The DOI matches its Crossref record; 221 of the 221 references it cites were found in a registry or an index.
Human beta cell mass and function in diabetes: Recent advances in knowledge and technologies to understand disease pathogenesis
Open accessAbstract
BACKGROUND: Plasma insulin levels are predominantly the product of the morphological mass of insulin producing beta cells in the pancreatic islets of Langerhans and the functional status of each of these beta cells. Thus, deficiency in either beta cell mass or function, or both, can lead to insufficient levels of insulin, resulting in hyperglycemia and diabetes. Nonetheless, the precise contribution of beta cell mass and function to the pathogenesis of diabetes as well as the underlying mechanisms are still unclear. In the past, this was largely due to the restricted number of technologies suitable for studying the scarcely accessible human beta cells. However, in recent years, a number of new platforms have been established to expand the available techniques and to facilitate deeper insight into the role of human beta cell mass and function as cause for diabetes and as potential treatment targets. SCOPE OF REVIEW: This review discusses the current knowledge about contribution of human beta cell mass and function to different stages of type 1 and type 2 diabetes pathogenesis. Furthermore, it highlights standard and newly developed technological platforms for the study of human beta cell biology, which can be used to increase our understanding of beta cell mass and function in human glucose homeostasis. MAJOR CONCLUSIONS: In contrast to early disease models, recent studies suggest that in type 1 and type 2 diabetes impairment of beta cell function is an early feature of disease pathogenesis while a substantial decrease in beta cell mass occurs more closely to clinical manifestation. This suggests that, in addition to beta cell mass replacement for late stage therapies, the development of novel strategies for protection and recovery of beta cell function could be most promising for successful diabetes treatment and prevention. The use of today's developing and wide range of technologies and platforms for the study of human beta cells will allow for a more detailed investigation of the underlying mechanisms and will facilitate development of treatment approaches to specifically target human beta cell mass and function.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280883 · Cited by 31,502
Toprak, O., Kurt, H., Sarı, Y., Şarkış, C., Us, H., & Kırık, A. (2017) · Kidney and Blood Pressure Research · doi:10.1159/000468530 · Cited by 29,627
Mechanisms linking obesity to insulin resistance and type 2 diabetes
Kahn, S. E., Hull, R. L., & Utzschneider, K. M. (2006) · Nature · doi:10.1038/nature05482 · Cited by 5,448
β-Cell Deficit and Increased β-Cell Apoptosis in Humans With Type 2 Diabetes
Butler, A. E., Janson, J., Bonner-Weir, S., Ritzel, R., Rizza, R. A., & Butler, P. C. (2003) · Diabetes · doi:10.2337/diabetes.52.1.102 · Cited by 4,011
DeFronzo, R. A. (2009) · Diabetes · doi:10.2337/db09-9028 · Cited by 3,016
Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels
Saxena, R., Voight, B. F., Lyssenko, V., Burtt, N. P., de Bakker, P. I. W., Chen, H., Roix, J. J., Kathiresan, S., Hirschhorn, J. N., Daly, M. J., et al. (2007) · Science · doi:10.1126/science.1142358 · Cited by 2,843
A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants
Scott, L. J., Mohlke, K. L., Bonnycastle, L. L., Willer, C. J., Li, Y., Duren, W. L., Erdos, M. R., Stringham, H. M., Chines, P. S., Jackson, A. U., et al. (2007) · Science · doi:10.1126/science.1142382 · Cited by 2,753
Replication of Genome-Wide Association Signals in UK Samples Reveals Risk Loci for Type 2 Diabetes
Zeggini, E., Weedon, M. N., Lindgren, C. M., Frayling, T. M., Elliott, K. S., Lango, H., Timpson, N. J., Perry, J. R. B., Rayner, N. W., Freathy, R. M., et al. (2007) · Science · doi:10.1126/science.1142364 · Cited by 2,194
Organoid models of human and mouse ductal pancreatic cancer
Boj, S. F., Hwang, C.-I., Baker, L. A., Chio, I. I. C., Engle, D. D., Corbo, V., Jager, M., Ponz-Sarvise, M., Tiriac, H., Spector, M. S., et al. (2015) · Cell · doi:10.1016/j.cell.2014.12.021 · Cited by 2,150
Generation of Functional Human Pancreatic β Cells In Vitro
Pagliuca, F. W., Millman, J. R., Gürtler, M., Segel, M., Van Dervort, A., Ryu, J. H., Peterson, Q. P., Greiner, D., & Melton, D. A. (2014) · Cell · doi:10.1016/j.cell.2014.09.040 · Cited by 2,025
Cited by
Ahmad, E., Lim, S., Lamptey, R., Webb, D. R., & Davies, M. J. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01655-5 · Cited by 1,120
Type 1 diabetes mellitus as a disease of the β-cell (do not blame the immune system?)
Roep, B. O., Thomaidou, S., van Tienhoven, R., & Zaldumbide, A. (2021) · Nature Reviews Endocrinology · doi:10.1038/s41574-020-00443-4 · Cited by 623
From NASH to diabetes and from diabetes to NASH: Mechanisms and treatment options
Gastaldelli, A., & Cusi, K. (2019) · JHEP Reports · doi:10.1016/j.jhepr.2019.07.002 · Cited by 455
Tan, S. Y., Mei Wong, J. L., Sim, Y. J., Wong, S. S., Mohamed Elhassan, S. A., Tan, S. H., Ling Lim, G. P., Rong Tay, N. W., Annan, N. C., Bhattamisra, S. K., et al. (2019) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2018.10.008 · Cited by 445
Endocrine toxicities of immune checkpoint inhibitors
Wright, J. J., Powers, A. C., & Johnson, D. B. (2021) · Nature Reviews Endocrinology · doi:10.1038/s41574-021-00484-3 · Cited by 441
Metadata
- Authors
- Chen, C., Cohrs, C. M., Stertmann, J., Bozsak, R., & Speier, S.
- Venue
- Molecular Metabolism
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 6
- Issue
- 9
- Pages
- 943-957
- ISSN
- 2212-8778 (ISSN-L)
- OpenAlex
- W2733324404
- Cited by
- 489
- References
- 221
- FWCI
- 39.16
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Human beta cell mass and function in diabetes: Recent advances in knowledge and technologies to understand disease pathogenesis. https://app.cochat.ai/papers/10.1016/j.molmet.2017.06.019
- The paper
- Chen, C., Cohrs, C. M., Stertmann, J., Bozsak, R., & Speier, S. (2017). Human beta cell mass and function in diabetes: Recent advances in knowledge and technologies to understand disease pathogenesis. Molecular Metabolism. https://doi.org/10.1016/j.molmet.2017.06.019
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.