Evidence map›Paper›PMID 32849535›Full record

ArticleFrontiers in immunology2020

Methylation vs. Protein Inflammatory Biomarkers and Their Associations With Cardiovascular Function.

Héléne Toinét Cronjé, Hannah R Elliott, Cornelie Nienaber-Rousseau, Fiona R Green, Aletta E Schutte, Marlien Pieters

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 23% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Accelerated epigenetic age in hypertension: a systematic review and meta-analysis.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
    Pooled it
  2. Review
  3. Article
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

6 authors at 3 institutions in 3 countries.

Héléne Toinét CronjéCentre of Excellence for Nutrition, North-West University, Potchefstroom, South Africa.
Hannah R ElliottMRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.
Cornelie Nienaber-RousseauCentre of Excellence for Nutrition, North-West University, Potchefstroom, South Africa.
Fiona R GreenFaculty of Health and Medical Sciences, Formerly School of Biosciences and Medicine, University of Surrey, Guildford, United Kingdom.
Aletta E SchutteHypertension in Africa Research Team, Medical Research Council Unit for Hypertension and Cardiovascular Disease, North-West University, Potchefstroom, South Africa.
Marlien PietersCentre of Excellence for Nutrition, North-West University, Potchefstroom, South Africa.
North-West University · ZAUniversity of Bristol · GBUniversity of Surrey · GB

Funding

Medical Research Council MC_UU_00011/5
6 · The paper itself

Abstract

DNA methylation data can be used to estimate proportions of leukocyte subsets retrospectively, when directly measured cell counts are unavailable. The methylation-derived neutrophil-to-lymphocyte and lymphocyte-to-monocyte ratios (mdNLRs and mdLMRs) have proven to be particularly useful as indicators of systemic inflammation. As with directly measured NLRs and LMRs, these methylation-derived ratios have been used as prognostic markers for cancer, although little is known about them in relation to other disorders with inflammatory components, such as cardiovascular disease (CVD). Recently, methylation of five genomic cytosine-phosphate-guanine sites (CpGs) was suggested as proxies for mdNLRs, potentially providing a cost-effective alternative when whole-genome methylation data are not available. This study compares seven methylation-derived inflammatory markers (mdNLR, mdLMR, and individual CpG sites) with five conventionally used protein-based inflammatory markers (C-reactive protein, interleukins 6 and 10, tumor-necrosis factor alpha, and interferon-gamma) and a protein-based inflammation score, in their associations with cardiovascular function (CVF) and risk. We found that markers of CVF were more strongly associated with methylation-derived than protein-based markers. In addition, the protein-based and methylation-derived inflammatory markers complemented rather than proxied one another in their contribution to the variance in CVF. There were no strong correlations between the methylation and protein markers either. Therefore, the methylation markers could offer unique information on the inflammatory process and are not just surrogate markers for inflammatory proteins. Although the five CpGs mirrored the mdNLR well in their capacity as proxies, they contributed to CVF above and beyond the mdNLR, suggesting possible added functional relevance. We conclude that methylation-derived indicators of inflammation enable individuals with increased CVD risk to be identified without measurement of protein-based inflammatory markers. In addition, the five CpGs investigated here could be useful surrogates for the NLR when the cost of array data cannot be met. Used in tandem, methylation-derived and protein-based inflammatory markers explain more variance than protein-based inflammatory markers alone.

Indexed as

BiomarkersCardiovascular Physiological PhenomenaAgedAged, 80 and overCardiovascular DiseasesCardiovascular SystemCross-Sectional StudiesDisease SusceptibilityHeart Disease Risk FactorsHumansInflammation MediatorsMaleMethylationMiddle AgedProteinsBiomarkersInflammation MediatorsProteinscell countsepidemiologyepigeneticsinflammationlymphocyte-to-monocyteneutrophil-to-lymphocyte

Identifiers

PMID32849535
PMCPMC7411149
OpenAlexW3040198455

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.