Prevalence of Familial Hypercholesterolemia Among the General Population and Patients With Atherosclerotic Cardiovascular Disease: A Systematic Review and Meta-Analysis
Abstract
Background:
Contemporary studies suggest that familial hypercholesterolemia (FH) is more frequent than previously reported and increasingly recognized as affecting individuals of all ethnicities and across many regions of the world. Precise estimation of its global prevalence and prevalence across World Health Organization regions is needed to inform policies aiming at early detection and atherosclerotic cardiovascular disease (ASCVD) prevention. The present study aims to provide a comprehensive assessment and more reliable estimation of the prevalence of FH than hitherto possible in the general population (GP) and among patients with ASCVD.
Methods:
We performed a systematic review and meta-analysis including studies reporting on the prevalence of heterozygous FH in the GP or among those with ASCVD. Studies reporting gene founder effects and focused on homozygous FH were excluded. The search was conducted through Medline, Embase, Cochrane, and Global Health, without time or language restrictions. A random-effects model was applied to estimate the overall pooled prevalence of FH in the general and ASCVD populations separately and by World Health Organization regions.
Results:
From 3225 articles, 42 studies from the GP and 20 from populations with ASCVD were eligible, reporting on 7 297 363 individuals/24 636 cases of FH and 48 158 patients/2827 cases of FH, respectively. More than 60% of the studies were from Europe. Use of the Dutch Lipid Clinic Network criteria was the commonest diagnostic method. Within the GP, the overall pooled prevalence of FH was 1:311 (95% CI, 1:250–1:397; similar between children [1:364] and adults [1:303], P=0.60; across World Health Organization regions where data were available, P=0.29; and between population-based and electronic health records–based studies, P=0.82). Studies with ≤10 000 participants reported a higher prevalence (1:200–289) compared with larger cohorts (1:365–407; P<0.001). The pooled prevalence among those with ASCVD was 18-fold higher than in the GP (1:17 [95% CI, 1:12–1:24]), driven mainly by coronary artery disease (1:16; [95% CI, 1:12–1:23]). Between-study heterogeneity was large (I2>95%). Tests assessing bias were nonsignificant (P>0.3).
Conclusions:
With an overall prevalence of 1:311, FH is among the commonest genetic disorders in the GP, similarly present across different regions of the world, and is more frequent among those with ASCVD. The present results support the advocacy for the institution of public health policies, including screening programs, to identify FH early and to prevent its global burden.
Clinical Perspective
What Is New?
•
This study provides a comprehensive assessment of the prevalence of familial hypercholesterolemia (FH) that is based on >7.3 million individuals from 62 studies, revealing an overall prevalence of 1:311 in the general population (without significant differences across the world regions where data are available), with data suggesting that ≈1% of those potentially affected are detected.
•
Different diagnostic criteria, applied to similar populations, provided variations in prevalence, and estimates from strategies to detect FH using traditional cohort versus electronic health records approaches did not differ.
•
Among individuals with atherosclerotic cardiovascular disease, the prevalence of FH was 18-fold higher (1:17) than among the general population.
What Are the Clinical Implications?
•
FH is frequent among general populations across different world regions, making a strong case for health policies that include screening programs for FH, tailoring strategies/diagnostic criteria to local populations/resources, to facilitate early identification and treatment strategies.
•
The high prevalence among individuals with atherosclerotic cardiovascular disease offers an opportunity for cascade testing from index cases with atherosclerotic cardiovascular disease and reinforces the need to consider FH as a cause of premature ASCVD.
•
As health data become digitized, identifying potential cases of FH through electronic health records may be a feasible and pragmatic option to screen large populations while being less resource intensive and potentially more cost-effective than other approaches.
Introduction
Editorial, see p 1760
Familial hypercholesterolemia (FH) is an autosomal dominantly inherited disorder that results in elevations in low-density lipoprotein cholesterol (LDL-C) from birth.1 Because LDL-C is both a causal and a cumulative risk factor for atherosclerotic cardiovascular disease (ASCVD),2 individuals with FH have a higher risk of premature ASCVD.3 Early reports suggested a prevalence of FH of ≈1:500 individuals in the general population (GP).4,5 However, these estimates were frequently derived from a limited number of data or from selected populations (eg, imputed from the known prevalence of homozygous FH with the Hardy-Weinberg equation).1,4,5 More recently, generally larger studies, using more systematic approaches, suggest that FH may be twice as common or higher.1,6 However, prevalence estimates still vary between studies.6 The prevalence of FH among individuals with ASCVD is likely to be much higher,7–9 and because this is a genetic condition, identifying FH index cases among those with ASCVD, in particular when it is premature, offers the opportunity for family cascade testing via the index case.
The adverse cardiovascular consequences of FH, although common, are preventable through both early detection of affected individuals and the initiation of therapies that significantly lower LDL-C.1,10–12 However, although FH is increasingly recognized as affecting individuals of all ethnicities, the proportion of individuals being identified globally is estimated to be <10%.1,13 Reliable quantification of the true prevalence of FH and its geographic distribution may help inform regional policies aimed at early detection and prevention of ASCVD. Recently, a considerable number of reports have been published from different regions of the world, allowing a reappraisal of evidence, with many studies now reporting the prevalence of FH among those with ASCVD. We conducted an extensive systematic review and meta-analysis to provide a comprehensive assessment and more reliable estimation than hitherto possible of the prevalence of FH in the GP and among those with ASCVD. In addition, the FH prevalence is estimated stratified by those World Health Organization (WHO) geographic regions with available data, age, diagnostic criteria, and data source, among other important variables.
Methods
Search Strategy and Selection Criteria
A PECOS (population, exposure, comparison, outcomes, study design) model was used to shape the clinical question and to build the search strategy (Table I in the Data Supplement). Articles were eligible if they reported the prevalence of FH either in the GP or in individuals with ASCVD and met the following inclusion criteria: (1) The study cohort was a representative GP (defined as a population not selected on the basis of the presence or absence of cardiovascular disease) or a cohort representing established cardiovascular disease (including coronary artery disease [CAD], cerebrovascular disease, and peripheral artery disease). (2) Criteria for FH diagnosis were clearly defined, as based on a genetic test or accepted clinical criteria (eg, Dutch Lipid Clinic Network [DLCN],1,14 Make Early Diagnosis to Prevent Early Deaths [MEDPED],14 Simon-Broome diagnostic criteria,14 Japanese Atherosclerosis Society guidelines criteria,15 Canadian FH criteria,16 or modifications thereof [provided that modified definitions were reported]) or using total cholesterol or LDL-C cutoff points (frequently with additional clinical criteria such as personal or family history). (3) The study reported on the sample size, number of individuals with FH, FH prevalence, or any 2 of them. Exclusion criteria included: (1) populations with founder effects (ie, population groups with reduced genetic diversity resulting from the expansion of a small number of ancestors; when some of these ancestors carry an FH mutation, it ultimately results in higher proportions of affected subjects [sharing the specific mutation/s introduced by the founders] than those expected for the GP),4 (2) those focused solely on homozygous FH, or (3) studies with unclear methodologies to obtain the estimates of FH frequency. Only full research articles were included (conference abstracts were not included because they provide limited information on methodologies and estimates and limited information to assess the study quality).
