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Wang F, Suo XG, Wang JN, Liu CY, Liu CC, Wang C, Li J, Duan ZH, Zhang FS, Xia YM, Jiang JJ, Hao YW, Li GY, Meng XM, Shao YX, Wang FC. SFN promotes renal fibrosis via binding with MYH9 in chronic kidney disease. Eur J Pharmacol 2024; 979:176806. [PMID: 38986830 DOI: 10.1016/j.ejphar.2024.176806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/19/2024] [Accepted: 07/08/2024] [Indexed: 07/12/2024]
Abstract
Chronic kidney disease (CKD) is a clinical syndrome characterized by persistent renal function decline. Renal fibrosis is the main pathological process in CKD, but an effective treatment does not exist. Stratifin (SFN) is a highly-conserved, multi-function soluble acidic protein. Therefore, this study explored the effects of SFN on renal fibrosis. First, we found that SFN was highly expressed in patients with CKD, as well as in renal fibrosis animal and cell models. Next, transforming growth factor-beta 1 (TGF-β1) induced injury and fibrosis in human renal tubule epithelial cells, and SFN knockdown reversed these effects. Furthermore, SFN knockdown mitigated unilateral ureteral obstruction (UUO)-induced renal tubular dilatation and renal interstitial fibrosis in mice. Liquid chromatography-tandem mass spectrometry/mass spectrometry (LC-MS/MS), co-immunoprecipitation (Co-IP), and immunofluorescence co-localization assays demonstrated that SFN bound the non-muscle myosin-encoding gene, myosin heavy chain 9 (MYH9), in the cytoplasm of renal tubular epithelial cells. MYH9 knockdown also reduced Col-1 and α-SMA expression, which are fibrosis markers. Finally, silencing SFN decreased MYH9 expression, alleviating renal fibrosis. These results suggest that SFN promotes renal fibrosis in CKD by interacting with MYH9. This study may provide potential strategies for the treatment of CKD.
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Affiliation(s)
- Fang Wang
- Department of Pharmacy, Lu'an Hospital of Anhui Medical University, Lu'an People's Hospital of Anhui Province, Lu'an, 237006, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, The Key Laboratory of Anti-inflammatory of Immune Medicines, Ministry of Education, Hefei, 230032, China
| | - Xiao-Guo Suo
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, The Key Laboratory of Anti-inflammatory of Immune Medicines, Ministry of Education, Hefei, 230032, China
| | - Jia-Nan Wang
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, The Key Laboratory of Anti-inflammatory of Immune Medicines, Ministry of Education, Hefei, 230032, China
| | - Cheng-Yi Liu
- Department of Urology, Lu'an Hospital of Anhui Medical University, Lu'an People's Hospital of Anhui Province, Lu'an, 237006, China
| | - Cheng-Cheng Liu
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, The Key Laboratory of Anti-inflammatory of Immune Medicines, Ministry of Education, Hefei, 230032, China
| | - Cong Wang
- Department of Pharmacy, Lu'an Hospital of Anhui Medical University, Lu'an People's Hospital of Anhui Province, Lu'an, 237006, China
| | - Jing Li
- Department of Pharmacy, Lu'an Hospital of Anhui Medical University, Lu'an People's Hospital of Anhui Province, Lu'an, 237006, China
| | - Zi-Hao Duan
- Department of Pharmacy, Lu'an Hospital of Anhui Medical University, Lu'an People's Hospital of Anhui Province, Lu'an, 237006, China
| | - Feng-Sen Zhang
- Department of Pharmacy, Lu'an Hospital of Anhui Medical University, Lu'an People's Hospital of Anhui Province, Lu'an, 237006, China
| | - Yi-Miao Xia
- Department of Pharmacy, Lu'an Hospital of Anhui Medical University, Lu'an People's Hospital of Anhui Province, Lu'an, 237006, China
| | - Jun-Jie Jiang
- Department of Pharmacy, Lu'an Hospital of Anhui Medical University, Lu'an People's Hospital of Anhui Province, Lu'an, 237006, China
| | - Yun-Wu Hao
- Department of Urology, Lu'an Hospital of Anhui Medical University, Lu'an People's Hospital of Anhui Province, Lu'an, 237006, China
| | - Guang-Yuan Li
- Department of Urology, Lu'an Hospital of Anhui Medical University, Lu'an People's Hospital of Anhui Province, Lu'an, 237006, China
| | - Xiao-Ming Meng
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, The Key Laboratory of Anti-inflammatory of Immune Medicines, Ministry of Education, Hefei, 230032, China
| | - Yun-Xia Shao
- College of Life Sciences, Anhui Normal University, Wuhu, 241000, China; Wuhu Hospital, East China Normal University (The Second People's Hospital of Wuhu), Wuhu, 241000, China.
| | - Fa-Cai Wang
- Department of Pharmacy, Lu'an Hospital of Anhui Medical University, Lu'an People's Hospital of Anhui Province, Lu'an, 237006, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, The Key Laboratory of Anti-inflammatory of Immune Medicines, Ministry of Education, Hefei, 230032, China.
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Abedini A, Levinsohn J, Klötzer KA, Dumoulin B, Ma Z, Frederick J, Dhillon P, Balzer MS, Shrestha R, Liu H, Vitale S, Bergeson AM, Devalaraja-Narashimha K, Grandi P, Bhattacharyya T, Hu E, Pullen SS, Boustany-Kari CM, Guarnieri P, Karihaloo A, Traum D, Yan H, Coleman K, Palmer M, Sarov-Blat L, Morton L, Hunter CA, Kaestner KH, Li M, Susztak K. Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression. Nat Genet 2024:10.1038/s41588-024-01802-x. [PMID: 39048792 DOI: 10.1038/s41588-024-01802-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 05/15/2024] [Indexed: 07/27/2024]
Abstract
Kidneys are intricate three-dimensional structures in the body, yet the spatial and molecular principles of kidney health and disease remain inadequately understood. We generated high-quality datasets for 81 samples, including single-cell, single-nuclear, spot-level (Visium) and single-cell resolution (CosMx) spatial-RNA expression and single-nuclear open chromatin, capturing cells from healthy, diabetic and hypertensive diseased human kidneys. Combining these data, we identify cell types and map them to their locations within the tissue. Unbiased deconvolution of the spatial data identifies the following four distinct microenvironments: glomerular, immune, tubule and fibrotic. We describe the complex organization of microenvironments in health and disease and find that the fibrotic microenvironment is able to molecularly classify human kidneys and offers an improved prognosis compared to traditional histopathology. We provide a comprehensive spatially resolved molecular roadmap of the human kidney and the fibrotic process, demonstrating the clinical utility of spatial transcriptomics.
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Affiliation(s)
- Amin Abedini
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Jonathan Levinsohn
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Konstantin A Klötzer
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Bernhard Dumoulin
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Ziyuan Ma
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Julia Frederick
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Poonam Dhillon
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Michael S Balzer
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Nephrology, Charité - Universitätsmedizin, Berlin, Germany
| | - Rojesh Shrestha
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Hongbo Liu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Steven Vitale
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Andi M Bergeson
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Paola Grandi
- Genomic Sciences, GSK-Cellzome, Heidelberg, Germany
| | | | - Erding Hu
- Research and Development, GSK, Crescent Drive, Philadelphia, PA, USA
| | - Steven S Pullen
- Department of Cardiometabolic Diseases Research, Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT, USA
| | - Carine M Boustany-Kari
- Department of Cardiometabolic Diseases Research, Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT, USA
| | - Paolo Guarnieri
- Department of Cardiometabolic Diseases Research, Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT, USA
| | | | - Daniel Traum
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Hanying Yan
- Department of Epidemiology, Biostatistics and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kyle Coleman
- Department of Epidemiology, Biostatistics and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Matthew Palmer
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Lea Sarov-Blat
- Research and Development, GSK, Crescent Drive, Philadelphia, PA, USA
| | - Lori Morton
- Cardiovascular and Renal Research, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Christopher A Hunter
- Department of Pathobiology, University of Pennsylvania School of Veterinary Medicine, Philadelphia, PA, USA
| | - Klaus H Kaestner
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Mingyao Li
- Department of Epidemiology, Biostatistics and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
- Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
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Zhou L, Li W. Effectiveness and safety of finerenone in Chinese CKD patients without diabetes: a retrospective, real-world study. Int Urol Nephrol 2024:10.1007/s11255-024-04142-1. [PMID: 38985246 DOI: 10.1007/s11255-024-04142-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 06/30/2024] [Indexed: 07/11/2024]
Abstract
BACKGROUND Finerenone, a non-steroidal mineralocorticoid receptor antagonist, has previously demonstrated its efficacy and safety in chronic kidney disease (CKD) associated with diabetes mellitus. Given its therapeutic potential, finerenone has been preliminarily explored in clinical practice for non-diabetic CKD patients. The effectiveness and safety in this population require further investigation in a real-world setting. METHODS This retrospective, real-world analysis included non-diabetic CKD patients receiving finerenone. The main clinical outcomes assessed were changes in urinary albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR). Serum potassium (sK+) levels were also monitored. Data were collected at baseline, and then at 1 month and 3 months following treatment initiation. RESULTS Totally, 16 patients were included. There was a notable decrease in UACR from 1-month post-treatment, with a further reduction at 3 months, resulting in a median reduction of 200.41 mg/g (IQR, 84.04-1057.10 mg/g; P = 0.028; percent change, 44.52% [IQR, 31.79-65.42%]). The average eGFR at baseline was 80.16 ml/min/1.73m2, with no significant change after 1 month (80.72 ml/min/1.73m2, P = 0.594) and a slight numerical increase to 83.45 ml/min/1.73m2 (P = 0.484) after 3 months. During the 3-month follow-up, sK+ levels showed only minor fluctuations, with no significant differences compared to baseline, and remained within the normal range throughout the treatment period. No treatment discontinuation or hospitalization due to hyperkalemia was observed. CONCLUSION In non-diabetic CKD patients, finerenone showed good effectiveness and safety within a 3-month follow-up period. This study provides valuable real-world evidence supporting the use of finerenone in non-diabetic CKD and highlights the need for future large-scale prospective research to further validate its efficacy.
