, 90, 120 min
Ha J., Satin L., Sherman A., Endocrinology 2016; Ha J. and Sherman A., AJP Endo. Metabolism 2020, Ha et al. Diabetes 2021
10. Mathematical DI with (mDI) and without Insulin (mDI-woI)
mDI with insulin
mDI = SI*s
• Uses glucose and insulin at 5 time points of OGTT
t=0, 30, 60, 90, 120 min
• Estimates SI and s separately
mDI-woI = SI*s
mDI without insulin
• Uses glucose only, t=0, 30, 60, 90, 120 min
• Cannot estimate SI and s separately
• Estimates mDI
ADA 2021, Young Investigator Award
Joon Ha 2021
11. Rationale for no requirement of insulin
Courtesy from Max Springer,
U of Maryland
12. Rationale for no requirement of insulin (cont’d)
Courtesy from Max Springer,
U of Maryland
13. mDI vs. mDI-woI
mDI-woI is excellently correlated with mDI
Data: Dr. Sangsoo Kim, the Pusan National University
Hospital, South Korea
N= 137, mean age=50.5, mean BMI=24.2, Male=48%.
14. Mean Glucose and Glycemic Excursion
• mDI-woI detects mean G and Glycemic excursion
• Large change in mDI-woI, but small change in mean G
16. : ROC Analysis to detect T2D and PreDM
T2D PreDM
Good Detector
Cross-sectional data
17. • N=5742 (Non-diabetes) and OGTTs
every two years
• Outcome: DM over the course of
14 year-longitudinal study
Good Predictor
: Longitudinal Confirmation
Korean Genome Epidemiology Study
(KoGes)
Data: Dr. Sangsoo Kim, PNUH, South Korea
18. Robustness of mDI –woI (longitudinal, N=215)
N=215 who developed from NGT at baseline to PDM and T2D
mDI-woI substantially changed
at onset of Prediabetes
19. Summary, so far
• Model insulin sensitivity, b-cell function, and DI agree well with
clamp parameters in obese youth
• mDI-woI correlates with mDI with insulin
• mDI-woI is a good and robust predictor
21. https://time.com/4703099/continuous-glucose-monitor-blood-sugar-diabetes/
Continuous Glucose Monitoring (CGM)
Real-time Glucose Reading
• Real-time glucose monitoring; daily average glucose and glycemic excursion
• glucose measurements collected in interstitial fluid (ISF G)
• Insulin measurements not collected
• Less invasive and relatively cheap
BG
ISF G
Peaks of ISF G tend to delay and
decrease
Diabetes Care. 2003;26(8):2405-2409. doi:10.2337/diacare.26.8.2405
Can we estimate relative beta-cell function with CGM?
• Current math models cannot do this because they require insulin measurements
• Our model can do (Ha and Sherman AJP 2020, Ha et al. Diabetes 2021)
• Goal: Estimate relative beta-cell function, mDI-CGM during CGM
BG vs. ISF G
22. Step 1: Relative Beta-cell function During an OGTT
wearing a CGM device
𝒅𝑮
𝒅𝒕
= 𝑶𝑮𝑻𝑻(𝒕) + 𝑯𝑮𝑷(𝑰, 𝑺𝑰) − 𝑬𝑮𝑶 + 𝑺𝑰𝑰 𝑮
𝒅𝑰
𝒅𝒕
=
𝜷
𝑽
𝑰𝑺𝑹(𝝈, 𝑮) − 𝒌𝑰 6,7
mDI-ISF = SI*s
• Estimation: SI and s
• Data: ISF at 5 time points:
t=0, 30, 60, 90, 120 min
Ha J., Satin L., Sherman A., Endocrinology 2016; Ha J. and Sherman A., AJP Endo. Metabolism 2020, Ha et al. Diabetes 2021
• Assumption: Discrepancy Between BG and ISF G is
uniform
• A data set for Step 1: BG and ISF G during OGTTs;
Patients wear a CGM sensor during an OGTT;
Glucose load same for all patients, 75 g
23. Mean ISF and Glycemic Excursion vs. mDI_ISF
mDI-ISF detects mean G and excursion G
24. Step 2: Relative Beta-cell function During CGM in a free
living environment
𝒅𝑮
𝒅𝒕
= 𝑶𝑮𝑻𝑻(𝒕) + 𝑯𝑮𝑷(𝑰, 𝑺𝑰) − 𝑬𝑮𝑶 + 𝑺𝑰𝑰 𝑮
𝒅𝑰
𝒅𝒕
=
𝜷
𝑽
𝑰𝑺𝑹(𝝈, 𝑮) − 𝒌𝑰 6,7
Output:
• Estimation: Si ,s, and Glucose Appearance
rate
• mDI-CGM = SI*s
Ha J., Satin L., Sherman A., Endocrinology 2016; Ha J. and Sherman A., AJP Endo. Metabolism 2020, Ha et al. Diabetes 2021
Input:
• CGMs at 25 time points during a meal
t=0, 10, 20, 30, … 240 min
• A total amount of carbo intakes during a meal
Carbohydrate intakes: Can-Pro 5.0 (web ver.), The Korean Nutrient Society
• Assumption: Discrepancy Between BG and ISF G is
uniform
• A CGM data set with free-living environments for Step
2; Glucose load estimated with carbo intakes
26. Summary, so far
• The mathematical model enables to estimate relative beta-
cell function during OGTTs and CGM without insulin
measurements
• mDI-CGM predicts mean G
• mDI-woI and mDI-CGM are cost effective for large scale
epidemiological studies and beneficial for patient care.
