Evidence map›Paper›PMID 32936666›Full record

ArticleAnnals of clinical biochemistry2021

Establishing reference intervals for triglyceride-containing lipoprotein subfraction metabolites measured using nuclear magnetic resonance spectroscopy in a UK population.

Roshni Joshi, Goya Wannamethee, Jorgen Engmann, Tom Gaunt, Deborah A Lawlor, Jackie Price, Olia Papacosta, Tina Shah, Therese Tillin, Peter Whincup and 10 more

Open access · hybridAbstract read
In one paragraph

Article in Annals of clinical biochemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 49% 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

3 citing papers in PubMed, 6 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

20 authors at 10 institutions in 3 countries.

Roshni JoshiInstitute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.ORCID 0000-0002-0236-572X
Goya WannametheeDepartment of Primary Care & Population Health, Faculty of Population Health, University College London, London, UK.
Jorgen EngmannInstitute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
Tom GauntDepartment of Primary Care & Population Health, Faculty of Population Health, University College London, London, UK.
Deborah A LawlorMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
Jackie PriceUsher Institute, University of Edinburgh, Edinburgh, UK.
Olia PapacostaDepartment of Primary Care & Population Health, Faculty of Population Health, University College London, London, UK.
Tina ShahInstitute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
Therese TillinDepartment of Epidemiology and Public Health, University College London, London, UK.
Peter WhincupPopulation Health Research Institute, St George's, University of London, London, UK.
Nishi ChaturvediMRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.
Mika KivimakiDepartment of Epidemiology and Public Health, University College London, London, UK.
Diana KuhMRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.
Meena KumariInstitute for Social and Economic Research, University of Essex, Colchester, UK.
Alun D HughesMRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.
Juan P CasasMassachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare, MA, USA.
Steve E HumphriesInstitute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
Aroon D HingoraniInstitute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
A Floriaan SchmidtInstitute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
UCLEB Consortium
University College London · GBMRC Unit for Lifelong Health and Ageing · GBPublic Health England · GBAt Bristol · GBBrigham and Women's Hospital · USDepartment of Health and Social Care · GBSt George's, University of London · GBThe Queen's Medical Research Institute · GBUniversity Medical Center Utrecht · NLUniversity of Essex · GB

Funding

British Heart Foundation CS/13/1/30327British Heart Foundation PG/17/90/33415British Heart Foundation RG/10/12/28456British Heart Foundation RG/19/4/34452British Heart Foundation SP/13/6/30554Medical Research Council G0600580Medical Research Council G0601354Medical Research Council G0801414Medical Research Council G0801566Medical Research Council G0802318Medical Research Council G0902037Medical Research Council MC_UU_00011/4Medical Research Council MC_UU_00011/6Medical Research Council MR/K006584/1Medical Research Council MR/K013351/1Medical Research Council MR/R024227/1Medical Research Council MR/S011676/1
6 · The paper itself

Abstract

backgroundNuclear magnetic resonance (NMR) spectroscopy allows triglycerides to be subclassified into 14 different classes based on particle size and lipid content. We recently showed that these subfractions have differential associations with cardiovascular disease events. Here we report the distributions and define reference interval ranges for 14 triglyceride-containing lipoprotein subfraction metabolites.

methodsLipoprotein subfractions using the Nightingale NMR platform were measured in 9073 participants from four cohort studies contributing to the UCL-Edinburgh-Bristol consortium. The distribution of each metabolite was assessed, and reference interval ranges were calculated for a disease-free population, by sex and age group (<55, 55-65, >65 years), and in a subgroup population of participants with cardiovascular disease or type 2 diabetes. We also determined the distribution across body mass index and smoking status.

resultsThe largest reference interval range was observed in the medium very-low density lipoprotein subclass (2.5th 97.5th percentile; 0.08 to 0.68 mmol/L). The reference intervals were comparable among male and female participants, with the exception of triglyceride in high-density lipoprotein. Triglyceride subfraction concentrations in very-low density lipoprotein, intermediate-density lipoprotein, low-density lipoprotein and high-density lipoprotein subclasses increased with increasing age and increasing body mass index. Triglyceride subfraction concentrations were significantly higher in ever smokers compared to never smokers, among those with clinical chemistry measured total triglyceride greater than 1.7 mmol/L, and in those with cardiovascular disease, and type 2 diabetes as compared to disease-free subjects.

conclusionThis is the first study to establish reference interval ranges for 14 triglyceride-containing lipoprotein subfractions in samples from the general population measured using the nuclear magnetic resonance platform. The utility of nuclear magnetic resonance lipid measures may lead to greater insights for the role of triglyceride in cardiovascular disease, emphasizing the importance of appropriate reference interval ranges for future clinical decision making.

Indexed as

Nuclear Magnetic Resonance, BiomolecularAgedCardiovascular DiseasesDiabetes Mellitus, Type 2FemaleHumansLipoproteinsMaleMiddle AgedReference StandardsTriglyceridesUnited KingdomLipoproteinslipoprotein triglycerideTriglyceridesAnalytesclinical studiesepidemiology studieslaboratory methodslipidsnuclear magnetic resonance

Identifiers

PMID32936666
PMCPMC7791273
OpenAlexW3087687602

What OpenQuestion holds

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LicenceCC BY
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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.