Evidence map›Paper›PMID 37463495›Full record

Trial reportDiabetes care2023

Diet-Related Lipidomic Signatures and Changed Type 2 Diabetes Risk in a Randomized Controlled Feeding Study With Mediterranean Diet and Traditional Chinese or Transitional Diets.

Yaogan Luo, Liang Sun, Qingqing Wu, Boyu Song, Yanpu Wu, Xiaowei Yang, Puchen Zhou, Zhenhua Niu, He Zheng, Huaixing Li and 5 more

Open access · bronzeAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Diabetes care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
4.3field-weighted citation impact, top 5% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

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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

15 authors at 4 institutions in 1 country.

Yaogan LuoShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Liang SunMinistry of Education Key Laboratory of Public Health Safety, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, China.
Qingqing WuShanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.
Boyu SongShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Yanpu WuShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Xiaowei YangShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Puchen ZhouShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Zhenhua NiuShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
He ZhengShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Huaixing LiShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Weiqiong GuDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiqiu WangDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0002-9383-7656
Guang NingDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0002-5754-7635
Rong ZengShanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.ORCID 0000-0003-0685-4333
Xu LinShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID 0000-0001-8876-9823
Shanghai Institute of Nutrition and Health · CNShanghai Jiao Tong University · CNChinese Academy of Sciences · CNFudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveFew trials studied the links of food components in different diets with their induced lipidomic changes and related metabolic outcomes. Thus, we investigated specific lipidomic signatures with habitual diets and modified diabetes risk by using a trial and a cohort. RESEARCH DESIGN AND

methodsWe included 231 Chinese with overweight and prediabetes in a randomized feeding trial with Mediterranean, traditional, or transitional diets (control diet) from February to September 2019. Plasma lipidomic profiles were measured at baseline, third month, and sixth month by high-throughput targeted liquid chromatography-mass spectrometry. Associations of the identified lipids with habitual dietary intakes were examined in another lipidomic database of a Chinese cohort (n = 1,117). The relationships between diet-induced changes of lipidomic species and diabetes risk factors were further investigated through both individual lipids and relevant modules in the trial.

resultsOut of 364 lipidomic species, 26 altered across groups, including 12 triglyceride (TAG) fractions, nine plasmalogens, four phosphatidylcholines (PCs), and one phosphatidylethanolamine. TAG fractions and PCs were associated with habitual fish intake while plasmalogens were associated with red meat intake in the cohort. Of the diet-related lipidomic metabolites, 10 TAG fractions and PC(16:0/22:6) were associated with improved Matsuda index (β = 0.12 to 0.42; PFDR < 0.030). Two plasmalogens were associated with deteriorated fasting glucose (β = 0.29 to 0.31; PFDR < 0.014). Similar results were observed for TAG and plasmalogen related modules.

conclusionsThese fish- and red meat-related lipidomic signatures sensitively reflected different diets and modified type 2 diabetes risk factors, critical for optimizing dietary patterns.

Indexed as

Diabetes Mellitus, Type 2Diet, MediterraneanAnimalsDietEast Asian PeopleHumansLipidomicsPlasmalogensPlasmalogens

Identifiers

PMID37463495
PMCPMC10465987
OpenAlexW4384663837

What OpenQuestion holds

Textmetadata
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.