Linear regression models were used to find Pearson correlation coefficients and part correlations, with adjustments for age as a covariate, between magnetic-resonance imaging outcomes of central adiposity and biomarkers of cardiometabolic disease.

Ryan M. Sumrell (5706611)
Thomas E. Nightingale (3778612)
Liron S. McCauley (5706614)
Ashraf S. Gorgey (5706617)

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Publication date

August 2018

DOI

10.1371/journal.pone.0203049.t002

Publisher

Public Library of Science (PLoS)

Abstract

Linear regression models were used to find Pearson correlation coefficients and part correlations, with adjustments for age as a covariate, between magnetic-resonance imaging outcomes of central adiposity and biomarkers of cardiometabolic disease.</p

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Linear regression models were used to find Pearson correlation coefficients and part correlations, with adjustments for age as a covariate, between magnetic-resonance imaging outcomes of central adiposity and biomarkers of cardiometabolic disease.

Abstract

Extracted data

Linear regression models were used to find Pearson correlation coefficients and part correlations, with adjustments for age as a covariate, between magnetic-resonance imaging outcomes of central adiposity and biomarkers of cardiometabolic disease.

Abstract

Extracted data

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