{"doi":"10.2337/dc23-0492","title":"Risk Assessment of Kidney Disease Progression and Efficacy of SGLT2 Inhibition in Patients With Type 2 Diabetes","abstract":"OBJECTIVE: To develop a risk assessment tool to identify patients with type 2 diabetes (T2D) at higher risk for kidney disease progression and who might benefit more from sodium-glucose cotransporter 2 (SGLT2) inhibition. RESEARCH DESIGN AND METHODS: A total of 41,204 patients with T2D from four Thrombolysis In Myocardial Infarction (TIMI) clinical trials were divided into derivation (70%) and validation cohorts (30%). Candidate predictors of kidney disease progression (composite of sustained ≥40% decline in estimated glomerular filtration rate [eGFR], end-stage kidney disease, or kidney death) were selected with multivariable Cox regression. Efficacy of dapagliflozin was assessed by risk categories (low: <0.5%; intermediate: 0.5 to <2%; high: ≥2%) in Dapagliflozin Effect on Cardiovascular Events (DECLARE)-TIMI 58. RESULTS: There were 695 events over a median follow-up of 2.4 years. The final model comprised eight independent predictors of kidney disease progression: atherosclerotic cardiovascular disease, heart failure, systolic blood pressure, T2D duration, glycated hemoglobin, eGFR, urine albumin-to-creatinine ratio, and hemoglobin. The c-indices were 0.798 (95% CI, 0.774-0.821) and 0.798 (95% CI, 0.765-0.831) in the derivation and validation cohort, respectively. The calibration plot slope (deciles of predicted vs. observed risk) was 0.98 (95% CI, 0.93-1.04) in the validation cohort. Whereas relative risk reductions with dapagliflozin did not differ across risk categories, there was greater absolute risk reduction in patients with higher baseline risk, with a 3.5% absolute risk reduction in kidney disease progression at 4 years in the highest risk group (≥1%/year). Results were similar with the 2022 Chronic Kidney Disease Prognosis Consortium risk prediction model. CONCLUSIONS: Risk models for kidney disease progression can be applied in patients with T2D to stratify risk and identify those who experience a greater magnitude of benefit from SGLT2 inhibition.","journal":"Diabetes Care","year":2023,"id":357211,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":14,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6259,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":420371,"name":"David D. Berg","orcid":"0000-0002-0366-5492","position":1,"is_corresponding":false},{"id":368527,"name":"Andrea Bellavia","orcid":"0000-0003-4988-4532","position":2,"is_corresponding":false},{"id":849529,"name":"Jamie P. Dwyer","orcid":"0000-0002-8402-8961","position":3,"is_corresponding":false},{"id":744858,"name":"Ofri Mosenzon","orcid":"0000-0002-5702-7584","position":4,"is_corresponding":false},{"id":95178,"name":"Benjamin M. Scirica","orcid":"0000-0002-7093-7048","position":5,"is_corresponding":false},{"id":853103,"name":"Stephen D. Wiviott","orcid":"0000-0002-4922-9880","position":6,"is_corresponding":false},{"id":3774,"name":"Deepak L. Bhatt","orcid":"0000-0002-1278-6245","position":7,"is_corresponding":false},{"id":606753,"name":"Itamar Raz","orcid":"0000-0003-0209-4453","position":8,"is_corresponding":false},{"id":251597,"name":"Mark W. Feinberg","orcid":"0000-0001-9523-3859","position":9,"is_corresponding":false},{"id":2019,"name":"Eugene Braunwald","orcid":"0000-0002-3472-626X","position":10,"is_corresponding":false},{"id":264819,"name":"David A. Morrow","orcid":"0000-0002-9589-5382","position":11,"is_corresponding":false},{"id":50019,"name":"Marc S. Sabatine","orcid":"0000-0002-0691-3359","position":12,"is_corresponding":false},{"id":1026457,"name":"Filipe A. Moura","orcid":"0000-0001-8017-1675","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":null,"created_at":"2026-07-19T01:13:34.795336Z","pmid":"37556796","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}