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Blood-Based Epigenetic Biomarkers Associated With Incident Chronic Kidney Disease in Individuals With Type 2 Diabetes

Marchiori, Marian LU ; Maguolo, Alice LU orcid ; Perfilyev, Alexander LU orcid ; Maziarz, Marlena LU orcid ; Martinell, Mats ; Gomez, Maria F. LU orcid ; Ahlqvist, Emma LU ; García-Calzón, Sonia LU and Ling, Charlotte LU orcid (2025) In Diabetes 74(3). p.439-450
Abstract

There is an increasing need for new biomarkers to improve prediction of chronic kidney disease (CKD) in individuals with type 2 diabetes (T2D). We aimed to identify blood-based epigenetic biomarkers associated with incident CKD and develop a methylation risk score (MRS) predicting CKD in individuals with newly diagnosed T2D. DNA methylation was analyzed epigenome wide in blood from 487 individuals with newly diagnosed T2D, of whom 88 developed CKD during an 11.5-year follow-up. Weighted Cox regression was used to associate methylation with incident CKD. Weighted logistic models and cross-validation (k = 5) were performed to test whether the MRS could predict CKD. Methylation at 37 sites was associated with CKD development based on a... (More)

There is an increasing need for new biomarkers to improve prediction of chronic kidney disease (CKD) in individuals with type 2 diabetes (T2D). We aimed to identify blood-based epigenetic biomarkers associated with incident CKD and develop a methylation risk score (MRS) predicting CKD in individuals with newly diagnosed T2D. DNA methylation was analyzed epigenome wide in blood from 487 individuals with newly diagnosed T2D, of whom 88 developed CKD during an 11.5-year follow-up. Weighted Cox regression was used to associate methylation with incident CKD. Weighted logistic models and cross-validation (k = 5) were performed to test whether the MRS could predict CKD. Methylation at 37 sites was associated with CKD development based on a false discovery rate of <5% and absolute methylation differences of $5% between individuals with incident CKD and those free of CKD during follow-up. Notably, 15 genes annotated to these sites, e.g., TGFBI, SHISA3, and SLC43A2 (encoding LAT4), have been linked to CKD or related risk factors, including blood pressure, BMI, and estimated glomerular filtration rate. Using an MRS including 37 sites and cross-validation for prediction of CKD, we generated receiver operating characteristic (ROC) curves with an area under the curve (AUC) of 0.82 for the MRS and AUC of 0.87 for the combination of MRS and clinical factors. Importantly, ROC curves including the MRS had significantly better AUCs versus the one only including clinical factors (AUC = 0.72). The combined epigenetic biomarker had high accuracy in identifying individuals free of future CKD (negative predictive value of 94.6%). We discovered a high-performance epigenetic biomarker for predicting CKD, encouraging its potential role in precisionmedicine, risk stratification, and targeted prevention in T2D.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Diabetes
volume
74
issue
3
pages
439 - 450
publisher
American Diabetes Association Inc.
external identifiers
  • scopus:85219497727
  • pmid:39715581
ISSN
0012-1797
DOI
10.2337/db24-0483
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2024 by the American Diabetes Association.
id
46d1a6a4-b09b-4bb1-8e9b-ea69d60d3193
date added to LUP
2025-03-24 12:22:16
date last changed
2025-06-30 18:24:12
@article{46d1a6a4-b09b-4bb1-8e9b-ea69d60d3193,
  abstract     = {{<p>There is an increasing need for new biomarkers to improve prediction of chronic kidney disease (CKD) in individuals with type 2 diabetes (T2D). We aimed to identify blood-based epigenetic biomarkers associated with incident CKD and develop a methylation risk score (MRS) predicting CKD in individuals with newly diagnosed T2D. DNA methylation was analyzed epigenome wide in blood from 487 individuals with newly diagnosed T2D, of whom 88 developed CKD during an 11.5-year follow-up. Weighted Cox regression was used to associate methylation with incident CKD. Weighted logistic models and cross-validation (k = 5) were performed to test whether the MRS could predict CKD. Methylation at 37 sites was associated with CKD development based on a false discovery rate of &lt;5% and absolute methylation differences of $5% between individuals with incident CKD and those free of CKD during follow-up. Notably, 15 genes annotated to these sites, e.g., TGFBI, SHISA3, and SLC43A2 (encoding LAT4), have been linked to CKD or related risk factors, including blood pressure, BMI, and estimated glomerular filtration rate. Using an MRS including 37 sites and cross-validation for prediction of CKD, we generated receiver operating characteristic (ROC) curves with an area under the curve (AUC) of 0.82 for the MRS and AUC of 0.87 for the combination of MRS and clinical factors. Importantly, ROC curves including the MRS had significantly better AUCs versus the one only including clinical factors (AUC = 0.72). The combined epigenetic biomarker had high accuracy in identifying individuals free of future CKD (negative predictive value of 94.6%). We discovered a high-performance epigenetic biomarker for predicting CKD, encouraging its potential role in precisionmedicine, risk stratification, and targeted prevention in T2D.</p>}},
  author       = {{Marchiori, Marian and Maguolo, Alice and Perfilyev, Alexander and Maziarz, Marlena and Martinell, Mats and Gomez, Maria F. and Ahlqvist, Emma and García-Calzón, Sonia and Ling, Charlotte}},
  issn         = {{0012-1797}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{439--450}},
  publisher    = {{American Diabetes Association Inc.}},
  series       = {{Diabetes}},
  title        = {{Blood-Based Epigenetic Biomarkers Associated With Incident Chronic Kidney Disease in Individuals With Type 2 Diabetes}},
  url          = {{http://dx.doi.org/10.2337/db24-0483}},
  doi          = {{10.2337/db24-0483}},
  volume       = {{74}},
  year         = {{2025}},
}