Evidence mapPaperPMID 38288339Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2024

The Association of HDL2b with Metabolic Syndrome Among Normal HDL-C Populations in Southern China.

Tong Chen, Shiquan Wu, Ling Feng, SiYu Long, Yu Liu, WenQian Lu, Wenya Chen, Guoai Hong, Li Zhou, Fang Wang and 2 more

Open access · goldAbstract read
In one paragraph

Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2024. 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
1.4field-weighted citation impact, top 18% of its field
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, 4 citations in OpenAlex.

  1. Article
  2. Patterns of lipid profile and genetic variations in South Asians.Cardiovascular diabetology. Endocrinology reports · 2025
    Review
  3. Article
  4. 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

12 authors at 3 institutions in 1 country.

Tong ChenDepartment of Nephrology, South China Hospital of Shenzhen University, Shenzhen, People's Republic of China.
Shiquan WuDepartment of Nephrology, South China Hospital of Shenzhen University, Shenzhen, People's Republic of China.
Ling FengDepartment of Nephrology, Shenzhen Hospital, Southern Medical University, Shenzhen, People's Republic of China.
SiYu LongDepartment of Nephrology, South China Hospital of Shenzhen University, Shenzhen, People's Republic of China.
Yu LiuDepartment of Nephrology, South China Hospital of Shenzhen University, Shenzhen, People's Republic of China.
WenQian LuDepartment of Medicine, The Chinese University of Hong Kong, Shenzhen, People's Republic of China.
Wenya ChenDepartment of Nephrology, South China Hospital of Shenzhen University, Shenzhen, People's Republic of China.
Guoai HongDepartment of Nephrology, South China Hospital of Shenzhen University, Shenzhen, People's Republic of China.ORCID 0009-0006-2367-4819
Li ZhouDepartment of Nephrology, South China Hospital of Shenzhen University, Shenzhen, People's Republic of China.
Fang WangDepartment of Nephrology, South China Hospital of Shenzhen University, Shenzhen, People's Republic of China.
Yuechan LuoDepartment of Nephrology, South China Hospital of Shenzhen University, Shenzhen, People's Republic of China.
Hequn ZouDepartment of Nephrology, South China Hospital of Shenzhen University, Shenzhen, People's Republic of China.
Shenzhen University · CNChinese University of Hong Kong, Shenzhen · CNSouthern Medical University Shenzhen Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The annual prevalence of metabolic syndrome (MetS) is increasing. Therefore, early screening and recognition of MetS are critical. This study aimed to evaluate the association between high-density lipoprotein (HDL) subclasses and MetS and to examine whether they could serve as early indicators in a Chinese community-based population with normal high-density lipoprotein cholesterol (HDL-C) levels. Methods: We used microfluidic chip technology to measure HDL subclasses in 463 people with normal HDL levels in 2018. We assessed how HDL subclasses correlated with and predicted insulin resistance (IR) and metabolic syndrome (MetS), evaluated by homeostatic model insulin resistance index (HOMA-IR) and the 2009 International Diabetes Federation (IDF), the American Heart Association (AHA), and the National Heart, Lung, and Blood Institute (NHLBI) criteria, respectively. We used correlation tests and ROC curves for the analysis. Results: The results indicate that there was a negative association between HDL2b% and the risk of IR and MetS in both sexes. Subjects in the highest quartile of HDL2b% had a significantly lower prevalence of IR and MetS than those in the lowest quartile (P<0.01). Correlation analysis between HDL2b% and metabolic risk factors showed that HDL2b% had a stronger association with these factors than HDL-C did in both sexes. ROC curve analysis also showed that HDL2b% had significant diagnostic value for IR and MetS compared to other lipid indicators. Conclusion: This study showed that MetS alters the distribution of HDL subclasses even when HDL-C levels are within the normal range. HDL-2b% has better diagnostic value for IR and MetS than HDL-C alone and may be a useful marker for early screening.

Indexed as

HDL subclassinsulin resistancemetabolic syndromenormal HDL-C population

Identifiers

PMID38288339
PMCPMC10822767
OpenAlexW4391146229

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.