The electronic databases Medline, Embase, Cochrane, and Global Health were searched for peer-reviewed literature using key words and MeSH terms related to FH and prevalence (Table II in the Data Supplement). Databases were searched from their inception to November 2018. In addition, the references from the selected articles and from selected reviews and previous systematic reviews were screened for additional relevant articles. The search, screening, and data extraction were performed independently by 2 reviewers (P.H. and K.I.D.); any disagreements were resolved through discussion with a third investigator (A.J.V.-V.). The data used to generate the results from the present systematic review and meta-analysis come from publications detailed in Tables 1 and 2 and Table III in the Data Supplement and are accessible from the electronic databases described above. Because the present work relied on already published, aggregated data, institutional review board approval was deemed not required. The methodology, including statistical methods used, and the study data are made available to other researchers in the present publication for purposes of reproducing the results or replicating the procedure.
Figure 1 shows the search and screening strategy. A total of 3225 articles were retrieved from the search after duplicates from the different databases were discarded. These articles were first screened by title and abstract, leading to 150 articles that were then subjected to full-text review. Finally, 62 studies were eligible for inclusion in the systematic review, of which 42 and 20 corresponded to the general and ASCVD populations, respectively (Tables 1 and 2 and Table III in the Data Supplement).3,7,8,17–75 When different articles were available from the same studies, we included all of them in the systematic review. However, in the meta-analysis, if the same cohort was used in different articles from the same study, then only 1 article/estimate of FH prevalence was included in each set of analyses (to avoid entering the same participants ≥2 times in the same set of analyses). In such cases, the selection of the article to be included was decided on an individual basis by consensus, considering factors such as sample size, representativeness, data provided, or quality of the articles, among others. If a study provides different FH prevalence estimates based on different diagnostic criteria, the estimate to be included for the overall pool meta-analysis was selected according to the following sequence of preference: first genetic testing (as the “gold standard” for FH diagnosis),1 next DLCN criteria (as the most widely used worldwide),13 next either Simon-Broome/MEDPED/Japanese Atherosclerosis Society guidelines criteria/Canadian FH criteria, and finally LDL-C cutoffs. For subgroup analyses based on different diagnosis criteria, each estimate was entered into the corresponding subgroup. Finally, for the purpose of the meta-analysis, when the diagnosis was classified on the basis of the likelihood of having FH (eg, DLCN criteria), only data that met the criteria for definite or probable FH diagnosis were included in the meta-analysis. Quality of studies (risk of bias) was assessed independently by 2 reviewers (P.H. and K.I.D.) using an adapted Assessment Tool for Prevalence Studies (Table IV in the Data Supplement),76 considering different items such as representativeness and selection of the study population, data collection, appropriateness of the case definition used and of the measurement of the parameter of interest (prevalence of FH), and data provided.
Data Analysis
Characteristics of studies, diagnostic criteria, and estimates of the FH prevalence are presented throughout stratified by general and ASCVD populations, and by WHO regions77 (Africa, America, Southeast Asia, Europe, Eastern Mediterranean, and Western Pacific). Meta-analyses were conducted separately for the GP and ASCVD population. A random-effects model (DerSimonian and Laird method) was applied to estimate the pooled prevalence across the studies. The 95% CIs for the prevalence reported in the individual studies (Tables 1 and 2) were estimated from the proportion of cases of FH and sample size using the binomial exact method (Clopper-Pearson method). The Freeman-Tukey double arcsine transformation was applied to the primary study data before meta-analysis. Further details are given in Methods in the Data Supplement. Between-study heterogeneity was assessed with the I2 statistic. Publication bias was examined formally with the Egger weighted regression and visually with funnel plots. When data allowed, additional subgroup analyses were conducted: children (<18 years of age) versus adults; FH diagnostic criteria; geographic location (WHO regions and by country with available data); type of ASCVD; data source; period of enrollment of patients in the study (contemporary versus older studies, arbitrarily set as before 2000 or thereafter); size of the study population (groups arbitrarily defined as ≤1000, 1000–10 000, 10 001–100 000, >100 000 participants); and background statin therapy, based on the proportion of participants taking statins (<20%/≥20% among the GP, <50%/≥50% in ASCVD cohorts). Analyses were performed with R (version 3.5.1). Statistical significance was set at P<0.05.
Results
A total of 62 studies (42 from the GP [Table 1 and Table IIIA in the Data Supplement]17–56 and 20 from ASCVD cohorts [Table 2 and Table IIIB in the Data Supplement]3,7,8,57-75), comprising >7.3 million different individuals, were included. Characteristics of the studies, stratified by WHO regions, are shown in Tables 1 and 2. In both the GP and ASCVD groups, most participants, and thus most cases of FH identified, are from studies published within the last 5 years (Figure IA and IB in the Data Supplement) as a result of a surge in publications from 2015 onward (Figure IC and ID in the Data Supplement). Only 8 articles from the GP and 1 article for ASCVD groups meeting the inclusion/exclusion criteria were published before 2015. However, the period of enrollment of participants, including studies published in 2015 or later, frequently dated back to 1990s or early 2000s, particularly for GP studies (Tables 1 and 2 and Figure IIA through IID in the Data Supplement). More than 60% of studies and >80% of cases of FH reported are from the region of Europe; there was no representation from the Southeast Asia and Africa regions. Reported FH prevalence in the individual studies was heterogeneous and varied among the different studies (and, when data were provided, within the same studies) on the basis of the diagnostic criteria used. DLCN criteria were the most widely used in both population groups. The FH prevalence reported for the GP ranged from 1:200 to 1:575 in the majority of studies and for any of the WHO regions with data available (Table 1). Among ASCVD populations, the prevalence of FH was higher than 1:80 in all individual studies, ranging from 1:10 to 1:50 in the majority of them (Table 2). ASCVD populations consisted mainly of patients with CAD. Only 1 study reported the prevalence in patients with stroke (1:75). Data from patients with peripheral artery disease were restricted to a study subgroup limited to 46 patients (2 cases of FH; Table 2). Quality assessment of articles included showed an overall low risk of bias for most studies and most items (Table IV in the Data Supplement).
Meta-Analysis
A total of 42 GP studies comprising 7 297 363 individuals (24 636 cases of FH) were included in the meta-analysis, resulting in a pooled FH prevalence of 1:311 (95% CI, 1:250–1:397; 0.32% [95% CI, 0.25–0.40]; Figure 2A). Although the pooled prevalence tended to be lower in children than adults (1:364 [0.28%] versus 1:303 [0.33%], respectively), the difference was not statistically significant (P=0.60; Figure 3 and Figure III in the Data Supplement). The ASCVD meta-analysis included 20 studies totaling 48 158 patients with established ASCVD (2827 cases of FH), leading to a pooled FH prevalence of 1:17 (95% CI, 1:12–1:24; 5.95% [95% CI, 4.15–8.04]; Figure 2B). In analyses stratified by type of ASCVD, the pooled prevalence of FH among patients with CAD was 1:16 (95% CI, 1:12–1:23; 6.33% [95% CI, 4.43–8.53]); the prevalence was similar when analyses were restricted to patients with either stable CAD, with any acute coronary syndrome, or in the subgroup with myocardial infarction (Figure 2C and Figure IV in the Data Supplement). Only 1 study reported specifically on patients with ischemic stroke, in which the prevalence was 1:75 (95% CI, 1:45–1:137; 1.33% [95% CI, 0.73–2.22]).
