Evidence map›Paper›PMID 36642660›Full record

ArticleAtherosclerosis2023

Healthy lifestyle, plasma metabolites, and risk of cardiovascular disease among individuals with diabetes.

Qi Lu, Junxiang Chen, Rui Li, Yi Wang, Zhouzheng Tu, Tingting Geng, Liegang Liu, An Pan, Gang Liu

Abstract read
PubMed Publisher
In one paragraph

Article in Atherosclerosis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

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

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  8. Identification of metabolite-disease associations based on knowledge graph.Metabolomics : Official journal of the Metabolomic Society · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Qi LuDepartment of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety (Huazhong University of Science and Technology), Ministry of Education Key Lab of Environment and Health, and State Key Laboratory of Environment Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Junxiang ChenDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Rui LiDepartment of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety (Huazhong University of Science and Technology), Ministry of Education Key Lab of Environment and Health, and State Key Laboratory of Environment Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yi WangDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Zhouzheng TuDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Tingting GengDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Liegang LiuDepartment of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety (Huazhong University of Science and Technology), Ministry of Education Key Lab of Environment and Health, and State Key Laboratory of Environment Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
An PanDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: panan@hust.edu.cn.
Gang LiuDepartment of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety (Huazhong University of Science and Technology), Ministry of Education Key Lab of Environment and Health, and State Key Laboratory of Environment Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: liugang026@hust.edu.cn.

Funding

Medical Research Council MC_PC_17228Medical Research Council MC_QA137853
6 · The paper itself

Abstract

BACKGROUND AND

aimsLifestyle management is a fundamental aspect of diabetes care to prevent cardiovascular disease (CVD); however, the underlying metabolic mechanism is not well established. We aimed to identify metabolites associated with different lifestyle factors, and estimate their mediating roles between lifestyle and CVD risk among people with diabetes.

methodsLifestyle and metabolomic data were available for 5072 participants with diabetes who were free of CVD at baseline in the UK Biobank. The healthy level of 5 lifestyle factors was defined as non-central obesity, non-current smoking, moderate alcohol intake, physically active, and healthy diet. A total of 44 biomarkers across 7 metabolic pathways including lipoprotein particles, fatty acids, amino acids, fluid balance, inflammation, ketone bodies, and glycolysis were quantified by nuclear magnetic resonance (NMR) spectroscopy.

resultsAll 44 assayed metabolites were significantly associated with at least one lifestyle factor. Approximately half of metabolites, which were mostly lipoprotein particles and fatty acids, showed a mediating effect between at least one lifestyle factor and CVD risk. NMR metabolites jointly mediated 43.4%, 30.0%, 16.8%, 43.4%, and 65.5% of the association of non-central obesity, non-current smoking, moderate alcohol intake, physically active, and healthy diet with lower CVD risk, respectively. In general, though metabolites that significantly associated with lifestyle were mostly different across the 5 lifestyle factors, the pattern of association was consistent between fatty acids and all 5 lifestyle factors. Further, fatty acids showed significant mediating effects in the association between all 5 lifestyle factors and CVD risk with mediation proportion ranging from 12.2% to 26.8%.

conclusionsThere were large-scale differences in circulating NMR metabolites between individuals with diabetes who adhered to a healthy lifestyle and those did not. Differences in metabolites, especial fatty acids, could partially explain the association between adherence to multiple healthy lifestyle and lower CVD risk among people with diabetes.

Indexed as

Cardiovascular DiseasesDiabetes MellitusHealthy LifestyleHumansLipoproteinsObesityRisk FactorsLipoproteinsCardiovascular diseaseDiabetesHealthy lifestyleMetabolomicsProspective study

Identifiers

PMID36642660

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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.