Android and Gynoid Fat Percentages and Serum Lipid Levels in United States Adults

Kyoung-Bok Min; Jin-Young Min

Disclosures

Clin Endocrinol. 2015;82(3):377-387. 

In This Article

Abstract and Introduction

Abstract

Context Accumulating evidence suggests that fat distribution is a better predictor of cardiovascular disease than body mass index (BMI).

Objective The aim of this study was to investigate the association of android and gynoid fat percentages with lipid profiles to determine whether android and/or gynoid fat percentages are associated with serum lipid levels.

Design A population-based cross-sectional study.

Patients Five thousand six hundred and ninety-six adults (20 years and older) who participated in the National Health and Nutrition Examination Survey 2003–2006.

Measurement The regional body composition in the android and gynoid regions was defined by dual energy X-ray absorptiometry (DXA). The estimation of lipid risk profiles included total cholesterol, high-density lipoprotein (HDL) -cholesterol, low-density lipoprotein (LDL) -cholesterol and triglycerides (TG).

Results Regardless of gender, android and gynoid body fat percentages were positively and significantly correlated with BMI and waist circumference. After adjustment for age, ethnicity, education, smoking, alcohol consumption, dyslipidaemia and BMI, increases in android fat percentage were significantly associated with total cholesterol, TG and HDL cholesterol in males, and total cholesterol, HDL cholesterol and LDL cholesterol in females. The gynoid fat percentages showed a positive correlation with total cholesterol in males, whereas gynoid fat accumulation in females showed a favourable association with TG and HDL cholesterol. The observed associations differed according to ethnic groups.

Conclusion Our results suggest that regional fat distribution in the android and gynoid regions have different effects on lipid profiles, and that fat in the android region, rather than the gynoid region, may be an important factor in determining the risk of cardiovascular disease.

Introduction

Several studies have shown an association between body fat, often determined by the body mass index (BMI), and cardiovascular morbidity and mortality.[1–3] However, an increasing amount of evidence indicates that fat distribution is a better predictor of cardiovascular disease than BMI.[4–7] Compared with overall fat volume, regional fat deposition results in a greater risk of cardiovascular events. Moreover, different health effects can occur according to the location of regional fat.[8–10]

Previous studies have indicated that an excess android fat pattern (an 'apple- shape' or central obesity) is significantly associated with increased lipid risk profiles [e.g., cholesterol and triglycerides (TG)], whereas gynoid fat distribution (a 'pear- shape' or fat accumulation around the hips and bottom), resulted in a lower risk.[11,12] In contrast, there is evidence of positive association between gynoid fat accumulation and the risk of metabolic complications or a protective effect of gynoid fat mass against cardiovascular disease.[11–16] The inconsistency of these findings may be due to the differences in the characteristics of study participants, especially gender, and differences in the fat accumulation measurement technique [i.e., anthropological methods, dual-energy X-ray absorptiometry (DXA) and computed tomography].

The aim of this study was to investigate the association of android and gynoid fat percentages with lipid profiles to determine whether android and/or gynoid fat percentages are associated with increases in total cholesterol, high-density lipoprotein (HDL) -cholesterol, low-density lipoprotein (LDL) -cholesterol and TG. Examining the major factors contributing to the heterogeneity of previous findings, we analysed the data from the Nutrition and Health Examination Survey (NHANES), 2003–2006, and used a representative sample of US adults and android and gynoid fat percentage measurements obtained from DXA, which has high precision, reliability and repeatability.[17,18]

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