Diagnostic Yield and Clinical Utility of Sequencing Familial Hypercholesterolemia Genes in Patients With Severe Hypercholesterolemia
Open accessAbstract
Background Approximately 7% of American adults have severe hypercholesterolemia (untreated low-density lipoprotein [LDL] cholesterol ≥190 mg/dl), which may be due to familial hypercholesterolemia (FH). Lifelong LDL cholesterol elevations in FH mutation carriers may confer coronary artery disease (CAD) risk beyond that captured by a single LDL cholesterol measurement. Objectives This study assessed the prevalence of an FH mutation among those with severe hypercholesterolemia and determined whether CAD risk varies according to mutation status beyond the observed LDL cholesterol level. Methods Three genes causative for FH (LDLR, APOB, and PCSK9) were sequenced in 26,025 participants from 7 case-control studies (5,540 CAD case subjects, 8,577 CAD-free control subjects) and 5 prospective cohort studies (11,908 participants). FH mutations included loss-of-function variants in LDLR, missense mutations in LDLR predicted to be damaging, and variants linked to FH in ClinVar, a clinical genetics database. Results Among 20,485 CAD-free control and prospective cohort participants, 1,386 (6.7%) had LDL cholesterol ≥190 mg/dl; of these, only 24 (1.7%) carried an FH mutation. Within any stratum of observed LDL cholesterol, risk of CAD was higher among FH mutation carriers than noncarriers. Compared with a reference group with LDL cholesterol <130 mg/dl and no mutation, participants with LDL cholesterol ≥190 mg/dl and no FH mutation had a 6-fold higher risk for CAD (odds ratio: 6.0; 95% confidence interval: 5.2 to 6.9), whereas those with both LDL cholesterol ≥190 mg/dl and an FH mutation demonstrated a 22-fold increased risk (odds ratio: 22.3; 95% confidence interval: 10.7 to 53.2). In an analysis of participants with serial lipid measurements over many years, FH mutation carriers had higher cumulative exposure to LDL cholesterol than noncarriers. Conclusions Among participants with LDL cholesterol ≥190 mg/dl, gene sequencing identified an FH mutation in <2%. However, for any observed LDL cholesterol, FH mutation carriers had substantially increased risk for CAD.
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Simple screening strategies for familial hypercholesterolemia have false positive rates greater than 95%.
“Simple screening strategies have false positive rates greater than 95%.”
From Finding missed cases of familial hypercholesterolemia in health systems using machine learning (2019) · cites this paper as [3]
The Results section (page not available) reports that only 24 of 1,386 people with LDL cholesterol ≥190 mg/dl carried an FH mutation; thus, more than 98% identified by this simple LDL-based threshold did not carry one. The paper does not establish a formal false-positive rate or evaluate simple screening strategies generally: the reported percentage is the proportion of screen-positive people without an identified mutation. (LLM, conf=0.86)
Experimental — accuracy not yet measuredUntreated lifelong elevation of low-density lipoprotein cholesterol in individuals with familial hypercholesterolemia causes up to a 20-fold excess risk of atherosclerotic cardiovascular disease versus those with normal LDL-C levels.
“Lifelong elevation of low-density lipoprotein cholesterol (LDL-C) in individuals with FH cause up to a 20-fold excess risk of atherosclerotic cardiovascular disease (ASCVD) versus those with normal LDL-C levels.2,3”
From Finding missed cases of familial hypercholesterolemia in health systems using machine learning (2019) · cites this paper as [3]
The Results (Clinical Impact of FH Mutation Identification on CAD Risk; page unavailable) report an OR of 22.3 for coronary artery disease in participants with LDL-C ≥190 mg/dl and an FH mutation, compared with those with LDL-C <130 mg/dl and no mutation. The Discussion describes lifelong LDL-C exposure as a hypothesis explaining the excess risk, not an established cause. The reported outcome is coronary artery disease rather than all ASCVD, and the analysis was not limited to untreated participants. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA prevalence of 1:70 is a reasonable estimate for evaluating the classifier in individuals with hypercholesterolemia or increased risk of ASCVD.
“In our case, we believe that this classifier would be most useful in flagging individuals within the healthcare system with an enhanced risk of ASCVD, we set a prevalence of 1:70 for evaluation, which is a reasonable estimate given the known prevalence of FH in individuals with hypercholesterolemia or increased risk of ASCVD (Supplementary Table 1).3”
From Finding missed cases of familial hypercholesterolemia in health systems using machine learning (2019) · cites this paper as [3]
The Results section (Diagnostic Yield of Gene Sequencing in Severe Hypercholesterolemia; page not provided) reports an FH-mutation prevalence of 1.7% among participants with LDL cholesterol ≥190 mg/dl, reasonably close to 1 in 70. It does not establish that prevalence for the broader group of people with increased ASCVD risk, so the proposed classifier-evaluation prevalence is only partly supported. (LLM, conf=0.82)
Experimental — accuracy not yet measuredIn adults with LDL-C greater than 190 mg/dl, less than 5% will be found to harbor a causal familial hypercholesterolemia gene mutation.
