4.7 Article

Predominance of small dense LDL differentiates metabolically unhealthy from metabolically healthy overweight adults in Korea

期刊

METABOLISM-CLINICAL AND EXPERIMENTAL
卷 63, 期 3, 页码 415-421

出版社

W B SAUNDERS CO-ELSEVIER INC
DOI: 10.1016/j.metabol.2013.11.015

关键词

Overweight; Small dense low-density lipoprotein (LDL); Lipoprotein subfraction; Visceral to subcutaneous adipose tissue ratio (VAT/SAT ratio)

资金

  1. Biomedical Technology Development Research Program through the National Research Foundation of Korea (NRF)
  2. Ministry of Science, ICT and Future Planning [NRF-2013M3A9B6046413]

向作者/读者索取更多资源

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. (C) 2014 Elsevier Inc. All rights reserved.

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