Evidence map›Paper›PMID 34838008›Full record

ArticleLipids in health and disease2021

A new phenotypic classification system for dyslipidemias based on the standard lipid panel.

Maureen Sampson, Rami A Ballout, Daniel Soffer, Anna Wolska, Sierra Wilson, Jeff Meeusen, Leslie J Donato, Erica Fatica, James D Otvos, Eliot A Brinton and 6 more

Open access · goldAbstract read
In one paragraph

Article in Lipids in health and disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.8field-weighted citation impact, top 6% 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

12 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. APOB to estimated APOB ratio for screening for the APOE2 genotype.medRxiv : the preprint server for health sciences · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
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

16 authors at 7 institutions in 1 country.

Maureen SampsonDepartment of Laboratory Medicine, Clinical Center, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0000-0003-0885-250X
Rami A BalloutLipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Drive, Bldg. 10/Rm. 2C433, Bethesda, MD, 20892, USA.
Daniel SofferDepartment of Internal Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Anna WolskaLipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Drive, Bldg. 10/Rm. 2C433, Bethesda, MD, 20892, USA.
Sierra WilsonLipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Drive, Bldg. 10/Rm. 2C433, Bethesda, MD, 20892, USA.
Jeff MeeusenDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA.
Leslie J DonatoDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA.
Erica FaticaDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA.
James D OtvosLaboratory Corporation of America Holdings (LabCorp), Burlington, NC, USA.
Eliot A BrintonUtah Lipid Center, Salt Lake City, UT, USA.
Robert S RosensonCardiometabolics Unit, Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Peter WilsonAtlanta VAMC and Emory Clinical Cardiovascular Research Institute, Atlanta, GA, 30322, USA.
Marcelo AmarLipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Drive, Bldg. 10/Rm. 2C433, Bethesda, MD, 20892, USA.
Robert ShamburekLipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Drive, Bldg. 10/Rm. 2C433, Bethesda, MD, 20892, USA.
Sotirios K KarathanasisLipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Drive, Bldg. 10/Rm. 2C433, Bethesda, MD, 20892, USA.
Alan T RemaleyLipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Drive, Bldg. 10/Rm. 2C433, Bethesda, MD, 20892, USA. aremaley1@nhlbi.nih.gov.
National Institutes of Health · USMayo Clinic in Arizona · USCardiovascular Institute of the South · USEmory Healthcare · USLouisville Metabolic and Atherosclerosis Research Center · USNational Institutes of Health Clinical Center · USUniversity of Pennsylvania · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDyslipoproteinemias can be classified by their distinct lipoprotein patterns, which helps determine atherosclerotic cardiovascular disease (ASCVD) risk and directs lipid management but this has required advanced laboratory testing.

objectiveTo develop a new algorithm for classifying lipoprotein disorders that only relies on the standard lipid panel.

methodsLipid thresholds for defining the different lipoprotein phenotypes were derived for Non-High-Density Lipoprotein-Cholesterol (NonHDL-C) and Triglycerides (TG) to be concordant when possible with the current US Multi-Society guidelines for blood cholesterol management.

resultsThe new classification method categorizes patients into all the classical Fredrickson-like phenotypes except for Type III dysbetalipoproteinemia. In addition, a new hypolipidemic phenotype (Type VI) due to genetic mutations in apoB-metabolism is described. The validity of the new algorithm was confirmed by lipid analysis by NMR (N = 11,365) and by concordance with classification by agarose gel electrophoresis/beta-quantification (N = 5504). Furthermore, based on the Atherosclerosis Risk in Communities (ARIC) cohort (N = 14,742), the lipoprotein phenotypes differ in their association with ASCVD (TypeV>IIb > IVb > IIa > IVa > normolipidemic) and can be used prognostically as risk enhancer conditions in the management of patients.

conclusionsWe describe a clinically useful lipoprotein phenotyping system that is only dependent upon the standard lipid panel. It, therefore, can be easily implemented for increasing compliance with current guidelines and for improving the care of patients at risk for ASCVD.

Indexed as

AdultAlgorithmsDyslipidemiasFemaleHeart Disease Risk FactorsHumansLipidsLipoproteinsMalePhenotypeTriglyceridesLipidsLipoproteinsTriglyceridesCardiovascular diseaseCholesterolGeneticsLDLLipidsLipoproteins

Identifiers

PMID34838008
PMCPMC8627634
OpenAlexW3204461078

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.