The FH prevalence in the GP was 1:232 (0.43%) in the only study from the Arabian Gulf region and ranged from 1:284 (0.35%) to 1:331 (0.30%) across the Americas, Europe, and Western Pacific (P=0.29 for comparison across regions; Figure 3 and Figure V in the Data Supplement). Analyses stratified by country showed wide variations in the estimated prevalence from one country to another (Figure 4 and Figure VI in the Data Supplement) and within each country, depending on the diagnostic criteria applied (Table V and Figure VII in the Data Supplement). Among ASCVD studies, the only study from the Arabian Gulf region reported a lower prevalence (1:27 [3.69%]) compared with Europe and Western Pacific regions (1:15–1:17 [5.81%–6.63%]; P=0.03 for comparison across regions; Figure 5 and Figure VIII in the Data Supplement).
Analysis stratified by FH diagnostic criteria did not show statistically significant differences for comparisons in the GP (P=0.49; Figure 3 and Figure IX in the Data Supplement). Among the ASCVD population, FH prevalence ranged from 1:7 (13.93%) with the Simon-Broome criteria to 1:37 (2.67%) with LDL-C cutoffs only (P<0.001 for comparison across the different diagnostic criteria; Figure 5 and Figure X in the Data Supplement).
Further subgroup analyses are shown in Figures 3 and 5 (see also Figures XI through XVII in the Data Supplement). Among the GP, cohorts including ≤10 000 participants resulted in higher pooled prevalence estimates (1:200–1:289 [0.35%–0.50%]) compared with those derived from larger cohorts (1:365–1:407 [0.25%–0.27%]; P<0.001 for comparison across subgroups). Subgroup analyses based on the data source for the GP studies revealed no significant differences (population-based versus electronic health records [EHRs], P=0.82; population-based versus primary care cohorts, P=0.48). Finally, although there was a trend toward a higher pooled FH prevalence in the subgroup of studies with a lower proportion of participants on statin therapy (versus larger proportions on statins) and among GP studies in which participants were enrolled in or after the year 2000 (versus earlier enrollment periods), the comparisons across subgroups did not reach statistical significance.
Heterogeneity was large in both the GP (I2=99.6%) and ASCVD (I2=98.6%) groups and in the different subgroups studied (Figures 2 through 5). Funnel plots to assess publication bias are shown in Figures XVIII and XIX in the Data Supplement. Although visually the graphs may suggest potential publication bias, the application of formal tests yielded nonsignificant results for both GP (P=0.36) and ASCVD (P=0.75) studies.
Discussion
Addressing the clinical and public health challenges posed by FH and instituting evidence-based policies require reliable prevalence estimates in the GP and among patients with ASCVD. The present report, including 62 studies and >7.3 million individuals, reveals an overall prevalence of FH of 1:311 (0.32%) among the GP. On the basis of this prevalence estimate and considering a world population of 7.7 billion in 2019,78 ≈25 million individuals may have FH worldwide. The FH prevalence estimates were not significantly different across the WHO regions from which data were available. However, noticeable variations by country were still found, including within the same world regions, suggesting that different patterns of prevalence may exist even in neighboring regions. Consistent with the higher ASCVD risk posed by FH, the prevalence among those with established ASCVD was found to be 18-fold higher (1:17 [5.95%]) than in the GP.
The present data reinforce the public health challenge of FH given that it occurs frequently in the GP and is substantially more prevalent among those with ASCVD. Because FH-causing mutations can be tested and because early detection and interventions significantly reduce FH-associated morbidity and mortality,1,6,7 systematic screening strategies should be considered. For instance, the US Centers for Disease Control and Prevention classifies FH as a tier 1 genomic application for cascade testing of relatives of index cases.79 However, FH is, in general, poorly ascertained by healthcare systems, even in resource-rich countries,13 which could be attributed partly to a lack of FH awareness and its recognition and challenges in implementation of screening strategies.13 Implementation of detection strategies earlier in life may provide greater benefit in terms of prevention. For instance, beginning statin treatment in childhood results in lower event rates than among affected parents for whom statins were implemented from adulthood onward.80 A greater focus on understanding the best screening strategies (eg, opportunistic/cascade/universal), population groups who may benefit the most, and barriers to implementation is needed (considering the specific contexts and that they may vary by region/countries).
Despite being a frequent condition, increasingly recognized as representing a global concern, there is still a large gap in the identification of patients with FH, particularly outside (but also within) Western populations. As the present report shows, most studies are from Western populations, and in particular, no studies reported prevalence from low- and lower-middle–income countries.81 The reported cases of FH from the included studies represent only ≈1% of the cases estimated globally (based on available data), with >80% of them coming from Western countries. The present systematic review shows a large surge in FH-related publications since 2015, which may suggest an increase in the recognition among the medical and scientific community of the burden posed by FH. However, this is yet to draw attention from policymakers and be translated into health policies widely.13
In subgroup analysis by age, there was a trend toward a lower pooled prevalence estimate in children (1:364 [0.28%]) than adults (1:303 [0.33%]). Because FH is an inherited condition, that is, present since birth, the prevalence would be expected to be concordant in both children and adults or, if anything, higher in children (because affected patients may have had a fatal ASCVD event over time, ie, an expected survivor bias, which would diminish prevalence estimates in adults as a function of increasing age). A lower observed prevalence in children may reflect (1) a lack of awareness and detection and lower screening rates in the first decades of life; (2) a lack of sensitivity of the present diagnostic criteria (eg, LDL-C thresholds; absence of ASCVD among children) to detect FH in the pediatric populations; and (3) the impact of false positives on the phenotypic diagnosis of FH in that metabolic abnormalities, familial combined hyperlipidemia, and other conditions may resemble the FH phenotype. Taken together, these reasons would contribute to cases of FH being identified late and thus missed opportunities for early FH detection and management and ASCVD prevention.