“While high LDL-C is a cardinal feature of FH, less than 5% of adults with an LDL-C > 190 mg/dl will be found to harbor a causal FH gene mutation.3”
From Finding missed cases of familial hypercholesterolemia in health systems using machine learning (2019) · cites this paper as [3]
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Cites
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447 · see citing works
Biological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487 · see citing works
Self-citationLoss-of-Function Mutations in <i>APOC3,</i> Triglycerides, and Coronary Disease
The TG and HDL Working Group of the Exome Sequencing Project, N. H. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1307095 · Cited by 1,071 · see citing works
Familial Hypercholesterolemia: Screening, diagnosis and management of pediatric and adult patients
Goldberg, A. C., Hopkins, P. N., Toth, P. P., Ballantyne, C. M., Rader, D. J., Robinson, J. G., Daniels, S. R., Gidding, S. S., de Ferranti, S. D., Ito, M. K., et al. (2011) · Journal of clinical lipidology · doi:10.1016/j.jacl.2011.03.001 · Cited by 622 · see citing works
Inactivating Mutations in <i>NPC1L1</i> and Protection from Coronary Heart Disease
The Myocardial Infarction Genetics Consortium Investigators (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1405386 · Cited by 419 · see citing works
Lowering LDL—not only how low, but how long?
Brown, M. S., & Goldstein, J. L. (2006) · Science · doi:10.1126/science.1125884 · Cited by 204 · see citing works
Flagged first, then by citations
Research works printed but not linked: 33
- [1]Emerging Risk Factors Collaboration Major lipids, apolipoproteins, and risk of vascular disease JAMA 2009 302 18 1993 2000 19903920 PMC3284229
- [2]Baigent C Keech A Kearney PM Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins Lancet 2005 366 9493 1267 1278 16214597
- [3]Stone NJ Robinson JG Lichtenstein AH 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines Circulation 2014 129 25 Suppl 2 S1 45 24222016
- [4]Gidding SS Ann Champagne M de Ferranti SD The Agenda for Familial Hypercholesterolemia: A Scientific Statement From the American Heart Association Circulation 2015 12 1 132 22 2167 92 26510694
- [5]Fouchier SW Defesche JC Umans-Eckenhausen MW Kastelein JP The molecular basis of familial hypercholesterolemia in The Netherlands Hum Genet 2001 109 6 602 15 11810272
- [6]Graham CA McIlhatton BP Kirk CW Genetic screening protocol for familial hypercholesterolemia which includes splicing defects gives an improved mutation detection rate Atherosclerosis 2005 10 182 2 331 40 16159606
- [7]Humphries SE Whittall RA Hubbart CS Genetic causes of familial hypercholesterolaemia in patients in the UK: relation to plasma lipid levels and coronary heart disease risk J Med Genet 2006 43 943 49 17142622 PMC2563208
- [8]Lombardi MP Redeker EJ van Gent DH Molecular genetic testing for familial hypercholesterolemia in the Netherlands: a stepwise screening strategy enhances the mutation detection rate Genet Test 2006 Summer 10 2 77 84 16792510
- [9]Civeira F Ros E Jarauta E Comparison of genetic versus clinical diagnosis in familial hypercholesterolemia Am J Cardiol 2008 102 9 1187 93 18940289
- [10]Taylor A Wang D Patel K Mutation detection rate and spectrum in familial hypercholesterolaemia patients in the UK pilot cascade project Clin Genet 2010 77 6 572 80 20236128
- [11]Medeiros AM Alves AC Francisco V Bourbon M investigators of the Portuguese FH Study Update of the Portuguese Familial Hypercholesterolaemia Study Atherosclerosis 2010 212 2 553 8 20828696
- [12]Chmara M Wasag B Zuk M Molecular characterization of Polish patients with familial hypercholesterolemia: novel and recurrent LDLR mutations J Appl Genet 2010 51 1 95 106 20145306
- [13]Marduel M Carrié A Sassolas A Molecular spectrum of autosomal dominant hypercholesterolemia in France Hum Mutat 2010 31 11 E1811 24 20809525 PMC3152176
- [14]van der Graaf A Avis HJ Molecular basis of autosomal dominant hypercholesterolemia: assessment in a large cohort of hypercholesterolemic children Circulation 2011 123 11 1167 73 21382890