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Affiliation(s)
- Li Zhou
- Department of Nephropathy, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Wenge Li
- Department of Nephropathy, China-Japan Friendship Hospital, Beijing, 100029, China.
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Kawada T. Albuminuria and chronic kidney disease progression in patients with type 2 diabetes mellitus. Clin Exp Nephrol 2024:10.1007/s10157-024-02533-3. [PMID: 38954310 DOI: 10.1007/s10157-024-02533-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 06/23/2024] [Indexed: 07/04/2024]
Affiliation(s)
- Tomoyuki Kawada
- Department of Hygiene and Public Health, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-Ku, Tokyo, 113-8602, Japan.
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Li H, Ren Y, Duan Y, Li P, Bian Y. Association of the longitudinal trajectory of urinary albumin/creatinine ratio in diabetic patients with adverse cardiac event risk: a retrospective cohort study. Front Endocrinol (Lausanne) 2024; 15:1355149. [PMID: 38745945 PMCID: PMC11091466 DOI: 10.3389/fendo.2024.1355149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 02/26/2024] [Indexed: 05/16/2024] Open
Abstract
Objective The baseline urinary albumin/creatinine ratio (uACR) has been proven to be significantly associated with the risk of major adverse cardiac events (MACE). However, data on the association between the longitudinal trajectory patterns of uACR, changes in glycated hemoglobin A1c (HbA1c), and the subsequent risk of MACE in patients with diabetes are sparse. Methods This is a retrospective cohort study including 601 patients with type 2 diabetes mellitus (T2DM; uACR < 300 mg/g) admitted to The First Hospital of Shanxi Medical University and The Second Hospital of Shanxi Medical University from January 2015 to December 2018. The uACR index was calculated as urinary albumin (in milligrams)/creatinine (in grams), and latent mixed modeling was used to identify the longitudinal trajectory of uACR during the exposure period (2016-2020). The deadline for follow-up was December 31, 2021. The primary outcome was the MACE [a composite outcome of cardiogenic death, hospitalization related to heart failure (HHF), non-fatal acute myocardial infarction, non-fatal stroke, and acute renal injury/dialysis indications]. The Kaplan-Meier survival analysis curve was used to compare the risk of MACE among four groups, while univariate and multivariate Cox proportional hazards models were employed to calculate the hazard ratio (HR) and 95% confidence interval (CI) for MACE risk among different uACR or HbA1c trajectory groups. The predictive performance of the model, both before and after the inclusion of changes in the uACR and HbA1c, was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Results Four distinct uACR trajectories were identified, namely, the low-stable group (uACR = 5.2-38.3 mg/g, n = 112), the moderate-stable group (uACR = 40.4-78.6 mg/g, n = 229), the high-stable group (uACR = 86.1-153.7 mg/g, n = 178), and the elevated-increasing group (uACR = 54.8-289.4 mg/g, n = 82). In addition, five distinct HbA1c trajectories were also identified: the low-stable group (HbA1c = 5.5%-6.8%, n = 113), the moderate-stable group (HbA1c = 6.0%-7.9%, n = 169), the moderate-decreasing group (HbA1c = 7.4%-6.1%, n = 67), the high-stable group (HbA1c = 7.7%-8.9%, n = 158), and the elevated-increasing group (HbA1c = 8.4%-10.3%, n = 94). Compared with the low-stable uACR group, patients in the high-stable and elevated-increasing uACR groups were more likely to be older, current smokers, and have a longer DM course, higher levels of 2-h plasma glucose (PG), HbA1c, N-terminal pro-B-type natriuretic peptide (NT-proBNP), uACR, and left ventricular mass index (LVMI), while featuring a higher prevalence of hypertension and a lower proportion of β-receptor blocker treatment (p < 0.05). During a median follow-up of 45 months (range, 24-57 months), 118 cases (19.6%) of MACE were identified, including 10 cases (1.7%) of cardiogenic death, 31 cases (5.2%) of HHF, 35 cases (5.8%) of non-fatal acute myocardial infarction (AMI), 18 cases (3.0%) of non-fatal stroke, and 24 cases (4.0%) of acute renal failure/dialysis. The Kaplan-Meier survival curve showed that, compared with that in the low-stable uACR group, the incidence of MACE in the high-stable (HR = 1.337, 95% CI = 1.083-1.652, p = 0.007) and elevated-increasing (HR = 1.648, 95% CI = 1.139-2.387, p = 0.009) uACR groups significantly increased. Similar results were observed for HHF, non-fatal AMI, and acute renal injury/dialysis indications (p < 0.05). The multivariate Cox proportional hazards models indicated that, after adjusting for potential confounders, the HRs for the risk of MACE were 1.145 (p = 0.132), 1.337 (p = 0.007), and 1.648 (p = 0.009) in the moderate-stable, high-stable, and elevated-increasing uACR groups, respectively. In addition, the HRs for the risk of MACE were 1.203 (p = 0.028), 0.872 (p = 0.024), 1.562 (p = 0.033), and 2.218 (p = 0.002) in the moderate-stable, moderate-decreasing, high-stable, and elevated-increasing groups, respectively. The ROC curve showed that, after adding uACR, HbA1c, or both, the AUCs were 0.773, 0.792, and 0.826, which all signified statistically significant improvements (p = 0.021, 0.035, and 0.019, respectively). Conclusion A long-term elevated uACR is associated with a significantly increased risk of MACE in patients with diabetes. This study implies that regular monitoring of uACR could be helpful in identifying diabetic patients with a higher risk of MACE.
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Affiliation(s)
- Hui Li
- Department of Cardiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yajuan Ren
- Department of Cardiology, Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Yongguang Duan
- Department of Cardiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Peng Li
- Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Yunfei Bian
- Department of Cardiology, Second Hospital of Shanxi Medical University, Taiyuan, China
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Ivković V, Bruchfeld A. Endothelin receptor antagonists in diabetic and non-diabetic chronic kidney disease. Clin Kidney J 2024; 17:sfae072. [PMID: 38660120 PMCID: PMC11040512 DOI: 10.1093/ckj/sfae072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Indexed: 04/26/2024] Open
Abstract
Chronic kidney disease (CKD) is one of the major causes of morbidity and mortality, affecting >800 million persons globally. While we still lack efficient, targeted therapies addressing the major underlying pathophysiologic processes in CKD, findings of several recent trials have brought about a shifting landscape of promising therapies. The endothelin system has been implicated in the pathophysiology of CKD and endothelin receptor antagonists are one class of drugs for which we have increasing evidence of efficacy in these patients. In this review we summarize the most recent findings on the safety and efficacy of endothelin receptor antagonists in diabetic and non-diabetic CKD, future directions of research and upcoming treatments.
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Affiliation(s)
- Vanja Ivković
- University Hospital Center Zagreb, Department of Nephrology, Hypertension, Dialysis and Transplantation, Zagreb, Croatia
- University of Rijeka, Faculty of Health Studies, Rijeka, Croatia
| | - Annette Bruchfeld
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Renal Medicine, Karolinska University Hospital and CLINTEC Karolinska Institutet, Stockholm, Sweden
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Verma A, Schmidt IM, Claudel S, Palsson R, Waikar SS, Srivastava A. Association of Albuminuria With Chronic Kidney Disease Progression in Persons With Chronic Kidney Disease and Normoalbuminuria : A Cohort Study. Ann Intern Med 2024; 177:467-475. [PMID: 38560911 DOI: 10.7326/m23-2814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Albuminuria is a major risk factor for chronic kidney disease (CKD) progression, especially when categorized as moderate (30 to 300 mg/g) or severe (>300 mg/g). However, there are limited data on the prognostic value of albuminuria within the normoalbuminuric range (<30 mg/g) in persons with CKD. OBJECTIVE To estimate the increase in the cumulative incidence of CKD progression with greater baseline levels of albuminuria among persons with CKD who had normoalbuminuria (<30 mg/g). DESIGN Multicenter prospective cohort study. SETTING 7 U.S. clinical centers. PARTICIPANTS 1629 participants meeting criteria from the CRIC (Chronic Renal Insufficiency Cohort) study with CKD (estimated glomerular filtration rate [eGFR], 20 to 70 mL/min/1.73 m2) and urine albumin-creatinine ratio (UACR) less than 30 mg/g. MEASUREMENTS Baseline spot urine albumin divided by spot urine creatinine to calculate UACR as the exposure variable. The 10-year adjusted cumulative incidences of CKD progression (composite of 50% eGFR decline or kidney failure [dialysis or kidney transplantation]) from confounder adjusted survival curves using the G-formula. RESULTS Over a median follow-up of 9.8 years, 182 of 1629 participants experienced CKD progression. The 10-year adjusted cumulative incidences of CKD progression were 8.7% (95% CI, 5.9% to 11.6%), 11.5% (CI, 8.8% to 14.3%), and 19.5% (CI, 15.4% to 23.5%) for UACR levels of 0 to less than 5 mg/g, 5 to less than 15 mg/g, and 15 mg/g or more, respectively. Comparing persons with UACR 15 mg/g or more to those with UACR 5 to less than 15 mg/g and 0 to less than 5 mg/g, the absolute risk differences were 7.9% (CI, 3.0% to 12.7%) and 10.7% (CI, 5.8% to 15.6%), respectively. The 10-year adjusted cumulative incidence increased linearly based on baseline UACR levels. LIMITATION UACR was measured once. CONCLUSION Persons with CKD and normoalbuminuria (<30 mg/g) had excess risk for CKD progression, which increased in a linear fashion with higher levels of albuminuria. PRIMARY FUNDING SOURCE None.
