Genetics of severe hypercholesterolemia in the general population: Insights from the STANISLAS cohort

J Atherosclerosis Prev Treat. 2021 Sep-Dec;12(3):73-83 | doi:10.53590/japt.02.1026


Constance Xhaard1*, João Pedro Ferreira1*, Edith Le Floch2, Zohra Lamiral1, Claire Dandine-Roulland2, Delphine Bacq-Daian2, Erwan Bozec1, Jean-Marc Boivin1, Nicolas Girerd1, Jean-François Deleuze2, Faiez Zannad1, Patrick Rossignol1

1Université de Lorraine, INSERM, Centre d’Investigations Cliniques Plurithématique 1433, INSERM 1116, CHRU de Nancy, FCRIN INI-CRCT, Nancy, France
2Centre National de Recherche en Génomique Humaine, Institut François Jacob, CEA, Université Paris-Saclay, Evry, France

*Co-first authors



Background: Severe hypercholesterolemia (SH) is a common condition characterized by increased levels of total and low-density lipoprotein cholesterol (LDLc). Methods: The aim of this study is to screen for prevalence of hypercholesterolemia, perform heritability estimation of circulating lipoproteins and study the association between SH cases and surrogate cardiovascular disease markers among participants of STANISLAS cohort. Gene candidate analyses were utilized to investigate the association between lipid levels, SH and polymorphisms from the three commonly reported genes (APOB, LDLR and PCSK9). Results: Participants with SH (n=102; 6.9%) were older (58 vs. 51yr), had higher total cholesterol (290 vs. 209mg/dL), LDLc (206 vs. 136mg/dL) and triglycerides (114 vs. 88 mg/dL). Despite smoking less, they had carotid plaques more frequently (21.2 vs. 9.3%), higher cIMT (676 vs. 597µm), and had more frequent family history cardiovascular disease. The circulating lipid levels have an important heritability: LDLc 51.6%, HDLc 66.6%, total cholesterol 49.8%, and triglycerides 41.4%. The SNPs located in LDLR gene present the strongest association with LDLc levels: rs55997232, rs17242395, rs1010679, and rs11668477. Conclusion: In a healthy cohort, participants with SH had premature vascular damage. LDLc had an important component of heritability and SNPs linked to the LDLR gene presented a strong association with LDLc. These findings reinforce the need for an early identification and treatment of SH subjects, which is mostly polygenic.

Key words: Severe hypercholesterolemia, LDL cholesterol, heritability, genetic, arterial stiffness, target organ damage

Corresponding author:Pr Patrick Rossignol, Centre d’Investigation Clinique 1433 module Plurithématique, CHRU Nancy – Hopitaux de Brabois, Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu, 4 rue du Morvan, 54500 Vandoeuvre les Nancy, Tel.: +33 3 83 15 73 15, Fax : +33 3 83 15 73 24, E-mail:

Submission: 25.08.2021, Acceptance: 31.10.2021


Hypercholesterolemia  is a common but underdiagnosed condition characterized by increased levels of total and low-density lipoprotein cholesterol (LDLc). If left untreated, it may lead to premature cardiovascular events and death1,2,3. Two kinds of hypercholesterolemia have been reported, the familial form (Familial Hypercholesterolemia FH) and the sporadic form. The estimated prevalence of FH is 0.4% (1 person per 250 people) but the true worldwide prevalence of hypercholesterolemia is unknown because most patients remain undiagnosed4,5.

Familial hypercholesterolemia is defined by several criteria (Supplemental Table 1) including genetic mutations in low-density lipoprotein receptor (LDLR), apolipoprotein B (APOB) and proprotein convertase subtilisin/kexin type 9 (PCSK9) genes6,7. However, beyond the “monogenic causes” responsible of FH, many people with hypercholesterolemia may present a polygenic severe form of hypercholesterolemia (SH, severe Hypercholesterolemia). Many of these polygenic forms remain to be characterized8.

