Evidence map›Paper›PMID 39600934›Full record

ArticleFrontiers in medicine2024

The relationship between high-sensitivity C-reactive protein and gallstones: a cross-sectional analysis.

Zhimeng Jiang, Huixin Jiang, Xingyu Zhu, Donglin Zhao, Feifei Su

Abstract read
In one paragraph

Article in Frontiers in medicine, 2024. 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
–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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

5 authors.

Zhimeng JiangGraduate School of Hebei North University, Zhangjiakou, China.
Huixin JiangSchool of Clinical Medicine, Haiyuan College of Kunming Medical University, Kunming, China.
Xingyu ZhuGraduate School of Hebei North University, Zhangjiakou, China.
Donglin ZhaoGraduate School of Hebei North University, Zhangjiakou, China.
Feifei SuDepartment of Cardiovascular Medicine, Air Force Medical Center, Chinese People's Liberation Army, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and objective: High-sensitivity C-reactive protein (hs-CRP), a classical indicator of inflammation, holds significant clinical value in various diseases. The relationship between hs-CRP and gallstones, however, remains poorly studied at present. The relationship between hs-CRP and gallstones will be investigated in this study. Methods: Data from the 2017-2020 National Health and Nutrition Examination Survey (NHANES) were analyzed, focusing on participants aged 20 years and older who provided complete hs-CRP and gallstone information. Due to the skewed distribution of hs-CRP, the data were log-transformed [Log (hs-CRP)] to achieve normalization. Logistic regression analysis, subgroup analysis, and smoothed fitted curves were applied to determine the relationship between Log (hs-CRP) and the presence of gallstones. Results: The study included 4,484 participants with an average Log (hs-CRP) of 1.18 ± 0.74. The prevalence of gallstones was 11.15%, increasing with higher Log (hs-CRP) levels (quartile 1: 8.31%; quartile 2: 8.76%; quartile 3: 11.98%; quartile 4: 16.36%; Conclusion: Higher Log (hs-CRP) levels are linked to a greater prevalence of gallstones. We still need to carry out further large prospective research to explore the causal relationship of this association.

Indexed as

cross-sectional studygallstoneshigh-sensitivity C-reactive proteininflammatoryNational Health and Nutrition Examination Survey

Identifiers

PMID39600934
PMCPMC11588438

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.