Evidence map›Paper›PMID 30021651›Full record

ArticleLipids in health and disease2018

Identification of a threshold to discriminate fasting hypertriglyceridemia with postprandial values.

Magdalena Del Rocío Sevilla-González, Carlos A Aguilar-Salinas, Liliana Muñóz-Hernández, Paloma Almeda-Valdés, Roopa Mehta, Rafael Zubirán, Omar Yaxmehen Bello-Chavolla, Donaji V Gómez-Velasco, Arsenio Vargas-Vázquez, Tannia Viveros-Ruíz and 2 more

Open access · goldAbstract read
In one paragraph

Article in Lipids in health and disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.5field-weighted citation impact, top 16% 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

5 citing papers in PubMed, 12 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

12 authors at 4 institutions in 1 country.

Magdalena Del Rocío Sevilla-GonzálezUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México.
Carlos A Aguilar-SalinasUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México.
Liliana Muñóz-HernándezUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México.
Paloma Almeda-ValdésUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México.
Roopa MehtaUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México.
Rafael ZubiránUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México.
Omar Yaxmehen Bello-ChavollaUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México.
Donaji V Gómez-VelascoUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México.
Arsenio Vargas-VázquezUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México.
Tannia Viveros-RuízUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México.
Alexandro J Martagón-RosadoUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México.
Ivette Cruz-BautistaUnidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de quiroga 15, 14200, México, México. ivette.cb27@gmail.com.
Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán · MXUniversidad Nacional Autónoma de México · MXConsejo Nacional de Humanidades, Ciencias y Tecnologías · MXTecnológico de Monterrey · MX

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPostprandial lipemia is an important cardiovascular risk factor. The assessment of postprandial lipid metabolism is a newly trend that several consortiums and countries have adopted. The aim of the study is to determine a postprandial triglyceride concentration cut-off point that accurately discriminate individuals with fasting normal triglyceride concentrations from those with fasting hypertriglyceridemia.

methodsCross sectional population-based study. A total of 212 subjects underwent an eight hours' oral fat tolerance test. Samples were taken fasting, three, four, five, six and eight hours after the meal. The area under the receiver operating characteristic curve (c-statistic) was computed using postprandial triglycerides concentrations as independent predictor, and fasting hypertriglyceridemia as dependent variable.

resultsThe best threshold of postprandial lipemia to discriminate fasting hypertriglyceridemia was 280 mg/dL at any hour area under the curve 0.816 (95% confidence interval 0.753-0.866), bootstrap-corrected c-statistic = 0.733 (95% confidence interval 0.68-0.86). The same value was compared with apolipoprotein B concentrations (>90th percentile) having a good performance: area under the curve 0.687 95% confidence interval 0.624-0.751). Likewise, subjects with high postprandial lipemia have higher Globo risk scores.

conclusionThe 280 mg/dL cut-off point value of postprandial triglycerides concentration any time after a test meal discriminate subjects with fasting hypertriglyceridemia. This threshold has a good performance in a heterogeneous population and has a good concordance with cardiovascular risk surrogates.

Indexed as

AdultAgedApolipoproteins BArea Under CurveBiomarkersCross-Sectional StudiesDietary FatsFastingFemaleHumansHypertriglyceridemiaLipid MetabolismMaleMiddle AgedPostprandial PeriodRiskApolipoproteins BBiomarkersDietary FatsTriglyceridesCardiovascular riskHypertriglyceridemiaPostprandial triglycerides

Identifiers

PMID30021651
PMCPMC6052549
OpenAlexW2884507647

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.