Hiddo J L Heerspink1, Tom Greene2, Hocine Tighiouart3, Ron T Gansevoort4, Josef Coresh5, Andrew L Simon6, Tak Mao Chan7, Fan Fan Hou8, Julia B Lewis9, Francesco Locatelli10, Manuel Praga11, Francesco Paolo Schena12, Andrew S Levey6, Lesley A Inker13. 1. Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. 2. Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA. 3. Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; Tufts Medical Center, Boston, MA, USA; Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, USA. 4. Department of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. 5. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 6. Division of Nephrology, Boston, MA, USA. 7. Department of Medicine, University of Hong Kong, Queen Mary Hospital, Pokfulam, Hong Kong. 8. Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China. 9. Division of Nephrology, Vanderbilt University, Nashville, TN, USA. 10. Department of Nephrology and Dialysis, Hospital Alessandro Manzoni, Lecco, Italy. 11. Nephrology Department, Hospital Universitario 12 de Octubre, Madrid, Spain. 12. Renal, Dialysis and Transplant Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy. 13. Division of Nephrology, Boston, MA, USA. Electronic address: linker@tuftsmedicalcenter.org.
Abstract
BACKGROUND: Change in albuminuria has strong biological plausibility as a surrogate endpoint for progression of chronic kidney disease, but empirical evidence to support its validity is lacking. We aimed to determine the association between treatment effects on early changes in albuminuria and treatment effects on clinical endpoints and surrograte endpoints, to inform the use of albuminuria as a surrogate endpoint in future randomised controlled trials. METHODS: In this meta-analysis, we searched PubMed for publications in English from Jan 1, 1946, to Dec 15, 2016, using search terms including "chronic kidney disease", "chronic renal insufficiency", "albuminuria", "proteinuria", and "randomized controlled trial"; key inclusion criteria were quantifiable measurements of albuminuria or proteinuria at baseline and within 12 months of follow-up and information on the incidence of end-stage kidney disease. We requested use of individual patient data from the authors of eligible studies. For all studies that the authors agreed to participate and that had sufficient data, we estimated treatment effects on 6-month change in albuminuria and the composite clinical endpoint of treated end-stage kidney disease, estimated glomerular filtration rate of less than 15 mL/min per 1·73 m2, or doubling of serum creatinine. We used a Bayesian mixed-effects meta-regression analysis to relate the treatment effects on albuminuria to those on the clinical endpoint across studies and developed a prediction model for the treatment effect on the clinical endpoint on the basis of the treatment effect on albuminuria. FINDINGS: We identified 41 eligible treatment comparisons from randomised trials (referred to as studies) that provided sufficient patient-level data on 29 979 participants (21 206 [71%] with diabetes). Over a median follow-up of 3·4 years (IQR 2·3-4·2), 3935 (13%) participants reached the composite clinical endpoint. Across all studies, with a meta-regression slope of 0·89 (95% Bayesian credible interval [BCI] 0·13-1·70), each 30% decrease in geometric mean albuminuria by the treatment relative to the control was associated with an average 27% lower hazard for the clinical endpoint (95% BCI 5-45%; median R2 0·47, 95% BCI 0·02-0·96). The association strengthened after restricting analyses to patients with baseline albuminuria of more than 30 mg/g (ie, 3·4 mg/mmol; R2 0·72, 0·05-0·99]). For future trials, the model predicts that treatments that decrease the geometric mean albuminuria to 0·7 (ie, 30% decrease in albuminuria) relative to the control will provide an average hazard ratio (HR) for the clinical endpoint of 0·68, and 95% of sufficiently large studies would have HRs between 0·47 and 0·95. INTERPRETATION: Our results support a role for change in albuminuria as a surrogate endpoint for the progression of chronic kidney disease, particularly in patients with high baseline albuminuria; for patients with low baseline levels of albuminuria this association is less certain. FUNDING: US National Kidney Foundation.
