Please use this identifier to cite or link to this item: http://hdl.handle.net/11189/7227
Title: Measured versus calculated small dense LDL-cholesterol and cardiometabolic traits in a South African population
Authors: Masoud, M. 
Kengne, Andre Pascal 
Erasmus, Rajiv T 
Hon, Gloudina Mary 
Macharia, Muiruri 
Matsha, Tandi Edith 
Keywords: Diabetes -- Africa;Lipoproteins;Membrane lipids;Lipids;Small-dense low density lipoprotein
Issue Date: 2018
Publisher: Springer
Source: Masoud, M., Kengne, A. P., Erasmus, R. T. et al. 2018. Measured versus calculated small dense LDL-cholesterol and cardiometabolic traits in a South African population. Indian journal of clinical Biochemistry, 34: 304-311. [https://doi.org/10.1007/s12291-018-0748-8]
Journal: Indian journal of clinical Biochemistry 
Abstract: Small-dense low density lipoprotein (sdLDL) is increasingly viewed as a marker for evaluating atherogenic risk, however its clinical uptake is hampered by the cumbersomeness of available methods. Consequently, a number of alternative methods for the estimation of sdLDL have been developed and none have been tested in a population from Africa. We evaluated an equation to estimate sdLDL-C from classic lipid parameters in South Africans. This is a cross-sectional study involving 1550 participants in which direct measurement of sdLDL in 237 participants was performed using a homogeneous enzymatic assay. Their mean age (standard deviation, SD) was 54.2 (14.7) years. 156 (65.8%) were normotolerant, 29 (12.2%) prediabetes, 17 (7.2%) screen detected diabetes and 35 (14.8%) known diabetes. Measured sdLDL values ranged from 0.17 to 3.39 versus—1.85 to 2.52 mmol/L calculated sdLDL. There was a significant positive correlation between the two measurements with a Pearson correlation coefficient of 0.659 (95%CI: 0.581–0.726). In a regression model, the adjusted R2 was 0.440 after adding age, 0.441 after further adding gender, then 0.443 with dysglycemia and lastly 0.447 upon adding body mass index. With the exception of HDL-cholesterol levels that decreased across increasing quintiles of calculated sdLDL, our data showed significant correlations between sdLDL and cardiometabolic risk factors, all p values < 0.0001. In conclusion, this study has shown that calculated sdLDL can be efficiently used to approximate population levels of sdLDL; however the modest correlation indicate that at the individual level, it will poorly approximate true sdLDL levels, with possible implications for risk stratification.
URI: http://hdl.handle.net/11189/7227
ISSN: 0970-1915
DOI: https://doi.org/10.1007/s12291-018-0748-8
Appears in Collections:HWSci - Journal Articles (DHET subsidised)

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