The lack of early diagnosis results in FH frequently being diagnosed after an ASCVD event.1,82 With a prevalence of 1:17 (5.95%), patients with ASCVD (particularly CAD and when it is premature) represent a particular target population and a unique opportunity for detecting index cases, triggering cascade testing. This high prevalence among patients with ASCVD was essentially driven by CAD (1:16 [6.33%], consistent for either stable or acute CAD). Conversely, the only study among patients with stroke reported a lower prevalence (1:75 [1.33%]), although still higher than in the GP. This finding fits with previous reports suggesting that cardiovascular risk in FH is associated mainly with CAD, whereas its association with the risk of stroke remains controversial. In fact, recent results from large cohorts support the lack of association with risk of stroke compared with the GP18,83,84 and even suggest that any observed association with stroke could potentially be attributable to previous CAD and might not be associated with high LDL-C per se.18
FH is diagnosed through different clinical criteria or genetic testing, with the latter being considered the “gold standard”.1,85 However, genetic testing is not widely available/accessible and is resource intensive and costly,13 particularly if intended for screening at a population level. Accordingly, although genetic testing prevalence data may provide the best estimates, the present analyses found that the majority of studies used DLCN clinical criteria. Because a major determinant of any prevalence is the diagnostic method, the use of different criteria to identify FH may influence the estimation of its burden. For instance, the present analyses reveal that different criteria applied to similar populations provided wide variations in the estimated prevalence, which may also suggest a lack of agreement among the phenotypic methods for diagnosing FH. In addition, only a few studies tailored the criteria to specific populations (eg, to population LDL-C levels). Because criteria to establish a diagnosis are derived from Western populations, it is unclear how well they fit other populations/regions. Although the implementation of genetic testing may not completely overcome the issue of FH definitions (because genetic data are frequently derived from Western populations), the use of diagnostic strategies that include genetic testing is likely to identify true cases of FH more accurately, reducing the variability resulting from different clinical criteria, avoiding misclassification of polygenic or other dyslipidemias as FH, and providing more reliable prevalence estimates. In addition, it may help assess risk more accurately because patients with FH carrying mutations have a higher risk compared with those without a mutation.3,86 Having said that, however, given the challenges of population-wide genetic screening and the current ease of and broad access to lipid measurement, using lipid test results in the first instance as an alternative to genetic testing could be a more feasible strategy in most countries and potentially a more cost-effective method. Undergoing a first screening step through lipid tests would allow identification of those in whom conducting further genetic testing would be of greater yield (higher pretest probability). Uniform but population-tailored criteria (eg, to population cholesterol levels) should be a feasible first step for which to aim.
Many studies use a population-based methodology to estimate FH prevalence. However, with an increase in the deployment of EHRs, studies have begun using this platform to apply clinical criteria to routinely collected data. The findings from the present systematic review show that, although the number of studies using a population-based approach was larger (24 versus 15 using EHRs), participants screened and cases of FH identified were substantially higher through EHRs (>6 million participants and 23 000 cases of FH versus 960 000 participants/1800 cases in population-based studies). Moreover, prevalence estimates were not different between traditional population-based and EHRs-based studies, supporting the latter as a valid strategy to identify new patients with FH. Because traditional observational studies can be resource intensive and with health data becoming digitized, identifying individuals with FH through EHRs may become more feasible and cost-effective.
A previous meta-analysis of the prevalence of FH in the GP was published in 2017,87 although there were several limitations. Furthermore, a large number of reports from the GP and ASCVD populations, including from world regions previously not captured, have been published during the last few years. In fact, that meta-analysis included 19 studies and ≈2.5 million individuals from the GP.87 The present study, in contrast, through a comprehensive and updated search includes 42 GP studies with ≈7.3 million individuals and 20 studies on ≈50 000 patients with ASCVD. Unlike the previous meta-analysis, the present study excluded populations with founder effects, which, by nature, can bias the estimates toward a higher prevalence than expected in the GP (which may, at least partly, explain the higher estimate of 1:250 [0.40%] in that previous meta-analysis). Populations with founder effects are characterized by a loss of genetic variability and the aggregation of 1 or a few particular FH mutations within the corresponding community (unlike the scenario in the GP), resulting in a relatively small number of individuals who carry a higher proportion of FH mutations by chance.4 These population groups are therefore not representative of the GP, and it also explains the several-fold higher prevalence observed in these groups versus the GP.4
Several limitations in the present study should be considered. For instance, data are still unavailable from large regions of the world, particularly the Eastern Mediterranean region, Asia, and Africa. There is large between-study heterogeneity. This was anticipated because of the broad scope of the systematic review and accordingly was accounted for a priori by the application of random-effects models for meta-analysis throughout and implementation of multiple subgroup analyses to assess potential sources of heterogeneity. The high heterogeneity likely corresponds to differences between individual studies related to factors such as study populations, designs, or different diagnostic criteria, among other factors. The limited number of studies in some subgroups limited the power of such subgroup analysis. Only full peer-reviewed articles were included; thus, it is possible that other relevant studies (eg, conference abstracts, gray literature) may not have been captured.
