Computational modeling enables individual assessment of postprandial glucose and insulin responses after bariatric surgery
Computational modeling with HMOGP regression can thus be used to, in detail, predict the combined responses of patient cohorts to ingested glucose or a mixed meal and help in assessing individual metabolic improvement after weight loss. This can lead to new knowledge in personalized metabolic interventions.
Authors
- Kirsi Hannele Pietiläinen (ORCID: https://orcid.org/0000-0002-8522-1288)
- Anne Juuti (ORCID: https://orcid.org/0000-0002-9836-662X)
- Sini Heinonen (ORCID: https://orcid.org/0000-0002-6342-0577)
- Onur Poyraz (ORCID: https://orcid.org/0000-0002-8257-2250)
- Tuure Saarinen (ORCID: https://orcid.org/0000-0002-7508-4849)
- Pekka Marttinen (ORCID: https://orcid.org/0000-0001-7078-7927)
- St. John (ORCID: https://orcid.org/0000-0002-4540-395X)
Institutions
- University of Helsinki (FI)
- Helsinki University Hospital (FI)
- Institute for Molecular Medicine Finland (FI)
- Aalto University (FI)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-10-23
- DOI
- https://doi.org/10.1038/s43856-025-01155-4
- Citations
- 1
- Primary Topic
- Bariatric Surgery and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- Suomen Lääketieteen Säätiö
- Novo Nordisk
- Paavo Nurmen Säätiö
- Novo Nordisk Fonden
- Diabetestutkimussäätiö
- Finnish Center for Artificial Intelligence
- Horizon 2020 Framework Programme