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Affiliation(s)
- Ashish Verma
- Boston Medical Center and Section of Nephrology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (A.V., S.S.W.)
| | - Insa M Schmidt
- Boston Medical Center and Section of Nephrology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts; and Hamburg Center for Kidney Health, University Medical Center Hamburg, Hamburg, Germany (I.M.S.)
| | - Sophie Claudel
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (S.C.)
| | - Ragnar Palsson
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts (R.P.)
| | - Sushrut S Waikar
- Boston Medical Center and Section of Nephrology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (A.V., S.S.W.)
| | - Anand Srivastava
- Division of Nephrology, Department of Medicine, University of Illinois Chicago, Chicago, Illinois (A.S.)
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Tan Y, Zhang Z, Zhou P, Zhang Q, Li N, Yan Q, Huang L, Yu J. Efficacy and safety of Abelmoschus manihot capsule combined with ACEI/ARB on diabetic kidney disease: a systematic review and meta analysis. Front Pharmacol 2024; 14:1288159. [PMID: 38249351 PMCID: PMC10796716 DOI: 10.3389/fphar.2023.1288159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 12/13/2023] [Indexed: 01/23/2024] Open
Abstract
Background: Diabetic kidney disease (DKD) is one of the most serious microvascular complications of diabetes, with the incidence rate increasing yearly, which is the leading cause of chronic kidney disease (CKD) and end-stage kidney disease. Abelmoschus Manihot capsule, as a proprietary Chinese patent medicine, is widely used for treating CKD in China. Currently, the combination of Abelmoschus Manihot (AM) capsule and renin-angiotensin-aldosterone system inhibitor (RASI) has gained popularity as a treatment option for DKD, with more and more randomized control trials (RCTs) in progress. However, the high-quality clinical evidence supporting its application in DKD is still insufficient. Aim of the study: To comprehensively and systematically evaluate the efficacy and safety of AM capsule combined with RASI in the treatment of DKD. Materials and methods: English and Chinese databases such as Pubmed, Cochrane Library, Embase, CNKI, SinoMed, WF, and VIP were searched to collect the RCTs of AM capsule in treatment of DKD. Then Two investigators independently reviewed and extracted data from the RCTs which met the inclusion criteria. The quality of the data was assessed using the Cochrane risk of bias assessment tool, and meta-analysis was performed using RevMan 5.4 software. Results: 32 RCTs with a total of 2,881 DKD patients (1,442 in the treatment group and 1,439 in the control group) were included. The study results showed that AM capsule combined with RASI could be more effective in decreasing 24h-UTP [MD = -442.05, 95% CI (-609.72, -274.38), p < 0.00001], UAER [MD = -30.53, 95% CI (-39.10, -21.96), p < 0.00001], UACR [MD = -157.93, 95% CI (-288.60, -27.25), p < 0.00001], Scr [MD = -6.80, 95% CI (-9.85, -3.74), p < 0.0001], and BUN [MD = -0.59, 95% CI (-1.07, -0.12), p = 0.01], compared to using RASI alone. According to the subgroup analyses, the combination of AM and ARB seems to be more effective in reducing UAER than the combination of ACEI, and the addition of AM may achieve a more significant clinical effect on decreasing Scr for DKD patients with 24h-UTP>2 g or Scr>110-133 μmol/L and >133 μmol/L. Furthermore, no additional adverse reactions were observed in the combination group [OR = 1.06; 95%CI: (0.66, 1.69), p = 0.82]. Conclusion: Combining AM with RASI may be a superior strategy for DKD treatment compared to RASI monotherapy. However, due to significant heterogeneity, the results should be interpreted with great caution, and more high-quality RCTs with multi-centers, different stages of DKD, large sample sizes, and long follow-up periods are still needed to improve the evidence quality of AM for DKD in the future. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/#recordDetails; Identifier CRD42022351422.
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Affiliation(s)
- Ying Tan
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
| | - Ziqi Zhang
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
| | - Peipei Zhou
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
| | - Qiling Zhang
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
| | - Nan Li
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Qianhua Yan
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Liji Huang
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Jiangyi Yu
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
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9
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Kellerer M, Wanner C. [Kidney diseases in type 2 diabetes mellitus : Overview and implementation of guidelines, position papers and practical recommendations for diagnostics and monitoring]. INNERE MEDIZIN (HEIDELBERG, GERMANY) 2023; 64:1211-1217. [PMID: 37955643 PMCID: PMC10667375 DOI: 10.1007/s00108-023-01610-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/25/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND National and international medical societies have published guidelines and recommendations pertaining to the diagnostics and monitoring of chronic kidney disease in patients with type 2 diabetes mellitus. Consistency and implementation in daily clinical practice are rarely reported. OBJECTIVE This article provides an overview on recommendations as a reflection of the global state of the art and assesses the implementation in daily practice in Germany, which was collected via a representative questionnaire. MATERIAL AND METHODS The current guidelines were compared with respect to the consistency of parameters, frequency of testing and recommendations for nephrological referrals. The results were then compared with the survey responses to estimate the level of their implementation in daily practice in Germany. RESULTS According to the recommendations the estimated glomerular filtration rate (eGFR) and the urine albumin to creatinine ratio (UACR) should be tested at least once per year in all patients with type 2 diabetes. In cases of more severe kidney impairment (above Kidney Disease:Improving Global Outcomes, KDIGO, stage 3b with eGFR < 45 ml/min/1,73 m2) or albuminuria (from stage A2), more frequent measurements and nephrological referrals are recommended; however, different threshold values and frequencies are recommended. The responses from the questionnaires indicate that eGFR is tested annually in 96.5% of all cases and albuminuria is tested in 77.2% of cases. An eGRF triggered referral to a nephrologist is implemented by 19.6% of all nonnephrological practitioners, albuminuria triggered referrals are implemented in the majority of cases. CONCLUSION Measurement of eGFR is the established standard in Germany. Potential improvement was found in albumin measurement, the frequency of testing and the time point for nephrological consultation. All guidelines emphasize the benefits of interdisciplinary cooperation.
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Affiliation(s)
- Monika Kellerer
- Klinik für Innere Medizin 1, Marienhospital Stuttgart, Böheimstr. 37, 70199, Stuttgart, Deutschland.
| | - Christoph Wanner
- Medizinische Klinik und Poliklinik mit Schwerpunkt Nephrologie, Universitätsklinikum Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Deutschland.
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10
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Zeng X, Zeng Q, Zhou L, Zhu H, Luo J. Prevalence of Chronic Kidney Disease Among US Adults With Hypertension, 1999 to 2018. Hypertension 2023; 80:2149-2158. [PMID: 37497635 DOI: 10.1161/hypertensionaha.123.21482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 07/18/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND Hypertension is a major cause of end-stage renal disease. Assessing temporal trends in the prevalence of chronic kidney disease (CKD) in hypertension could provide information for public health policies and plans. METHODS From the National Health and Nutrition Examination Survey from 1999 to 2018, a probability sample of adults aged ≥20 years was collected. The primary outcomes were classified according to the estimated glomerular filtration rate and urinary albumin. Trend tests were performed to assess age-standardized prevalence trends of CKD, albuminuria, and macroalbuminuria in US adults with hypertension. RESULTS A total of 23 120 US adults with hypertension were included in this study. The prevalence of any CKD, albuminuria, or macroalbuminuria in hypertension remained relatively stable. However, the age-standardized prevalence of stage 1 CKD in hypertension increased from 4.9% in 2003 to 2006 to 7.0% in 2015 to 2018 (P=0.0077 for trend). The age-standardized prevalence of stage 3b CKD in hypertension decreased from 2.9% in 2011 to 2014 to 2.1% in 2015 to 2018 (P=0.0350 for trend). A similar trend was observed for the age-standardized prevalence of stages 3 to 5 CKD in hypertension, which declined from 10.9% in 2011 to 2014 to 8.9% in 2015 to 2018 (P=0.0160 for trend). CONCLUSIONS Among US adults with hypertension, the prevalence of any CKD, albuminuria, and macroalbuminuria remained relatively stable from 1999 to 2018, whereas the hypertensive population showed an increasing trend in stage 1 CKD from 2003 to 2006 to 2015 to 2018 and a decreasing trend in the prevalence of stages 3 to 5 and 3b CKD from 2011 to 2014 to 2015 to 2018.
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Affiliation(s)
- Xianghui Zeng
- Department of Cardiology, Ganzhou Hospital of Guangdong Provincial People's Hospital, Ganzhou Municipal Hospital, China (X.Z., H.Z.)
- Department of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, China (X.Z.)
| | - Qingfeng Zeng
- Emergency Department, The Second Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China (Q.Z.)
| | - Lanqian Zhou
- Department of Anesthesiology, The Second Affiliated Hospital of Nanchang University, Jiangxi, China (L.Z.)
| | - Hengqing Zhu
- Department of Cardiology, Ganzhou Hospital of Guangdong Provincial People's Hospital, Ganzhou Municipal Hospital, China (X.Z., H.Z.)
| | - Jianping Luo
- Department of Cardiology, Ganzhou People's Hospital, Jiangxi, China (J.L.)