28. Personalized intervention with estimated SI and BCF
• Model-derived beta-cell function and insulin sensitivity characterize glucose tolerance status
• Insulin resistance is a manageable risk factor
-Lifestyle intervention could be more effective with insulin resistance subjects than weak beta function
• Insulin resistance and weak beta-cell function groups have the same level of mDI
Weak beta-cell function
Insulin resistance
Which group is
at higher risk?
30. Six subtypes of Non-diabetes:
Machine learning algorithm
N=325, Non-diabetes
Age=48.2,
BMI=24.3, PNUH
Normal Function
Insulin Sensitive
(NF IS)
Weak Function
Mild Insulin Resistance
(WF MIR)
Weak Function
Insulin Resistance
(WF IR)
Normal Function
Insulin Resistance
(MDF IR)
Mild DysFunction
Insulin Resistance
(MDF SIR)
Strong Function
Severe Insulin Resistance
(SF SIR)
A B F
E
D
C
G
I
31. Survival Analysis
Insulin sensitivity vs. beta-cell function
Risk Assessment of six subtypes of Non-diabetes
(KoGes, 16 years follow-up)
• Cluster C is at the highest risk and followed by E, B, D, F, A
• A+B+C: 82%, D:8.5%, E:7.9%, F:1.2%
• Weakest beta-cell function class C is at the highest risk
32. Longitudinal Changes of Clusters
Transition Matrix Table
• AàB and BàC are most frequently observed
• AàBàC is the most common pathway to progression to diabetes
A B
Longitudinal Changes of Clusters
33. Most common pathway to T2D:
Koreans vs. Pima Indians
Koreans Pima Indians
Beta-cell function increases and decrease,
as insulin resistance worsens
Population Fit
42. Progressors vs. Non-progressors
AUC G vs. mDI-woI
Progressors have smaller mDI at baseline
Non-progressor
baseline
Progressor
baseline
0 2 4 6
mDI-woI
43. Progressors vs. Non-progressors
2h-PG, 1h-PG, FPG vs. mDI-woI, mDI
Fasting Glucose of the two groups at baseline are not different, suggesting that
Fasting Glucose is not a good predictor to progression to diabetes in Koreans
Progressors have smaller mDI at baseline
mDI
44. Summary, overall
• Model insulin sensitivity, b-cell function, and DI agree well with
conventional surrogates, but outperform to predict dysglycemia
• Model-derived insulin sensitivity and beta-cell function could have
potential to be implemented in personalized therapies.
• mDI-woI and mDI best predict diabetes, compared to current diabetes
criteria, G0, G120, A1c, and oDI, based on a longitudinal study
• A combination of machine learning algorithm and model parameters
identifies 6 subtypes of Non-diabetes and reveals the most common
pathway of progression to diabetes in a cohort of Korean population;
potential to apply for other cohorts
45. Conclusions
• A longitudinal Confirmation of mDI-CGM with
Young African Americans, Howard University
Future Directions
•mDI-woI is a reliable indicator of dysglycemia
(PreDM and DM)
•mDI-woI is suitable for large scale observational
and interventional studies to assess b-cell function
• s and SI are good surrogates for beta-cell function
and insulin sensitivity
46. Minimum Data points for math model derived metabolic surrogates
Outcomes
5G + 5I
5G + 2I or 3G + 3I
5G
3G (t=0, 60, 120)
CGM + Carbo
FPG + FPI + A1c
SI, beta-cell function, DI
SI, beta-cell function, DI
DI
DI
DI (ongoing)
Six subtypes with a machine
learning algorithm (ongoing)
Data
47. Funding Source
• dkNET, Pilot Study of Bioinformatics for a new PI, NIDDK, NIH
with title “Estimating Relative Beta-Cell Function During Cont
inuous Glucose Monitoring”
• Brain Pool Program of South Korea, Department of Endocrin
ology and Metabolism Pusan National University Hospital, Pu
san: May 2022 – Dec 2024 titled “Finding a Robust Early Bio
marker of Progression to Type 2 Diabetes Mellitus Using a No
vel Mathematical Model”
• Howard University Start-up Fund 49
48. Sangsoo Kim, MD, Division of Endocrinology,
Pusan National University Hospital, South Korea
Collaborators and data sources
Jinmi Kim, Ph.D, Department of Biostatistics,
Clinical Trial Center, Biomedical Research Institute,
Pusan National University Hospital, South Korea
Team of Division of Endo. and Meta., PNUH
Wook Yi, MD
Sori Yang, MD
Myoungsoo Lim, MD
Doohwa Kim, MD
Minsoo Kim, Ph.D. Candidate
Hyejung Jae, RN
49. 15-year longitudinal data of Pima Indians
51
Pima Indian Data:
Clifton Bogardus,
Phoenix, NIDDK, NIH,
A community
with very high
rates of obesity
and diabetes
50. Consultants and data sources
Stephanie Chung
Anne Sumner
Diabetes, Endocrinology and Obesity Branch, NIDDK, NIH.
Arthur Sherman, Ph. D.
NIDDK, NIH, MD