The present study confirms that FH is a frequent condition among the GP across different world regions, with a particularly high prevalence among individuals with established ASCVD. However, available data suggest that only an estimated 1% of those potentially affected are detected. These data reinforce the case for public health policies that improve detection both in GPs and opportunistically among index cases presenting with ASCVD.
| Study | Country | Year of Publication | Data Source | Period of Enrollment of Participants | Age, y* | Women, % | FH Diagnostic Criteria | FH Cases/Overall Study Population, n | FH Prevalence | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| % | 95% CI | 1 in xxx | 95% CI | |||||||||
| European region | ||||||||||||
| CCH study17,18 | Denmark | 1998 | Population-based study | 1991–1994 | NR | 45.0 | Genetic test | 22/9255 | 0.24 | 0.15–0.36 | 1:421 | 1:278–1:667 |
| 2018 | Population-based study | 1991–1994 and 2001–2004 | NR | NR | Genetic test | 18/10 372 | 0.17 | 0.10–0.27 | 1:576 | 1:370–1:1000 | ||
| CGP study18–22 | Denmark | 2018 | Population-based study | 2003–NR | 20–100 | 55.0 | Genetic test | 185/106 412 | 0.17 | 0.15–0.20 | 1:575 | 1:503–1:672 |
| 2012 | Population-based study | 2003–2014 | 47–67 | 55.0 | DLCN | 502/69 016 | 0.73 | 0.66–0.79 | 1:137 | 1:126–1:151 | ||
| 2016 | Population-based study | 2003–2015 | 20–100 | NR | Genetic test | 174/98 098 | 0.18 | 0.15–0.20 | 1:565 | 1:491–1:662 | ||
| DLCN | 341/98 098 | 0.35 | 0.31–0.38 | 1:288 | 1:260–1:322 | |||||||
| Simon-Broome | 3905/98 098 | 3.98 | 3.86–4.10 | 1:25 | 1:24–1:26 | |||||||
| MEDPED | 789/98 098 | 0.80 | 0.75–0.86 | 1:124 | 1:116–1:134 | |||||||
| 2016 | Population-based study | 2003–NR | 20–100 | 54.0 | Modified DLCN† | 184/46 200 | 0.40 | 0.34–0.46 | 1:251 | 1:219–1:293 | ||
| 2018 | Population-based study | 2003–NR | 20–100 | NR | DLCN | 488/106 172 | 0.46 | 0.42–0.50 | 1:218 | 1:200–1:239 | ||
| Lahtinen et al23 | Finland | 2015 | National FINRISK Study and the Health 2000 cohort studies | 1999–2001 | 25–74 | NR | Genetic test | 35/28 465 | 0.12 | 0.09–0.17 | 1:813 | 1:388–1:1111 |
| DETECT study24 | Germany | 2017 | Sampled from 3188 primary care offices | 2003 | 18–95 | 62.0 | DLCN | 17/4722 | 0.36 | 0.21–0.58 | 1:278 | 1:172–1:476 |
| MEDPED | 16/4722 | 0.34 | 0.19–0.55 | 1:295 | 1:182–1:526 | |||||||
| Kalina et al25 | Hungary | 2001 | Family doctors’ register | NR | NR | NR | MEDPED | 39/21 000 | 0.19 | 0.13–0.25 | 1:538 | 1:400–1:784 |
| Guglielmi et al26 | Italy | 2016 | EHRs (IMSHealth Longitudinal Patient Database) | 2014–NR | ≥15 | NR | DLCN | 2043/1 135 000 | 0.18 | 0.17–0.19 | 1:556 | 1:532–1:581 |
| Casula et al27 | Italy | 2017 | EHRs from 600 Primary Care consortium | NR | NR | NR | DLCN | 237/162 864 | 0.15 | 0.13–0.16 | 1:687 | 1:610–1:787 |
| MEDPED | 160/162 864 | 0.10 | 0.08–0.11 | 1:1018 | 1:881–1:1200 | |||||||
| Scicali et al28 | Italy | 2018 | GP screened subjects and in healthcare workers | 2015–2017 | NR | NR | DLCN and genetic test | 15/1575 | 0.95 | 0.53–1.57 | 1:105 | 1:64–1:189 |
| Zafrir et al29 | Israel | 2017 | Regional database | 2000–2015 | <85 | NR | MEDPED | 1932/685 314 | 0.28 | 0.27–0.29 | 1:355 | 1:340–1:371 |
| POL-MONICA Krakow30 | Poland | 2016 | Population-based study | 1983–1993 | 35–64 | 55.0 | DLCN | 23/5159 | 0.45 | 0.28–0.67 | 1:224 | 1:149–1:357 |
| POL-MONICA Warszawa30 | Poland | 2016 | Population-based study | 1984–1993 | 35–64 | 51.0 | DLCN | 27/5385 | 0.50 | 0.33–0.73 | 1:199 | 1:137–1:303 |
| WOBASZ30 | Poland | 2016 | Population-based study | 2003–2004 | 20–74 | 53.0 | DLCN | 35/14 011 | 0.25 | 0.18–0.35 | 1:400 | 1:286–1:556 |
| Pilot HAPIEE30 | Poland | 2016 | Population-based study | 2001–2002 | 45–64 | 50.0 | DLCN | 11/2043 | 0.54 | 0.27–0.96 | 1:186 | 1:104–1:370 |
| HAPIEE30 | Poland | 2016 | Population-based study | 2003–2005 | 45–70 | 52.0 | DLCN | 50/9128 | 0.55 | 0.39–0.69 | 1:183 | 1:143–1:252 |
| NATPOL 201130 | Poland | 2016 | Population-based study | 2001–NR | 20–74 | 32.0 | DLCN | 5/2163 | 0.23 | 0.08–0.54 | 1:433 | 1:185–1:1250 |
| Dumitrescu et al31 | Romania | 2018 | Single ambulatory practice, Electronic database, ICMed | 2015–2018 | NR | NR | DLCN | 39/8329 | 0.47 | 0.33–0.64 | 1:214 | 1:156–1:303 |
| ESSE study32 | Russia | 2017 | Multicenter population-based study | 2012–2013 | 25–64 | 64.0 | DLCN | 30/3252 | 0.92 | 0.62–1.31 | 1:108 | 1:76–1:161 |
| SIDIAP study33 | Spain | 2017 | Database from 3414 general practitioners and 274 primary care practices | 2006–2014 | ≥8–NR | NR | LDL-C cutoff‡ | 14699/2 554 644 | 0.58 | 0.58–0.60 | 1:174 | 1:171–1:177 |
| SPREAD study34 | Switzerland | 2018 | Population-based study | NR | 20 (median, IQR 20–21) | NR | Genetic test | 0/717 | 0.00 | 0.00–0.00 | – | – |
| SINOPE study34 | Switzerland | 2018 | Population-based study | NR | 57 (median, IQR 51–65) | NR | Genetic test | 2/1097 | 0.18 | 0.02–0.66 | 1:549 | 1:152–1:5000 |
| IDA study34 | Switzerland | 2018 | Population-based study | NR | 75 (median, IQR 70–81) | NR | Genetic test | 5/407 | 1.23 | 0.40–2.85 | 1:81 | 1:35–1:250 |
| Neil et al35 | UK | 2000 | Computerized diagnostic register and primary care records | 1980 | All ages | 50 | Simon-Broome | 334/616 707 | 0.05 | 0.05–0.06 | 1:1846 | 1:1668–1:2068 |
| Gray et al36 | UK | 2008 | Single primary care center, EHRs | NR | NR | NR | DLCN | 20/12 100 | 0.17 | 0.10–0.25 | 1:605 | 1:400–1:1000 |
| Green et al37 | UK | 2016 | 56 Primary care practices, EHRs | 2011–2014 | >65 | NR | DLCN | 546/281 655 | 0.19 | 0.18–0.21 | 1:516 | 1:476–1:563 |
| Wald et al38 | UK | 2016 | Screening program | 2012–2015 | 12.7 mo (median, IQR12.4–13.3) | NR | Genetic test | 37/10 095 | 0.37 | 0.26–0.50 | 1:273 | 1:200–1:385 |
| ALSPAC study39 | UK | 2017 | Population-based study | 1991–1992 | 7–15 | 50 | Genetic test | 6/1512 | 0.40 | 0.15–0.86 | 1:252 | 1:116–1:667 |