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11
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Dattani R, Ul-Haq Z, Shah M, Goldet G, Darzi LA, Ashrafian H, Kamalati T, Frankel AH, Tam FW. Association and progression of multi-morbidity with Chronic Kidney Disease stage 3a secondary to Type 2 Diabetes Mellitus, grouped by albuminuria status in the multi-ethnic population of Northwest London: A real-world study. PLoS One 2023; 18:e0289838. [PMID: 37624842 PMCID: PMC10456138 DOI: 10.1371/journal.pone.0289838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 07/26/2023] [Indexed: 08/27/2023] Open
Abstract
INTRODUCTION The prevalence of Diabetic Kidney Disease (DKD) secondary to Type 2 Diabetes Mellitus (T2DM) is rising worldwide. However, real-world data linking glomerular function and albuminuria to the degree of multi-morbidity is lacking. We thus utilised the Discover dataset, to determine this association. METHOD Patients with T2DM diagnosed prior to 1st January 2015 with no available biochemical evidence of CKD were included. Patients subsequently diagnosed and coded for CKD3a in 2015, were grouped by the degree of albuminuria. Baseline and 5-year co-morbidity was determined, as were prescribing practices with regards to prognostically beneficial medication. RESULTS We identified 56,261 patients with T2DM, of which 1082 had CKD stage 3a diagnosed in 2015 (224-CKD3aA1,154-CKD3aA2,93-CKD3aA1; 611 patients with CKD3a but no uACR available in 2015 were excluded from follow up). No statistically significant difference was observed in the degree of co-morbidities at baseline. A significant difference in the degree of hypertension, retinopathy, ischaemic heart disease and vascular disease from baseline compared to study end point was observed for all 3 study groups. Comparing co-morbidities developed at study end point, highlighted a statistical difference between CKD3aA1 Vs CKD3aA3 for retinopathy alone and for hypertension and heart failure between CKD3aA2 Vs CKD3aA3. 40.8% of patients with CKD3aA2 or A3 were prescribed Renin Angiotensin Aldosterone inhibitors (RAASi) therapy between June-December 2021. Survival analysis showed 15% of patients with CKD3aA3 developed CKD stage 5 within 5 years of diagnosis. DISCUSSION CKD3a secondary to DKD is associated with significant multimorbidity at baseline and 5 years post diagnosis, with CKD3aA3 most strongly associated with CKD progression to CKD 5, heart failure, hypertension and retinopathy compared to CKD3aA1 or CKD3aA2 at 5 years post diagnosis. The lack of uACR testing upon diagnosis and poor prescribing of RAASi, in those with CKD3aA2/A3, raises significant cause for concern. CONCLUSION DKD is associated with significant multimorbidity. Significant work is needed to be done to ensure patients undergo testing for uACR, to allow for future risk stratification and ability to be started on prognostically beneficial medication.
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Affiliation(s)
| | - Zia Ul-Haq
- Imperial College Health Partners, London, United Kingdom
| | - Moulesh Shah
- Imperial College Health Partners, London, United Kingdom
| | | | | | | | | | | | - Frederick W.K. Tam
- Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
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12
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Heerspink HJ, Inker LA, Tighiouart H, Collier WH, Haaland B, Luo J, Appel GB, Chan TM, Estacio RO, Fervenza F, Floege J, Imai E, Jafar TH, Lewis JB, Kam-Tao Li P, Locatelli F, Maes BD, Perna A, Perrone RD, Praga M, Schena FP, Wanner C, Xie D, Greene T. Change in Albuminuria and GFR Slope as Joint Surrogate End Points for Kidney Failure: Implications for Phase 2 Clinical Trials in CKD. J Am Soc Nephrol 2023; 34:955-968. [PMID: 36918388 PMCID: PMC10278784 DOI: 10.1681/asn.0000000000000117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 01/19/2023] [Indexed: 03/16/2023] Open
Abstract
SIGNIFICANCE STATEMENT Changes in albuminuria and GFR slope are individually used as surrogate end points in clinical trials of CKD progression, and studies have demonstrated that each is associated with treatment effects on clinical end points. In this study, the authors sought to develop a conceptual framework that combines both surrogate end points to better predict treatment effects on clinical end points in Phase 2 trials. The results demonstrate that information from the combined treatment effects on albuminuria and GFR slope improves the prediction of treatment effects on the clinical end point for Phase 2 trials with sample sizes between 100 and 200 patients and duration of follow-up ranging from 1 to 2 years. These findings may help inform design of clinical trials for interventions aimed at slowing CKD progression. BACKGROUND Changes in log urinary albumin-to-creatinine ratio (UACR) and GFR slope are individually used as surrogate end points in clinical trials of CKD progression. Whether combining these surrogate end points might strengthen inferences about clinical benefit is unknown. METHODS Using Bayesian meta-regressions across 41 randomized trials of CKD progression, we characterized the combined relationship between the treatment effects on the clinical end point (sustained doubling of serum creatinine, GFR <15 ml/min per 1.73 m 2 , or kidney failure) and treatment effects on UACR change and chronic GFR slope after 3 months. We applied the results to the design of Phase 2 trials on the basis of UACR change and chronic GFR slope in combination. RESULTS Treatment effects on the clinical end point were strongly associated with the combination of treatment effects on UACR change and chronic slope. The posterior median meta-regression coefficients for treatment effects were -0.41 (95% Bayesian Credible Interval, -0.64 to -0.17) per 1 ml/min per 1.73 m 2 per year for the treatment effect on GFR slope and -0.06 (95% Bayesian Credible Interval, -0.90 to 0.77) for the treatment effect on UACR change. The predicted probability of clinical benefit when considering both surrogates was determined primarily by estimated treatment effects on UACR when sample size was small (approximately 60 patients per treatment arm) and follow-up brief (approximately 1 year), with the importance of GFR slope increasing for larger sample sizes and longer follow-up. CONCLUSIONS In Phase 2 trials of CKD with sample sizes of 100-200 patients per arm and follow-up between 1 and 2 years, combining information from treatment effects on UACR change and GFR slope improved the prediction of treatment effects on clinical end points.
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Affiliation(s)
- Hiddo J.L. Heerspink
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Lesley A. Inker
- Division of Nephrology, Tufts Medical Center, Boston, Massachusetts
| | - Hocine Tighiouart
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts
- Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts
| | - Willem H. Collier
- Division of Biostatistics, Department of Population Health Sciences, University of Utah Health, Salt Lake City, Utah
| | - Benjamin Haaland
- Department of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah
| | - Jiyu Luo
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California
| | - Gerald B. Appel
- Division of Nephrology, Columbia University Medical Center and the New York Presbyterian Hospital, New York, New York
| | - Tak Mao Chan
- Department of Medicine, University of Hong Kong, Queen Mary Hospital, Pokfulam, Hong Kong
| | | | - Fernando Fervenza
- Division of Nephrology and Hypertension and Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Jürgen Floege
- Division of Nephrology, RWTH Aachen University, Aachen, Germany
| | - Enyu Imai
- Nakayamadera Imai Clinic, Takarazuka, Japan
| | - Tazeen H. Jafar
- Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore
| | - Julia B. Lewis
- Division of Nephrology, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Francesco Locatelli
- Department of Nephrology, Alessandro Manzoni Hospital (past Director), ASST Lecco, Italy
| | - Bart D. Maes
- Department of Nephrology, AZ Delta, Roeselare, Belgium
| | - Annalisa Perna
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | | | - Manuel Praga
- Nephrology Department, Hospital Universitario 12 de Octubre, Department of Medicine, Complutense University, Madrid, Spain
| | - Francesco P. Schena
- Renal, Dialysis and Transplant Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Christoph Wanner
- Division of Nephrology, University Hospital of Würzburg, Würzburg, Germany
| | - Di Xie
- Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Tom Greene
- Division of Biostatistics, Department of Population Health Sciences, University of Utah Health, Salt Lake City, Utah
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Liu G, Zhong X, Zheng J, Zhang J, Kong W, Hu X, Min J, Xia W, Zeng T, Chen L. Comparative Efficacy of Novel Antidiabetic Drugs on Albuminuria Outcomes in Type 2 Diabetes: A Systematic Review. Diabetes Ther 2023; 14:789-822. [PMID: 36913143 PMCID: PMC10126195 DOI: 10.1007/s13300-023-01391-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/24/2023] [Indexed: 03/14/2023] Open
Abstract
INTRODUCTION Albuminuria, or elevated urinary albumin-to-creatine ratio (UACR), is a biomarker for chronic kidney disease that is routinely monitored in patients with type 2 diabetes (T2D). Head-to-head comparisons of novel antidiabetic drugs on albuminuria outcomes remain limited. This systematic review qualitatively compared the efficacy of novel antidiabetic drugs on improving albuminuria outcomes in patients with T2D. METHODS We searched the MEDLINE database until December 2022 for Phase 3 or 4 randomized, placebo-controlled trials that evaluated the effects of sodium-glucose co-transporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dipeptidyl peptidase-4 (DPP-4) inhibitors on changes in UACR and albuminuria categories in patients with T2D. RESULTS Among 211 records identified, 27 were included, which reported on 16 trials. SGLT2 inhibitors and GLP-1 RAs decreased UACR by 19-22% and 17-33%, respectively, versus placebo (P < 0.05 for all studies) over median follow-up of ≥ 2 years; DPP-4 inhibitors showed varying effects on UACR. Compared with placebo, SGLT2 inhibitors decreased the risk for albuminuria onset by 16-20% and for albuminuria progression by 27-48% (P < 0.05 for all studies) and promoted albuminuria regression (P < 0.05 for all studies) over median follow-up of ≥ 2 years. Evidence on changes in albuminuria categories with GLP-1 RA or DPP-4 inhibitor treatment were limited with varying outcome definitions across studies and potential drug-specific effects within each class. The effect of novel antidiabetic drugs on UACR or albuminuria outcomes at ≤ 1 year remains poorly studied. CONCLUSION Among the novel antidiabetic drugs, SGLT2 inhibitors consistently improved UACR and albuminuria outcomes in patients with T2D, with continuous treatment showing long-term benefit.
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Affiliation(s)
- Geng Liu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, Hubei, China
| | - Xueyu Zhong
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, Hubei, China
| | - Juan Zheng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, Hubei, China
| | - Jiaoyue Zhang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, Hubei, China
| | - Wen Kong
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, Hubei, China
| | - Xiang Hu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, Hubei, China
| | - Jie Min
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, Hubei, China
| | - Wenfang Xia
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, Hubei, China
| | - Tianshu Zeng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, Hubei, China
| | - Lulu Chen
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, Hubei, China.
