Proteomic Analysis Uncovers Multi-Protein Signatures Associated with Early Diabetic Kidney Disease in Youth with Type 2 Diabetes Mellitus.


Journal

Clinical journal of the American Society of Nephrology : CJASN
ISSN: 1555-905X
Titre abrégé: Clin J Am Soc Nephrol
Pays: United States
ID NLM: 101271570

Informations de publication

Date de publication:
21 Oct 2024
Historique:
received: 12 02 2024
accepted: 08 10 2024
medline: 21 10 2024
pubmed: 21 10 2024
entrez: 21 10 2024
Statut: aheadofprint

Résumé

The onset of diabetic kidney disease (DKD) in youth with type 2 diabetes mellitus often occurs early, leading to complications in young adulthood. Risk biomarkers associated with the early onset of DKD are urgently needed in youth with type 2 diabetes. We conducted an in-depth analysis of 6596 proteins (SomaScan 7K) in 374 baseline plasma samples from the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study to identify multi-protein signatures associated with the onset of albuminuria (urine albumin-to-creatinine ratio [UACR] ≥30 mg/g), a rapid decline in estimated glomerular filtration rate (eGFR) [annual eGFR decline >3 mL/min/1.73m2 and/or ≥3.3% at two consecutive visits], and hyperfiltration (≥135 mL/min/1.73m2 at two consecutive visits). Elastic net Cox regression with 10-fold cross-validation was applied to the top 100 proteins (ranked by p-value) to identify multi-protein signatures of time to development of DKD outcomes. Participants in the TODAY study (14±2 years old, 63% female, 7±6 months diabetes duration) experienced high rates of early DKD: 43% developed albuminuria, 48% hyperfiltration, and 16% rapid eGFR decline. Increased levels of seven and three proteins were predictive of shorter time to develop albuminuria and rapid eGFR decline, respectively; 118 proteins predicted time to development of hyperfiltration. Elastic net Cox proportional hazards model identified multi-protein signatures of time to incident early DKD with concordance for models with clinical covariates and selected proteins between 0.81 and 0.96, while the concordance for models with clinical covariates only was between 0.56 and 0.63. Our research sheds new light on proteomic changes early in the course of youth-onset type 2 diabetes that associate with DKD. Proteomic analyses identified promising risk factors that predict DKD risk in youth with type 2 diabetes and could deepen our understanding of DKD mechanisms and potential interventions.

Sections du résumé

BACKGROUND BACKGROUND
The onset of diabetic kidney disease (DKD) in youth with type 2 diabetes mellitus often occurs early, leading to complications in young adulthood. Risk biomarkers associated with the early onset of DKD are urgently needed in youth with type 2 diabetes.
METHODS METHODS
We conducted an in-depth analysis of 6596 proteins (SomaScan 7K) in 374 baseline plasma samples from the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study to identify multi-protein signatures associated with the onset of albuminuria (urine albumin-to-creatinine ratio [UACR] ≥30 mg/g), a rapid decline in estimated glomerular filtration rate (eGFR) [annual eGFR decline >3 mL/min/1.73m2 and/or ≥3.3% at two consecutive visits], and hyperfiltration (≥135 mL/min/1.73m2 at two consecutive visits). Elastic net Cox regression with 10-fold cross-validation was applied to the top 100 proteins (ranked by p-value) to identify multi-protein signatures of time to development of DKD outcomes.
RESULTS RESULTS
Participants in the TODAY study (14±2 years old, 63% female, 7±6 months diabetes duration) experienced high rates of early DKD: 43% developed albuminuria, 48% hyperfiltration, and 16% rapid eGFR decline. Increased levels of seven and three proteins were predictive of shorter time to develop albuminuria and rapid eGFR decline, respectively; 118 proteins predicted time to development of hyperfiltration. Elastic net Cox proportional hazards model identified multi-protein signatures of time to incident early DKD with concordance for models with clinical covariates and selected proteins between 0.81 and 0.96, while the concordance for models with clinical covariates only was between 0.56 and 0.63.
CONCLUSIONS CONCLUSIONS
Our research sheds new light on proteomic changes early in the course of youth-onset type 2 diabetes that associate with DKD. Proteomic analyses identified promising risk factors that predict DKD risk in youth with type 2 diabetes and could deepen our understanding of DKD mechanisms and potential interventions.

Identifiants

pubmed: 39432369
doi: 10.2215/CJN.0000000000000559
pii: 01277230-990000000-00489
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 by the American Society of Nephrology.

Auteurs

Laura Pyle (L)

Department of Pediatrics, Section of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, USA.
Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA.

Ye Ji Choi (YJ)

Department of Pediatrics, Section of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, USA.

Phoom Narongkiatikhun (P)

Department of Medicine, Division of Endocrinology, Metabolism and Nutrition, University of Washington School of Medicine, Seattle, WA, USA.
Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.

Kumar Sharma (K)

Department of Medicine, Section of Nephrology, University of Texas Health San Antonio, San Antonio, TX, USA.

Sushrut Waikar (S)

Department of Medicine, Section of Nephrology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.

Anita Layton (A)

Departments of Applied Mathematics, Computer Science, Pharmacy, and Biology, University of Waterloo, Waterloo, ON, Canada.

Kalie L Tommerdahl (KL)

Department of Pediatrics, Section of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, USA.

Ian de Boer (I)

Division of Nephrology, University of Washington School of Medicine, Seattle, WA, USA.

Timothy Vigers (T)

Department of Pediatrics, Section of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, USA.

Robert G Nelson (RG)

Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA.

Jane Lynch (J)

Department of Pediatrics, Section of Pediatric Endocrinology, University of Texas Health San Antonio, San Antonio, TX, USA.

Frank Brosius (F)

Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI USA.
Division of Nephrology, The University of Arizona College of Medicine Tucson, Tucson, AZ, USA.

Pierre J Saulnier (PJ)

Clinical Investigation Center INSERM, School of Medicine, Poitiers University, Poitiers, France.

Jesse A Goodrich (JA)

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

Jeanie B Tryggestad (JB)

Section of Diabetes and Endocrinology, Department of Pediatrics, University of Oklahoma Health Sciences and Center, Oklahoma City, OK.

Elvira Isganaitis (E)

Research Division, Joslin Diabetes Center, Boston and Department of Pediatrics, Harvard Medical School, Boston, MA.

Fida Bacha (F)

Division of Pediatric Endocrinology and Diabetes, Texas Children's Hospital, Baylor College of Medicine.

Kristen J Nadeau (KJ)

Department of Pediatrics, Section of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, USA.

Daniel van Raalte (D)

Diabetes Center, Department of Internal Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands.

Matthias Kretzler (M)

Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI USA.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USA.

Hiddo Heerspink (H)

Faculty of Medical Sciences, University of Groningen, Groningen, Netherlands.

Petter Bjornstad (P)

Department of Medicine, Division of Endocrinology, Metabolism and Nutrition, University of Washington School of Medicine, Seattle, WA, USA.

Classifications MeSH