The STANISLAS (Suivi Temporaire Annuel Non-Invasif de la Santé des Lorrains Assurés Sociaux) cohort is a single-centre familial longitudinal cohort from the Lorraine region of France, where the circulating levels of cholesterol and triglycerides were determined along with a detailed cardiovascular phenotyping9. Importantly, the familial design of this cohort allows the estimation of heritability of many variables including circulating lipoproteins.

The objectives of this study are to: 1) screen for prevalence of hypercholesterolemia among the participants, 2) describe the characteristics of the participants with hypercholesterolemia , 3) perform heritability estimation of circulating lipoproteins, 4) assess for potential familial form of hypercholesterolemia accordingly to genetic characteristics, and 5) study the association between SH cases and surrogate cardiovascular disease markers.


Study population

The design of the STANISLAS cohort has been previously published9. In brief, the STANISLAS cohort is a single-center familial longitudinal cohort composed of 1,006 families (4,295 subjects) from the Nancy region recruited in 1993–1995 at the Center for Preventive Medicine. The cohort was established with the primary objective of investigating gene-gene and gene–environment interactions in the field of cardiovascular diseases. The study protocols for all examinations were reviewed and approved by the local Ethic Committee of CPP Est 3, France. All participants provided written informed consent to participate in the study. The most recent visit (fourth visit, V4) included 1705 subjects. For the present analysis, we excluded 213 subjects that were treated with a statin, and 17 participants who had missing LDLc values. The study population included a total of 1475 subjects, among whom, 1377 were successfully genotyped (Figure 1).

FIGURE 1. Study flow chart.

Study design

All participants were observed at the Centre d’Investigation Clinique Plurithématique Pierre Drouin at the University Center Hospital of Nancy (CIC-P de Nancy). Samples were collected early morning after an overnight fasting period. Standardized sample handling procedures enabled the collection of serum and plasma. Blood lipids were determined after plasma centrifugation in the central laboratory of the hospital using the Roche Cobas® lipid panel. LDL cholesterol levels were automatically calculated in the central laboratory using the Friedwald formula, excluding subjects with triglyceride levels superior to 4 g/L in whom LDLc was not calculated. Personal and familial medical history, medications, anthropometric parameters, blood pressure, pulse-wave velocity (PWV), carotid intima-media thickness (cIMT) and echography (including carotid plaque assessment and echocardiography) were recorded10-12. Participants more likely to have any form of SH were identified on the basis of LDLc levels: LDLc ≥190 mg/dL if age ≥20yr and LDLc ≥160 mg/dL if age <20yr13,14.

Genetic analyses and heritability estimations

Blood DNA was extracted using Gentra Puregene Blood Kit (Qiagen, Hilden Germany®) and stored at -20°C. Genotyping was conducted at the Centre National de Recherche en Génomique Humaine (CNRGH, Evry, France) using two chips: 1) the Illumina Global Screening Array (GSA) which is composed of 687572 intronic and exonic markers, and 2) the Illumina Exome Array, which is constituted of 244330 SNPs, mostly exonic.

For heritability estimation, we used a linear mixed model, which allowed to simultaneously include additive genetic effects across the genome, common environment effects shared by nuclear family and fixed effects (sex and age). The additive genetic effects were assessed using the Genetic Relatedness Matrix (GRM) which has been computed using polymorphic SNPs of included subjects and which is based on genotype correlations. The use of the GRM allowed a better inference of relatedness between siblings, instead of an expected average15.

Gene candidate association analyses were utilized in order to investigate the association between lipid levels, SH and genetic polymorphisms from the three commonly reported genes (APOB, LDLR and PCSK9). For each gene, we defined an interval that encompassed their boundaries (±20kb) based on the reference genome built 37 from the Ensembl database ( Then we selected the SNPs from the two chips and performed usual quality control steps (Supplemental Table 2). A total of 7 SNPs were excluded for monomorphism, no SNP had >5% of missing data and no SNP deviated from the Hardy–Weinberg equilibrium at a threshold of p < 1.10-8. Then 29 other SNPs were excluded because they were on linkage disequilibrium (r² < 0.9).