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14
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Changes in urinary albumin as a surrogate for kidney disease progression in people with type 2 diabetes. Clin Exp Nephrol 2023; 27:465-472. [PMID: 36840900 DOI: 10.1007/s10157-023-02328-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/07/2023] [Indexed: 02/26/2023]
Abstract
BACKGROUND It remains unclear whether urinary albumin changes can predict subsequent kidney disease progression in people with diabetes. METHODS This retrospective cohort study included 4570 Japanese adults with type 2 diabetes (T2D). The exposure was changes in urinary albumin-to-creatinine ratio (UACR) over 3 years, categorized into three categories: ≤ - 30%, minor change, or ≥ 30%. During the exposure period, eGFR decline was also examined and categorized into two categories: < 30% or ≥ 30% decline. The primary outcome was the composite of eGFR halving or initiation of kidney replacement therapy (KRT). The secondary outcome was the initiation of KRT. RESULTS In the spline model, the hazard ratio for the primary outcome increased linearly on the log2 scale of UACR changes. When classified into six groups based on the categories of UACR changes and eGFR decline, people with a ≤ - 30% UACR change and < 30% eGFR decline had a 38% lower incidence of the outcome compared to those with a minor UACR change and < 30% eGFR decline. Meanwhile, the risk in those with a ≤ - 30% UACR change and ≥ 30% eGFR decline was 2.89 times. People with a ≥ 30% UACR change had the higher risk, regardless of whether a ≥ 30% eGFR decline occurred. Similar results were obtained in the secondary outcome. CONCLUSIONS UACR changes can be a useful surrogate for kidney disease progression in people with T2D. However, when setting a decrease in UACR as the surrogate, it may be necessary to simultaneously evaluate kidney function decline.
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Carrero JJ, Fu EL, Vestergaard SV, Jensen SK, Gasparini A, Mahalingasivam V, Bell S, Birn H, Heide-Jørgensen U, Clase CM, Cleary F, Coresh J, Dekker FW, Gansevoort RT, Hemmelgarn BR, Jager KJ, Jafar TH, Kovesdy CP, Sood MM, Stengel B, Christiansen CF, Iwagami M, Nitsch D. Defining measures of kidney function in observational studies using routine health care data: methodological and reporting considerations. Kidney Int 2023; 103:53-69. [PMID: 36280224 DOI: 10.1016/j.kint.2022.09.020] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 08/31/2022] [Accepted: 09/09/2022] [Indexed: 11/06/2022]
Abstract
The availability of electronic health records and access to a large number of routine measurements of serum creatinine and urinary albumin enhance the possibilities for epidemiologic research in kidney disease. However, the frequency of health care use and laboratory testing is determined by health status and indication, imposing certain challenges when identifying patients with kidney injury or disease, when using markers of kidney function as covariates, or when evaluating kidney outcomes. Depending on the specific research question, this may influence the interpretation, generalizability, and/or validity of study results. This review illustrates the heterogeneity of working definitions of kidney disease in the scientific literature and discusses advantages and limitations of the most commonly used approaches using 3 examples. We summarize ways to identify and overcome possible biases and conclude by proposing a framework for reporting definitions of exposures and outcomes in studies of kidney disease using routinely collected health care data.
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Affiliation(s)
- Juan Jesus Carrero
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.
| | - Edouard L Fu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Søren V Vestergaard
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Simon Kok Jensen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Alessandro Gasparini
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Viyaasan Mahalingasivam
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Samira Bell
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - Henrik Birn
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Biomedicine, Aarhus University, Aarhus, Denmark; Department of Renal Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Uffe Heide-Jørgensen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Catherine M Clase
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada; Department of Health Research and Methodology, McMaster University, Hamilton, Ontario, Canada
| | - Faye Cleary
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Friedo W Dekker
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ron T Gansevoort
- Department of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Kitty J Jager
- ERA Registry, Amsterdam UMC location University of Amsterdam, Medical Informatics, Meibergdreef, Amsterdam, Netherlands; Amsterdam Public Health Research Institute, Quality of Care, Amsterdam, the Netherlands
| | - Tazeen H Jafar
- Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore
| | - Csaba P Kovesdy
- Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Manish M Sood
- Department of Medicine, the Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Bénédicte Stengel
- CESP (Center for Research in Epidemiology and Population Health), Clinical Epidemiology Team, University Paris-Saclay, University Versailles-Saint Quentin, Inserm U1018, Villejuif, France
| | - Christian F Christiansen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Masao Iwagami
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK; Department of Health Services Research, University of Tsukuba, Ibaraki, Japan
| | - Dorothea Nitsch
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK; Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; UK Renal Registry, UK Kidney Association, Bristol, UK.
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Hanai K, Mori T, Yamamoto Y, Yoshida N, Murata H, Babazono T. Association of Estimated Glomerular Filtration Rate With Progression of Albuminuria in Individuals With Type 2 Diabetes. Diabetes Care 2023; 46:183-189. [PMID: 36399781 DOI: 10.2337/dc22-1582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/24/2022] [Indexed: 11/19/2022]
Abstract
OBJECTIVE To elucidate the association of glomerular filtration rate (GFR) at baseline with subsequent progression of albuminuria in individuals with type 2 diabetes. RESEARCH DESIGN AND METHODS This was a single-center retrospective cohort study of 6,618 Japanese adults with type 2 diabetes and urinary albumin-to-creatinine ratio of <300 mg/g, comprising 2,459 women and 4,159 men with a mean (± SD) age of 60 ± 12 years. The exposure was baseline estimated GFR (eGFR) (mL/min/1.73 m2), treated as a categorical variable and classified into five categories: ≥90, 75-90, 60-75, 45-60, and <45, as well as a continuous variable. The outcome was progression of albuminuria category (i.e., from normoalbuminuria to micro- or macroalbuminuria or from micro- to macroalbuminuria). Hazard ratios (HRs) for the outcome were estimated using the multivariable Cox proportional hazards model. In the analysis treating baseline eGFR as a continuous variable, the multivariable-adjusted restricted cubic spline model was used. RESULTS During the median follow-up period of 6.3 years, 1,190 individuals reached the outcome. When those with a baseline eGFR of 75-90 mL/min/1.73 m2 were considered the reference group, HRs (95% CIs) for the outcome in those with a baseline eGFR of ≥90, 60-75, 45-60, or <45 mL/min/1.73 m2 were 1.38 (1.14-1.66), 1.34 (1.14-1.58), 1.81 (1.50-2.20), or 2.37 (1.84-3.05), respectively. Furthermore, the inverse J-shaped curve was more clearly shown by the spline model. CONCLUSIONS This study of Japanese adults with type 2 diabetes suggests that both high and low GFRs are implicated in the pathogenesis of albuminuria progression.
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Niu X, Zhang S, Shao C, Guo Z, Wu J, Tao J, Zheng K, Ye W, Cai G, Sun W, Li M. Urinary complement proteins in IgA nephropathy progression from a relative quantitative proteomic analysis. PeerJ 2023; 11:e15125. [PMID: 37065697 PMCID: PMC10103701 DOI: 10.7717/peerj.15125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 03/03/2023] [Indexed: 04/18/2023] Open
Abstract
Aim IgA nephropathy (IgAN) is one of the leading causes of end-stage renal disease (ESRD). Urine testing is a non-invasive way to track the biomarkers used for measuring renal injury. This study aimed to analyse urinary complement proteins during IgAN progression using quantitative proteomics. Methods In the discovery phase, we analysed 22 IgAN patients who were divided into three groups (IgAN 1-3) according to their estimated glomerular filtration rate (eGFR). Eight patients with primary membranous nephropathy (pMN) were used as controls. Isobaric tags for relative and absolute quantitation (iTRAQ) labelling, coupled with liquid chromatography-tandem mass spectrometry, was used to analyse global urinary protein expression. In the validation phase, western blotting and parallel reaction monitoring (PRM) were used to verify the iTRAQ results in an independent cohort (N = 64). Results In the discovery phase, 747 proteins were identified in the urine of IgAN and pMN patients. There were different urine protein profiles in IgAN and pMN patients, and the bioinformatics analysis revealed that the complement and coagulation pathways were most activated. We identified a total of 27 urinary complement proteins related to IgAN. The relative abundance of C3, the membrane attack complex (MAC), the complement regulatory proteins of the alternative pathway (AP), and MBL (mannose-binding lectin) and MASP1 (MBL associated serine protease 2) in the lectin pathway (LP) increased during IgAN progression. This was especially true for MAC, which was found to be involved prominently in disease progression. Alpha-N-acetylglucosaminidase (NAGLU) and α-galactosidase A (GLA) were validated by western blot and the results were consistent with the iTRAQ results. Ten proteins were validated in a PRM analysis, and these results were also consistent with the iTRAQ results. Complement factor B (CFB) and complement component C8 alpha chain (C8A) both increased with the progression of IgAN. The combination of CFB and mucosal addressin cell adhesion molecule-1 (MAdCAM-1) also showed potential as a urinary biomarker for monitoring IgAN development. Conclusion There were abundant complement components in the urine of IgAN patients, indicating that the activation of AP and LP is involved in IgAN progression. Urinary complement proteins may be used as biomarkers for evaluating IgAN progression in the future.
