Mutations causative of familial hypercholesterolaemia: screening of 98 098 individuals from the Copenhagen General Population Study estimated a prevalence of 1 in 217
Authors: Gerald F Watts
Anne Tybjærg-Hansen
Børge G Nordestgaard
Marianne Benn
Journal: European Heart Journal (2016)
Discipline: CLINICAL MEDICINE
Keywords: APOB mutation; Coronary artery disease; Familial hypercholesterolaemia; General population; Ischaemic heart disease
Affiliations: Department of Clinical Biochemistry, Gentofte Hospital, Copenhagen University Hospital, Kildegårdsvej 28, DK-2900 Gentofte, Denmark The Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Denmark Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark ., School of Medicine and Pharmacology, Lipid Disorders Clinic, Cardiovascular Medicine, Royal Perth Hospital, University of Western Australia, Crawley, Australia., The Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Denmark Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark., The Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Denmark Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Denmark.

Abstract:  Aims: Ideally, familial hypercholesterolaemia (FH) is diagnosed by testing for mutations that decrease the catabolism of low-density lipoprotein (LDL) cholesterol; however, genetic testing is not universally available. The aim of the present study was to assess the frequency and predictors of FH causing mutations in 98 098 participants from the general population, the Copenhagen General Population Study. Methods and results: We genotyped for LDLR[W23X;W66G;W556S] and APOB[R3500Q] accounting for 38.7% of pathogenic FH mutations in Copenhagen. Clinical FH assessment excluded mutation information. The prevalence of the four FH mutations was 0.18% (1:565), suggesting a total prevalence of FH mutations of 0.46% (1:217). Using the Dutch Lipid Clinic Network (DLCN) criteria, odds ratios for an FH mutation were 439 (95% CI: 170-1 138) for definite FH, 90 (53-152) for probable FH, and 18 (13-25) for possible FH vs. unlikely FH. Using the Simon Broome criteria, the odds ratio was 27 (20-36) for possible vs. unlikely FH, and using the Make Early Diagnosis to Prevent Early Death (MEDPED) criteria, 40 (28-58) for probable vs. unlikely FH. Odds ratios for an FH mutation were 17 (9-31) for LDL-cholesterol of 4-4.9 mmol/L, 69 (37-126) for LDL-cholesterol of 5-5.9 mmol/L, 132 (66-263) for LDL-cholesterol of 6-6.9 mmol/L, 264 (109-637) for LDL-cholesterol of 7-7.9 mmol/L, and 320 (129-798) for LDL-cholesterol above 7.9 mmol/L vs. LDL-cholesterol below 4 mmol/L. The most optimal threshold for LDL-cholesterol concentration to discriminate between mutation carriers and non-carriers was 4.4 mmol/L. Conclusion: Familial hypercholesterolaemia-causing mutations are estimated to occur in 1:217 in the general population and are best identified by a definite or probable phenotypic diagnosis of FH based on the DLCN criteria or an LDL-cholesterol above 4.4 mmol/L. 

Retracted: No

PubPeer comments:
 In Figure 3, Page 1390, is 1.7 mmol L the correct number or is it under 1 mmol L? ![file] , 
 Total FH prevalence is here estimated to be 1 217. Its components however are presented separately, and they add up to 1 91. From the Results: u201c u2026 total prevalence of known FH-causing mutations of 0.46% (1:217); considered separately by gene, the corresponding numbers were 0.25% (1:395) for LDLR mutations and 0.85% (1:118) for APOB mutations.u201d 1 118 APOB plus 1 395 LDLR equals 1 91 FH (LDLR+APOB), not 1 217 FH (LDLR+APOB). From the perspective of FH carriers per million people: ![file] , 
 Regarding comment #2 above, one reason why the components do not add up to the total is due to the asymmetry between the proportions found in the spectrum and those found in the survey. The report derives the percentage u2013 38.7% u2013 by using the top four mutations in the spectrum as a numerator and the total spectrum as a denominator. It then carries this 38.7% over to the survey in order to account for the untargeted LDLR and include them in the total. ![file] However, if the report uses the percentage derived from the spectrum to gauge and account for the untargeted LDLR in the 2016 survey, then the two sets need to be roughly symmetrical. Yet looking at the percentages of APOB to top 3 LDLR in both tables, it is clear that they are not merely asymmetrical, but the predominance of one over the other is actually reversed: in the spectrum the count for the three LDLR mutations predominates over the APOB count, whereas it is the APOB count that predominates over the same three LDLR in the survey. ![file] The asymmetry precludes the mathematical method. This is one of the reasons why the components to FH prevalence do not add up to the total FH prevalence in the results. , 
