The DOI matches its Crossref record; 166 of the 166 references it cites were found in a registry or an index.
Molecular pathways that drive diabetic kidney disease
Open accessAbstract
Kidney disease is a major driver of mortality among patients with diabetes and diabetic kidney disease (DKD) is responsible for close to half of all chronic kidney disease cases. DKD usually develops in a genetically susceptible individual as a result of poor metabolic (glycemic) control. Molecular and genetic studies indicate the key role of podocytes and endothelial cells in driving albuminuria and early kidney disease in diabetes. Proximal tubule changes show a strong association with the glomerular filtration rate. Hyperglycemia represents a key cellular stress in the kidney by altering cellular metabolism in endothelial cells and podocytes and by imposing an excess workload requiring energy and oxygen for proximal tubule cells. Changes in metabolism induce early adaptive cellular hypertrophy and reorganization of the actin cytoskeleton. Later, mitochondrial defects contribute to increased oxidative stress and activation of inflammatory pathways, causing progressive kidney function decline and fibrosis. Blockade of the renin-angiotensin system or the sodium-glucose cotransporter is associated with cellular protection and slowing kidney function decline. Newly identified molecular pathways could provide the basis for the development of much-needed novel therapeutics.
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
Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy
Perkovic, V., Jardine, M. J., Neal, B., Bompoint, S., Heerspink, H. J. L., Charytan, D. M., Edwards, R., Agarwal, R., Bakris, G., Bull, S., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1811744 · Cited by 6,189
Dapagliflozin in Patients with Chronic Kidney Disease
Heerspink, H. J. L., Stefánsson, B. V., Correa-Rotter, R., Chertow, G. M., Greene, T., Hou, F.-F., Mann, J. F. E., McMurray, J. J. V., Lindberg, M., Rossing, P., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2024816 · Cited by 5,618
Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes
(2005) · New England Journal of Medicine · doi:10.1056/nejmoa052187 · Cited by 5,138
Ferroptosis: molecular mechanisms and health implications
Tang, D., Chen, X., Kang, R., & Kroemer, G. (2021) · Cell Research · doi:10.1038/s41422-020-00441-1 · Cited by 4,256
Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes
Bakris, G. L., Agarwal, R., Anker, S. D., Pitt, B., Ruilope, L. M., Rossing, P., Kolkhof, P., Nowack, C., Schloemer, P., Joseph, A., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2025845 · Cited by 2,935 · cited 2×
“The addition of finerenone also led to a reduction of albuminuria in the ARTS-DN study (163) and decreased progression of kidney disease and improved cardiovascular outcomes in the FIDELIO-DKD and FIGARO-DKD studies (161, 164).”
The molecular machinery of regulated cell death
Tang, D., Kang, R., Berghe, T. V., Vandenabeele, P., & Kroemer, G. (2019) · Cell Research · doi:10.1038/s41422-019-0164-5 · Cited by 2,549
The pathways of mitophagy for quality control and clearance of mitochondria
Ashrafi, G., & Schwarz, T. L. (2013) · Cell Death & Differentiation · doi:10.1038/cdd.2012.81 · Cited by 1,900
Cardiovascular events with finerenone in kidney disease and type 2 diabetes
Pitt, B., Filippatos, G., Agarwal, R., Anker, S. D., Bakris, G. L., Rossing, P., Joseph, A., Kolkhof, P., Nowack, C., Schloemer, P., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2110956 · Cited by 1,814
Pathologic Classification of Diabetic Nephropathy
Tervaert, T. W. C., Mooyaart, A. L., Amann, K., Cohen, A. H., Cook, H. T., Drachenberg, C. B., Ferrario, F., Fogo, A. B., Haas, M., de Heer, E., et al. (2010) · Journal of the American Society of Nephrology · doi:10.1681/asn.2010010010 · Cited by 1,690
Sattar, N., Lee, M. M. Y., Kristensen, S. L., Branch, K. R. H., Del Prato, S., Khurmi, N. S., Lam, C. S. P., Lopes, R. D., McMurray, J. J. V., Pratley, R. E., et al. (2021) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(21)00203-5 · Cited by 1,599 · cited 1×
“GLP1 receptor agonists lower albuminuria, but their effect on composite renal outcome is not yet available (168).”
Cited by
Diabetes and its complications: molecular mechanisms, prevention and treatment
Zhao, L., Yuan, J., Yang, Q., Ma, J., Yang, F., Zou, Y., Liu, K., & Liu, F. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02401-w · Cited by 80
Insights into the molecular underpinning of type 2 diabetes complications
Singh, A., Bocher, O., & Zeggini, E. (2025) · Human Molecular Genetics · doi:10.1093/hmg/ddae203 · Cited by 10
Metadata
- Authors
- Mohandes, S., Doke, T., Hu, H., Mukhi, D., Dhillon, P., & Susztak, K.
- Venue
- Journal of Clinical Investigation
- Published
- 2023-02-15
- Volume
- 133
- Issue
- 4
- ISSN
- 0021-9738 (ISSN-L)
- 1558-8238
- OpenAlex
- W4320709875
- Cited by
- 341
- References
- 166
- FWCI
- 56.10
- 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: Molecular pathways that drive diabetic kidney disease. https://app.cochat.ai/papers/10.1172/jci165654
- The paper
- Mohandes, S., Doke, T., Hu, H., Mukhi, D., Dhillon, P., & Susztak, K. (2023). Molecular pathways that drive diabetic kidney disease. Journal of Clinical Investigation. https://doi.org/10.1172/jci165654
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.