Genetic analyses were performed using linear mixed-effect model in order to take into account pedigree data, under an additive genetic model (with age and sex as covariates). Results were considered statistically significant at p <0.05 after correction for multiple testing with a 1% false discovery rate. These analyses were performed with R (version 3.5.0) using the R package Gaston (gaston: Genetic Data Handling & Linear Mixed Models) for heritability estimation and GWAS analysis.

Statistical analyses

Continuous variables are expressed as means ± standard deviation (SD) or median (interquartile range), categorical variables as frequencies (percentages), and odd ratios (ORs) as the point estimate and the respective 95% confidence interval (95% CI). Comparisons of characteristics were performed using Student’s t‐test or Wilcoxon non parametric tests for continuous variables and χ2 or exact Fisher tests for categorical variables. Factors associated with SH were assessed using both univariate and multivariate logistic regression with SH as binary outcome variable. First, univariate logistic regression was performed between SH and each variable significantly associated with SH with a p <0.01 in Table 1. Second, multivariable logistic regression was performed using a stepwise forward selection process, retaining only the variables associated with SH with a p <0.05 in the multivariable model. Assumptions of log linearity for continuous variables were checked using restricted cubic spline analyses. When log linearity hypothesis was not respected, continuous variables were categorized according to the shape of the spline curve. Accordingly, triglycerides, glycated hemoglobin and body mass index were expressed as binary covariates (≤ vs. >88 mg/dL, ≤ vs. >5.5% and ≤ vs. >25kg/m2, respectively). These analyses were performed using SAS version 9.4.6 (SAS Institute Inc., Cary, NC, USA) and the R software (version 3.5.0). A two‐tailed significance level p <0.05 was used.


Characteristics of the study population

The comparison of the population characteristics by SH status is depicted in the Table 1. Participants with possible SH (n=102; 6.9%) were older (58 vs. 51yr), had higher total cholesterol (290 vs. 209mg/dL), LDL cholesterol (206 vs. 136mg/dL) and triglycerides (114 vs. 88 mg/dL), higher glycated hemoglobin (5.7 vs. 5.5%), had higher BMI (26 vs. 25 kg/m²), had more often hypertension (23.8 vs. 15.2%), had lower eGFR (94 vs.98 mL/min/1.73m²), had carotid plaques more often (21.2 vs. 9.3%), higher cIMT (676 vs. 597µm), and higher PWV (8.1 vs 8.0m/s) despite smoking less (13.9 vs. 22.7%), and had more frequent family history of myocardial infarction (26.5 vs. 14.6%), stroke (28.4 vs. 16.0%), and hyperlipidemia (66.3 vs. 46.9%) (p <0.05 for all). The HDLc levels did not differ between groups. Table 1. In the multivariable logistic model higher cIMT, glycated hemoglobin and triglyceride levels, and family history of hyperlipemia remained independently associated with SH. Supplemental Table 3.

Heritability estimations

The circulating lipid levels have an important heritability component: LDLc 51.6%, HDLc 66.6%, total cholesterol 49.8%, and triglycerides 41.4%. The variance in the lipid levels explained by environmental factors shared by nuclear family was low, especially for LDL and total cholesterol: LDLc 3.9%, HDLc 14.7%, total cholesterol 4.8%, and triglycerides 13.1%. About 45% of the variance of LDLc, triglycerides and total cholesterol levels remain unexplained neither by genetic nor by common environment effects, except for HDLc for which unexplained variance represent only 18.7%. Table 2.

