Research Unmuted

SLC
  • 5,0 (1)
  • NATURVIDENSKAB

Research Unmuted is a experimental scientific audio journal presenting contemporary research in diabetes, cardiometabolic diseases, prediabetes, wearable technology, digital health, medical informatics, data science, AI, machine learning, and related topics. Episodes discuss peer-reviewed studies, methodological insights, and clinical implications, translating complex research into an accessible format.

Episoder

  1. 2 DAGE SIDEN

    The A1c Mystery

    Abstract This study aims to characterize the temporal discordance between CGM-derived glucose exposure and HbA1c over time in individuals with type 1 diabetes, and to explore the development of a statistical model to adjust the relationship between these measures based on previously observed individual discrepancies. We paired CGM-data in a 60-day window prior to each HbA1c measurement and included individuals with type 1 diabetes with multiple pairs to assess and model discordance over time. Discordance was defined as difference between HbA1c and Glucose Management Indicator at each pair. At baseline (first pair), participants were categorized into three groups based on the degree of discordance: positive (≥0.5%), negative (≤–0.5%), and neutral (within ±0.5%). A multiple linear regression model incorporating historical discordance values, HbA1c levels, and the current GMI was utilized for an adjustment. 477 individuals were included and 1,523 instances of paired HbA1c and CGM-data were analyzed. Absolute discordance of ≥0.5% was observed in 31% of cases. In 51% of instances, the direction of discordance in each pair was maintained. In the modeling analysis, GMI accounted for 69% of the variance in HbA1c levels (r = 0.83, p  0.001, MAE = 0.42%). Adjusting improved variance explainability to 82% (r = 0.90, p  0.001, MAE = 0.33%). HbA1c-CGM discordance is highly prevalent, and while inter-individual discordance shows some degree of persistence, it also appears to vary over time for a substantial proportion of individuals. Adjusting for individual discordance in the short term can improve the alignment between adjusted GMI and laboratory-measured HbA1c. Citation Cichosz, S. (2026, februar 19). Research Unmuted – Episode 6: The A1c Mystery. Research Unmuted. https://doi.org/10.5281/zenodo.18697228 References Narrowing the A1c gap: personalized modeling of HbA1c– continuous glucose monitor discordance in type 1 diabetes. Cichosz, S. L., Thomsen, C. H. N., Klonoff, D. C., Hirsch, I. B. & Jensen, M. H., 17 Feb 2026, (E-pub ahead of print) In: PLOS digital health. Tags #Diabetes #Data_Science #Digital_Health

    4 min.

  2. 20. JAN.

    What Research Should be Prioritized in Prediabetes

    Abstract Aims/hypothesis: Research agendas are typically set by researchers and funders, meaning that priorities of end users, such as patients and healthcare professionals (HCPs), could be missed or overlooked in research. To ensure future research in prediabetes is of relevance and benefit to people with prediabetes and HCPs, it is important to involve these stakeholders in setting the research agenda. The aim of this study was to establish a top-10 list of the most important research questions in prediabetes (HbA1c 42-47 mmol/mol [6.0-6.4%]) by involving and collaborating with patients, relatives, patient organisations, HCPs and researchers.Methods: We used a modified James Lind Alliance Priority Setting Partnership methodology, following the four-step process including: (1) Gathering uncertainties; (2) Organising uncertainties; (3) Interim priority setting; and (4) Final priority setting in a workshop. Further, the international relevance of the final top-10 list was assessed.Results: A total of 1142 responses were submitted by 405 people to: 'What questions about prediabetes would you like to see answered by research?'. The collected uncertainties were categorised and condensed into 35 indicative questions. Through prioritisation, patients and relatives had different preferences from researchers and HCPs. The jointly agreed top-10 list included questions on prevention strategies, risk factors, diet advice, screening and personalised treatment. Highest prioritisation was given to: 'What is the best prevention of diabetes and will early prevention strategies reduce the number of people with type 2 diabetes?'.Conclusions/interpretation: An iterative and collaborative process identified shared priorities between patients, HCPs and relevant stakeholders in prediabetes. Findings should support academia, funders and the healthcare industry to target research within prediabetes specifically to the needs of patients and HCPs. Citation Jensen, MH. (2026, januar 20). Research Unmuted – Episode 5: What Research Should be Prioritized in Prediabetes. Research Unmuted. https://doi.org/10.5281/zenodo.18311318 References Andersen AK, Lyng KD, Færch K, Vistisen D, von Scholten BJ, Rathleff MS, Thomsen JL, Jensen MH. What are the most important research questions within prediabetes? A priority setting partnership in collaboration with patients, healthcare professionals and researchers. Diabetologia. 2025 Oct;68(10):2156-2167 Tags #Prediabetes #Research_Priorities #Diabetes #Cardiometabolic #CVD #AI_Modeling #Data_Science #Digital_Health

    5 min.

  3. 20. JAN.

    Reproducibility in the Age of Data-Driven Research

    Abstract Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH’s Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article’s cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH’s GSS principles. Citation Cichosz, S. (2026, januar 20). Research Unmuted – Episode 4: Reproducibility in the Age of Data-Driven Research. Research Unmuted. https://doi.org/10.5281/zenodo.18311031 References Klonoff DC, Espinoza J, Mader JK, et al. Research Code Sharing in Support of Gold Standard Science. Journal of Diabetes Science and Technology. 2026;0(0). doi:10.1177/19322968251391819 Tags #Diabetes #Cardiometabolic #AI_Modeling #Digital_Health

    5 min.