| Eastern Mediterranean region | ||||||||||||
| Al-Rasadi et al40 | 5 Countries in the Arabian Gulf region++ | 2018 | Recruited from EHR systems | NR | NR | NR | Genetic test | 148/34 366 | 0.43 | 0.36–0.50 | 1:232 | 1:200–1:277 |
| Region of the Americas | ||||||||||||
| ELSA-Brasil study41 | Brazil | 2018 | Population-based study | NR | 35–75 | 54.0 | DLCN | 55/14 460 | 0.38 | 0.28–0.48 | 1:263 | 1:208–1:357 |
| ERICA study42 | Brazil | 2018 | Population-based study | 2013–2014 | 12–17 | 59.9 | LDL-C cutoff (>160 mg/dL) | 209/38 069 | 0.55 | 0.62– 0.74 | 1:182 | 1:160–1:211 |
| CARDIAC Project43 | US | 2013 | Population-based study | 1998–2012 | 10–12 | 53.0 | LDL-C cutoff (≥190 mg/dL) | 107/81 156 | 0.13 | 0.11–0.16 | 1:758 | 1:638–1:936 |
| Abul-Husn et al44 | US | 2016 | Genomic sequencing and EHR data | NR | ≥18 | 59.0 | Genetic test | 229/50 726 | 0.45 | 0.39–0.51 | 1:222 | 1:196–1:254 |
| Knickelbine et al45 | US | 2016 | EHRs | 2009–2012 | NR | 55.0 | LDL-C cutoff§ | 841/391 166 | 0.21 | 0.20–0.23 | 1:465 | 1:436–1:499 |
| NHANES study46–48 | US | 2016 | Population-based study | 1999–2012 | ≥20 | NR | DLCN | NR/36 949 | 0.40 | 0.32–0.48 | 1:250 | 1:209–1:311 |
| <20 | NR | LDL-C cutoff (≥4.9 mmol/L) | NR/13 343 | 0.42 | 0.15–0.70 | 1:237 | 1:144–1:684 | |||||
| US | 2018 | Population-based study | 1999–2002 | ≥18 | NR | DLCN | 25/5326 | 0.47 | 0.30–0.69 | 1:213 | 1:145–1:333 | |
| US | 2018 | Population-based study | 1999-2014 | >20 | NR | Modified DLCN‖ | 200/42 471 | 0.47 | 0.41–0.54 | 1:212 | 1:187–1:246 | |
| Safarova et al49 | US | 2016 | EHRs | 1993–2014 | Mean age 52 | 59.0 | DLCN | 423/131 000 | 0.32 | 0.29–0.35 | 1:310 | 1:283–1:342 |
| Western Pacific region | ||||||||||||
| Bell et al50 | Australia | 2012 | Private community laboratory | 2010–2011 | NR | NR | MEDPED | 176/84 823 | 0.21 | 0.18–0.24 | 1:482 | 1:420–1:565 |
| LDL-C cutoff (≥6.5 mmol/L) | 213/84 823 | 0.25 | 0.22–0.28 | 1:398 | 1:351–1:460 | |||||||
| AusDiab study51 | Australia | 2015 | Population-based study | 1999–2000 | ≥25 | 45.1 | DLCN | 31/10 904 | 0.28 | 0.19–0.40 | 1:352 | 1:250–1:526 |
| Baker IDI study51 | Australia | 2015 | Population-based study | 2005–2012 | ≥26 | 66.3 | DLCN | 32/7318 | 0.44 | 0.30–0.62 | 1:229 | 1:170–1:333 |
| Pang et al52 | Australia | 2016 | Western Australian Pregnancy Cohort (Raine) Study | 1989–1991 | 14–17 | 48.1 | LDL-C cutoff and family history of ASCVD¶ | 6/1602 | 0.37 | 0.13–0.81 | 1:267 | 1:123–1:769 |
| Troeung et al53 | Australia | 2016 | Screening of EHRs | 2012–2014 | 43.5 (mean, SD 24.6) | 53.3 | DLCN | 32/3708 | 0.86 | 0.59–1.12 | 1:116 | 1:89–1:169 |
| Vickery et al54 | Australia | 2017 | Screening of EHRs | NR | 18-70 | NR | Modified DLCN** | 1081/157 290 | 0.69 | 0.65–0.73 | 1:146 | 1:137–1:155 |
| Ohta et al55 | Japan | 2002 | Children screening program | 1990–1999 | 18 mo | NR | Genetic test | 91/56 181 | 0.16 | 0.13–0.20 | 1:617 | 1:512–1:777 |
| KMSMS56 | Korea | 2018 | Cohort study | 1994–2004 | >20 | 43.0 | MEDPED | 540/502 966 | 0.11 | 0.10–0.12 | 1:931 | 1:859–1:1017 |
ALSPAC indicates Avon Longitudinal Study of Parents and Children; ASCVD, atherosclerotic cardiovascular disease; AusDiab, Australian Diabetes, Obesity, and Lifestyle Study; CAD, coronary artery disease; CARDIAC, Coronary Artery Risk Detection in Appalachian Communities; CCH, Copenhagen City Heart; CGP, Copenhagen General Population; DETECT, Diabetes Cardiovascular Risk Evaluation: Targets and Essential Data for Commitment of Treatment; DLCN, Dutch Lipid Clinic Network; EHR, electronic health record; ELSA, Longitudinal Study of Adult Health; ERICA, Study of Cardiovascular Risk in Adolescents; ESSE, Epidemiology of Cardiovascular Risk Factors and Diseases in Regions of the Russian Federation Study; FH, familial hypercholesterolemia; GP, general population; HAPIEE, Health, Alcohol and Psychosocial factors In Eastern Europe; IDA, Inter-Disciplinary study on Aging; IQR, interquartile range; KMSMS, Korean Metabolic Syndrome Mortality Study; LDL-C, low-density lipoprotein cholesterol; MEDPED, Make Early Diagnosis to Prevent Early Deaths criteria; NATPOL, Arterial hypertension and other CVD risk factors in Poland; NHANES, National Health and Nutrition Examination Surveys; NR, not reported; POL-MONICA, Poland and U.S. Collaborative Study on Cardiovascular Epidemiology Hypertension in the Community; SIDIAP, Information System for the Development of Research in Primary Care; SINOPE, Screening In Normocholesterolemic Persons study; SPREAD, Swiss Prevalence of Apolipoprotein Defects study; WHO, World Health Organization; and WOBASZ, Multi-center National Population Health Examination Survey.
*
Age is presented as range of age of participants (age inclusion criteria). When this is not reported, mean or median age of participants is shown, as specified in each case; age is presented in years unless otherwise specified.
†
Modified DLCN, as follows: (1) family history of a first-degree relative with premature CAD and/or a first-degree relative with known hypercholesterolemia (1 point); (2) personal history of premature CAD at baseline (2 points) or premature cerebral vascular disease or peripheral vascular disease at baseline (1 point if not already 2 points for premature CAD); (3) LDL-C >8.5 mmol/L (8 points), 6.5 to 8.4 mmol/L (5 points), 5.0 to 6.4 mmol/L (3 points), and 4.0 to 4.9 mmol/L (1 point); and (4) presence of a LDLR W23X, W66G, or W556S or APOB R3500Q mutation (8 points).
‡
In adults, FH defined as untreated LDL-C >230 mg/dL for 18- to 30-year-olds, >239 mg/dL for 30- to 39-year-olds, >269 mg/dL for 40- to 48-year-olds, >255 mg/dL for >48-year-olds. In children and adolescents (8–18 years of age), FH defined as untreated LDL-C >190 mg/dL.
§
Saudi Arabia, Oman, United Arab Emirates, Kuwait, and Bahrain.
‖
In adults, FH defined as untreated LDL-C >190 mg/dL for >20-year-olds, LDL-C >220 mg/dL for 20- to 29-year-olds, and LDL-C >250 mg/dL for >30-year-olds.
¶
Modified DLCN, as follows: (1) family history of a first-degree relative with premature CAD and/or a first-degree relative with known hypercholesterolemia (1 point); (2) personal history of ASCVD at baseline (2 points); and (3) LDL-C >8.5 mmol/L (8 points), 6.5 to 8.4 mmol/L (5 points), 5.0 to 6.4 mmol/L (3 points), and 4.0-4.9 mmol/L (1 point).