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Affiliation(s)
- Xia Niu
- Core Facility of Instruments, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Shuyu Zhang
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Chen Shao
- Core Facility of Instruments, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Zhengguang Guo
- Core Facility of Instruments, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Jianqiang Wu
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianling Tao
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Ke Zheng
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Wenling Ye
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Guangyan Cai
- Department of Nephrology, The First Medical Centre, Chinese PLA General Hospital, Medical School of Chinese PLA, Beijing, China
| | - Wei Sun
- Core Facility of Instruments, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Mingxi Li
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
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18
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Zhou Y, Huang H, Yan X, Hapca S, Bell S, Qu F, Liu L, Chen X, Zhang S, Shi Q, Zeng X, Wang M, Li N, Du H, Meng W, Su B, Tian H, Li S. Glycated Haemoglobin A1c Variability Score Elicits Kidney Function Decline in Chinese People Living with Type 2 Diabetes. J Clin Med 2022; 11:6692. [PMID: 36431169 PMCID: PMC9692466 DOI: 10.3390/jcm11226692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 10/28/2022] [Accepted: 11/09/2022] [Indexed: 11/16/2022] Open
Abstract
Our aim was to investigate the association of glycated haemoglobin A1c (HbA1c) variability score (HVS) with estimated glomerular filtration rate (eGFR) slope in Chinese adults living with type 2 diabetes. This cohort study included adults with type 2 diabetes attending outpatient clinics between 2011 and 2019 from a large electronic medical record-based database of diabetes in China (WECODe). We estimated the individual-level visit-to-visit HbA1c variability using HVS, a proportion of changes in HbA1c of ≥0.5% (5.5 mmol/mol). We estimated the odds of people experiencing a rapid eGFR annual decline using a logistic regression and differences across HVS categories in the mean eGFR slope using a mixed-effect model. The analysis involved 2397 individuals and a median follow-up of 4.7 years. Compared with people with HVS ≤ 20%, those with HVS of 60% to 80% had 11% higher odds of experiencing rapid eGFR annual decline, with an extra eGFR decline of 0.93 mL/min/1.73 m2 per year on average; those with HVS > 80% showed 26% higher odds of experiencing a rapid eGFR annual decline, with an extra decline of 1.83 mL/min/1.73 m2 per year on average. Chinese adults with type 2 diabetes and HVS > 60% could experience a more rapid eGFR decline.
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Affiliation(s)
- Yiling Zhou
- Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hongmei Huang
- Department of Endocrinology and Metabolism, The First People’s Hospital of Shuangliu District, Chengdu 610200, China
| | - Xueqin Yan
- Department of Chronic Disease Management, Pidu District Second People’s Hospital, Chengdu 610000, China
| | - Simona Hapca
- Division of Computing Science and Mathematics, University of Stirling, Stirling FK9 4LA, UK
| | - Samira Bell
- Division of Population Health Science and Genomics, School of Medicine, University of Dundee, Dundee DD2 4BF, UK
| | - Furong Qu
- Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of General Practice, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Li Liu
- Department of Endocrinology and Metabolism, Second People’s Hospital of Ya’an City, Ya’an 625000, China
| | - Xiangyang Chen
- Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Endocrinology and Metabolism, The First People’s Hospital of Shuangliu District, Chengdu 610200, China
| | - Shengzhao Zhang
- Department of Pharmacy, Karamay Central Hospital, Karamay 834000, China
- Department of Pharmacy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qingyang Shi
- Chinese Evidence-Based Medicine Center, Cochrane China Center, MAGIC China Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiaoxi Zeng
- Department of Nephrology, West China Hospital, Sichuan University, Chengdu 610041, China
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Miye Wang
- Department of Informatics, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Nan Li
- Department of Informatics, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Heyue Du
- Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Nephrology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Wentong Meng
- Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Baihai Su
- Department of Nephrology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Haoming Tian
- Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Sheyu Li
- Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, China
- Chinese Evidence-Based Medicine Center, Cochrane China Center, MAGIC China Center, West China Hospital, Sichuan University, Chengdu 610041, China
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19
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Gorski M, Rasheed H, Teumer A, Thomas LF, Graham SE, Sveinbjornsson G, Winkler TW, Günther F, Stark KJ, Chai JF, Tayo BO, Wuttke M, Li Y, Tin A, Ahluwalia TS, Ärnlöv J, Åsvold BO, Bakker SJL, Banas B, Bansal N, Biggs ML, Biino G, Böhnke M, Boerwinkle E, Bottinger EP, Brenner H, Brumpton B, Carroll RJ, Chaker L, Chalmers J, Chee ML, Chee ML, Cheng CY, Chu AY, Ciullo M, Cocca M, Cook JP, Coresh J, Cusi D, de Borst MH, Degenhardt F, Eckardt KU, Endlich K, Evans MK, Feitosa MF, Franke A, Freitag-Wolf S, Fuchsberger C, Gampawar P, Gansevoort RT, Ghanbari M, Ghasemi S, Giedraitis V, Gieger C, Gudbjartsson DF, Hallan S, Hamet P, Hishida A, Ho K, Hofer E, Holleczek B, Holm H, Hoppmann A, Horn K, Hutri-Kähönen N, Hveem K, Hwang SJ, Ikram MA, Josyula NS, Jung B, Kähönen M, Karabegović I, Khor CC, Koenig W, Kramer H, Krämer BK, Kühnel B, Kuusisto J, Laakso M, Lange LA, Lehtimäki T, Li M, Lieb W, Lind L, Lindgren CM, Loos RJF, Lukas MA, Lyytikäinen LP, Mahajan A, Matias-Garcia PR, Meisinger C, Meitinger T, Melander O, Milaneschi Y, Mishra PP, Mononen N, Morris AP, Mychaleckyj JC, Nadkarni GN, Naito M, Nakatochi M, Nalls MA, Nauck M, Nikus K, Ning B, Nolte IM, Nutile T, O'Donoghue ML, O'Connell J, Olafsson I, Orho-Melander M, Parsa A, Pendergrass SA, Penninx BWJH, Pirastu M, Preuss MH, Psaty BM, Raffield LM, Raitakari OT, Rheinberger M, Rice KM, Rizzi F, Rosenkranz AR, Rossing P, Rotter JI, Ruggiero D, Ryan KA, Sabanayagam C, Salvi E, Schmidt H, Schmidt R, Scholz M, Schöttker B, Schulz CA, Sedaghat S, Shaffer CM, Sieber KB, Sim X, Sims M, Snieder H, Stanzick KJ, Thorsteinsdottir U, Stocker H, Strauch K, Stringham HM, Sulem P, Szymczak S, Taylor KD, Thio CHL, Tremblay J, Vaccargiu S, van der Harst P, van der Most PJ, Verweij N, Völker U, Wakai K, Waldenberger M, Wallentin L, Wallner S, Wang J, Waterworth DM, White HD, Willer CJ, Wong TY, Woodward M, Yang Q, Yerges-Armstrong LM, Zimmermann M, Zonderman AB, Bergler T, Stefansson K, Böger CA, Pattaro C, Köttgen A, Kronenberg F, Heid IM. Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies. Kidney Int 2022; 102:624-639. [PMID: 35716955 PMCID: PMC10034922 DOI: 10.1016/j.kint.2022.05.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 04/19/2022] [Accepted: 05/11/2022] [Indexed: 12/15/2022]
Abstract
Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics.
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Affiliation(s)
- Mathias Gorski
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany; Department of Nephrology, University Hospital Regensburg, Regensburg, Germany.
| | - Humaira Rasheed
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway; MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany; Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
| | - Laurent F Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway; Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway; BioCore-Bioinformatics Core Facility, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sarah E Graham
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Felix Günther
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany; Statistical Consulting Unit StaBLab, Department of Statistics, LMU Munich, Munich, Germany
| | - Klaus J Stark
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Bamidele O Tayo
- Department of Public Health Sciences, Loyola University Chicago, Maywood, Illinois, USA
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany; Renal Division, Department of Medicine IV, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Yong Li
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Adrienne Tin
- Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, Mississippi, USA; Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Tarunveer S Ahluwalia
- Steno Diabetes Center Copenhagen, Gentofte, Denmark; The Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Johan Ärnlöv
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; School of Health and Social Studies, Dalarna University, Stockholm, Sweden
| | - Bjørn Olav Åsvold
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway; Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Stephan J L Bakker
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Bernhard Banas
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Nisha Bansal
- Division of Nephrology, University of Washington, Seattle, Washington, USA; Kidney Research Institute, University of Washington, Seattle, Washington, USA
| | - Mary L Biggs
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, USA; Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Ginevra Biino
- Institute of Molecular Genetics, National Research Council of Italy, Pavia, Italy
| | - Michael Böhnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, Texas, USA
| | - Erwin P Bottinger
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Digital Health Center, Hasso Plattner Institute and University of Potsdam, Potsdam, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Network Aging Research, Heidelberg University, Heidelberg, Germany
| | - Ben Brumpton
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway; MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Clinic of Thoracic and Occupational Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Layal Chaker
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - John Chalmers
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Miao-Ling Chee
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Audrey Y Chu
- Genetics, Merck & Co, Inc., Kenilworth, New Jersey, USA
| | - Marina Ciullo
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso"-CNR, Naples, Italy; IRCCS Neuromed, Pozzilli, Italy
| | - Massimiliano Cocca
- Institute for Maternal and Child Health, IRCCS "Burlo Garofolo," Trieste, Italy
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Daniele Cusi
- Institute of Biomedical Technologies, National Research Council of Italy, Milan, Italy; Bio4Dreams-Business Nursery for Life Sciences, Milan, Italy
| | - Martin H de Borst
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Frauke Degenhardt
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany; Department of Nephrology and Hypertension, Friedrich Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Karlhans Endlich
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany; Department of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Baltimore, Maryland, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Sandra Freitag-Wolf
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA; Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Piyush Gampawar
- Institute of Molecular Biology and Biochemistry, Center for Molecular Medicine, Medical University of Graz, Graz, Austria
| | - Ron T Gansevoort
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Sahar Ghasemi
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Vilmantas Giedraitis
- Molecular Geriatrics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland; Iceland School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Stein Hallan
- Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway; Department of Nephrology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Pavel Hamet
- Montreal University Hospital Research Center, CHUM, Montreal, Quebec, Canada; Medpharmgene, Montreal, Quebec, Canada; CRCHUM, Montreal, Quebec, Canada
| | - Asahi Hishida
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kevin Ho
- Kidney Health Research Institute (KHRI), Geisinger, Danville, Pennsylvania, USA; Department of Nephrology, Geisinger, Danville, Pennsylvania, USA
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria; Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Bernd Holleczek
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hilma Holm
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Anselm Hoppmann
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany; LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Nina Hutri-Kähönen
- Department of Pediatrics, Tampere University Hospital, Tampere, Finland; Department of Pediatrics, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Shih-Jen Hwang
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Navya Shilpa Josyula
- Geisinger Research, Biomedical and Translational Informatics Institute, Rockville, Maryland, USA
| | - Bettina Jung
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany; Department of Nephrology and Rheumatology, Kliniken Südostbayern, Traunstein, Germany; KfH Kidney Centre Traunstein, Traunstein, Germany
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland; Department of Clinical Physiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Irma Karabegović