  4. 6. JAN.

    Learning From Irregular Data

    Abstract Accurately predicting blood glucose (BG) levels of ICU patients is critical, as both hypoglycemia (BG 180 mg/dL) are associated with increased morbidity and mortality. This study presents a proof-of-concept machine learning framework, the Multi-source Irregular Time-Series Transformer (MITST), designed to predict BG levels in ICU patients. In contrast to existing methods that rely heavily on manual feature engineering or utilize limited Electronic Health Record (EHR) data sources, MITST integrates diverse clinical data-including laboratory results, medications, and vital signs-without predefined aggregation. The model leverages a hierarchical Transformer architecture, designed to capture interactions among features within individual timestamps, temporal dependencies across different timestamps, and semantic relationships across multiple data sources. Evaluated using the extensive eICU database (200,859 ICU stays across 208 hospitals), MITST achieves a statistically significant (p0.001) average improvement of 1.7 percentage points (pp) in AUROC and 1.8 pp in AUPRC over a state-of-the-art random forest baseline. Crucially, for hypoglycemia-a rare but life-threatening condition-MITST increases sensitivity by 7.2 pp, potentially enabling hundreds of earlier interventions across ICU populations. The flexible architecture of MITST allows seamless integration of new data sources without retraining the entire model, enhancing its adaptability for clinical decision support. While this study focuses on predicting BG levels, we also demonstrate MITST's ability to generalize to a distinct clinical task (in-hospital mortality prediction), highlighting its potential for broader applicability in ICU settings. MITST thus offers a robust and extensible solution for analyzing complex, multi-source, irregular time-series data. Citation Cichosz, S. (2026, januar 6). Research Unmuted – Episode 3: Learning From Irregular Data. Research Unmuted. https://doi.org/10.5281/zenodo.18169955 References Mehdizavareh H, Khan A, Cichosz SL. Enhancing glucose level prediction of ICU patients through hierarchical modeling of irregular time-series. Comput Struct Biotechnol J. 2025 Jul 1;27:2898-2914. doi: 10.1016/j.csbj.2025.06.039. PMID: 40687991; PMCID: PMC12270796. Tags #Diabetes #Cardiometabolic #AI_Modeling #Digital_Health

    4 min.

  5. 2. JAN.

    What CGM Data Gaps Are Really Telling Us

    Abstract The aim was to investigate the association between continuous glucose monitoring (CGM) data coverage and glycemic metrics. This study included over 97,000 clinical study participants and real-world data from type 1 or type 2 diabetes treated with multiple daily insulin injections, closed-loop systems, or basal-only insulin regimens. Over 35 million days of CGM data were analyzed with multilevel modeling. Low coverage was observed in 6.4%–10.1% of days and was significantly associated with lower time in range (TIR) across sources (P  0.001). Each 1% increase in coverage was associated with a within-person increase of 0.07%–0.13% in mean daily TIR (P  0.001). Our analysis shows that higher daily sensor coverage is significantly associated with higher daily TIR, suggesting that missing CGM data may be missing not-at-random. Although low-coverage days are included in TIR calculations, they contribute fewer measurements and may underrepresent periods of poor glycemic control, potentially leading to a systematic overestimation and bias of overall TIR. Citation Cichosz, S. (2026, januar 2). Research Unmuted – Episode 2: What CGM Data Gaps Are Really Telling Us. Research Unmuted. https://doi.org/10.5281/zenodo.18170881 References Cichosz SL, Hartvig NV, Kronborg T, Hangaard S, Vestergaard P, Jensen MH. Biases in Glucose Metrics Are Directly Related to Low Coverage of Continuous Glucose Monitoring: Insights from Diverse Populations. Diabetes Technol Ther. 2025 Sep 3. doi: 10.1177/15209156251376007. PMID: 40897432. Tags #Diabetes #Wearables #Digital_Health #Data_Science

    4 min.

  6. 2. JAN.

    Editorial

    Research Unmuted beta is a experimental audio overview platform designed for the researcher, students and people interested in science. We transform complex, peer-reviewed publications into high-fidelity audio summaries and podcasts, allowing you to listen. Citation Cichosz, S. (2026, januar 2). Research Unmuted – Episode 1: Editorial. Research Unmuted. https://doi.org/10.5281/zenodo.18171055 Tags #Diabetes#Cardiometabolic#Prediabetes#AI_Modeling#Data_Science#Wearables#Digital_Health

    3 min.
  • 6 episoder

Om

Research Unmuted is a experimental scientific audio journal presenting contemporary research in diabetes, cardiometabolic diseases, prediabetes, wearable technology, digital health, medical informatics, data science, AI, machine learning, and related topics. Episodes discuss peer-reviewed studies, methodological insights, and clinical implications, translating complex research into an accessible format.

Oplysninger

  • Skaber
    SLC
  • Aktive år
    2 t
  • Episoder
    6
  • Vurdering
    Ikke anstødeligt
  • Copyright
    © SLC
  • Websted for serie
    Research Unmuted