Abstract
Individuals with familial hypercholesterolemia (FH) have a high risk of coronary artery disease, but their risk of peripheral arterial disease (PAD) and chronic kidney disease (CKD) is unknown.
In individuals with clinical FH, we tested the hypotheses (1) that the risks of PAD and CKD are elevated and (2) that low ankle-brachial index (ABI) and estimated glomerular filtration rate (eGFR) are associated with a high risk of myocardial infarction.
Prospective cohort study of the general population.
A total of 106,172 individuals, of whom 7109 were diagnosed with FH.
PAD, CKD, and myocardial infarction.
Compared with individuals with unlikely FH, multivariable adjusted ORs (95% CIs) of PAD were 1.84 (1.70 to 2.00) in those with possible FH and 1.36 (1.00 to 1.84) in individuals with probable/definite FH. For CKD, the corresponding ORs (95% CIs) were 1.92 (1.78 to 2.07) and 2.42 (1.86 to 3.26). Compared with individuals with unlikely FH and ABI >0.9, the multivariable adjusted hazard ratio (95% CI) of myocardial infarction was 4.60 (2.36 to 8.97) in those with possible/probable/definite FH and ABI ≤0.9. Compared with individuals with unlikely FH and eGFR ≥60 mL/min/1.73 m2, the corresponding value was 2.19 (1.71 to 2.82) in those with possible/probable/definite FH and eGFR <60 mL/min/1.73 m2.
Individuals with clinical FH have increased risks of PAD and CKD, and low ABI and eGFR are associated with high risk of myocardial infarction. Consequently, individuals with FH should be screened for PAD and CKD, and ABI and eGFR may be used as prognostic tools in the management and treatment of FH to identify those at very high risk of myocardial infarction.
Heterozygous familial hypercholesterolemia (FH) is an autosomal dominant disorder of lipoprotein metabolism, estimated to affect one out of 250 individuals in Western populations (1, 2). Mutations in genes involved in the recycling pathways of the low-density lipoprotein (LDL) receptor lead to decreased clearance of LDL cholesterol from plasma and consequently substantially increased total and LDL cholesterol concentrations (3, 4). Pathogenic variants in the LDL receptor (LDLR), apolipoprotein B (APOB), and protein convertase subtilisin/kexin type 9 (PCSK9) genes account for the majority of known FH-causing mutations. Lifelong vascular exposure to elevated concentrations of LDL cholesterol leads to atherosclerosis, the major clinical manifestation of FH. Accordingly, FH is a well-established cause of premature coronary artery disease (CAD) (4, 5). A similar involvement of the peripheral arteries seems likely but is largely unknown for individuals with FH.
Lower extremity peripheral arterial disease (PAD) and chronic kidney disease (CKD) are two manifestations of peripheral atherosclerotic disease that significantly contribute to morbidity and mortality (6, 7). Furthermore, both diseases constitute independent risk factors for CAD in the general population (6, 7). Ankle-brachial index (ABI) and estimated glomerular filtration rate (eGFR) are noninvasive, low-cost tools that are widely used in the diagnosis and management of PAD and CKD, respectively. Both have been shown to be markers of cardiovascular risk in individuals in the general population (7, 8) but hitherto not in individuals with FH. Consequently, signs and symptoms of PAD and CKD may not be routinely screened for as part of the FH diagnosis, and the results are not included in the overall cardiovascular risk assessment in FH, thus influencing decisions on treatment, treatment intensity, and clinical follow-up (3, 9).
In individuals with clinical FH, we tested the hypotheses that (1) the risks of PAD and CKD are elevated and (2) that ABI and eGFR, as markers of PAD and CKD, are associated with high risk of myocardial infarction (MI), independent of age, birth year, sex, current smoking, pack-years, body mass index (BMI), lipoprotein(a) hypertension, diabetes mellitus (DM), lipid-lowering medication use, and menopausal status. For these purposes, we used the Dutch Lipid Clinic Network (DLCN) criteria to diagnose clinical FH in individuals in the Copenhagen General Population Study.
