Quantification of beta-cell carrying capacity in prediabetes
Prediabetes, a subclinical state of high glucose, carries a risk of transitioning to diabetes. One cause of prediabetes is insulin resistance, which impairs the ability of insulin to control blood glucose. However, many individuals with high insulin resistance retain normal glucose due to compensation by enhanced insulin secretion by beta cells. Individuals seem to differ in their maximum compensation level, termed beta cell carrying capacity, such that low carrying capacity is associated with a higher risk of prediabetes and diabetes. Carrying capacity has not been quantified using a mathematical model and at present cannot be estimated from measured glucose and insulin levels in patients, unlike insulin resistance and beta cell function which can be estimated using HOMA-IR and HOMA-B formula. Here we present a mathematical model of beta cell compensation and carrying capacity, and develop a new formula called HOMA-C to estimate it from glucose and insulin measurements. HOMA-C estimates the maximal potential beta cell function of an individual, rather than the current beta cell function. It uses prediabetes as a stress and estimates carrying capacity using the gap between secreted insulin and the amount of insulin needed for homeostasis. We test this approach using longitudinal cohorts of prediabetic people, finding 10-fold variation in carrying capacity. Low HOMA-C associates with higher risk of transitioning to diabetes in a one-year follow up, more strongly than beta-cell function HOMA-B and insulin resistance HOMA-IR, but slightly less or similarly to only-glucose dependent parameters. The interpretation of HOMA-C as a carrying capacity relies on a mathematical model and requires further experimental testing. Quantification of beta cell carrying capacity may help to assess the risk of diabetes in individuals with prediabetes.
Authors
- Smadar Shilo (ORCID: https://orcid.org/0000-0003-2399-1866)
- Yoel Toledano (ORCID: https://orcid.org/0000-0001-9360-8987)
- Eran Segal (ORCID: https://orcid.org/0000-0002-6859-1164)
- Yuval Tamir
- Aurore Woller
- Anastasia Godneva (ORCID: https://orcid.org/0000-0001-6992-5552)
- Didier Gonze (ORCID: https://orcid.org/0000-0002-9800-2412)
- A. Bar (ORCID: https://orcid.org/0000-0003-0176-876X)
- Avi Mayo (ORCID: https://orcid.org/0000-0002-4479-3423)
- Uri Alon
- Michal Rein
- Netta Mendelson Cohen
Institutions
- Université Libre de Bruxelles (BE)
- Tel Aviv University (IL)
- Diabetes Australia (AU)
- Rabin Medical Center (IL)
- Weizmann Institute of Science (IL)
Publication Details
- Journal
- PLoS Computational Biology
- Published
- 2026-09-15
- DOI
- https://doi.org/10.1371/journal.pcbi.1014589
- Primary Topic
- Diabetes, Cardiovascular Risks, and Lipoproteins
- Type
- article
- Field-Weighted Citation Impact
- 0.00