{"doi":"10.1016/j.ekir.2023.09.027","title":"Obesity Weight Loss Phenotypes in CKD: Findings from the Chronic Renal Insufficiency Cohort Study","abstract":"Letter to the Editor – Author’s Response The authors reply: We thank Dr. Rico-Fontalvo and colleagues for their interest in our study. They suggest to further adjust our statistical model for baseline albuminuria, which is an important predictor of kidney disease progression and cardiovascular outcomes. Due to missingness of baseline albuminuria data in our study population (33%), we present results after adjustment for baseline urine protein to creatinine ratio (UPCR), given that urinary protein excretion is an independent predictor of kidney function trajectory1Koye D.N. Magliano D.J. Reid C.M. et al.Risk of Progression of Nonalbuminuric CKD to End-Stage Kidney Disease in People With Diabetes: The CRIC (Chronic Renal Insufficiency Cohort) Study.Am J Kidney Dis. 2018; 72: 653-661Abstract Full Text Full Text PDF PubMed Scopus (85) Google Scholar and cardiovascular risk.2Cohen J.B. Yang W. Li L. et al.Time-Updated Changes in Estimated GFR and Proteinuria and Major Adverse Cardiac Events: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study.Am J Kidney Dis. 2022; 79: 36-44 e31Abstract Full Text Full Text PDF Scopus (0) Google Scholar To examine the association between the estimated latent classes from the 6-class model in the primary analysis and the risk of death after further adjustment for baseline UPCR (mg/g), we excluded 150 CRIC participants (5.3%) with missing baseline UPCR information, leaving 2681 participants that were eligible to be included in the analysis. We fit a Cox model with the six estimated latent classes from our primary analysis as predictors, adjusting for baseline age, sex, race/ethnicity, baseline diabetes status, baseline eGFR, baseline body mass index, baseline systolic blood pressure, baseline serum albumin level, initiation of dialysis or transplantation, weight loss intention at baseline, and baseline standardized UPCR (i.e., per standard deviation increase). Stratified by latent class, mean (standard deviation) UPCR levels are shown in the Table. Similar to findings in our original analysis, we observed that mortality was highest in Classes 1, 2, 4, and 6, and lowest in Classes 3 and 5 after adjustment for UPCR and other covariates. Relative to Class 3, adjusted Hazard Ratios (aHRs) for weight loss phenotypes 1 and 6, which were associated with the highest mortality in our original analysis, were 3.95 (95% Confidence Interval [CI] 3.22, 4.84) and 3.77 (95% CI 2.86, 4.97), respectively. Findings were also consistent when adjusting for baseline urine albumin to creatinine ratio (UACR) instead of UPCR, after excluding those participants with missing albuminuria data.TableUrine protein to creatinine ratio of the study cohort at baseline, overall and stratified by latent class membership.OverallClass 1Class 2Class 3Class 4Class 5Class 6UPCR mg/g895 (2170)546 (1220)1130 (1940)733 (1690)426 (1030)5410 (6080)1140 (2210)Abbreviations: UPCR—urine protein to creatinine ratio; mg—milligrams; g—grams Open table in a new tab Abbreviations: UPCR—urine protein to creatinine ratio; mg—milligrams; g—grams Obesity Weight Loss Phenotypes in CKD: Measured GFR and Albumin-to-Creatinine Excretion Ratio Place for StratificationKidney International ReportsPreviewWe have read with interest the recent published paper in Kidney International Reports entitled “Obesity Weight Loss Phenotypes in CKD: Findings From the Chronic Renal Insufficiency Cohort Study.”1 The authors of this interesting manuscript revealed that the pattern of weight loss (rapid vs. slow) and concurrent trends of nutritional, hemodynamic, and body composition indicators are important for understanding long-term mortality risk in persons with obesity and chronic kidney disease (CKD). Currently, obesity has been clearly identified as a cause of CKD with different phenotypes. Full-Text PDF Open Access","journal":"Kidney International Reports","year":2023,"id":355864,"datarank":0.26876392038420827,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.0,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9609,"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":1104217,"name":"Yuna Kim","orcid":"0000-0003-1574-9121","position":1,"is_corresponding":false},{"id":408773,"name":"Brandy‐Joe Milliron","orcid":"0000-0003-1113-9043","position":2,"is_corresponding":false},{"id":408772,"name":"Lucy Robinson","orcid":"0009-0003-1605-9410","position":3,"is_corresponding":false},{"id":270989,"name":"Meera N. Harhay","orcid":"0000-0001-6500-2814","position":0,"is_corresponding":true}],"reference_count":2,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:13:21.145955Z","pmid":"38025237","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":[]}