Evidence map›Paper›PMID 26219736›Full record

ReviewAging cell2015

A roadmap for the genetic analysis of renal aging.

Gerda A Noordmans, Jan-Luuk Hillebrands, Harry van Goor, Ron Korstanje

Open access · goldAbstract readReview
In one paragraph

Review in Aging cell, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.0field-weighted citation impact, top 24% 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

8 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Renal Aging: Causes and Consequences.Journal of the American Society of Nephrology : JASN · 2017
    Review
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

4 authors at 3 institutions in 2 countries.

Gerda A NoordmansDepartment of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Jan-Luuk HillebrandsDepartment of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Harry van GoorDepartment of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Ron KorstanjeThe Jackson Laboratory, Bar Harbor, ME, USA.
University Medical Center Groningen · NLJackson Laboratory · USUniversity of Groningen · NL

Funding

Shared Resource ManagementP30CA034196 · NCI · JACKSON LABORATORY · PI William Carl Skarnes · 1985 to 2026
$61.9M
UNC and PERLEGEN, IncP50GM076468 · NIGMS · JACKSON LABORATORY · PI CHESLER, ELISSA J · 2006 to 2015
$31.0M
Translational CoreP30AG038070 · NIA · JACKSON LABORATORY · PI Gary A Churchill · 2010 to 2026
$19.3M
NCI NIH HHS CA034196NIA NIH HHS AG038070NIGMS NIH HHS GM076468NIGMS NIH HHS P50 GM076468
6 · The paper itself

Abstract

Several studies show evidence for the genetic basis of renal disease, which renders some individuals more prone than others to accelerated renal aging. Studying the genetics of renal aging can help us to identify genes involved in this process and to unravel the underlying pathways. First, this opinion article will give an overview of the phenotypes that can be observed in age-related kidney disease. Accurate phenotyping is essential in performing genetic analysis. For kidney aging, this could include both functional and structural changes. Subsequently, this article reviews the studies that report on candidate genes associated with renal aging in humans and mice. Several loci or candidate genes have been found associated with kidney disease, but identification of the specific genetic variants involved has proven to be difficult. CUBN, UMOD, and SHROOM3 were identified by human GWAS as being associated with albuminuria, kidney function, and chronic kidney disease (CKD). These are promising examples of genes that could be involved in renal aging, and were further mechanistically evaluated in animal models. Eventually, we will provide approaches for performing genetic analysis. We should leverage the power of mouse models, as testing in humans is limited. Mouse and other animal models can be used to explain the underlying biological mechanisms of genes and loci identified by human GWAS. Furthermore, mouse models can be used to identify genetic variants associated with age-associated histological changes, of which Far2, Wisp2, and Esrrg are examples. A new outbred mouse population with high genetic diversity will facilitate the identification of genes associated with renal aging by enabling high-resolution genetic mapping while also allowing the control of environmental factors, and by enabling access to renal tissues at specific time points for histology, proteomics, and gene expression.

Indexed as

AgingAnimalsGene ExpressionHumansKidneyPhenotypeaginggeneticshumankidneymousephenotype

Identifiers

PMID26219736
PMCPMC4568960
OpenAlexW2105450170

What OpenQuestion holds

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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.