- [15]Ahmad Z Adams-Huet B Chen C Garg A Low prevalence of mutations in known loci for autosomal dominant hypercholesterolemia in a multiethnic patient cohort Circ Cardiovasc Genet 2012 5 6 666 75 23064986 PMC3774009
- [16]Klančar G Grošelj U Kovač J Universal Screening for Familial Hypercholesterolemia in Children J Am Coll Cardiol 2015 66 11 1250 7 26361156
- [18]Nordestgaard BG Chapman MJ Humphries SE Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society Eur Heart J 2013 34 3478 90a 23956253 PMC3844152
- [19]Atherosclerosis Thrombosis, and Vascular Biology Italian Study Group No evidence of association between prothrombotic gene polymorphisms and the development of acute myocardial infarction at a young age Circulation 2003 107 1117 22 12615788
- [20]Do R Stitziel NO Won H-H Exome sequencing identifies multiple rare alleles at LDLR and APOA5 that confer risk for myocardial infarction Nature 2015 519 102 106 25487149 PMC4319990
- [21]Taylor HA Jr The Jackson Heart Study: an overview Ethnicity & disease 2005 15 S6 1-3 16317981
- [23]McPherson R Pertsemlidis A Kavaslar N A common allele on chromosome 9 associated with coronary heart disease Science 2007 316 1488 91 17478681 PMC2711874
- [24]Clarke R Peden JF Hopewell JC Genetic variants associated with Lp(a) lipoprotein level and coronary disease N Engl J Med 2009 361 2518 28 20032323
- [25]Saleheen D Zaidi M Rasheed A The Pakistan Risk of Myocardial Infarction Study: a resource for the study of genetic, lifestyle and other determinants of myocardial infarction in South Asia European journal of epidemiology 2009 24 329 38 19404752 PMC2697028
- [26]Baigent C Keech A Kearney PM Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins Lancet 2005 366 9493 1267 1278 16214597
- [28]Benn M Watts GF Tybjærg-Hansen A Nordestgaard BG Mutations causative of familial hypercholesterolaemia: screening of 98 098 individuals from the Copenhagen General Population Study estimated a prevalence of 1 in 217 Eur Heart J 2016 2 22 Epub ahead of print 26908947
- [29]Psaty BM O’Donnell CJ Gudnason V Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from five cohorts Circ Cardiovasc Genet 2 73 80 2009 20031568 PMC2875693
- [30]Purcell SM Moran JL Fromer M A polygenic burden of rare disruptive mutations in schizophrenia Nature 2014 506 185 90 24463508 PMC4136494
- [31]Landrum MJ Lee JM Riley GR Jang W Rubinstein WS Church DM Maglott DR ClinVar: public archive of relationships among sequence variation and human phenotype Nucleic Acids Res 2014 42 D980 5 24234437 PMC3965032
- [32]Borén J Ekström U Agren B Nilsson-Ehle P Innerarity TL The molecular mechanism for the genetic disorder familial defective apolipoprotein B100 J Biol Chem 2001 276 9214 9218 11115503
- [33]McLaren W Pritchard B Rios D Chen Y Flicek P Cunningham F Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor Bioinformatics 2010 26 2069 70 20562413 PMC2916720
- [35]Liu X Jian X Boerwinkle E dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations Hum Mutat 2013 9 34 9 E2393 402 23843252 PMC4109890
- [38]Talmud PJ Shah S Whittall R Use of low-density lipoprotein cholesterol gene score to distinguish patients with polygenic and monogenic familial hypercholesterolaemia: a case-control study Lancet 2013 381 1293 301 23433573
- [40]Alonso R Andres E Mata N Lipoprotein(a) levels in familial hypercholesterolemia: an important predictor of cardiovascular disease independent of the type of LDL receptor mutation J Am Coll Cardiol 63 19 1982 9 24632281
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- Authors
- Khera, A. V., Won, H.-H., Peloso, G. M., Lawson, K. S., Bartz, T. M., Deng, X., van Leeuwen, E. M., Natarajan, P., Emdin, C. A., Bick, A. G., Morrison, A. C., Brody, J. A., Gupta, N., Nomura, A., Kessler, T., Duga, S., Bis, J. C., van Duijn, C. M., Cupples, L. A., Psaty, B., Rader, D. J., Danesh, J., Schunkert, H., McPherson, R., Farrall, M., Watkins, H., Lander, E., Wilson, J. G., Correa, A., Boerwinkle, E., Merlini, P. A., Ardissino, D., Saleheen, D., Gabriel, S., & Kathiresan, S.