#
FH defined as LDL-C >4.0 mmol/L plus either personal family history of premature CAD or at least 1 parent with high cholesterol.
**
DLCN applied without family history evidence.
| Study | Country | Year of Publication | Data Source | Type of ASCVD | Period of Enrollment of Participant | Age, y* | Women, % | FH Diagnostic Criteria | FH Cases/ Overall Study Population, n | FH Prevalence | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| % | 95% CI | 1 in xxx | 95% CI | ||||||||||
| European region | |||||||||||||
| STROKE CARD project57 | Austria | 2018 | Patients admitted to a neurology department | Acute ischemic stroke or TIA | 2014–2016 | 69.3 (±13.5) | 42.3 | DLCN | 14/1054 | 1.30 | 0.72–2.22 | 1:75 | 1:45–1:139 |
| Mortensen et al58 | Denmark | 2016 | Patients with a first MI in 4 Danish hospitals | MI | 2012 | 67.2 (±13.4) | 34.0 | DLCN | 28/1381 | 2.00 | 1.35– 2.91 | 1:49 | 1:34–1:74 |
| 67.2 (±13.4) | 34.0 | Simon-Broome | 69/1381 | 5.00 | 3.85–6.15 | 1:20 | 1:16–1:26 | ||||||
| Rerup et al59 | Denmark | 2016 | Eastern Danish Heart Registry and national administrative registries or Multiple Danish registries linked through the civil registration number | MI (STEMI and NSTEMI) | 1998–2012 | 56.3–74.7 | 29.8 | DLCN | 1281/13 174 | 9.72 | 9.22–10.23 | 1:10 | 1:10–1:11 |
| Koivisto et al60 | Finland | 1993 | Consecutive patients with MI (at hospital) and effort-induced angina (cardiology outpatient ward) | CAD (MI and angina) | 1987–1991 | ≤45 | NR | Genetic test | Overall: 8/90 | 8.89 | 3.92–16.77 | 1:11 | 1:6–1:25 |
| MI: 4/55 | 7.27 | 2.02–17.59 | 1:14 | 1:6–1:50 | |||||||||
| Angina: 4/35 | 11.43 | 3.20–26.74 | 1:9 | 1:4–1:31 | |||||||||
| Rallidis et al61 | Greece | 2016 | Consecutive patients surviving their first STEMI from 2 hospitals | STEMI | 1996–2014 | ≤35 | 12.8 | DLCN | 65/320 | 20.31 | 15.90–24.72 | 1:5 | 1:4–1:6 |
| LAERTES study62 | Greece | 2018 | Consecutive prospective, hospital-based registry | Stable CAD | NR | ≤80 | NR | DLCN | 150/1629 | 9.20 | 7.80–10.61 | 1:11 | 1:9–1:13 |
| START registry63 | Italy | 2018 | Prospective, observational, nationwide registry (patients seen by cardiologists, enrollment at outpatient or day-hospital or at hospital discharge) | Stable CAD | 2016–2017 | No FH, 68.0 (±11); FH, 61.0 (±10) | 16.3 | DLCN | 92/4030 | 2.28 | 1.82–2.74 | 1:44 | 1:36–1:55 |
| HEREDITY survey64 | Italy | 2018 | Observational multi-center nationwide survey, in- and out-patients at cardiac rehabilitation and secondary prevention units | CAD (ACS, percutaneous/surgical myocardial revascularization, stable angina) and lower extremity PAD | 2015 | 65.9 (±10.6) | 16.3 | DLCN | Overall: 53/1438 | 3.69 | 2.71–4.66 | 1:26 | 1:21–1:35 |
| Stable CAD: 14/381 | 3.67 | 2.02–6.09 | 1:27 | 1:16–1:50 | |||||||||
| Post-ACS: 34/755 | 4.50 | 3.14–6.24 | 1:22 | 1:16–1:32 | |||||||||
| PAD: 2/46 | 4.35 | 0.53–14.83 | 1:23 | 1:7–1:189 | |||||||||
| Amor-Salamanca et al65 | Spain | 2017 | Clinical records from 1 hospital† | ACS | 2012–2016 | ≤65 | 12.6 | Genetic test | 41/103 | 39.81 | 30.29–49.92 | 1:3 | 1:2–1:3 |
| DLCN | 28/103 | 27.18 | 18.88 –36.84 | 1:4 | 1:3–1:5 | ||||||||
| Simon-Broome | 28/103 | 27.18 | 18.88–36.84 | 1:4 | 1:3–1:5 | ||||||||
| SWEDEHEART registry66 | Sweden | 2018 | Patients admitted to a coronary care or similar specialized unit‡ | Suspected or definite ACS | 2009–2015 | 55–70 | 37.1 | Genetic test | 8/116 | 6.90 | 3.02–13.14 | 1:15 | 1:8–1:33 |
| SPUM-ACS study67 | Switzerland | 2015 | Prospective, multicenter, and observational cohort | ACS | 2009–2014 | No FH, 64.8 (±11.5); FH, 49.5 (±9.3) | 21.1 | DLCN | 78/4778 | 1.60 | 1.30–2.00 | 1:61 | 1:50–1:79 |
| No FH, 63.8 (±12.2); FH, 51.6 (±9.8) | Simon-Broome | 259/4778 | 5.42 | 4.80–6.10 | 1:18 | 1:16–1:21 | |||||||
| ELIPS study (subproject from SPUM-ACS Study)68 | Switzerland | 2017 | ELIPS study from SPUM-ACS study | ACS | 2009–2014 | 61.7 | 19.7 | DLCN | 67/2023 | 3.31 | 2.53–4.09 | 1:71 | 1:58–1:94 |
| Wald et al7 | United Kingdom | 2015 | Cardiac care wards in 2 hospitals | MI (STEMI or non-STEMI) | 2011–2013 | 41–48 | 14.0 | Genetic test | 3/231 | 1.30 | 0.27–3.75 | 1:77 | 1:30–1:370 |
| EUROASPIRE IV study8 | 24 European countries | 2015 | EUROASPIRE IV study | Coronary event or surgical or percutaneous revascularization | NR | 18–80 | 24.3 | DLCN | 587/7044 | 8.33 | 7.70–9.00 | 1:12 | 1:11–1:13 |
| Eastern Mediterranean region | |||||||||||||
| Gulf COAST registry69 | Bahrain, Kuwait, Oman and United Arab Emirates | 2018 | Prospective, multicenter, observational, cohort-based registry of consecutive citizens | ACS | 2012–2013 | ≥18 | NR | DLCN | 119/3224 | 3.69 | 3.04–4.34 | 1:27 | 1:23–1:33 |
| Western Pacific region | |||||||||||||
| Auckle et al70 | China | 2017 | Patients with STEMI admitted to 1 hospital | STEMI | 2013–2015 | No FH, 53.5 (±4.2); FH 50.1 (±3.9) | 27.4 | DLCN | 19/498 | 3.82 | 2.13–5.50 | 1:26 | 1:18–1:47 |
| EXPLORE-J registry71 | Japan | 2018 | Prospective, observational study at 59 sites | ACS (STEMI, NSTEMI, or unstable angina) | 2015–2016 | 66.0 (12.2) | 19.7 | 2012 JAS guidelines criteria | Overall: 52/1944 | 2.67 | 1.96–3.39 | 1:37 | 1:29–1:51 |
| STEMI: 31/1195 | 2.59 | 1.77–3.66 | 1:39 | 1:27–1:56 | |||||||||
| NSTEMI: 8/309 | 2.59 | 1.12–5.04 | 1:39 | 1:20–1:89 | |||||||||
| Unstable angina: 13/440 | 2.95 | 1.58–5.00 | 1:34 | 1:20–1:63 | |||||||||
| Li et al72,73 | China | 2017 | Patients referred for coronary angiography at 1 hospital | Coronary angiography because of suspected CAD | 2011–2016 | Unlikely FH, 59.3 (±10.0); FH, 50.2 (±12.5) | 46.6 | DLCN | 281/8050 | 3.49 | 3.09–3.89 | 1:29 | 1:26–1:32 |
