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Mănescu IB, Demian L, Dobreanu M. Low-Density Lipoprotein Cholesterol Gymnastics: Exploring the Advantages and Limitations of the Friedewald, Martin-Hopkins, and Sampson Equations for Personalized Lipid Management. J Pers Med 2024; 14:1000. [PMID: 39338254 PMCID: PMC11433184 DOI: 10.3390/jpm14091000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Revised: 09/16/2024] [Accepted: 09/19/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND The most commonly used method for low-density lipoprotein cholesterol (LDL-C) estimation is the Friedewald equation, which has notable limitations. However, more accurate methods have been proposed. This study investigates the advantages and limitations of these methods and identifies the contexts in which each equation is the most or least applicable. METHODS A cohort of 222 individuals underwent a standard lipid profile assessment, including directly measuring their LDL-C (dLDL-C). LDL-C was also estimated using the Friedewald, Martin-Hopkins, and Sampson equations. The differences (%Delta) between the estimated and measured LDL-C were analyzed in relation to dLDL-C, high-density lipoprotein cholesterol (HDL-C), and triglyceride levels. RESULTS The %Delta was significantly lower (p < 0.0001) for the Martin-Hopkins (-8.8 ± 9.8) and Sampson (-9.5 ± 9.2) equations compared to Friedewald (-12.2 ± 9.2). All equations increasingly underestimated LDL-C as the dLDL-C levels decreased. The %Delta of the Martin-Hopkins equation showed significant positive correlations with dLDL-C (≤130 mg/dL) and triglycerides and a significant negative correlation with HDL-C. In a subgroup of 30 individuals with extreme %Delta values, patterns of gross underestimation were observed, particularly when low LDL-C, low triglycerides, and high HDL-C coincided. CONCLUSIONS The Martin-Hopkins equation is a superior method for LDL-C estimation and a valuable tool in precision medicine. However, clinicians and laboratory professionals must be aware of its limitations and recognize patterns that could lead to significant LDL-C underestimation. We propose an algorithm for clinical laboratories to provide personalized LDL-C assessments.
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Affiliation(s)
- Ion Bogdan Mănescu
- Department of Laboratory Medicine, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu, 540142 Targu Mures, Romania
- Doctoral School, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu, 540142 Targu Mures, Romania
| | - Liliana Demian
- Clinical Laboratory, Emergency County Clinical Hospital of Targu Mures, 50 Gheorghe Marinescu, 540136 Targu Mures, Romania
- Immunology Laboratory, Center for Advanced Medical and Pharmaceutical Research (CCAMF), George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu, 540142 Targu Mures, Romania
| | - Minodora Dobreanu
- Department of Laboratory Medicine, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu, 540142 Targu Mures, Romania
- Clinical Laboratory, Emergency County Clinical Hospital of Targu Mures, 50 Gheorghe Marinescu, 540136 Targu Mures, Romania
- Immunology Laboratory, Center for Advanced Medical and Pharmaceutical Research (CCAMF), George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu, 540142 Targu Mures, Romania
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Hedegaard BS, Nordestgaard BG, Kanstrup HL, Thomsen KK, Bech J, Bang LE, Henriksen FL, Andersen LJ, Gohr T, Larsen LH, Soja AMB, Elpert FP, Jakobsen TJ, Sjøl A, Joensen AM, Klausen IC, Schmidt EB, Bork CS. High Lipoprotein(a) May Explain One-Quarter of Clinical Familial Hypercholesterolemia Diagnoses in Danish Lipid Clinics. J Clin Endocrinol Metab 2024; 109:659-667. [PMID: 37862146 DOI: 10.1210/clinem/dgad625] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/13/2023] [Accepted: 10/17/2023] [Indexed: 10/22/2023]
Abstract
CONTEXT Cholesterol carried in lipoprotein(a) adds to measured low-density lipoprotein cholesterol (LDL-C) and may therefore drive some diagnoses of clinical familial hypercholesterolemia (FH). OBJECTIVE We investigated plasma lipoprotein(a) in individuals referred to Danish lipid clinics and evaluated the effect of plasma lipoprotein(a) on a diagnosis of FH. METHODS Individuals referred to 15 Danish lipid clinics who were suspected of having FH according to nationwide referral criteria were recruited between September 1, 2020 and November 30, 2021. All individuals were classified according to the Dutch Lipid Clinical Network criteria for FH before and after LDL-C was adjusted for 30% cholesterol content in lipoprotein(a). We calculated the fraction of individuals fulfilling a clinical diagnosis of FH partly due to elevated lipoprotein(a). RESULTS We included a total of 1166 individuals for analysis, of whom 206 fulfilled a clinical diagnosis of FH. Median lipoprotein(a) was 15 mg/dL (29 nmol/L) in those referred and 28% had lipoprotein(a) greater than or equal to 50 mg/dL (105 nmol/L), while 2% had levels greater than or equal to 180 mg/dL (389 nmol/L). We found that in 27% (55/206) of those fulfilling a clinical diagnosis of FH, this was partly due to high lipoprotein(a). CONCLUSION Elevated lipoprotein(a) was common in individuals referred to Danish lipid clinics and in one-quarter of individuals who fulfilled a clinical diagnosis of FH, this was partly due to elevated lipoprotein(a). These findings support the notion that the LPA gene should be considered an important causative gene in patients with clinical FH and further support the importance of measuring lipoprotein(a) when diagnosing FH as well as for stratification of cardiovascular risk.