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Chiea-Chuen Khor
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore; Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany; DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany; Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
| | - Holly Kramer
- Department of Public Health Sciences, Loyola University Chicago, Maywood, Illinois, USA; Division of Nephrology and Hypertension, Loyola University Chicago, Chicago, Illinois, USA
| | - Bernhard K Krämer
- Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Brigitte Kühnel
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Johanna Kuusisto
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland; Centre for Medicine and Clinical Research, University of Eastern Finland School of Medicine, Kuopio, Finland
| | - Markku Laakso
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland; Centre for Medicine and Clinical Research, University of Eastern Finland School of Medicine, Kuopio, Finland
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado Denver-Anschutz Medical Campus, Aurora, Colorado, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Man Li
- Division of Nephrology and Hypertension, Department of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank Popgen, Kiel University, Kiel, Germany
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Cecilia M Lindgren
- Nuffield Department of Population Health, University of Oxford, Oxford, UK; Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA; Wellcome Center for Human Genetics, University of Oxford, Oxford, UK; Nuffield Department of Women's and Reproductive Health, University of Oxford, Level 3, Women's Centre, John Radcliffe Hospital, Oxford, UK; Li Ka Shing Centre for Health Information and Discovery, The Big Data Institute, University of Oxford, Oxford, UK
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mary Ann Lukas
- Clinical Sciences, GlaxoSmithKline, Albuquerque, New Mexico, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Anubha Mahajan
- Wellcome Center for Human Genetics, University of Oxford, Oxford, UK; Oxford Center for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Pamela R Matias-Garcia
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, Augsburg, Germany
| | - Thomas Meitinger
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany; Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany; Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Olle Melander
- Hypertension and Cardiovascular Disease, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC/Vrije Universiteit and GGZ inGeest, Amsterdam, the Netherlands
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Nina Mononen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Andrew P Morris
- Department of Health Data Science, University of Liverpool, Liverpool, UK; Wellcome Center for Human Genetics, University of Oxford, Oxford, UK; Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, Charlottesville, Virginia, USA
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mariko Naito
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan; Department of Oral Epidemiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA; Data Tecnica International, Glen Echo, Maryland, USA
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany; Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Kjell Nikus
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland; Department of Cardiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Boting Ning
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Teresa Nutile
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso"-CNR, Naples, Italy
| | - Michelle L O'Donoghue
- Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts, USA; TIMI Study Group, Boston, Massachusetts, USA
| | | | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Marju Orho-Melander
- Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Afshin Parsa
- Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA; Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Sarah A Pendergrass
- Geisinger Research, Biomedical and Translational Informatics Institute, Danville, Pennsylvania, USA
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC/Vrije Universiteit and GGZ inGeest, Amsterdam, the Netherlands
| | - Mario Pirastu
- Institute of Genetic and Biomedical Research, National Research Council of Italy, UOS of Sassari, Li Punti, Sassari, Italy
| | - Michael H Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Department of Epidemiology, Department of Health Services, University of Washington, Seattle, Washington, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland; Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland; Research Center of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Myriam Rheinberger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany; Department of Nephrology and Rheumatology, Kliniken Südostbayern, Traunstein, Germany; KfH Kidney Centre Traunstein, Traunstein, Germany
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Federica Rizzi
- Department of Health Sciences, University of Milan, Milano, Italy; ePhood Scientific Unit, ePhood SRL, Milano, Italy
| | - Alexander R Rosenkranz
- Division of Nephrology, Department of Internal Medicine, Medical University Graz, Graz, Austria
| | - Peter Rossing
- Steno Diabetes Center Copenhagen, Gentofte, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso"-CNR, Naples, Italy; IRCCS Neuromed, Pozzilli, Italy
| | - Kathleen A Ryan
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Erika Salvi
- Department of Health Sciences, University of Milan, Milano, Italy; Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico "Carlo Besta," Milan, Italy
| | - Helena Schmidt
- Institute of Molecular Biology and Biochemistry, Center for Molecular Medicine, Medical University of Graz, Graz, Austria
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany; LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Network Aging Research, Heidelberg University, Heidelberg, Germany
| | - Christina-Alexandra Schulz
- Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Sanaz Sedaghat
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, Illinois, USA
| | - Christian M Shaffer
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Karsten B Sieber
- Human Genetics, GlaxoSmithKline, Collegeville, Pennsylvania, USA
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mario Sims
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Kira J Stanzick
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland; Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Hannah Stocker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Network Aging Research, Heidelberg University, Heidelberg, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Chair of Genetic Epidemiology, IBE, Faculty of Medicine, Ludwig-Maximilians-Universität München, München, Germany; Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Silke Szymczak
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany; Institute of Medical Biometry and Statistics, University of Lübeck, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Chris H L Thio
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Johanne Tremblay
- Montreal University Hospital Research Center, CHUM, Montreal, Quebec, Canada; CRCHUM, Montreal, Quebec, Canada; Medpharmgene, Montreal, Quebec, Canada
| | - Simona Vaccargiu
- Institute of Genetic and Biomedical Research, National Research Council of Italy, UOS of Sassari, Li Punti, Sassari, Italy
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Durrer Center for Cardiovascular Research, The Netherlands Heart Institute, Utrecht, the Netherlands
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany; Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Lars Wallentin
- Cardiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden; Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Stefan Wallner
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Judy Wang
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | | | - Harvey D White
- Green Lane Cardiovascular Service, Auckland City Hospital and University of Auckland, Auckland, New Zealand
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA; Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Mark Woodward
- The George Institute for Global Health, University of New South Wales, Sydney, Australia; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; The George Institute for Global Health, University of Oxford, Oxford, UK
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | | | - Martina Zimmermann
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Baltimore, Maryland, USA
| | - Tobias Bergler
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Kari Stefansson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland; Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Carsten A Böger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany; Department of Nephrology and Rheumatology, Kliniken Südostbayern, Traunstein, Germany; KfH Kidney Centre Traunstein, Traunstein, Germany
| | - Cristian Pattaro
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
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Xu J, Xue Y, Chen Q, Han X, Cai M, Tian J, Jin S, Lu H. Identifying Distinct Risk Thresholds of Glycated Hemoglobin and Systolic Blood Pressure for Rapid Albuminuria Progression in Type 2 Diabetes From NHANES (1999–2018). Front Med (Lausanne) 2022; 9:928825. [PMID: 35795642 PMCID: PMC9251013 DOI: 10.3389/fmed.2022.928825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 05/12/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundIt is widely recognized that glycated hemoglobin (HbA1c) and systolic blood pressure (SBP) are two key risk factors for albuminuria and renal function impairment in patients with type 2 diabetes mellitus (T2DM). Our study aimed to identify the specific numerical relationship of albumin/creatinine ratio (ACR) with HbA1c and SBP among a large population of adults with T2DM.MethodA total of 8,626 patients with T2DM were included in the data analysis from the National Health and Nutrition Examination Surveys (NHANES) (1999-2018). The multiple linear regressions were used to examine the associations of ACR with HbA1c and SBP. Generalized additive models with smooth functions were performed to identify the non-linear relations between variables and interactions were also tested.ResultsSignificantly threshold effects were observed between ACR and HbA1c or SBP after multivariable adjustment, with the risk threshold values HbA1c = 6.4% and SBP = 127 mmHg, respectively. Once above thresholds were exceeded, the lnACR increased dramatically with higher levels of HbA1c (β = 0.23, 95 CI%:0.14, 0.32, P < 0.001) and SBP (β = 0.03, 95 CI%:0.03, 0.04, P < 0.001). Subgroup analysis showed high protein diet was related to higher ACR. In addition, a higher risk of ACR progression was observed in central obesity participants with HbA1C ≥ 6.4% or hyperuricemia participants with SBP ≥ 127 mmHg among patients withT2DM.ConclusionWe identified thresholds of HbA1c and SBP to stratify patients with T2DM through rapid albuminuria progression. These might provide a clinical reference value for preventing and controlling diabetes kidney disease.
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Affiliation(s)
- Jiahui Xu
- Department of Endocrinology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yan Xue
- Laboratory of Cellular Immunity, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qingguang Chen
- Department of Endocrinology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu Han
- Department of Endocrinology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mengjie Cai
- Department of Endocrinology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jing Tian
- Department of Endocrinology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Shenyi Jin
- Department of Endocrinology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hao Lu
- Department of Endocrinology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Hao Lu,
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Urinary C5b-9 as a Prognostic Marker in IgA Nephropathy. J Clin Med 2022; 11:jcm11030820. [PMID: 35160271 PMCID: PMC8836759 DOI: 10.3390/jcm11030820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/24/2022] [Accepted: 02/02/2022] [Indexed: 01/27/2023] Open
Abstract
C5b-9 plays an important role in the pathogenesis of immunoglobin A nephropathy (IgAN). We evaluated C5b-9 as a prognostic marker for IgAN. We prospectively enrolled 33 patients with biopsy-proven IgAN. We analyzed the correlation between baseline urinary C5b-9 levels, posttreatment changes in their levels, and clinical outcomes, including changes in proteinuria, estimated glomerular filtration rate (eGFR), and treatment response. Baseline urinary C5b-9 levels were positively correlated with proteinuria (r = 0.548, p = 0.001) at the time of diagnosis. Changes in urinary C5b-9 levels were positively correlated with changes in proteinuria (r = 0.644, p < 0.001) and inversely correlated with changes in eGFR (r = −0.410, p = 0.018) at 6 months after treatment. Changes in urinary C5b-9 levels were positively correlated with time-averaged proteinuria during the follow-up period (r= 0.461, p = 0.007) but were not correlated with the mean annual rate of eGFR decline (r = −0.282, p = 0.112). Baseline urinary C5b-9 levels were not a significant independent factor that could predict the treatment response in logistic regression analyses (odds ratio 0.997; 95% confidence interval, 0.993 to 1.000; p = 0.078). Currently, urinary C5b-9 is not a promising prognostic biomarker for IgAN, and further studies are needed.
