Evidence map›Paper›PMID 42488352›Full record

ArticleFrontiers in genetics2026

A genetic and clinical risk factor algorithm to aid in identifying new cases of chronic kidney disease from the general population.

Graham Rodwell, John P A Ioannidis, Stuart K Kim

Abstract read
In one paragraph

Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

3 authors.

Graham RodwellDivision of Nephrology, Palo Alto Medical Foundation, Palo Alto, CA, United States.
John P A IoannidisDepartment of Medicine, Stanford Prevention Research Center, Stanford, CA, United States.
Stuart K KimDepartment of Developmental Biology, Stanford University Medical Center, Stanford, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The purpose of this study is to develop a genetic test to aid in diagnosing chronic kidney disease (CKD). One challenge in treating CKD is that 80%-90% of people with it are undiagnosed and thus do not access healthcare promptly. The problem arises because early-stage CKD has no overt symptoms, and the current policy is to perform diagnostic tests only when accompanied by risk factors such as old age, hypertension, and diabetes. Methods: This study describes the development of the RICK (RIsk for Chronic Kidney disease) algorithm that employs a polygenic risk score for CKD plus clinical risk factors to identify people at risk. Results: In data from the United Kingdom biobank, those in the top decile of RICK have a ten-fold increased risk of CKD, and approximately 49% of all those with CKD are included in this decile. Furthermore, targeted creatinine testing for those in the highest RICK decile would potentially increase the number of individuals diagnosed with CKD by 7.4%. However, the RICK algorithm adds little value for detecting CKD defined by elevated uACR (albuminuria). Conclusion: Using RICK to selectively test those in the general population with highest risk may help in the early identification of CKD and facilitate early access to renal healthcare. The effectiveness and cost-effectiveness of such testing require further study.

Indexed as

chronic kidney diseasegenetic testingkidney disease - diagnosispersonalized and precision medicinepolygenic risk score

Identifiers

PMID42488352
PMCPMC13391044

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.