Gene candidate analyses

Gene candidate analysis for the 3 genes implicated in definition of familial form of hypercholesterolemia is shown in the Table 3. All allele frequencies are in concordance with those from European population panels of reference. Compared with the APOB and PCSK9 SNPs, those located in LDLR gene present the strongest association with LDLc levels. After correction for test multiplicity, the LDLR SNPs rs55997232, rs17242395, rs1010679, and rs11668477, and the APOB SNPs rs1367117, rs6548010, rs6754295, rs481069, and rs61743299, remained associated with LDLc. No SNPs located on PSCK9 gene reach the significant threshold after correction for test multiplicity. Table 3. Several LDLR, PCSK9, and APOB SNPs were associated with SH, but none remained significant after correction for test multiplicity, they are almost the same than those associated with LDLc levels. Table 3. We did not find monogenic mutations of familial hypercholesterolemia, but all subjects with SH showed at least one mutated polymorphism from one of the 3 genes of interest.

Sensitive analysis are shown in Supplemental Table 4, where analyses without subjects that were not fasting (n=30) have been run, results are similar than those in the Table 3, and in Supplemental Table 5, association tests between subjects taking statin or not taking statin for the 3 candidate genes implicated in familial form of hypercholesterolemia occurrence are shown, we find some polymorphisms in APOB gene linked with taking statin. However, results for association with LDL levels are similar with or without taking into account subjects with statin treatment (data not shown).


The present study shows that in a generally healthy populational cohort not taking statins, the proportion of participants with possible polygenic hypercholesterolemia was nearly 7%; these participants had more frequently a family history of cardiovascular disease and carotid plaques, and also had higher cIMT and PWV, indicating more advanced arterial ageing and vascular damage.

The LDLc had an important heritability with more than 50% of the variance explained by additive genetic effects, however less than 4% of the variance resulted from common environmental effects, that may support the notion that lifestyle intervention has little impact on the LDLc levels or that we may have missed to take into account some other shared environmental factors. Moreover, in the GWAS analysis, SNPs located on the LDLR gene presented a strong association with the plasmatic LDLc levels. These findings of a main genetic origin reinforce the need for an early identification and treatment of individuals with SH. The prevalence of polygenic forms of SH in the general population is high, easy to identify, and the cardiovascular damage is potentially preventable.

In our cohort the prevalence of participants with SH was higher than the prevalence usually described in the literature4,5. It should be noted that if these individuals had not been included in our cohort they would otherwise have not be identified. These findings are reflective of the “real-world” where a low proportion of people are diagnosed and treated. Supporting our findings, the prevalence of LDLc ≥190 mg/dL among American adults free of coronary artery disease was also 7%, and they had a multiple fold higher risk of cardiovascular events16. Moreover, a familial hypercholesterolemia monogenic mutation identified by gene sequencing was rare (<2% among those with a LDLc ≥190 mg/dL)16. To date, there is no gold standard for the clinical diagnosis of SH. Nonetheless, the first step is to assess the cholesterol levels and to perform a thorough clinical examination looking for features of hypercholesterolemia (e.g., xanthomas, xanthelasmas) and premature cardiovascular disease (e.g., angina pectoris, intermittent claudication, erectile dysfunction)17. Although these features may be useful in the case of severe autosomal dominant hypercholesterolemia, they are of little utility for the great majority of people with polygenic forms of hypercholesterolemia. In our cohort we did not systematically evaluate the presence of xanthomas or xanthelasmas, and the proportion of patients with any of myocardial infarction, stroke, or peripheral artery disease was low (≤10%) is not different between people with SH or not. On the other hand, a simple LDLc measurement could identify patients with carotid plaques, signals of premature vascular ageing (as supported by higher PWV and cIMT), and family history of premature cardiovascular disease18,19. Given the important hereditary component of LDLc, this simple and inexpensive laboratorial measurement (i.e. Blood measurement of LDLc) should be used for screening of SH. As supported by our findings, many of these patients will not present an identifiable gene mutation, but rather present polygenic profiles that will make them more prone to have elevated LDLc.

We found that variants located on LDLR gene (rs55997232, rs17242395, rs1010679, and rs11668477) were those with stronger association with LDLc levels in our cohort. However, none of these 4 SNPs have been identified in previous studies. Variants located on the APOB gene (rs1367117, rs6548010, rs6754295, rs481069, rs61743299 and rs515135) presented lower association with LDLc. The variant rs1367117 had already been previously associated with higher concentrations of LDLc20. Furthermore, some SNPs located on PCSK9 gene, which did not reach the statistically significant threshold in our study, are known for their associations with LDLc21-25.