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Affiliation(s)
- Berit Storgaard Hedegaard
- The Danish FH Study Group, Denmark
- Department of Cardiology, Regional Hospital Central Jutland, Viborg DK-8800, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg DK-9000, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital, Herlev-Gentofte DK-2730, Copenhagen, Denmark
| | - Børge Grønne Nordestgaard
- The Danish FH Study Group, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital, Herlev-Gentofte DK-2730, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N DK-2200, Denmark
- The Copenhagen General Population Study, Copenhagen University Hospital, Herlev-Gentofte DK-2730, Copenhagen, Denmark
| | - Helle Lynge Kanstrup
- The Danish FH Study Group, Denmark
- Department of Cardiology, Aarhus University Hospital, Aarhus DK-8200, Denmark
| | - Kristian Korsgaard Thomsen
- The Danish FH Study Group, Denmark
- Department of Cardiology, Hospital South West Jutland, University Hospital of Southern Denmark, Esbjerg DK-6700, Denmark
| | - Jan Bech
- The Danish FH Study Group, Denmark
| | - Lia Evi Bang
- The Danish FH Study Group, Denmark
- The Heart Center, Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Copenhagen Ø DK-2100, Denmark
| | - Finn Lund Henriksen
- The Danish FH Study Group, Denmark
- Department of Cardiology, Odense University Hospital, Odense DK-5000, Denmark
| | - Lars Juel Andersen
- The Danish FH Study Group, Denmark
- Department of Cardiology, Zealand University Hospital, Roskilde DK-4000, Denmark
| | - Thomas Gohr
- The Danish FH Study Group, Denmark
- Department of Cardiology, Lillebælt Hospital, Kolding DK-6000, Denmark
| | - Linnea Hornbech Larsen
- The Danish FH Study Group, Denmark
- Department of Cardiology, Copenhagen University Hospital, Herlev-Gentofte, Hellerup DK-2900, Denmark
| | - Anne Merete Boas Soja
- The Danish FH Study Group, Denmark
- Department of Internal Medicine, Section of Cardiology, Holbæk Hospital, Holbæk DK-4300, Denmark
| | - Frank-Peter Elpert
- The Danish FH Study Group, Denmark
- Department of Cardiology, Hospital of Southern Jutland, Aabenraa DK-6200, Denmark
| | - Tomas Joen Jakobsen
- The Danish FH Study Group, Denmark
- Department of Cardiology, North Zealand Hospital, Frederikssund DK-3600, Denmark
| | - Anette Sjøl
- The Danish FH Study Group, Denmark
- Department of Cardiology, Amager-Hvidovre Hospital, Hvidovre DK-2650, Denmark
| | - Albert Marni Joensen
- The Danish FH Study Group, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg DK-9000, Denmark
- Department of Cardiology, North Denmark Regional Hospital, Hjørring DK-9800, Denmark
| | - Ib Christian Klausen
- The Danish FH Study Group, Denmark
- Department of Cardiology, Regional Hospital Central Jutland, Viborg DK-8800, Denmark
| | - Erik Berg Schmidt
- The Danish FH Study Group, Denmark
- Department of Cardiology, Regional Hospital Central Jutland, Viborg DK-8800, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg DK-9000, Denmark
| | - Christian Sørensen Bork
- The Danish FH Study Group, Denmark
- Department of Cardiology, Aalborg University Hospital, Aalborg DK-9000, Denmark
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Chen X, Song C, Ma X, Tao J, Hu L, Xu Y, Yi Y, Yang X, Jiang L. High lipoprotein(a) concentration is associated with moyamoya disease. Lipids Health Dis 2024; 23:21. [PMID: 38254149 PMCID: PMC10802057 DOI: 10.1186/s12944-024-02015-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Moyamoya disease (MMD) has attracted the attention of scholars because of its rarity and unknown etiology. METHODS Data for this study were sourced from the Second Affiliated Hospital of Nanchang University. Regression analyses were conducted to examine the association in Lipoprotein [Lp(a)] and MMD. R and IBM SPSS were conducted. RESULTS A cohort comprising 1012 MMD patients and 2024 controls was established through the propensity score matching method. Compared with controls, MMD patients showed higher median Lp(a) concentrations [18.5 (9.6-37.8) mg/dL vs. 14.9 (7.8-30.5) mg/dL, P < 0.001]. The odds ratios and 95% confidence intervals for Lp(a) were calculated in three models: unadjusted model, model 1 (adjusted for body mass index and systolic blood pressure), and model 2 (adjusted for model 1 plus triglyceride, C-reactive protein, homocysteine, and low-density lipoprotein cholesterol). Results were [1.613 (1.299-2.002), P < 0.001], [1.598 (1.286-1.986), P < 0.001], and [1.661 (1.330-2.074), P < 0.001], respectively. Furthermore, age, sex, or hypertension status had nothing to do with this relationship. CONCLUSIONS Positive relationship exists between Lp(a) and MMD.
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Affiliation(s)
- Xinyue Chen
- Department of Cardiovascular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- The Second Clinical Medical College of Nanchang University, The Second Affiliated Hospital of Nanchang University, Jiangxi, Nanchang, 330006, China
| | - Chenxin Song
- Department of Cardiovascular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- The Second Clinical Medical College of Nanchang University, The Second Affiliated Hospital of Nanchang University, Jiangxi, Nanchang, 330006, China
| | - Xianrun Ma
- School of Basic Medical Sciences, Nanchang University, Jiangxi, Nanchang, 330006, China
| | - Junjie Tao
- The Second Clinical Medical College of Nanchang University, The Second Affiliated Hospital of Nanchang University, Jiangxi, Nanchang, 330006, China
| | - Lijuan Hu
- Department of Nursing, Nanchang Medical College, Nanchang, Jiangxi, China
| | - Yuan Xu
- Department of Medical Big Data Center, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yingping Yi
- Department of Medical Big Data Center, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xinlei Yang
- Biobank center, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
| | - Long Jiang
- Department of Cardiovascular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
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