- 作者：Sue Kim ; Hyangkyu Lee ; Duk-Chul Lee ; Hye-Sun Lee ; Ji-Won Lee
- 关键词：MHO ; metabolically healthy overweight ; non-MHO ; metabolically unhealthy overweight ; VAT/SAT ratio ; visceral adipose tissue area to subcutaneous adipose tissue area ratio ; sdLDL ; small ; dense low-density lipoprotein ; LDL ; low-density lipoprotein ; HDL ; high-density lipoprotein ; VLDL ; very low-density lipoprotein ; CT ; computed tomography ; HOMA-IR ; homeostasis model assessment of insulin resistance ; QUICKI ; quantitative insulin sensitivity check index ; HTN ; hypertension ; DM ; diabetes mellitus ; hsCRP
- 刊名：Metabolism
- 出版年：March, 2014
- 年：2014
- 卷：63
- 期：3
- 页码：415-421
- 全文大小：228 K
Objective
The purposes of this study were (1) to determine the association between lipoprotein subfraction profiles and metabolically healthy overweight (MHO) phenotype, as defined by visceral adiposity; and (2) to identify the strongest predictor of metabolic health among the lipoprotein measurements.Materials/Methods
This cross-sectional study was comprised of 462 overweight patients, who were classified as MHO or non-MHO based on their visceral adipose tissue (VAT) area to subcutaneous adipose tissue area (SAT) ratio (VAT/SAT ratio). Serum lipoprotein subfraction analyses and other metabolic parameters were measured.
Results
Among the overweight participants, two hundred fifty-five individuals (53.7%) had the MHO phenotype. After adjusting for age, sex, medication, lifestyle factors, and confounding metabolic characteristics, the non-MHO group showed significantly higher lipid levels and a greater prevalence of unfavorable lipid profiles. LDL subclass pattern type B was the most significant predictor of the non-MHO phenotype (odds ratio 2.70; 95% CI 1.55-4.69), while serum LDL cholesterol level was not a significant predictor of the non-MHO phenotype.
Conclusions
Lipoprotein subfraction particle measurements were significantly associated with the non-MHO phenotype and a higher VAT/SAT ratio, with small dense LDL predominance being the most significant predictor of MHO phenotype. These findings will help identify MHO and non-MHO phenotypes and perhaps lead to a development of cost-effective individualized treatments.