- Affiliations
- Broad Institute (US)
- Samsung Medical Center (KR)
- The University of Texas Health Science Center at Houston (US)
- University of Washington (US)
- Boston University (US)
- Kanazawa University (JP)
- German Centre for Cardiovascular Research (DE)
- Humanitas University (IT)
- University of Pennsylvania (US)
- University of Cambridge (GB)
- Ottawa Heart Institute (CA)
- Centre for Human Genetics (GB)
- University of Mississippi Medical Center (US)
- Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda (IT)
- University of Parma (IT)
- Corresponding author
- Kathiresan, Sekar
- ORCID
- Khera, Amit V.
- Won, Hong-Hee
- Peloso, Gina M.
- Bartz, Traci M.
- Deng, Xuan
- Natarajan, Pradeep
- Emdin, Connor A.
- Bick, Alexander G.
- Morrison, Alanna C.
- Brody, Jennifer A.
- Gupta, Namrata
- Nomura, Akihiro
- Kessler, Thorsten
- Duga, Stefano
- Bis, Joshua C.
- Cupples, L. Adrienne
- Psaty, Bruce
- Rader, Daniel J.
- Danesh, John
- Schunkert, Heribert
- McPherson, Ruth
- Farrall, Martin
- Watkins, Hugh
- Lander, Eric
- Wilson, James G.
- Correa, Adolfo
- Boerwinkle, Eric
- Ardissino, Diego
- Saleheen, Danish
- Gabriel, Stacey
- Kathiresan, Sekar
- Venue
- Journal of the American College of Cardiology
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 67
- Issue
- 22
- Pages
- 2578-2589
- ISSN
- 0735-1097 (ISSN-L)
- PMC
- PMC5405769
- OpenAlex
- W2410774145
- Cited by
- 942
- References
- 7
- Citations by year
- 2026: 58 · 2025: 63 · 2024: 84 · 2023: 107 · 2022: 106 · 2021: 104 · 2020: 96 · 2019: 97 · 2018: 98 · 2017: 87 · 2016: 40 · 2015: 1 · 2012: 1
- FWCI
- 118.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of the American College of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Funders
- Wellcome Trust
- National Institute for Health and Care Research (NF-SI-0513-10087)
- British Heart Foundation (RG/13/13/30194)
- National Institutes of Health (n01hc085082-026, n01hc085081-021, n01hc085079-024, 1r01hl120393-01a1, n01hc085083-027, n01hc085080-026, n01hc085086-022, n01hc25195-41-0-1, 1rc2hl103010-01, n01hc55222-0-0-0, n02hl64278-6-0-0)
- Medical Research Council (MR/L003120/1)
- National Institute on Aging (R01AG023629)
- National Heart, Lung, and Blood Institute (HHSN268201 100012C, HHSN268201300050C, HHSN268201100007I, N01 HC085086, R01HL127564, RC2HL102925, HHSN268201100007C, HHSN268201300046C, HHSN2682 01100008C, HH-SN-268201100006C, HHSN268201100011C, HHSN2682011-00005I, N01 HC085079, RC2HL102926, R01-HL120393, N01 HC085083, RC2HL102923, HHSN2682011-00009I, N01 HC085080, R01HL087652, HHSN268201100005C, R01HL103612, HHSN 268200800007C, N02 HL064278, HHSN268201300048C, HHSN268201100005G, HHSN268201100010C, RC2HL-103010, HHSN268201100011I, HHSN268201300049C, N01-HC-025195, UC2HL103010, K01HL125751, HHSN268201200036C, HHSN26820 1100009C, N01HC085082, R01HL105756, U01HL080295, N01 HC055222, HHSN2682011-00008I, RC2HL102924, N01 HC085081, HHSN268201300047C, RC2HL102419)
- National Human Genome Research Institute (U54HG003067, U54HG003273)
- Topics
- Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipid metabolism and disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- familial hypercholesterolemia, severe hypercholesterolemia, LDL cholesterol, coronary artery disease risk, LDLR mutations, APOB mutations, PCSK9 mutations, loss-of-function mutations, mutation prevalence, genetic testing, diagnostic yield
- MeSH terms
- Proprotein Convertase 9, Coronary Artery Disease, Female, Heterozygote, Humans, Hypercholesterolemia, Hyperlipoproteinemia Type II, Cholesterol, LDL, Male, Middle Aged, Receptors, LDL, Genetic Variation, Cohort Studies, Case-Control Studies, Sequence Analysis, Apolipoprotein B-100
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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