| 2018 | Patients referred for coronary angiography at 1 hospital | Coronary angiography for first MI | 2009–2017 | 31.6 (±3.6) | 3.2 | DLCN | 71/1093 | 6.50 | 5.03–7.96 | 1:15 | 1:13–1:20 | ||
| Ohmura et al74 | Japan | 2017 | Registry of patients with ACS admitted in 4 hospitals | ACS | 2012–2013 | ≥18 | 20.6 | 2012 JAS guidelines criteria | 17/296 | 5.70 | 3.38–9.04 | 1:17 | 1:11–1:30 |
| Pang et al75 | Australia | 2015 | Coronary care unit; medical records within 1 hospital | CAD (ACS, coronary revascularization, or angina) | 2011 and 2013 | 50.3 (±7.1) | 19.0 | Modified DLCN§ | 25/175 | 14.30 | 9.46–20.36 | 1:7 | 1:5–1:11 |
| Multiple regions | |||||||||||||
| Khera et al3 | Cohorts from multiple regions‖ | 2016 | 7 CAD case-control cohorts of the MI Genetics Consortium | CAD | 1987–1998 | 53.0 (±14.0) | 26.0 | Genetic test | 116/5540 | 2.09 | 1.72–2.47 | 1:48 | 1:40–1:58 |
ACS indicates acute coronary syndrome; ASCVD, atherosclerotic cardiovascular disease; CAD, coronary artery disease; DLCN, Dutch Lipid Clinic Network; ELIPS, Multidimensional Prevention Program After Acute Coronary Syndrome; EUROASPIRE, European Action on Secondary and Primary Prevention by Intervention to Reduce Events; EXPLORE-J, Exploration Into Lipid Management and Persistent Risk in Patients Hospitalised for Acute Coronary Syndrome in Japan; FH, familial hypercholesterolemia; Gulf COAST, Gulf Locals With Acute Coronary Syndrome Events; HEREDITY, Heterozygous Familial Hypercholesterolemia in Patients Admitted to Cardiac Rehabilitation Programs in Italy; JAS, Japanese Atherosclerosis Society; LAERTES, Lipoprotein-Associated Phospholipase A2 in Stable Coronary Artery Disease; MEDPED, Make Early Diagnosis to Prevent Early Deaths; MI, myocardial infarction; NR, not reported; NSTEMI, non–ST-segment–elevation myocardial infarction; PAD, peripheral artery disease; SPUM-ACS, Special Program University Medicine–Acute Coronary Syndromes; START, Stable Coronary Artery Diseases Registry; STEMI, ST-segment–elevation myocardial infarction; STROKE CARD, Post-Stroke Disease Management - Stroke Card; SWEDEHEART, Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies; TIA, transient ischemic attack; and WHO, World Health Organization.
*
Age is presented as range of age of participants (age inclusion criteria). When this is not reported, mean or median age of participants is shown, as specified in each case; age is presented in years unless otherwise specified.
†
Screening participants’ lipid profile: low-density lipoprotein cholesterol >160 mg/dL.
‡
Screening participants’ lipid profile: total cholesterol ≥7 mmol/L combined with triglycerides ≤2.6 mmol/L or total cholesterol level >4.9 mmol/L combined with triglycerides ≤2.6 mmol/L treated with lipid-lowering medications.
§
DLCN without family history evidence.
‖
Italian Atherosclerosis, Thrombosis, and Vascular Biology study, ESP-EOMI (Exome Sequencing Project Early–Onset Myocardial Infarction) study, a nested case-control of the Jackson Heart Study, Munich Myocardial Infarction study, Ottawa Heart Study, PROCARDIS (Precocious Coronary Artery Disease) study, and PROMIS (Pakistan Risk of Myocardial Infarction Study).
Acknowledgments
Dr Ray acknowledges support from the National Institute for Health Research Imperial Biomedical Research Center. A.J.V.-V. and K.K.R. conceived the original study concept and overall study design and supervised the subsequent steps of the study. G.F.W. and J.G. contributed via their original studies to study concept. P.H., K.I.D., and R.S.J. designed the search strategy. P.H. and K.I.D., supported by A.J.V.-V., conducted the literature search and data extraction. P.H. and K.I.D. conducted quality assessment of studies. P.H. and M.T.A.S. conducted the statistical analysis. P.H., K.I.D., C.A.T.S., and A.J.V.-V. created the tables and figures. G.F.W. reviewed the literature search and data analyses. K.I.D. and A.J.V.-V. wrote the first draft of the manuscript. All authors participated in the interpretation of data, have critically reviewed the manuscript and provided edits and comments, and approved its final submission. A.J.V.-V., K.I.D., and P.H. had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Supplemental Material
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Received: 11 November 2019
Accepted: 20 March 2020
Published online: 29 May 2020
Published in print: 2 June 2020
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Dr Watts has received honoraria for advisory boards and research grants and support from Amgen, Sanofi, Regeneron, Arrowhead, and Novartis. Dr Genest leads FHCanada, a registry of FH in Canada, and reports research support from Amgen and Sanofi. Dr Ray reports personal fees for consultancy from AbbVie, Amgen, AstraZeneca, Sanofi, Regeneron, Merck Sharp & Dohme, Pfizer, Resverlogix, Akcea, Boehringer Ingelheim, Novo Nordisk, Takeda, Kowa, Algorithm, Cipla, Cerenis, Dr. Reddys, Lilly, Zuellig Pharma, Bayer, Daiichi Sankyo, The Medicines Company, and Esperion, as well as research grant support from Pfizer, Amgen, Sanofi, Regeneron, and Merck Sharp & Dohme. Dr Vallejo-Vaz reports honoraria for lectures from Amgen, Mylan, and Akcea; nonfinancial support from Regeneron; and participation in research grants from Amgen, Sanofi, MSD, Pfizer, and Daiichi Sankyo to Imperial College London/European Atherosclerosis Society. The other authors report no conflicts.
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