Evidence map›Paper›PMID 38404868›Full record

ArticleHeliyon2024

Lipidomics based on UHPLC/Q-TOF-MS to characterize lipid metabolic profiling in patients with newly diagnosed type 2 diabetes mellitus with dyslipidemia.

Xunlong Zhong, Chang Xiao, Ruolun Wang, Yunfeng Deng, Tao Du, Wangen Li, Yanmei Zhong, Yongzhen Tan

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.6field-weighted citation impact, top 11% 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

7 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Serum Lipidomic Analysis of T2DM Patients: A Potential Biomarker Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025
    Article
  5. Article
  6. Metabolomics Analysis of the Mechanism of Yuquan Capsules in the Treatment of T2DM Rats Using UPLC/MS.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025
    Article
  7. 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

8 authors at 3 institutions in 1 country.

Xunlong ZhongDepartment of Pharmacy, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China.
Chang XiaoDepartment of Pharmacy, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China.
Ruolun WangDepartment of Pharmacy, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China.
Yunfeng DengDepartment of Pharmacy, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China.
Tao DuDepartment of Endocrinology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China.
Wangen LiDepartment of Endocrinology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China.
Yanmei ZhongNew Drug Research and Development Center, Guangdong Pharmaceutical University, Guangzhou, 510006, China.
Yongzhen TanDepartment of Traditional Chinese Medicine, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China.
Second Affiliated Hospital of Guangzhou Medical University · CNGuangzhou Medical University · CNGuangdong Pharmaceutical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dyslipidemia often accompanies type 2 diabetes mellitus (T2DM). Elevated blood glucose in patients commonly leads to high levels of lipids. Lipid molecules can play a crucial role in early detection, treatment, and prognosis of T2DM with dyslipidemia. Previous lipid studies on T2DM mainly focused on Western diabetic populations with elevated blood glucose. In this research, we investigate both high blood sugar and high lipid levels to better understand changes in plasma lipid metabolism in newly diagnosed Chinese T2DM patients with dyslipidemia (NDDD). We used a plasma lipid analysis method based on ultra-high performance liquid chromatography coupled with mass spectrometry technology (UHPLC-MS) and statistical analysis to characterize lipid profiles and identify potential biomarkers in NDDD patients compared to healthy control (HC) subjects. Additionally, we examined the differences in lipid profiles between hyperlipidemia (HL) patients and HC subjects. We found significant changes in 15 and 23 lipid molecules, including lysophosphatidylcholine (LysoPC), phosphatidylcholine (PC), phosphatidylethanolamine (PE), sphingomyelin (SM), and ceramide (Cer), in the NDDD and HL groups compared to the HC group. These altered lipid molecules are associated with five metabolic pathways, with sphingolipid metabolism and glycerophospholipid metabolism being the most relevant to glucose and lipid metabolism changes. These lipid biomarkers are strongly correlated with traditional markers of glucose and lipid metabolism. Notably, Cer(d18:1/24:0), SM(d18:1/24:0), SM(d18:1/16:1), SM(d18:1/24:1), and SM(d18:2/24:1) were identified as essential potential biomarkers closely linked to clinical parameters through synthetic analysis of receiver operating characteristic curves, random forest analysis, and Pearson matrix correlation. These lipid biomarkers can enhance the risk prediction for the development of T2DM in individuals with dyslipidemia but no clinical signs of high blood sugar. Furthermore, they offer insights into the pathological mechanisms of T2DM with dyslipidemia.

Indexed as

HyperlipidemiaLipidomicsLipid profilesType 2 diabetes mellitus with dyslipidemiaUHPLC-MS

Identifiers

PMID38404868
PMCPMC10884851
OpenAlexW4391788381

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.