Sue Kim1, Hyangkyu Lee2, Duk-Chul Lee1, Hye-Sun Lee3, Ji-Won Lee4. 1. Department of Family Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea. 2. Department of Clinical Nursing Science, Yonsei University College of Nursing, Nursing Policy Research Institute, Biobehavioral Research Center, Seoul, Korea. 3. Biostatistics Collaboration Units, Department of Research Affairs, Yonsei University College of Medicine, Seoul, Korea. 4. Department of Family Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea. Electronic address: indi5645@yuhs.ac.
Abstract
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.
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.
Authors: Jelena Vekic; Aleksandra Zeljkovic; Arrigo F G Cicero; Andrej Janez; Anca Pantea Stoian; Alper Sonmez; Manfredi Rizzo Journal: Medicina (Kaunas) Date: 2022-02-16 Impact factor: 2.430
Authors: Enrique Rodriguez-Garcia; Josefina Ruiz-Nava; Sonia Santamaria-Fernandez; Jose Carlos Fernandez-Garcia; Antonio Vargas-Candela; Raquel Yahyaoui; Francisco J Tinahones; Maria Rosa Bernal-Lopez; Ricardo Gomez-Huelgas Journal: Medicine (Baltimore) Date: 2017-07 Impact factor: 1.889