BACKGROUND: Change in albuminuria has strong biological plausibility as a surrogate endpoint for progression of chronic kidney disease, but empirical evidence to support its validity is lacking. We aimed to determine the association between treatment effects on early changes in albuminuria and treatment effects on clinical endpoints and surrograte endpoints, to inform the use of albuminuria as a surrogate endpoint in future randomised controlled trials. METHODS: In this meta-analysis, we searched PubMed for publications in English from Jan 1, 1946, to Dec 15, 2016, using search terms including "chronic kidney disease", "chronic renal insufficiency", "albuminuria", "proteinuria", and "randomized controlled trial"; key inclusion criteria were quantifiable measurements of albuminuria or proteinuria at baseline and within 12 months of follow-up and information on the incidence of end-stage kidney disease. We requested use of individual patient data from the authors of eligible studies. For all studies that the authors agreed to participate and that had sufficient data, we estimated treatment effects on 6-month change in albuminuria and the composite clinical endpoint of treated end-stage kidney disease, estimated glomerular filtration rate of less than 15 mL/min per 1·73 m2, or doubling of serum creatinine. We used a Bayesian mixed-effects meta-regression analysis to relate the treatment effects on albuminuria to those on the clinical endpoint across studies and developed a prediction model for the treatment effect on the clinical endpoint on the basis of the treatment effect on albuminuria. FINDINGS: We identified 41 eligible treatment comparisons from randomised trials (referred to as studies) that provided sufficient patient-level data on 29 979 participants (21 206 [71%] with diabetes). Over a median follow-up of 3·4 years (IQR 2·3-4·2), 3935 (13%) participants reached the composite clinical endpoint. Across all studies, with a meta-regression slope of 0·89 (95% Bayesian credible interval [BCI] 0·13-1·70), each 30% decrease in geometric mean albuminuria by the treatment relative to the control was associated with an average 27% lower hazard for the clinical endpoint (95% BCI 5-45%; median R2 0·47, 95% BCI 0·02-0·96). The association strengthened after restricting analyses to patients with baseline albuminuria of more than 30 mg/g (ie, 3·4 mg/mmol; R2 0·72, 0·05-0·99]). For future trials, the model predicts that treatments that decrease the geometric mean albuminuria to 0·7 (ie, 30% decrease in albuminuria) relative to the control will provide an average hazard ratio (HR) for the clinical endpoint of 0·68, and 95% of sufficiently large studies would have HRs between 0·47 and 0·95. INTERPRETATION: Our results support a role for change in albuminuria as a surrogate endpoint for the progression of chronic kidney disease, particularly in patients with high baseline albuminuria; for patients with low baseline levels of albuminuria this association is less certain. FUNDING: US National Kidney Foundation.
Authors: Brendon L Neuen; Toshiaki Ohkuma; Bruce Neal; David R Matthews; Dick de Zeeuw; Kenneth W Mahaffey; Greg Fulcher; Qiang Li; Meg Jardine; Richard Oh; Hiddo L Heerspink; Vlado Perkovic Journal: J Am Soc Nephrol Date: 2019-09-17 Impact factor: 10.121
Authors: Josef Coresh; Hiddo J L Heerspink; Yingying Sang; Kunihiro Matsushita; Johan Arnlov; Brad C Astor; Corri Black; Nigel J Brunskill; Juan-Jesus Carrero; Harold I Feldman; Caroline S Fox; Lesley A Inker; Areef Ishani; Sadayoshi Ito; Simerjot Jassal; Tsuneo Konta; Kevan Polkinghorne; Solfrid Romundstad; Marit D Solbu; Nikita Stempniewicz; Benedicte Stengel; Marcello Tonelli; Mitsumasa Umesawa; Sushrut S Waikar; Chi-Pang Wen; Jack F M Wetzels; Mark Woodward; Morgan E Grams; Csaba P Kovesdy; Andrew S Levey; Ron T Gansevoort Journal: Lancet Diabetes Endocrinol Date: 2019-01-08 Impact factor: 32.069
Authors: Yuliya Lytvyn; Petter Bjornstad; Daniel H van Raalte; Hiddo L Heerspink; David Z I Cherney Journal: Endocr Rev Date: 2020-04-01 Impact factor: 19.871