Despite the fact that our findings are not in total concordance with those already reported in other cohorts and populations of different ethnic backgrounds8,14,26, the density of our genotyping is not sufficient to find a mutation of one of the 3 genes commonly implicated in familial form; if we consider a 0.5% or lower prevalence of hypercholesteromemia-associated variants, it is very plausible that we had found none among our 102 patients more likely to have any form of hypercholesterolemia. In this regard, the heritability of LDLc was important, highlighting the need for an early detection and treatment of hypercholesterolemia, thus limiting the lifetime cumulative exposure to elevated LDLc16,27. For limiting the exposure to excessive LDLc levels in this high-risk population, and beyond lifestyle changes, an early initiation of statins is required (at least in the great majority of patients who have any form of SH). Early (during childhood and adolescence) statin initiation with the goal of lowering LDLc below 100 mg/dL may delay the progression of vascular damage and reduce the incidence of cardiovascular events19,28,29. In addition to statins, PCSK9 inhibitors and Ezetimibe may be considered in people unable to tolerate intensive statin therapy or in whom statins are ineffective to lower LDLc to the desired target. Using the STANISLAS cohort, we have previously shown that the circulating PCSK9 levels and the missense mutation coding the SNP rs562556 were associated with the advent of carotid arterial plaques30. This SNP was not associated with the LDLc levels in the present report. Whenever required other lipid-lowering strategies can also be tried (e.g., ezetimibe and bile acid sequestrants)14.

Our findings add to the literature that milder polygenic forms of SH may be much more prevalent that initially thought. An important proportion of people carrying these SH forms develop premature vascular damage and may have increased risk of cardiovascular events. Both the diagnosis and effective treatment are relatively easy to achieve (e.g., family history and LDLc measurement), but need to be performed early in life to avoid lifetime exposure to high LDLc levels and reduce the incidence of cardiovascular events.


This study has several limitations. First, this is an observational study and no causality can be established. Second, our sample size might be underpowered to detect genetic mutations and monogenic alterations and the STANISLAS cohort had participants mostly from the Lorraine region of France. Therefore, the low incidence of monogenic familial hypercholesterolemia found herein cannot be generalized to other populations. Third, this is a cohort of mostly healthy people and the impact of LDLc in major adverse cardiovascular events cannot be determined. Fourth, family history was reported by the participants and not confirmed using medical records. Fifth, the presence of xanthomas and xanthelasmas was not systematically assessed. Sixth, we used age and LDLc for SH classification. These criteria are sensitive but less specific i.e., they may incorporate people with sporadic forms of hypercholesterolemia; notwithstanding, these forms likely have an important hereditary component (as here described) and more attention should be payed to them.


In a generally healthy populational cohort not taking statins, the proportion of participants with SH was nearly 7%. These participants had much higher LDL and total cholesterol levels, family history of cardiovascular disease, and premature vascular damage including carotid plaques. LDLc had an important component of heritability with little environmental effect. SNPs linked to the LDLR gene presented a strong association with the blood LDLc levels. These findings reinforce the need for an early identification and treatment of individuals with possible FH as identified by elevated LDLc levels.


We acknowledge Robert Olaso and his lab “Production Platforms in Human Genomics” at CNRGH for genotyping data production. Anne Boland at CNRGH for management of the genotyping study.  This biomarker study was funded by the French National Research Agency Fighting Heart Failure (ANR-15-RHU-0004) and FEDER Lorraine, and all coauthors are supported by the French PIA project “Lorraine Université d’Excellence” GEENAGE (ANR-15-IDEX-04-LUE) programmes, and the Contrat de Plan Etat Région Lorraine and FEDER IT2MP.

