Logistic regression model showing significant risk factors for the individual components of the metabolic syndrome.

<p>Logistic regression model showing significant risk factors for the individual components of the metabolic syndrome.</p

Metabolic and Body Composition Risk Factors Associated with Metabolic Syndrome in a Cohort of Women with a High Prevalence of Cardiometabolic Disease - Fig 1

<p>Risk of metabolic syndrome across hexiles/quintiles of: A. trunk fat-free, soft-tissue mass, B. abdominal subcutaneous fat thickness, C. HOMA, D. adiponectin and E. age. Lighter bars represent unadjusted odds ratios whilst darker bars represent odds ratios with adjustment for smoking and all 4 of the other variables shown in this figure; *p<0.05, **p<0.005, ***p<0.0005 vs hexile 1.</p

Anthropometric and metabolic variables in women with and without metabolic syndrome^a.

<p>Anthropometric and metabolic variables in women with and without metabolic syndrome<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162247#t001fn001" target="_blank">^a</a>.</p

Sample selection flow chart.

<p>Sample selection flow chart.</p

Regression coefficients from multiple robust linear models for the prediction of blood pressure at follow-up by DXA-derived measures, accounting for potential effects of age.

<p>Regression coefficients from multiple robust linear models for the prediction of blood pressure at follow-up by DXA-derived measures, accounting for potential effects of age.</p

DXA-derived measures of body composition as predictors of hypertension at follow-up (n = 273).

<p>DXA-derived measures of body composition as predictors of hypertension at follow-up (n = 273).</p

Anthropometric-derived measures of body composition as predictors of hypertension at follow-up (n = 473).

<p>Anthropometric-derived measures of body composition as predictors of hypertension at follow-up (n = 473).</p

Subject characteristics at baseline and follow-up.

<p>Subject characteristics at baseline and follow-up.</p