Evidence map›Paper›PMID 36414942›Full record

ArticleBMC medicine2022

Plasma metabolomic profiling of dietary patterns associated with glucose metabolism status: The Maastricht Study.

Evan Yi-Wen Yu, Zhewen Ren, Siamak Mehrkanoon, Coen D A Stehouwer, Marleen M J van Greevenbroek, Simone J P M Eussen, Maurice P Zeegers, Anke Wesselius

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 3 pooled it
1.8field-weighted citation impact, top 15% 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

11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 11 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

8 authors at 2 institutions in 3 countries.

Evan Yi-Wen YuKey Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing, 210009, China. evan.yu@maastrichtuniversity.nl.ORCID 0000-0001-7825-5087
Zhewen RenDepartment of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Universiteitssingel 40 (Room C5.570), Maastricht, 6229ER, The Netherlands.
Siamak MehrkanoonDepartment of Data Science and Knowledge Engineering, Maastricht University, Maastricht, 6229ER, The Netherlands.
Coen D A StehouwerCARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, 6229ER, The Netherlands.
Marleen M J van GreevenbroekCARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, 6229ER, The Netherlands.
Simone J P M EussenDepartment of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Universiteitssingel 40 (Room C5.570), Maastricht, 6229ER, The Netherlands.
Maurice P ZeegersDepartment of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Universiteitssingel 40 (Room C5.570), Maastricht, 6229ER, The Netherlands.
Anke WesseliusDepartment of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Universiteitssingel 40 (Room C5.570), Maastricht, 6229ER, The Netherlands. anke.wesselius@maastrichtuniversity.nl.ORCID 0000-0003-4474-9665
Maastricht University · NLMaastricht University Medical Centre · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGlucose metabolism has been reported to be affected by dietary patterns, while the underlying mechanisms involved remain unclear. This study aimed to investigate the potential mediation role of circulating metabolites in relation to dietary patterns for prediabetes and type 2 diabetes.

methodsData was derived from The Maastricht Study that comprised of 3441 participants (mean age of 60 years) with 28% type 2 diabetes patients by design. Dietary patterns were assessed using a validated food frequency questionnaire (FFQ), and the glucose metabolism status (GMS) was defined according to WHO guidelines. Both cross-sectional and prospective analyses were performed for the circulating metabolome to investigate their associations and mediations with responses to dietary patterns and GMS.

resultsAmong 226 eligible metabolite measures obtained from targeted metabolomics, 14 were identified to be associated and mediated with three dietary patterns (i.e. Mediterranean Diet (MED), Dietary Approaches to Stop Hypertension Diet (DASH), and Dutch Healthy Diet (DHD)) and overall GMS. Of these, the mediation effects of 5 metabolite measures were consistent for all three dietary patterns and GMS. Based on a 7-year follow-up, a decreased risk for apolipoprotein A1 (APOA1) and docosahexaenoic acid (DHA) (RR 0.60, 95% CI 0.55, 0.65; RR 0.89, 95% CI 0.83, 0.97, respectively) but an increased risk for ratio of ω-6 to ω-3 fatty acids (RR 1.29, 95% CI 1.05, 1.43) of type 2 diabetes were observed from prediabetes, while APOA1 showed a decreased risk of type 2 diabetes from normal glucose metabolism (NGM; RR 0.82, 95% CI 0.75, 0.89).

conclusionsIn summary, this study suggests that adherence to a healthy dietary pattern (i.e. MED, DASH, or DHD) could affect the GMS through circulating metabolites, which provides novel insights into understanding the biological mechanisms of diet on glucose metabolism and leads to facilitating prevention strategy for type 2 diabetes.

Indexed as

Diabetes Mellitus, Type 2Diet, MediterraneanPrediabetic StateCross-Sectional StudiesGlucoseHumansMetabolomicsMiddle AgedProspective StudiesGlucoseCohort studyDietary patternsGlucose metabolismMetabolomicsMolecular epidemiology

Identifiers

PMID36414942
PMCPMC9682653
OpenAlexW4309650699

What OpenQuestion holds

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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.