Evidence mapPaperPMID 32248841Full record

ArticleClinical epigenetics2020

DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes.

Delnaz Roshandel, Zhuo Chen, Angelo J Canty, Shelley B Bull, Rama Natarajan, Andrew D Paterson, DCCT/EDIC Research Group

Open access · goldAbstract read
In one paragraph

Article in Clinical epigenetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
2.7field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 44 citations in OpenAlex.

  1. Article
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  9. Advances in the Epigenetic Mechanisms of Diabetic Nephropathy Pathogenesis.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025
    Review
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  14. Within subject cross-tissue analyzes of epigenetic clocks in substance use disorder postmortem brain and blood.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 2 countries.

Delnaz RoshandelGenetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada.
Zhuo ChenDepartment of Diabetes Complications and Metabolism, Beckman Research Institute of City of Hope, Duarte, CA, USA.
Angelo J CantyDepartment of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada.
Shelley B BullLunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Rama NatarajanDepartment of Diabetes Complications and Metabolism, Beckman Research Institute of City of Hope, Duarte, CA, USA.
Andrew D PatersonGenetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada. Andrew.paterson@sickkids.ca.
DCCT/EDIC Research Group
City of Hope · USHospital for Sick Children · CALunenfeld-Tanenbaum Research Institute · CAMcMaster University · CAUniversity of Toronto · CA

Funding

Small Animal Studies CoreP30CA033572 · NCI · CITY OF HOPE/BECKMAN RESEARCH INSTITUTE · 1985 to 2025
$31.1M
Yale Diabetes Research CenterP30DK045735 · YALE UNIVERSITY · 1993 to 2025
$10.2M
Epidemiology of Diabetes Interventions and Complications (EDIC) StudyU01DK094157 · CASE WESTERN RESERVE UNIVERSITY · 2025 to 2025
$8.0M
Continuation of Epidemiology of Diabetes Interventions and Complications (EDIC) Study Biostatistics CenterU01DK094176 · NIDDK · GEORGE WASHINGTON UNIVERSITY · PI Ionut Bebu, Barbara Halina Braffett · 2022 to 2022
$4.3M
Inflammatory Gene Transcription In Diabetic ConditionsR01DK065073 · CITY OF HOPE/BECKMAN RESEARCH INSTITUTE · 2003 to 2005
$1.2M
NCI NIH HHS P30 CA033572NHLBI NIH HHS R01 HL106089NIDDK NIH HHS DP3 DK106917NIDDK NIH HHS P30 DK045735NIDDK NIH HHS R01 DK065073NIDDK NIH HHS R01 DK081705NIDDK NIH HHS U01 DK094157NIDDK NIH HHS U01 DK094176
6 · The paper itself

Abstract

backgroundMany CpGs become hyper or hypo-methylated with age. Multiple methods have been developed by Horvath et al. to estimate DNA methylation (DNAm) age including Pan-tissue, Skin & Blood, PhenoAge, and GrimAge. Pan-tissue and Skin & Blood try to estimate chronological age in the normal population whereas PhenoAge and GrimAge use surrogate markers associated with mortality to estimate biological age and its departure from chronological age. Here, we applied Horvath's four methods to calculate and compare DNAm age in 499 subjects with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study using DNAm data measured by Illumina EPIC array in the whole blood. Association of the four DNAm ages with development of diabetic complications including cardiovascular diseases (CVD), nephropathy, retinopathy, and neuropathy, and their risk factors were investigated.

resultsPan-tissue and GrimAge were higher whereas Skin & Blood and PhenoAge were lower than chronological age (p < 0.0001). DNAm age was not associated with the risk of CVD or retinopathy over 18-20 years after DNAm measurement. However, higher PhenoAge (β = 0.023, p = 0.007) and GrimAge (β = 0.029, p = 0.002) were associated with higher albumin excretion rate (AER), an indicator of diabetic renal disease, measured over time. GrimAge was also associated with development of both diabetic peripheral neuropathy (OR = 1.07, p = 9.24E-3) and cardiovascular autonomic neuropathy (OR = 1.06, p = 0.011). Both HbA1c (β = 0.38, p = 0.026) and T1D duration (β = 0.01, p = 0.043) were associated with higher PhenoAge. Employment (β = - 1.99, p = 0.045) and leisure time (β = - 0.81, p = 0.022) physical activity were associated with lower Pan-tissue and Skin & Blood, respectively. BMI (β = 0.09, p = 0.048) and current smoking (β = 7.13, p = 9.03E-50) were positively associated with Skin & Blood and GrimAge, respectively. Blood pressure, lipid levels, pulse rate, and alcohol consumption were not associated with DNAm age regardless of the method used.

conclusionsVarious methods of measuring DNAm age are sub-optimal in detecting people at higher risk of developing diabetic complications although some work better than the others.

Indexed as

DNA MethylationAdolescentAdultAlbuminsCpG IslandsDiabetes Mellitus, Type 1Diabetic NeuropathiesEpigenesis, GeneticFemaleGenome-Wide Association StudyHumansMaleOligonucleotide Array Sequence AnalysisYoung AdultAlbuminsDiabetic complicationsDNA methylation ageType 1 diabetes

Identifiers

PMID32248841
PMCPMC7132894
OpenAlexW3014518127

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.