We are highly grateful to the Vandoeuvre-Lès Nancy Centre de Médecine Préventive staff, and to Dr Sophie Visvikis-Siest (Inserm U1122) who managed the STANISLAS Cohort for first three visits. The authors deeply thank the Staff of the Clinical Investigation Center and other personnel involved in the Stanislas Cohort management: Biostatisticians: Fay R, Lamiral Z, Machu JL. Computer scientists: Boucenna N, Gallina-Muller C, Maclot PL, Sas T. Co-investigators: Chau K, Di Patrizio P, Dobre D, Gonthier D, Huttin O, Malingrey L, Mauffrey V, Olivier A, Poyeton T, Steyer E, Watfa G. Datamanagers: Cimon P, Eby E, Merckle L. Data entryoperators: Batsh M, Blanger O, Bottelin C, Haskour N, Jacquet V, Przybylski MC, Saribekyan Y, Thomas H, Vallee M. Echocardiographists, echographists: Ben Sassi M, Cario S, Camara Y, Coiro S, Frikha Z, Kearney-Schwartz A, Selton-Suty C, Watfa G. Imaging engineer: Bozec E. Laboratory Engineer Nuee-Capiaumont J and Technicians: Fruminet J, Kuntz M, Ravey J, Rousseau E, Tachet C. Project manager: Bouali S, Hertz C. Quality engineer: Lepage X. Registered Nurses: Giansily M, Poinsignon L, Robin N, Schmartz M, Senn M, Micor-Patrignani E, Toutlemonde M. Hospital technician: Fleurot MT. Resident doctors: Alvarez- Vasquez R, Amiot M, Angotti M, Babel E, Balland M, Bannay A, Basselin P, Benoit P, Bercand J, Bouazzi M, Boubel E, Boucherab- Brik N, Boyer F, Champagne C, Chenna SA, Clochey J, Czolnowski D, Dal-Pozzolo J, Desse L, Donetti B, Dugelay G, Friang C, Galante M, Garel M, Gellenoncourt A, Guillin A, Hariton ML, Hinsiger M, Haudiquet E, Hubert JM, Hurtaud A, Jabbour J, Jeckel S, Kecha A, Kelche G, Kieffert C, Laurie`re E, Legay M, Mansuy A, Millet-Muresan O, Meyer N, Mourton E, Naude´ AL, Pikus AC, Poucher M, Prot M, Quartino A, Saintot M, Schiavi A, Schumman R, Serot M, Sert C, Siboescu R, Terrier-de-la-Chaise S, Thiesse A, Thietry L, Vanesson M, Viellard M. Secretaries: De Amorin E, Villemain C, Ziegler N. Study Coordinators: Dauchy E, Laurent S, and all persons not listed above who helped to the funding, initiation, accrual, management and analysis of the fourth visit of the STANISLAS cohort. They also thank the CRB Lorrain of the Nancy CHRU for management of the biobank. Steering committee: Pierre Mutzenhardt, Mehdy Siaghy, Patrick Lacolley, Marie-Ange Luc, Pierre Yves Marie, Jean Michel Vignaud. Advisory members: Sophie Visvikis Siest, F Zannad. Technical committee: Christiane Branlant, Isabelle Behm-Ansmant, Jean-Michel Vignaud, Christophe Philippe, Jacques Magdalou, Faiez Zannad, Patrick Rossignol. Scientific committee: Laurence Tiret, Denis Wahl, Athanase Benetos, Javier Diez, Maurizio Ferrari, Jean Louis Gueant, Georges Dedoussis, François Alla, Franc¸ois Gueyffier, Pierre-Yves Scarabin, Claire Bonithon Kopp, Xavier Jouven, Jean-Claude Voegel, Jan Staessen.


This study was funded by the French National Research Agency Fighting Heart Failure (ANR-15-RHU-0004) and FEDER Lorraine, and all coauthors are supported by the French PIA project “Lorraine Université d’Excellence” GEENAGE (ANR-15-IDEX-04-LUE) programs, and the Contrat de Plan Etat Région Lorraine and FEDER IT2MP.

Conflict of Interest

The authors declare that they have no competing interests


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