The DOI matches its Crossref record; 65 of the 66 references it cites were found in a registry or an index.
β Cell Aging Markers Have Heterogeneous Distribution and Are Induced by Insulin Resistance
Open accessAbstract
We hypothesized that the known heterogeneity of pancreatic β cells was due to subpopulations of β cells at different stages of their life cycle with different functional capacities and that further changes occur with metabolic stress and aging. We identified new markers of aging in β cells, including IGF1R. In β cells IGF1R expression correlated with age, dysfunction, and expression of known age markers p16 ink4a, p53BP1, and senescence-associated β-galactosidase. The new markers showed striking heterogeneity both within and between islets in both mouse and human pancreas. Acute induction of insulin resistance with an insulin receptor antagonist or chronic ER stress resulted in increased expression of aging markers, providing insight into how metabolic stress might accelerate dysfunction and decline of β cells. These novel findings about β cell and islet heterogeneity, and how they change with age, open up an entirely new set of questions about the pathogenesis of type 2 diabetes.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“No statistical methods were used to predetermine sample sizes, but our sample sizes are similar to those reported in our previous publications8,22,49.”
From Exercise activates AMPK in mouse and human pancreatic islets to decrease senescence (2024) · cites this paper as [49]
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
1 retracted
RETRACTED: Betatrophin: A Hormone that Controls Pancreatic β Cell Proliferation
Yi, P., Park, J.-S., & Melton, D. A. (2013) · Cell · doi:10.1016/j.cell.2013.04.008 · Cited by 449
Retractedlimma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Cellular senescence: when bad things happen to good cells
Campisi, J., & d'Adda di Fagagna, F. (2007) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2233 · Cited by 4,441
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
Self-citationClearance of p16Ink4a-positive senescent cells delays ageing-associated disorders
Baker, D. J., Wijshake, T., Tchkonia, T., LeBrasseur, N. K., Childs, B. G., van de Sluis, B., Kirkland, J. L., & van Deursen, J. M. (2011) · Nature · doi:10.1038/nature10600 · Cited by 3,670
Naturally occurring p16Ink4a-positive cells shorten healthy lifespan
Baker, D. J., Childs, B. G., Durik, M., Wijers, M. E., Sieben, C. J., Zhong, J., A. Saltness, R., Jeganathan, K. B., Verzosa, G. C., Pezeshki, A., et al. (2016) · Nature · doi:10.1038/nature16932 · Cited by 2,876
The role of senescent cells in ageing
van Deursen, J. M. (2014) · Nature · doi:10.1038/nature13193 · Cited by 2,623
Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes
Segerstolpe, Å., Palasantza, A., Eliasson, P., Andersson, E.-M., Andréasson, A.-C., Sun, X., Picelli, S., Sabirsh, A., Clausen, M., Bjursell, M. K., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.020 · Cited by 1,550
Ink4a/Arf expression is a biomarker of aging
Krishnamurthy, J., Torrice, C., Ramsey, M. R., Kovalev, G. I., Al-Regaiey, K., Su, L., & Sharpless, N. E. (2004) · Journal of Clinical Investigation · doi:10.1172/jci22475 · Cited by 1,486
p16INK4a induces an age-dependent decline in islet regenerative potential
Krishnamurthy, J., Ramsey, M. R., Ligon, K. L., Torrice, C., Koh, A., Bonner-Weir, S., & Sharpless, N. E. (2006) · Nature · doi:10.1038/nature05092 · Cited by 1,003
Self-citation
Cited by
Sorted by citations
Acceleration of β Cell Aging Determines Diabetes and Senolysis Improves Disease Outcomes
Aguayo-Mazzucato, C., Andle, J., Lee, T. B., Midha, A., Talemal, L., Chipashvili, V., Hollister-Lock, J., van Deursen, J., Weir, G., & Bonner-Weir, S. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.05.006 · Cited by 469
Self-citationThe role of macrophages in obesity-associated islet inflammation and β-cell abnormalities
Ying, W., Fu, W., Lee, Y. S., & Olefsky, J. M. (2019) · Nature Reviews Endocrinology · doi:10.1038/s41574-019-0286-3 · Cited by 410
Obesity and ageing: Two sides of the same coin
Tam, B. T., Morais, J. A., & Santosa, S. (2020) · Obesity Reviews · doi:10.1111/obr.12991 · Cited by 274
Inflammageing and metaflammation: The yin and yang of type 2 diabetes
Prattichizzo, F., Nigris, V. D., Spiga, R., Mancuso, E., Sala, L. L., Antonicelli, R., Testa, R., Procopio, A. D., Olivieri, F., & Ceriello, A. (2017) · Ageing Research Reviews · doi:10.1016/j.arr.2017.10.003 · Cited by 255
Single-nucleus and in situ RNA-sequencing reveal cell topographies in the human pancreas
Tosti, L., Hang, Y., Debnath, O., Tiesmeyer, S., Trefzer, T., Steiger, K., Ten, F. W., Lukassen, S., Ballke, S., Kühl, A. A., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.11.010 · Cited by 228
Metadata
- Authors
- Aguayo-Mazzucato, C., van Haaren, M., Mruk, M., Lee, T. B., Crawford, C., Hollister-Lock, J., Sullivan, B. A., Johnson, J. W., Ebrahimi, A., Dreyfuss, J. M., Van Deursen, J., Weir, G. C., & Bonner-Weir, S.
- Affiliations
- Joslin Diabetes Center (US)
- Boston University (US)
- Mayo Clinic (US)
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 25
- Issue
- 4
- Pages
- 898-910.e5
- ISSN
- 1550-4131 (ISSN-L)
- PMC
- PMC5471618
- OpenAlex
- W2604579271
- Cited by
- 198
- References
- 66
- Citations by year
- 2026: 9 · 2025: 25 · 2024: 19 · 2023: 26 · 2022: 20 · 2021: 25 · 2020: 24 · 2019: 25 · 2018: 19 · 2017: 6
- FWCI
- 20.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Metabolism (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- NIH (R01 DK093909, P30 DK036836, P30 DK057521)
- Diabetes Research and Wellness Foundation
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Keywords
- beta-cell heterogeneity, IGF-1 receptor, insulin resistance, islet heterogeneity, cellular senescence, p16INK4a, senescence-associated β-galactosidase, endoplasmic reticulum stress, insulin receptor antagonist, type 2 diabetes pathogenesis, metabolic stress
- MeSH terms
- Tumor Suppressor p53-Binding Protein 1, Adolescent, Adult, Aged, Aging, Animals, Diabetes Mellitus, Type 2, Female, Flow Cytometry, Glucose, Hemolytic Plaque Technique, Humans, Insulin, Insulin Resistance, Male, Mice, Inbred C57BL, Middle Aged, RNA, Messenger, Stress, Physiological, Biomarkers, Cellular Senescence, Receptor, IGF Type 1, Gene Expression Regulation, Developmental, Green Fluorescent Proteins, Insulin-Secreting Cells, Young Adult
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