 Regarding comment #2: The report estimates LDLR prevalence u201cseparatelyu201d from APOB prevalence. There is a portion of the LDLR that were *not* targeted in the 2016 survey. Given the numbers in the spectrum (left) how does one account for and include the *untargeted* LDLR in the surveyu2019s LDLR total (right) *and also arrive at the authorsu2019 LDLR prevalence of 1 in 395?* (Note that there are multiple population sizes printed in the report. I take 98,098 from the title. This particular issue involves mathematical errors and is best left to a separate, more detailed comment.) ![file] In comment #3, when deriving prevalence for both APOB and LDLR combined, we employed the equation, 55 142. However, now we are trying derive the *untargeted* LDLR in the survey u201cseparatelyu201d from the APOB, so we must not include the APOB in either the denominator or the numerator when we derive the percentage from the spectrum table. Rather, we subtract the 19 APOB in the spectrum table from both the numerator and the denominator, and then follow through with the procedure: 55 u2013 19 = 36 ttfor the LDLR numerator in the spectrum. 142 u2013 19 = 123 ttfor the LDLR denominator in the spectrum. 36 123 = 0.2927 tto derive the percentage from the spectrum. 63 .2927 = 215 tto include untargeted LDLR in LDLR survey total. 98,098 215 = 456tu2026 arriving at a prevalence of 1 in 456 LDLR. , 
 With the authorsu2019 APOB prevalence estimate in this 2016 Copenhagen study,[1] there are two points that deserve either red flags or further explanation. First, an APOB prevalence of 1 in 118 is about 4 to 8 times higher than established estimates. 1 in 1,000 is common.[2] The Merck Manual has APOB (familial defective apo B-100) prevalence at 1 in 700,[3] and we see it between 1 500 to 1 700 in Arup Laboratoriesu2019 test directory.[4] Here are two estimates put forward, the 1 118 in this 2016 Copenhagen study and 1 1,000 in an earlier Copenhagen study[5] by some of the same authors. ![file] Second, the reason the 38.7% was applied to the authorsu2019 2016 Copenhagen results was that not all LDLR mutations were targeted in genetic screening, only three of them. Using the percentage, the authors mathematically derived a quantity for the *LDLR* mutations that were *not* screened for. (See illustration below.) Crucially, a fourth mutation was targeted in this 2016 Copenhagen study. It was not LDLR, but APOB. And there is only one APOB relevant to the Danish. Here are some of the same authors again in 1998 presenting results from the Copenhagen City Heart Study. *u201cIn conclusion, our results suggest that the Arg3500Gln mutation is at present the only known APOB mutation worth screening for in white patients with hypercholesterolemia or ischemic heart disease and their relatives.u201d ~ TYBJu00c6RG-HANSEN, Bu00d8RGE G. NORDESTGAARD et al.[5] Note that u201cArg3500glnu201d is an alias for the u201cR3500Qu201d in Supplementary Table 2 and that this 2016 Copenhagen study is restricted to a u201cWhite Danish population of Danish descent.u201d The 1998 Copenhagen study screened 9,255, 99% of which were whites of Danish descent. They found 7 R3500Q Arg3500gln. They dismissed R3500W Arg3500trp: *u201cu2026 Arg3500trp is probably very rare and also not associated with hypercholesterolemia.u201d*[5] (R3500W is found mostly in Asia and among those of Asian descent.[6]) Here are three of the authors in 2013: u201cA single mutation, Arg3500Gln, is the only common FH-related mutation in APOB, while 20 different mutations have been detected in PCSK9.u201d Bu00f8rge G. Nordestgaard, Gerald F. Watts, Anne Tybju00e6rg-Hansen, et al.[7]* So when we look at the 111 APOB mutations detected in the 2016 Copenhagen study we have the complete APOB count already. No derivation is necessary. There is no u201cremainderu201d to account for, so we donu2019t employ a percentage in a preliminary step; we employ simple division in a single, final step. If the population is 98,098, we divide by the 111 APOB detected and arrive at an APOB prevalence of 1 in 884. And this estimate is consistent with prior prevalence estimates. It is however at odds with this 2016 studyu2019s conclusion of 1 in 118. To emphasize, when it comes to whites of Danish descent, because there are no other u201cAPOB mutation[s] worth screening for,u201d there is no need to employ the mutation spectrum presented in Supplementary Table 2. ![file] For