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22
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Neuen BL, Tighiouart H, Heerspink HJ, Vonesh EF, Chaudhari J, Miao S, Chan TM, Fervenza FC, Floege J, Goicoechea M, Herrington WG, Imai E, Jafar TH, Lewis JB, Li PKT, Locatelli F, Maes BD, Perrone RD, Praga M, Perna A, Schena FP, Wanner C, Wetzels JF, Woodward M, Xie D, Greene T, Inker LA. Acute Treatment Effects on GFR in Randomized Clinical Trials of Kidney Disease Progression. J Am Soc Nephrol 2022; 33:291-303. [PMID: 34862238 PMCID: PMC8819983 DOI: 10.1681/asn.2021070948] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 10/28/2021] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Acute changes in GFR can occur after initiation of interventions targeting progression of CKD. These acute changes complicate the interpretation of long-term treatment effects. METHODS To assess the magnitude and consistency of acute effects in randomized clinical trials and explore factors that might affect them, we performed a meta-analysis of 53 randomized clinical trials for CKD progression, enrolling 56,413 participants with at least one estimated GFR measurement by 6 months after randomization. We defined acute treatment effects as the mean difference in GFR slope from baseline to 3 months between randomized groups. We performed univariable and multivariable metaregression to assess the effect of intervention type, disease state, baseline GFR, and albuminuria on the magnitude of acute effects. RESULTS The mean acute effect across all studies was -0.21 ml/min per 1.73 m2 (95% confidence interval, -0.63 to 0.22) over 3 months, with substantial heterogeneity across interventions (95% coverage interval across studies, -2.50 to +2.08 ml/min per 1.73 m2). We observed negative average acute effects in renin angiotensin system blockade, BP lowering, and sodium-glucose cotransporter 2 inhibitor trials, and positive acute effects in trials of immunosuppressive agents. Larger negative acute effects were observed in trials with a higher mean baseline GFR. CONCLUSION The magnitude and consistency of acute GFR effects vary across different interventions, and are larger at higher baseline GFR. Understanding the nature and magnitude of acute effects can help inform the optimal design of randomized clinical trials evaluating disease progression in CKD.
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Affiliation(s)
- Brendon L. Neuen
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Hocine Tighiouart
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts
- Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts
| | - Hiddo J.L. Heerspink
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, Groningen, Netherlands
| | - Edward F. Vonesh
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois
| | - Juhi Chaudhari
- Division of Nephrology, Tufts Medical Center, Boston, Massachusetts
| | - Shiyuan Miao
- Division of Nephrology, Tufts Medical Center, Boston, Massachusetts
| | - Tak Mao Chan
- Department of Medicine, University of Hong Kong, Pokfulam, Hong Kong
| | - Fernando C. Fervenza
- Division of Nephrology and Hypertension and Department of Medicine, Mayo Clinic Rochester, Minnesota
| | - Jürgen Floege
- Division of Nephrology, RWTH Aachen University, Aachen, Germany
| | - Marian Goicoechea
- Department of Nephrology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - William G. Herrington
- Medical Research Council Population Health Research Unit at the University of Oxford Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Enyu Imai
- Nakayamadera Imai Clinic, Takarazuka, Japan
| | - Tazeen H. Jafar
- Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Duke Global Health Institute, Duke University, Durham, North Carolina
| | - Julia B. Lewis
- Division of Nephrology, Vanderbilt University, Nashville, Tennessee
| | - Philip Kam-Tao Li
- Division of Nephrology, Prince of Wales Hospital, Chinese University of Hong Kong, Shatin, Hong Kong
| | | | - Bart D. Maes
- Department of Nephrology, AZ Delta, Roeselare, Belgium
| | | | - Manuel Praga
- Nephrology Department, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Annalisa Perna
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - Francesco P. Schena
- Renal, Dialysis and Transplant Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Christoph Wanner
- Division of Nephrology, University Hospital of Würzburg, Würzburg, Germany
| | - Jack F.M. Wetzels
- Department of Nephrology, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Mark Woodward
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- The George Institute for Global Health, Imperial College London, United Kingdom
| | - Di Xie
- Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Tom Greene
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, Utah
| | - Lesley A. Inker
- Division of Nephrology, Tufts Medical Center, Boston, Massachusetts
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Wang AA, Cai X, Srivastava A, Prasad PV, Sprague SM, Carr J, Wolf M, Ix JH, Block GA, Chonchol M, Raphael KL, Cheung AK, Raj DS, Gassman JJ, Rahsepar AA, Middleton JP, Fried LF, Sarnari R, Isakova T, Mehta R. Abnormalities in Cardiac Structure and Function among Individuals with CKD: The COMBINE Trial. KIDNEY360 2021; 3:258-268. [PMID: 35373122 PMCID: PMC8967624 DOI: 10.34067/kid.0005022021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/10/2021] [Indexed: 01/10/2023]
Abstract
Background Individuals with CKD have a high burden of cardiovascular disease (CVD). Abnormalities in cardiac structure and function represent subclinical CVD and can be assessed by cardiac magnetic resonance imaging (cMRI). Methods We investigated differences in cMRI parameters in 140 individuals with CKD stages 3b-4 who participated in the CKD Optimal Management with BInders and NicotinamidE (COMBINE) trial and in 24 age- and sex-matched healthy volunteers. Among COMBINE participants, we examined the associations of eGFR, urine albumin-creatinine ratio (UACR), phosphate, fibroblast growth factor 23 (FGF23), and parathyroid hormone (PTH) with baseline (N=140) and 12-month change (N=112) in cMRI parameters. Results Mean (SD) ages of the COMBINE participants and healthy volunteers were 64.9 (11.9) and 60.4 (7.3) years, respectively. The mean (SD) baseline eGFR values in COMBINE participants were 32.1 (8.0) and 85.9 (16.0) ml/min per 1.73 m2 in healthy volunteers. The median (interquartile range [IQR]) UACR in COMBINE participants was 154 (20.3-540.0) mg/g. Individuals with CKD had lower mitral valve E/A ratio compared with healthy volunteers (for CKD versus non-CKD, β estimate, -0.13; 95% CI, -0.24 to -0.012). Among COMBINE participants, multivariable linear regression analyses showed that higher UACR was significantly associated with lower mitral valve E/A ratio (β estimate per 1 unit increase in natural-log UACR, -0.06; 95% CI, -0.09 to -0.03). This finding was preserved among individuals without baseline CVD. UACR was not associated with 12-month change in any cMRI parameter. eGFR, phosphate, FGF23, and PTH were not associated with any cMRI parameter in cross-sectional or change analyses. Conclusions Individuals with CKD stages 3b-4 have evidence of cMRI abnormalities. Albuminuria was independently associated with diastolic dysfunction, as assessed by mitral valve E/A ratio, in individuals with CKD with and without clinical CVD. Albuminuria was not associated with change in any cMRI parameter.
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Affiliation(s)
- Ann A. Wang
- Graduate Medical Education, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Xuan Cai
- Center for Translational Metabolism and Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Anand Srivastava
- Center for Translational Metabolism and Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois,Division of Nephrology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Pottumarthi V. Prasad
- Department of Radiology, NorthShore University Health System Evanston, Evanston, Illinois
| | - Stuart M. Sprague
- Division of Nephrology and Hypertension, NorthShore University Health System, Evanston, Illinois,University of Chicago Pritzker School of Medicine, Chicago, Illinois
| | - James Carr
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Myles Wolf
- Division of Nephrology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina,Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
| | - Joachim H. Ix
- Division of Nephrology, Department of Medicine, University of San Diego School of Medicine and Veterans Affairs San Diego Healthcare System, San Diego, California
| | | | - Michel Chonchol
- Division of Renal Disease/Hypertension, Department of Internal Medicine, University of Colorado Hospitals, Aurora, Colorado
| | - Kalani L. Raphael
- Division of Nephrology and Hypertension, Department of Medicine, Oregon Health and Science University and Veterans Affairs Portland Health Care System, Portland, Oregon
| | - Alfred K. Cheung
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Utah Health, Salt Lake City, Utah
| | - Dominic S. Raj
- Division of Kidney Diseases and Hypertension, George Washington University School of Medicine, Washington, DC
| | | | - Amir Ali Rahsepar
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - John P. Middleton
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
| | - Linda F. Fried
- Renal Section, Veterans Affairs Pittsburgh Healthcare System and Department of Medicine, Renal-Electrolyte Division, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Roberto Sarnari
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Tamara Isakova
- Center for Translational Metabolism and Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois,Division of Nephrology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Rupal Mehta
- Center for Translational Metabolism and Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois,Division of Nephrology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois,Division of Nephrology, Department of Medicine, Jesse Brown Veterans Administration Medical Center, Chicago, Illinois
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Coresh J, Grams ME, Chen TK. Using GFR, Albuminuria, and Their Changes in Clinical Trials and Clinical Care. Am J Kidney Dis 2021; 78:333-334. [PMID: 34059333 DOI: 10.1053/j.ajkd.2021.04.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 04/13/2021] [Indexed: 11/11/2022]
Affiliation(s)
- Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Teresa K Chen
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
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