Evidence map›Paper›PMID 36271454›Full record

SynthesisHuman genomics2022

Nidogen-1 could play a role in diabetic kidney disease development in type 2 diabetes: a genome-wide association meta-analysis.

Ahmed Khattab, Ali Torkamani

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Human genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 10% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Exome-Wide Analysis Identifies a RareKidney international reports · 2026
    Article
  7. Article
  8. Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease.Diabetic medicine : a journal of the British Diabetic Association · 2025
    Review
  9. Article
  10. Article
  11. Article
  12. Pathomechanisms of Diabetic Kidney Disease.Journal of clinical medicine · 2023
    Review
  13. Review
  14. 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

2 authors at 2 institutions in 1 country.

Ahmed KhattabIntegrative Structural and Computational Biology, Scripps Research, 3344 North Torrey Pines Court, Suite 300, La Jolla, CA, 92037, USA.
Ali TorkamaniIntegrative Structural and Computational Biology, Scripps Research, 3344 North Torrey Pines Court, Suite 300, La Jolla, CA, 92037, USA. atorkama@scripps.edu.
Scripps Institution of Oceanography · USScripps Research Institute · US

Funding

Genotype First: Actionable Genetic Risk through Genotype-to-Phenotype PredictionR01HG010881 · NHGRI · SCRIPPS RESEARCH INSTITUTE, THE · PI TORKAMANI, ALI · 2020 to 2023
$3.1M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS R01 HG010881
6 · The paper itself

Abstract

backgroundDiabetic kidney disease (DKD) affects about 40% of patients with diabetes. It is incurable and usually leads to end-stage renal disease (ESRD). The pathogenesis of DKD is still not fully understood, and the genetics of DKD have not yet been extensively studied. In this study, we investigate the genetic basis of DKD in type 2 diabetes (T2D) to provide more insights into the pathogenesis of the disease.

resultsUsing the data provided by the UK Biobank (UKBB), we performed a DKD genome-wide association study (GWAS) in 13,123 individuals with T2D as well as two creatinine estimated glomerular filtration rate (eGFR) GWA studies: one in 26,786 individuals with T2D and the other in 339,080 non-diabetic individuals. We also conducted a DKD GWAS meta-analysis combining our results with those published by the surrogate markers for micro- and macro-vascular hard endpoints for Innovative diabetes Tools (SUMMIT) consortium. We confirm two loci previously reported to be associated with chronic kidney disease (CKD) and eGFR in T2D. The UMOD-PDILT locus is associated with DKD (P = 1.17E-09) as well as creatinine eGFR in both people with T2D (P = 1.31E-15) and people without diabetes (P = 3.95E-73). The PRKAG2 locus is associated with creatinine eGFR in people with (P = 2.78E-10) and without (P = 5.65E-72) T2D. Our meta-analysis reveals a novel association between DKD and variant rs72763500 (chr1:236116561) which is a splicing quantitative trait locus (sQTL) for nidogen-1 (NID1) gene.

conclusionOur data confirm two loci previously reported in association with CKD and creatinine eGFR in T2D. It also suggests that NID1, a major component of the renal tubular basement membrane, could play a role in DKD development in T2D. While our NID1 finding remains to be replicated, it is a step toward a more comprehensive understanding of DKD pathogenesis.

Indexed as

Diabetes Mellitus, Type 2Diabetic NephropathiesMembrane GlycoproteinsBiomarkersCreatinineGenome-Wide Association StudyHumansProtein Disulfide-IsomerasesRenal Insufficiency, ChronicBiomarkersCreatinineMembrane GlycoproteinsnidogenPDILT protein, humanProtein Disulfide-Isomerases

Identifiers

PMID36271454
PMCPMC9587571
OpenAlexW4307168560

What OpenQuestion holds

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LicenceCC BY
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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.