Evidence map›Paper›PMID 41834967›Full record

ArticleSichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition2026

[The Relationship Between Serum Very Long-chain Saturated Fatty Acids and Chronic Kidney Disease].

Kaixiang Liu, Yi Wang, Li Wang

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Kaixiang Liu（ 610054）School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China.ORCID 0000-0002-8053-4701
Yi Wang（ 610054）School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
Li Wang（ 610054）School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China.ORCID 0000-0002-4204-3768

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the relationship between very long chain saturated fatty acids (VLCSFAs) levels in the serum of American adults and chronic kidney disease (CKD), thereby providing new insights for the prevention and treatment of CKD. Methods: Using data from the 2011-2014 National Health and Nutrition Examination Survey (NHANES), individuals under 20 years of age and those lacking serum creatinine, blood urea nitrogen, urine albumin-to-creatinine ratio (uACR), or other covariate data were excluded. Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI (2009) equation. The independent relationship between VLCSFAs and CKD was examined using weighted multivariate logistic regression, the XGBoost machine learning model, and subgroup analyses. Restricted cubic splines (RCS) were used to test for nonlinear associations. Results: A total of 4164 participants were analyzed. Serum VLCSFAs levels differed significantly between the CKD and non-CKD groups ( Conclusion: Elevated circulating VLCSFAs levels in US adults are associated with reduced CKD risk. Further large-scale prospective studies are needed to validate these findings.

Indexed as

Fatty AcidsRenal Insufficiency, ChronicAdultBoosting Machine Learning AlgorithmsFemaleGlomerular Filtration RateHumansLogistic ModelsMaleMiddle AgedNutrition SurveysRisk FactorsFatty AcidsChronic kidney diseaseCross-sectional studyExtreme gradient boostingVery long-chain saturated fatty acids

Identifiers

PMID41834967
PMCPMC12980000

What Socratic holds

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LicenceCC BY-NC
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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 Socratic graph.