Evidence map›Paper›PMID 39916754›Full record

ArticleFrontiers in endocrinology2024

The potential of the serum uric acid to high-density lipoprotein cholesterol ratio as a predictive biomarker of diabetes risk: a study based on NHANES 2005-2018.

Jianming Yin, Chuanjie Zheng, Xiaoqian Lin, Chaoqiang Huang, Zhanhui Hu, Shuyuan Lin, Yiqian Qu

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

7 authors.

Jianming Yin *School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
Chuanjie Zheng *School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
Xiaoqian Lin *School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
Chaoqiang Huang *School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
Zhanhui Hu *School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
Shuyuan LinSchool of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
Yiqian QuSchool of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Previous studies have indicated an association between UHR and diabetes risk, but evidence from large-scale and diverse populations remains limited. This study aims to verify UHR's independent role in diabetes risk prediction in a large sample population and assess its applicability across different populations. We drew upon data from 30,813 participants collected during the 2005-2018 NHANES cycle. The association between UHR and the risk of diabetes was explored using multivariate logistic regression models, with key predictive factors identified through LASSO regression. Model effectiveness was evaluated through receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration metrics. Additionally, restricted cubic spline (RCS) and threshold effect assessments were applied to examine the nonlinear association between UHR and diabetes risk. The results showed that UHR levels were notably elevated in individuals with diabetes when compared to those without diabetes (

Indexed as

BiomarkersCholesterol, HDLDiabetes MellitusUric AcidAdultAgedFemaleHumansMaleMiddle AgedNutrition SurveysPrognosisRisk FactorsBiomarkersCholesterol, HDLUric AciddiabetesHDL-CNHANESserum uric acidUHR

Identifiers

PMID39916754
PMCPMC11798810

What OpenQuestion holds

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Registered trials

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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.