Evidence map›Paper›PMID 39609681›Full record

ArticleBMC gastroenterology2024

Novel non-HDLc/HDLc ratio for predicting MASLD: a cross-sectional study in a Chinese health screening population.

Yan Hong, Xinrong Chen, Hangtao Yuan, Zixuan Huang, Shaohong Tao, Fang Xie, Weining Xie

Abstract read
In one paragraph

Article in BMC gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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.

Yan HongAffiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine, Foshan, Guangdong Province, China.
Xinrong ChenThe First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, China.
Hangtao YuanAffiliated Dongfang Hospital of Beijing University of Chinese Medicine, Beijing University of Chinese Medicine, Fengtai District, Beijing, China.
Zixuan HuangGuangxi University of Chinese Medicine, Nanning, Guangxi Zhuang Autonomous Region, China.
Shaohong TaoAffiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine, Foshan, Guangdong Province, China.
Fang XieDepartment of Liver Disease, Jinling Hospital affiliated to Medical College of Nanjing University, Nanjing, Jiangsu Province, China.
Weining XieInfectious Disease Department, Guangdong Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Foshan, Guangdong Province, China. xwn1219@qq.com.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2023A1515140125Medical Scientific Research Foundation of Guangdong Province of China B2021265the Science and Technology Innovation Research Program of Jinling Hospital 2023JCYJZD078
6 · The paper itself

Abstract

backgroundPrevious studies have shown a positive correlation between the non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol(non-HDLc/HDLc ratio) and metabolism-associated steatohepatitis (MASLD), suggesting it is a superior predictor of MASLD.The aim of this study is to assess the predictive value of the non-HDLc/HDLc ratio for MASLD.

methodsIn this study, we retrospectively analyzed data from 4,498 adult patients at the Health Management Centre of Guangdong Provincial Hospital of Integrative Medicine, regardless of gender, and the diagnostic criteria for MASLD were derived from guidelines. We used univariate and multivariate logistic regression to verify the relationship between the non-HDLc/HDLc ratio and MASLD and assessed the stability of the results through interaction tests. ROC curve analysis was then employed to compare the diagnostic efficacy of several lipid indices, including the non-HDLc/HDLc ratio, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), total cholesterol (TC), and the composite lipid profile in diagnosing MASLD.

resultsThese data suggested that non-HDLc/HDLc, LDL-C, TG, and TC were positively correlated with MASLD, while HDL-C was inversely correlated, even after adjusting for confounders such as gender, age, body mass index, ALT, AST, uric acid, blood glucose, and hypertension. Among them, non-HDLc/HDLc ratio(OR: 2.709, 95% CI: 2.316-3.169, p < 0.001), LDL-C (OR: 1.294, 95% CI: 1.125-1.489, p < 0.001), TG (OR: 2.854, 95% CI: 2.473-3.293, p < 0.001), TC (OR: 1.242, 95% CI: 1.122-1.374, p < 0.001), and HDL-C (OR: 0.074, 95% CI: 0.044-0.123, p < 0.001) were identified. The interaction results showed that gender did not affect lipid levels in MASLD (p > 0.05). ROC analysis confirmed the validity of the non-HDL cholesterol/HDL cholesterol ratio in predicting MASLD incidence, with an AUC of 0.801. Moreover, the composite lipid indices demonstrated greater predictive power for MASLD compared to individual indices (AUC: 0.857), and validation set results were consistent with those of the training set.

conclusionThe present study revealed that the non-HDLc/HDLc ratio was positively correlated with the development of MASLD, and the ratio was also effective in predicting MASLD.

Indexed as

Cholesterol, HDLAdultAgedBiomarkersChinaCholesterolCholesterol, LDLCross-Sectional StudiesFemaleHumansMaleMass ScreeningMiddle AgedNon-alcoholic Fatty Liver DiseasePredictive Value of TestsRetrospective StudiesBiomarkersCholesterolCholesterol, HDLCholesterol, LDLTriglyceridesA cross-sectional studyChinese health screening populationMetabolic dysfunction-associated steatotic liver diseaseNon-HDLc/HDLc ratioPredictive model

Identifiers

PMID39609681
PMCPMC11603918

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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.