Evidence map›Paper›PMID 35896726›Full record

ArticleNPJ digital medicine2022

Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk.

Marcus D R Klarqvist, Saaket Agrawal, Nathaniel Diamant, Patrick T Ellinor, Anthony Philippakis, Kenney Ng, Puneet Batra, Amit V Khera

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Article
  2. Observational
  3. Article
  4. Article
  5. Article
  6. 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.

Marcus D R Klarqvist *Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-7630-2708
Saaket Agrawal *Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-2535-4759
Nathaniel DiamantData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-1738-304X
Patrick T EllinorCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Anthony PhilippakisData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Kenney NgCenter for Computational Health, IBM Research, Cambridge, MA, USA.
Puneet BatraData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-6822-0593
Amit V KheraCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA. avkhera@mgh.harvard.edu.ORCID http://orcid.org/0000-0001-6535-5839

Funding

Enabling improved applicability and transferability of polygenic scores across populationsU01HG011719 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI Alicia Martin · 2021 to 2026
$5.5M
Polygenic prediction of common, complex diseasesK08HG010155 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI KHERA, AMIT VIKRAM · 2018 to 2021
$1.1M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS K08 HG010155NHGRI NIH HHS U01 HG011719
6 · The paper itself

Abstract

Inter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice, in part because medical imaging has not been practical to deploy at scale for this task. Here, we report a deep learning model trained on an individual's body shape outline-or "silhouette" -that enables accurate estimation of specific fat depots of interest, including visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes, and VAT/ASAT ratio. Two-dimensional coronal and sagittal silhouettes are constructed from whole-body magnetic resonance images in 40,032 participants of the UK Biobank and used as inputs for a convolutional neural network to predict each of these quantities. Mean age of the study participants is 65 years and 51% are female. A cross-validated deep learning model trained on silhouettes enables accurate estimation of VAT, ASAT, and GFAT volumes (R

Identifiers

PMID35896726
PMCPMC9329470

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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.