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Le Gall L, Harambat J, Combe C, Philipps V, Proust-Lima C, Dussartre M, Drüeke T, Choukroun G, Fouque D, Frimat L, Jacquelinet C, Laville M, Liabeuf S, Pecoits-Filho R, Massy ZA, Stengel B, Alencar de Pinho N, Leffondré K, Prezelin-Reydit M. Haemoglobin trajectories in chronic kidney disease and risk of major adverse cardiovascular events. Nephrol Dial Transplant 2024; 39:669-682. [PMID: 37935529 DOI: 10.1093/ndt/gfad235] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND The trajectories of haemoglobin in patients with chronic kidney disease (CKD) have been poorly described. In such patients, we aimed to identify typical haemoglobin trajectory profiles and estimate their risks of major adverse cardiovascular events (MACE). METHODS We used 5-year longitudinal data from the CKD-REIN cohort patients with moderate to severe CKD enrolled from 40 nationally representative nephrology clinics in France. A joint latent class model was used to estimate, in different classes of haemoglobin trajectory, the competing risks of (i) MACE + defined as the first event among cardiovascular death, non-fatal myocardial infarction, stroke or hospitalization for acute heart failure, (ii) initiation of kidney replacement therapy (KRT) and (iii) non-cardiovascular death. RESULTS During the follow-up, we gathered 33 874 haemoglobin measurements from 3011 subjects (median, 10 per patient). We identified five distinct haemoglobin trajectory profiles. The predominant profile (n = 1885, 62.6%) showed an overall stable trajectory and low risks of events. The four other profiles had nonlinear declining trajectories: early strong decline (n = 257, 8.5%), late strong decline (n = 75, 2.5%), early moderate decline (n = 356, 11.8%) and late moderate decline (n = 438, 14.6%). The four profiles had different risks of MACE, while the risks of KRT and non-cardiovascular death consistently increased from the haemoglobin decline. CONCLUSION In this study, we observed that two-thirds of patients had a stable haemoglobin trajectory and low risks of adverse events. The other third had a nonlinear trajectory declining at different rates, with increased risks of events. Better attention should be paid to dynamic changes of haemoglobin in CKD.
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Affiliation(s)
- Lisa Le Gall
- University Bordeaux, INSERM, Bordeaux Population Health, UMR1219, Bordeaux, France
- University Bordeaux, INSERM, CIC-1401-EC, Bordeaux, France
| | - Jérôme Harambat
- University Bordeaux, INSERM, Bordeaux Population Health, UMR1219, Bordeaux, France
- University Bordeaux, INSERM, CIC-1401-EC, Bordeaux, France
- Bordeaux University Hospital, Pediatric Nephrology Unit, Centre de Référence des Maladies Rénales Rares Sorare, Pellegrin-Enfants Hospital, Bordeaux, France
| | - Christian Combe
- Bordeaux University Hospital, Department of Nephrology, transplantation, dialysis, Bordeaux, France
- University Bordeaux, INSERM U1026, Bordeaux, France
| | - Viviane Philipps
- University Bordeaux, INSERM, Bordeaux Population Health, UMR1219, Bordeaux, France
| | - Cécile Proust-Lima
- University Bordeaux, INSERM, Bordeaux Population Health, UMR1219, Bordeaux, France
| | - Maris Dussartre
- University Bordeaux, INSERM, Bordeaux Population Health, UMR1219, Bordeaux, France
| | - Tilman Drüeke
- Centre for research in Epidemiology and Population Health (CESP), Paris-Saclay University, Versailles Saint-Quentin University, Inserm U1018 Clinical Epidemiology Team, Villejuif, France
| | - Gabriel Choukroun
- Amiens Picardie University Hospital, Department of Nephrology Dialysis Transplantation, Amiens, France
- University of Picardie Jules Verne, MP3CV Research Unit, Amiens, France
| | - Denis Fouque
- Hopital Lyon Sud, Département de néphrologie, Lyon, France
- Université Claude Bernard Lyon 1, Carmen INSERM U1060, Pierre-Bénite, France
| | - Luc Frimat
- CHRU de Nancy, Department of Nephrology, Vandoeuvre-lès-Nancy, France
- Lorraine University, APEMAC, Nancy, France
| | - Christian Jacquelinet
