Evidence mapPaperPMID 41199849Full record

SynthesisFrontiers in oncology2025

Inverse association between serum lipid profiles and hepatocellular carcinoma risk: a meta-analysis of epidemiological studies.

Chongshi Zeng, Shuran Liu, Haining Li, Xiao Han

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

4 authors.

Chongshi ZengChina Three Gorges University School of Science and Technology, Yichang, China.
Shuran LiuCollege of Biological Science and Engineering, Fuzhou University, Fuzhou, China.
Haining LiCollege of Biological Science and Engineering, Fuzhou University, Fuzhou, China.
Xiao HanCollege of Biological Science and Engineering, Fuzhou University, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

​Background: The relationship between serum lipid profiles and hepatocellular carcinoma (HCC) risk remains controversial. We aimed to clarify this association through a systematic meta-analysis of epidemiological studies. ​Methods: A systematic literature search was conducted in PubMed, Embase, and Web of Science (2000-May 2023) for prospective, retrospective, and cross-sectional studies reporting adjusted risk estimates (HR/OR) of HCC associated with serum lipids. Pooled effect sizes were calculated using random-effects models, with heterogeneity assessed via Cochran's Q and I² statistics. ​Results: Twenty-three studies (16 cohorts, 7 case-control) involving 1.2 million participants ((including both healthy individuals and patients with chronic liver diseases)​​) were included. Elevated serum total cholesterol (TC) was inversely associated with HCC risk (HR = 0.71, 95% CI: 0.64-0.78; I²=0%). Similar protective effects were observed for high LDL (HR = 0.46, 95% CI: 0.36-0.59; I²=97%), triglycerides (HR = 0.79, 95% CI: 0.62-0.99; I²=94%), and dyslipidemia (HR = 0.64, 95% CI: 0.50-0.83; I²=81%). No significant association was found for high-density lipoprotein (HDL). Sensitivity analyses confirmed robustness for TC and LDL, while TG results were influenced by a single study. ​Conclusion: This meta-analysis provides robust evidence that elevated serum cholesterol and specific lipid subfractions are associated with reduced HCC risk. Further mechanistic studies are warranted to elucidate the role of lipid metabolism in hepatocarcinogenesis.

Indexed as

cholesterolHDLLDLliver cancermeta-analysisriskserum lipidtriglycerides

Identifiers

PMID41199849
PMCPMC12585945

What Socratic 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 Socratic graph.