example, if I left home for a picnic with 30% of an apple pie and *all* of a strawberry pie, I might use that 30% measurement to derive how much of the *apple pie* I left on the counter at home. But the strawberry pie on the picnic blanket is not partial, but whole. Iu2019ve left no strawberry pie at home. No percentage should be employed to derive a u201cremainingu201d portion. But if for some reason I first said that the pies -- *considered together* -- are *incomplete* and if I then carry over this mathematical procedure to derive a remainder of the *strawberry pie,* then this dislocated math conjures up a remainder, not of strawberry pie on the counter at home, but of irrelevant numbers on a piece of paper. [1] Mutations causative of familial hypercholesterolaemia: screening of 98 098individuals from the Copenhagen General Population Study estimated a prevalence of 1 in 217; 2016; Marianne Benn, Gerald F. Watts, Anne Tybju00e6rg-Hansen, and Bu00f8rge G. Nordestgaard; European Heart Journal (2016) 37, 1384u20131394doi:10.1093 eurheartj ehw028 [2] Monogenic hypercholesterolemia: new insights in pathogenesis and treatment; 2003; Daniel J. Rader, Jonathan Cohen, and Helen H. Hobbs; J. Clin. Invest.111:1795u20131803 (2003). doi:10.1172 JCI200318925 [3] https: www.merckmanuals.com professional endocrine-and-metabolic-disorders lipid-disorders dyslipidemia [4] https: ltd.aruplab.com api ltd pdf 139 [5] ASSOCIATION OF MUTATIONS IN THE APOLIPOPROTEIN B GENE WITH HYPERCHOLESTEROLEMIA AND THE RISK OF ISCHEMIC HEART DISEASE; 1998; TYBJu00c6RG-HANSEN, Bu00d8RGE G. NORDESTGAARD et al.; N Engl J Med 1998;338:1577-84 [6] https: pubmed.ncbi.nlm.nih.gov 27919345 , https: pubmed.ncbi.nlm.nih.gov 17964958 , https: ltd.aruplab.com api ltd pdf 139 [7] Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease; 2013; Bu00f8rge G. Nordestgaard, Gerald F. Watts, Anne Tybju00e6rg-Hansen, et al. doi:10.1093 eurheartj eht273![file] , 
 **How many participants were in this 2016 study?** Here is a screenshot of Table 5 from the Supplementary material. A reader expects the final column u201cAllu201d to represent *all* of its constituents. ![file] The following issue seems insignificant, until one tries to understand it. Although the title of the report says that there were 98,098 participants, the actual constituents shown in the Dutch Lipid Clinic Network (DLCN) and Simone Broome (SB) criteria in the main document were *not* rounded and they each totaled exactly 98,000 u2013 so the number, 98,000, is not a product of rounding. Here is a screenshot. ![file] Nonetheless, it seems that the authors used 98,098 for the results. The first of the [two different APOB prevalence results] makes this clear: 98,098 u00f7 111 APOB = 883.77, which rounds to the authorsu2019 published result, 884. Whereas using 98,000 comes out to 882.88. The use of 98,098 is also illustrated with the calculation of prevalence for the top three LDLR mutations: 1557 is derived from 98,098 while 1556 is derived from 98,000. The authors use 1557 in the paper. ![file] Of course, one can imagine reasons for the slight mismatch between 98,098 and 98,000, and possibly these could be coupled with disclosure missteps. However, when one tries to track those reasons down, the additional information hurts more than it helps. To reconcile the different quantities claimed, we return to the main documentu2019s Table 1. The Make Early Diagnosis to Prevent Early Death (MEDPED) listing seems to be an exception to the 98,000 to 98,098 range, coming in at 93,398. However, when we consult Supplementary Table 5, although we see the same numbers in the final column totals, 98,000 DLCN, 98,000 SB, and 93,398 MEDPED, the *constituents* of the MEDPED total do not add up to 93,398; *they add up to 98,000 again.* At a minimum, this is a striking coincidence. ![file] For MEDPED, why would a reader have more confidence in 93,398 than in the otherwise usual 98,000? Consulting the u201cMethodsu201d leaves the reader with more questions than answers. ![file] 99,372 minus 274, equals 99,098 and not 98,098. The number of participants here is at odds with the title by 1,000. -tM. Benn, G. F. Watts, A. Tybjaerg-Hansen and B. G. Nordestgaard,  Corrigendum: Familial Hypercholesterolemia in the Danish General Population: Prevalence, Coronary Artery Disease, and Cholesterol-Lowering Medication, vol. 97, no. 11, 2014. - tM. Benn, G. F. Watts, A. Tybjaerg-Hansen and B. G. Nordestgaard,  Familial Hypercholesterolemia in the Danish General Population: Prevalence, Coronary Artery Disease, and Cholesterol-Lowering Medication, vol. 97, no. 11, 2012. ,