Evolocumab for the treatment of homozygous familial hypercholesterolaemia

Blom, Dirk; Marais, A. David

doi:10.1080/21678707.2016.1189323

Cited by 4 publications

(2 citation statements)

References 74 publications

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“…22 As mentioned, FH is very common in South Africa among certain White and Indian ethnicities, but the prevalence among Black South Africans has not been extensively studied. 8,9 The recent study by Raal et al highlighted this disparity, where only ≈4% of individuals diagnosed with a FH mutation at a large South African academic hospital were of Black African ethnicity, despite contributing ≈81% of the country's population. 41,42 This is probably not due to targeting the wrong mutations in these individuals, as the same study demonstrated that the 'clinical FH' group with no positive mutation consisted of only ≈2% black individuals.…”

Section: Discussionmentioning

confidence: 99%

Familial Hypercholesterolemia Identification by Machine Learning Using Lipid Profile Data Performs as Well as Clinical Diagnostic Criteria

Hesse

Raal²,

Endo

et al. 2022

Circ: Genomic and Precision Medicine

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Background: Familial hypercholesterolemia (FH) is a common genetic disorder and, if not diagnosed and treated early, results in premature cardiovascular disease. Most individuals with FH are undiagnosed and machine learning offers a new prospect to improve FH identification. Our objective was to create a machine learning model from basic lipid profile data with better screening performance than LDL-C (low-density lipoprotein cholesterol) cutoff levels and diagnostic performance comparable to the Dutch Lipid Clinic Network criteria. Methods: The model was developed combining logistic regression, deep learning, and random forest classification and trained on a 70% split of a data set of individuals clinically suspected of having FH. Model performance, as well as that of the LDL-C cutoff and Dutch Lipid Clinic Network criteria, were assessed on the internal 30% testing data set and an external data set by comparing the area under the receiver operator characteristic (AUROC) curves. All methodologies were measured against the gold standard of FH diagnosis by mutation identification. Furthermore, the model was also tested on 2 lower prevalence data sets. Results: The machine learning model achieved an AUROC curve of 0.711 on the external data set (n=1376; FH prevalence=64%), which was superior to the LDL-C cutoff (AUROC=0.642) and comparable to the Dutch Lipid Clinic Network criteria (AUROC=0.705). The model performed even better when tested on the medium-prevalence (n=2655; FH prevalence=20%) and low-prevalence (n=1616; FH prevalence=1%) data sets, with AUROC curve values of 0.801 and 0.856, respectively. Conclusions: Despite absence of clinical information, the model better identified genetically confirmed FH in a cohort of individuals suspected of having FH than LDL-C cutoff values and was comparable to the Dutch Lipid Clinic Network criteria. The model achieved higher accuracy when tested on 2 cohorts with lower FH prevalence. The application of machine learning is, therefore, a promising tool in both the screening for, and diagnosis of, individuals with FH.

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Section: Discussionmentioning

confidence: 99%

Familial Hypercholesterolemia Identification by Machine Learning Using Lipid Profile Data Performs as Well as Clinical Diagnostic Criteria

Hesse

Raal²,

Endo

et al. 2022

Circ: Genomic and Precision Medicine

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“…So the need for an add-on drug to enhance the effect of first-line treatments is suggested. New ideas include altering the absorption of cholesterol in the brush border of the intestine [ 7 ], affecting the related receptor on the liver [ 8 ], altering the pathway of producing cholesterol, or using inhibitors of proprotein convertase subtilisin/kexin type 9 (PCSK9) [ 9 ]. Apolipoprotein B-100 is an essential component of all atherogenic lipoproteins, including LDL.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Mipomersen in the Management of Patients with Familial Hypercholesterolemia: A Systematic Review and Meta-Analysis of Clinical Trials

Astaneh

Makhdami

Astaneh

et al. 2021

JCDD

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Background: Familial hypercholesterolemia (FH) lead to significant adverse effects in coronary arteries. Mipomersen is a second-generation antisense oligonucleotide that inhibits the synthesis of apolipoprotein B-100, an essential component of low density lipoprotein (LDL), and thus decreases the production of LDL. We aimed to determine the effect of mipomersen in patients with FH. Methods: We searched Ovid Medline, Ovid EMBASE, WHO ICTRP search portal, ISI database, the reference lists of relevant articles, and also Google Scholar to retrieve articles. All randomized controlled trials (RCTs) comparing patients with FH receiving mipomersen as an add-on and a parallel group receiving a placebo or no intervention were selected. Results: Five studies with more than 500 patients were included. All had low risk of bias. Pooling data showed that mipomersen probably reduces LDL compared with placebo [mean difference: −24.79, 95% CI (−30.15, −19.43)] but with a moderate level of certainty. There was a high level of evidence for injection site reactions [RR = 2.56, CI (1.47–4.44)] and a low level for increased serum alanine transaminase (ALT) > 3 times upper limit of normal (ULN) [RR = 5.19, CI (1.01–26.69)]. Conclusion: A moderate level of evidence in decreasing serum LDL indicates that we are uncertain if this drug provides benefit in any outcome important to patients. Although a low level of evidence for an increase in serum ALT leaves uncertainty about this adverse effect, injection site reactions in 10% or more of patients can be an important concern.

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Lomitapide and Mipomersen—Inhibiting Microsomal Triglyceride Transfer Protein (MTP) and apoB100 Synthesis

Blom

Raal

Santos

et al. 2019

Curr Atheroscler Rep

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Evolocumab for the treatment of homozygous familial hypercholesterolaemia

Cited by 4 publications

References 74 publications

Familial Hypercholesterolemia Identification by Machine Learning Using Lipid Profile Data Performs as Well as Clinical Diagnostic Criteria

Familial Hypercholesterolemia Identification by Machine Learning Using Lipid Profile Data Performs as Well as Clinical Diagnostic Criteria

The Effect of Mipomersen in the Management of Patients with Familial Hypercholesterolemia: A Systematic Review and Meta-Analysis of Clinical Trials

Lomitapide and Mipomersen—Inhibiting Microsomal Triglyceride Transfer Protein (MTP) and apoB100 Synthesis

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