Biomarker Panels for Discriminating Risk of CKD Progression in Children

被引：0

作者：

Greenberg, Jason H. ^{[1
,2
]}

Abraham, Alison G. ^{[3
]}

Xu, Yunwen ^{[4
]}

Schelling, Jeffrey R. ^{[5
]}

Coca, Steven G. ^{[6
]}

Schrauben, Sarah J. ^{[7
,8
]}

Wilson, F. Perry ^{[2
]}

Waikar, Sushrut S. ^{[9
,10
]}

Vasan, Ramachandran S. ^{[11
,12
]}

Gutierrez, Orlando M. ^{[13
]}

Shlipak, Michael G. ^{[14
]}

Ix, Joachim H. ^{[15
]}

Warady, Bradley A. ^{[16
]}

Kimmel, Paul L. ^{[17
]}

Bonventre, Joseph V. ^{[18
]}

Parikh, Chirag R. ^{[19
]}

Denburg, Michelle ^{[20
]}

Furth, Susan ^{[1
,20
]}

CKD Biomarkers Consortium

机构：

[1] Yale Univ, Sch Med, Dept Pediat, Sect Nephrol, New Haven, CT 06520 USA

[2] Yale Univ, Sch Med, Clin & Translat Res Accelerator, New Haven, CT 06520 USA

[3] Univ Colorado, Dept Epidemiol, Denver, CO USA

[4] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Epidemiol, Baltimore, NY USA

[5] Case Western Reserve Univ, Sch Med, Dept Physiol & Biophys & Med, Div Nephrol, Cleveland, OH USA

[6] Icahn Sch Med Mt Sinai, Dept Internal Med, Sect Nephrol, New York, NY USA

[7] Univ Penn, Clin Ctr Biostat & Epidemiol, Perelman Sch Med, Dept Med, Philadelphia, PA USA

[8] Univ Penn, Clin Ctr Biostat & Epidemiol, Perelman Sch Med, Dept Epidemiol, Philadelphia, PA USA

[9] Boston Univ, Sch Med, Sect Nephrol, Boston, MA 02118 USA

[10] Boston Med Ctr, Boston, MA USA

[11] Boston Univ, Sch Med, Dept Med, Boston, MA 02118 USA

[12] Boston Univ, Sch Med, Dept Epidemiol, Boston, MA USA

[13] Univ Alabama Birmingham, Dept Med, Sect Nephrol, Birmingham, AL USA

[14] Univ Calif San Francisco, San Francisco Vet Affairs Healthcare Syst, Dept Med, Kidney Hlth Res Collaborat, San Francisco, CA USA

[15] Univ Calif San Diego, San Francisco Vet Affairs Healthcare Syst, Dept Med, Div Nephrol Hypertens,Nephrol Sect, San Diego, CA 92161 USA

[16] Childrens Mercy Kansas City, Dept Pediat, Div Nephrol, Kansas City, MO USA

[17] NIH, Natl Inst Diabet & Digest & Kidney Dis, Bethesda, MD USA

[18] Brigham & Womens Hosp, Dept Internal Med, Sect Nephrol, Boston, MA USA

[19] Johns Hopkins Sch Med, Dept Internal Med, Sect Nephrol, Baltimore, NY USA

[20] Univ Penn, Childrens Hosp Philadelphia, Perelman Sch Med, Dept Pediat,Div Nephrol, Philadelphia, PA USA

来源：

JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY | 2025年

关键词：

children; chronic GN; CKD; pediatric nephrology; progression of renal failure; proteinuria; risk factors; CHRONIC KIDNEY-DISEASE; SURVIVAL;

D O I：

10.1681/ASN.0000000602

中图分类号：

R5 [内科学]; R69 [泌尿科学（泌尿生殖系疾病）];

学科分类号：

1002 ; 100201 ;

摘要：

Key PointsPlasma biomarkers (kidney injury molecule-1, KIM-1), urine biomarkers (EGF/creatinine and urine albumin-creatinine ratio), and eGFR identified four prognostic groups in children with CKD progression.A panel of biomarkers may better capture the complexity of kidney disease in children and may allow for a broader assessment of kidney health.BackgroundWe have previously studied biomarkers of tubular health (EGF), injury (kidney injury molecule-1 [KIM-1]), dysfunction (alpha-1 microglobulin), and inflammation (TNF receptor-1, TNF receptor-2, monocyte chemoattractant protein-1, YKL-40, and soluble urokinase plasminogen activator receptor) and demonstrated that plasma KIM-1, TNF receptor-1, TNF receptor-2, urine KIM-1, EGF, monocyte chemoattractant protein-1, and urine alpha-1 microglobulin are each independently associated with CKD progression in children. In this study, we used bootstrapped survival trees to identify a combination of biomarkers to predict CKD progression in children.MethodsThe Chronic Kidney Disease in Children (CKiD) Cohort Study prospectively enrolled children aged 6 months to 16 years with an eGFR of 30-90 ml/min per 1.73 m2. We measured biomarkers in stored plasma and urine collected 5 months after study enrollment. The primary outcome of CKD progression was a composite of 50% eGFR decline or kidney failure. We constructed a regression tree-based model for predicting the time to the composite event, using a panel of clinically relevant biomarkers with empirically derived thresholds, in addition to conventional risk factors.ResultsOf the 599 children included, the median age was 12 years (interquartile range [IQR], 8-15), 371 (62%) were male, baseline urine protein-creatinine ratio was 0.33 (IQR, 0.12-0.95) mg/mg, and baseline eGFR was 53 (IQR, 40-66) ml/min per 1.73 m2. Overall, 205 children (34%) reached the primary outcome of CKD progression. A single regression tree-based model using the most informative predictors with data-driven biomarker thresholds suggested a final set of four prognosis groups. In the final model, urine albumin/creatinine was the variable with the highest importance and along with urine EGF/creatinine identified the highest risk group of 24 children, 100% of whom developed CKD progression at a median time of 1.3 years (95% confidence interval [CI], 1.0 to 1.7). When the regression tree-derived risk group classifications were added to prediction models including the clinical risk factors, the C-statistic increased from 0.76 (95% CI, 0.71 to 0.80) to 0.85 (95% CI, 0.81 to 0.88).ConclusionsUsing regression tree-based methods, we identified a biomarker panel of urine albumin/creatinine, urine EGF/creatinine, plasma KIM-1, and eGFR, which significantly improved discrimination for CKD progression.

引用

页数：11

共 23 条

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