Searchable abstracts of presentations at key conferences in endocrinology
Endocrine Abstracts (2014) 35 P787 | DOI: 10.1530/endoabs.35.P787

ECE2014 Poster Presentations Obesity (53 abstracts)

The association between metabolic syndrome and android fat mass and android to gynoid fat mass ratio- are Classification and Regression Trees models helpful?

Anna Brona & Andrzej Milewicz


Wroclaw Medical Univercity, Wroclaw, Poland.


The aim of the study was to estimate thresholds of android fat mass and android to gynoid fat mass ratio at which metabolic disorders and blood pressure typical of metabolic syndrome appear. Classification and Regression Trees models were used to estimate these thresholds for glucose, HDL cholesterol, triglycerides and systolic and diastolic blood pressure.

Methods: healthy postmenopausal women were recruited for the study. Blood samples were collected for the measurement of plasma concentration of HDL cholesterol, triglycerides and fasting glucose. Blood pressure was measured. Android and gynoid fat mass were assessed using dual energy X-ray absorptiometry.

Results: Metabolic disorders and hypertension in android to gynoid fat ratio groups were more frequent in range from 0,943220 to 1,011458 (glucose from 0,952078, triglycerides 0,965859, HDL cholesterol 1,011458, SBP 0,943220, DBP 0,961549).

Metabolic disorders and hypertension in android fat groups were more frequent in range from 33,25% to 42,15% (glucose from 33,25%, triglycerides 42,15%, HDL cholesterol 35,55%, SBP 34,65%, DBP 34,25%)

Conclusion: Classification and Regression Tress model is applicable to searching for postmenopausal women at risk of metabolic disorders.

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