Evidence map›Paper›PMID 38124898›Full record

ArticleFrontiers in cardiovascular medicine2023

Relationships between body composition, anthropometrics, and standard lipid panels in a normative population.

Marcus Weeks, Andrew D Delgado, Jamie Wood, Bodi Zhang, Sarah Pesce, Laura Kunces, Loukia Lili, David Putrino

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 37% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Trial
  2. 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

8 authors at 2 institutions in 1 country.

Marcus WeeksDepartment of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Andrew D DelgadoDepartment of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Jamie WoodDepartment of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Bodi ZhangThorne HealthTech Inc., New York, NY, United States.
Sarah PesceThorne HealthTech Inc., New York, NY, United States.
Laura KuncesThorne HealthTech Inc., New York, NY, United States.
Loukia LiliThorne HealthTech Inc., New York, NY, United States.
David PutrinoDepartment of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Hamilton Thorne (United States) · USIcahn School of Medicine at Mount Sinai · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: More than one third of adults in the United States (US) meet the clinical criteria for a diagnosis of metabolic syndrome, but often diagnosis is challenging due to healthcare access, costs and discomfort with the process and invasiveness associated with a standard medical examination. Less invasive and more accessible approaches to collecting biometric data may have utility in identifying individuals at risk of diagnoses, such as metabolic syndrome or dyslipidemia diagnoses. Body composition is one such source of biometric data that can be non-invasively acquired in a home or community setting that may provide insight into an individual's propensity for a metabolic syndrome diagnosis. Here we investigate possible associations between body composition, anthropometrics and lipid panels in a normative population. Methods: Healthy participants visited the Lab100 clinic location at a hospital setting in New York City and engaged in a wellness visit led by a nurse practitioner. Blood was analyzed at point-of-care using the Abbott Piccolo Xpress portable diagnostic analyzer (Abbott Laboratories, IL, USA) and produced direct measures of total cholesterol (TC), high density lipoprotein (HDL-C), low density lipoprotein (LDL-C), very-low density lipoprotein (VLDL-C), and triglycerides (TG). Body composition and anthropometric data were collected using two separate pieces of equipment during the same visit (Fit3D and InBody570). Regression analysis was performed to evaluate associations between all variables, after adjusting for age, sex, race, AUDIT-C total score (alcohol use), and current smoking status. Results: Data from 199 participants were included in the analysis. After adjusting for variables, percentage body fat (%BF) and visceral fat levels were significantly associated with every laboratory lipid value, while waist-to-hip ratio also showed some significant associations. The strongest associations were detected between %BF and VLDL-C cholesterol levels ( Discussion: This initial, exploratory analysis shows early feasibility in using body composition and anthropometric data, that can easily be acquired in community settings, to identify people with dyslipidemia in a normative population.

Indexed as

biometricsbody compositionlipid panelslipidsmetabolic syndrome

Identifiers

PMID38124898
PMCPMC10731366
OpenAlexW4389397590

What Socratic 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 Socratic graph.