- Centre for research in Epidemiology and Population Health (CESP), Paris-Saclay University, Versailles Saint-Quentin University, Inserm U1018 Clinical Epidemiology Team, Villejuif, France
- Agence de la biomedecine, La Plaine-Saint-Denis, France
| | - Maurice Laville
- Université Claude Bernard Lyon 1, Carmen INSERM U1060, Pierre-Bénite, France
| | - Sophie Liabeuf
- University of Picardie Jules Verne, MP3CV Research Unit, Amiens, France
- Amiens-Picardie University Medical Center, Pharmacoepidemiology Unit, Department of Clinical Pharmacology, Amiens, France
| | - Roberto Pecoits-Filho
- DOPPS Program Area, Arbor Research Collaborative for Health, Ann Arbor, MI, USA
- School of Medicine, Pontificia Universidade Catolica do Parana, Cutitiba, PR, Brazil
| | - Ziad A Massy
- Centre for research in Epidemiology and Population Health (CESP), Paris-Saclay University, Versailles Saint-Quentin University, Inserm U1018 Clinical Epidemiology Team, Villejuif, France
- Ambroise Paré University Hospital, APHP, Department of Nephrology, Boulogne-Billancourt/Paris, France
| | - Bénédicte Stengel
- Centre for research in Epidemiology and Population Health (CESP), Paris-Saclay University, Versailles Saint-Quentin University, Inserm U1018 Clinical Epidemiology Team, Villejuif, France
| | - Natalia Alencar de Pinho
- Centre for research in Epidemiology and Population Health (CESP), Paris-Saclay University, Versailles Saint-Quentin University, Inserm U1018 Clinical Epidemiology Team, Villejuif, France
| | - Karen Leffondré
- University Bordeaux, INSERM, Bordeaux Population Health, UMR1219, Bordeaux, France
- University Bordeaux, INSERM, CIC-1401-EC, Bordeaux, France
| | - Mathilde Prezelin-Reydit
- University Bordeaux, INSERM, Bordeaux Population Health, UMR1219, Bordeaux, France
- University Bordeaux, INSERM, CIC-1401-EC, Bordeaux, France
- Maison du REIN AURAD Aquitaine, Néphrologie, Gradignan, Nouvelle-Aquitaine, FR
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Roy M, Saroha S, Sarma U, Sarathy H, Kumar R. Quantitative systems pharmacology model of erythropoiesis to simulate therapies targeting anemia due to chronic kidney disease. Front Pharmacol 2023; 14:1274490. [PMID: 38125882 PMCID: PMC10731587 DOI: 10.3389/fphar.2023.1274490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023] Open
Abstract
Anemia induced by chronic kidney disease (CKD) has multiple underlying mechanistic causes and generally worsens as CKD progresses. Erythropoietin (EPO) is a key endogenous protein which increases the number of erythrocyte progenitors that mature into red blood cells that carry hemoglobin (Hb). Recombinant human erythropoietin (rHuEPO) in its native and re-engineered forms is used as a therapeutic to alleviate CKD-induced anemia by stimulating erythropoiesis. However, due to safety risks associated with erythropoiesis-stimulating agents (ESAs), a new class of drugs, prolyl hydroxylase inhibitors (PHIs), has been developed. Instead of administering exogenous EPO, PHIs facilitate the accumulation of HIF-α, which results in the increased production of endogenous EPO. Clinical trials for ESAs and PHIs generally involve balancing decisions related to safety and efficacy by carefully evaluating the criteria for patient selection and adaptive trial design. To enable such decisions, we developed a quantitative systems pharmacology (QSP) model of erythropoiesis which captures key aspects of physiology and its disruption in CKD. Furthermore, CKD virtual populations of varying severities were developed, calibrated, and validated against public data. Such a model can be used to simulate alternative trial protocols while designing phase 3 clinical trials, as well as an asset for reverse translation in understanding emerging clinical data.
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Affiliation(s)
| | | | | | - Harini Sarathy
- Division of Nephrology, University of California San Francisco, Zuckerberg San Francisco General Hospital, San Francisco, CA, United States
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