{"id":674,"date":"2020-08-01T15:36:37","date_gmt":"2020-08-01T15:36:37","guid":{"rendered":"http:\/\/garoufi.markware.gr\/?p=674"},"modified":"2020-08-01T15:36:37","modified_gmt":"2020-08-01T15:36:37","slug":"comparing-calculated-ldl-c-with-directly-measured-ldl-c-in-healthy-and-in-dyslipidemic-children","status":"publish","type":"post","link":"https:\/\/garoufi.gr\/?p=674","title":{"rendered":"Comparing calculated LDL-C with directly measured LDL-C in healthy and in dyslipidemic children"},"content":{"rendered":"
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Background: LDL-C is one of the strongest markers for atherosclerosis and therapeutic decisions in children are based on its levels. Friedewald formula (FF) which is usually used for the calculation of LDL-C (cLDL-C); and Anandaraja’s formula (AF) may under- or overestimate actual levels.<\/p>\n

Objective: To compare cLDL-C with directly measured LDL-C (dLDL-C) as a screening tool and to evaluate dyslipidemic children.<\/p>\n

Methods: The study population consisted of 1005 children, 2\u201318 years, 688 of whom underwent lipid screen- ing in a regular check-up (group A); and 317 were dyslipidemic (LDL-C \u2265130 mg\/dl) (group B). A fasting serum lipid profile was assessed. LDL-C was measured using a homogenous assay and was calculated using FF and AF.<\/p>\n

Results: Each method of calculating LDL-C was highly correlated to dLDL-C. Using FF, cLDL-C was lower than dLDL-C in 75.6% (group A) and in 77.3% (group B) of children; the mean difference was significant in dyslipidemic group. Moreover, in group B, 25% of children with boundary high and 12% with high dLDL-C would be misclassified. Using AF, LDL-C was higher than dLDL-C; the mean difference was significant in group A. Based on cLDL-C, 52% of group A with borderline dLDL-C and 27.5% of group B children with boundary high dLDL-C would be considered as dyslipidemic and eligible for medication respectively.<\/p>\n

Conclusions: Comparing two methods of calculated LDL-C with directly measured LDL-C. FF was more accu- rate as a screening tool while AF was more accurate in the evaluation and follow-up of the dyslipidemic group. \u00a9 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

Garoufi et al-cLDL vs mLDL-Clin Biochemistry 2016<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Background: LDL-C is one of the strongest markers for atherosclerosis and therapeutic decisions in children are based on its levels. Friedewald formula (FF) which is usually used for the calculation of LDL-C (cLDL-C); and Anandaraja’s formula (AF) may under- or overestimate actual levels. Objective: To compare cLDL-C with directly measured LDL-C (dLDL-C) as a screening […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-674","post","type-post","status-publish","format-standard","hentry","category-1"],"yoast_head":"\nComparing calculated LDL-C with directly measured LDL-C in healthy and in dyslipidemic children » \u0393\u03b1\u03c1\u03bf\u03cd\u03c6\u03b7 A\u03bd\u03b1\u03c3\u03c4\u03b1\u03c3\u03af\u03b1<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/garoufi.gr\/?p=674\" \/>\n<meta property=\"og:locale\" content=\"el_GR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Comparing calculated LDL-C with directly measured LDL-C in healthy and in dyslipidemic children » \u0393\u03b1\u03c1\u03bf\u03cd\u03c6\u03b7 A\u03bd\u03b1\u03c3\u03c4\u03b1\u03c3\u03af\u03b1\" \/>\n<meta property=\"og:description\" content=\"Background: LDL-C is one of the strongest markers for atherosclerosis and therapeutic decisions in children are based on its levels. Friedewald formula (FF) which is usually used for the calculation of LDL-C (cLDL-C); and Anandaraja’s formula (AF) may under- or overestimate actual levels. 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