Materials and Methods
The Copenhagen General Population Study
The Copenhagen General Population Study is a prospective cohort study initiated in 2003 with ongoing enrollment (1). White individuals of Danish descent (i.e., the person and both parents were born in Denmark and were Danish citizens) were randomly selected on the basis of the national Danish Civil Registration System to reflect the Danish population aged 20 to 100 years. Data were obtained from a self-administrated questionnaire that was reviewed together with an investigator on the day of attendance, a physical examination, and blood samples including DNA extraction. The study was approved by local institutional review boards and a Danish ethical committee (H-KF-01-144/01) and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all 106,506 individuals available for the study. Because hypothyroidism may mimic FH in terms of elevated LDL cholesterol concentrations, 334 individuals with hypothyroidism (TSH level >5 mIU/L and a total T4 level <50 nmol/L and/or a total T3 level <0.9 nmol/L) were excluded, leaving 106,172 individuals for the analyses.
Diagnostic criteria for clinical FH
FH was diagnosed by using the DLCN criteria, a set of clinical criteria that are widely used and recommended by lipid and FH guidelines (3, 9, 10) The criteria were slightly modified, as done previously (1, 11), because information on LDL cholesterol concentrations in children and family members and personal details of tendon xanthoma or corneal arcus were lacking (Supplemental Table 1).
A clinical FH diagnosis was considered definite when the total score was >8 points, probable when the score was 6 to 8 points, possible when the score was 3 to 5 points, and unlikely when the score was <3 points. The score was calculated using points assigned for (1) family history of a first-degree relative with premature CAD (<55 years of age for men and <60 years for women) and/or a first-degree relative with known hypercholesterolemia (1 point); (2) personal history of premature CAD at baseline (ages as given previously, 2 points) or premature cerebral vascular disease (ages as given previously) or peripheral vascular disease at baseline (1 point if not already 2 points for premature CAD); (3) LDL cholesterol ≥8.5 mmol/L (330 mg/dL, 8 points), 6.5 to 8.4 mmol/L (250 to 329 mg/dL, 5 points), 5.0 to 6.4 mmol/L (190 to 249 mg/dL, 3 points), and 4.0 to 4.9 mmol/L (155 to 189 mg/dL, 1 point); and (4) presence of an LDLR W23X, W66G or W556S, or APOB R3500Q mutation (8 points).
PAD, CKD, and MI
Endpoints were based on the World Health Organization’s codes for International Classification of Diseases, Eighth Revision and 10th Revision (ICD-8 and ICD-10) and were collected from 1 January 1977 through 9 March 2017 by reviewing all hospital admissions, including outpatient visits (because many PAD and CKD diagnoses do not require hospital admission), and diagnoses in the national Danish Patient Registry and causes of death in the national Danish Registry of Causes of Death. The International Classification of Diseases, Ninth Revision was not used because this version of the ICD was never introduced in Denmark, and ICD-10 replaced ICD-8 as of 1 January 1994. PAD was defined as ICD-8 440 to 441, 443.99, and 445 and ICD-10 as I70 to I72 and I73.9; CKD as ICD-8 582 to 584, 403 to 404, and 440.19 and ICD-10 as N18 to N19, I12 to I13, N28.0, and N70.1; and MI as ICD-8 410 and ICD-10 I21 to I22.
In all, 3736 individuals had a PAD diagnosis (2337 during follow-up); 1993 individuals had a CKD diagnosis (1382 during follow-up), and 4365 individuals had an MI diagnosis (2157 during follow-up). Follow-up began at the first inclusion into the study and ended with censoring at the date of death, occurrence of an event, or emigration or on 9 March 2017 (corresponding to the end of follow-up for the least updated register), whichever came first. Follow-up was 100% complete, as all individuals living in Denmark have a Danish Civil Registration System number that provides daily updated information on emigration and death. The follow-up time was up to 13 years (median, 7.7 years).
In the cross-sectional analysis, PAD was defined as a composite of the previously stated PAD ICD-8 and ICD-10 diagnoses and/or self-reported intermittent claudication and/or ABI ≤0.9, corresponding to the European Society of Cardiology definition of lower extremity PAD (7), yielding 6705 individuals with an event. Likewise, CKD was defined as a composite of the previously stated CKD ICD-8 and ICD-10 diagnoses and/or a calculated eGFR<60 mL/min/1.73 m2, coinciding with the Kidney Disease: Improving Global Outcomes definition of mildly to moderately decreased (stage G3) kidney function or worse (12), yielding 11,283 individuals with an event. Blood pressure measurements were performed in both arms and legs by a trained technician (medical student), with the participant resting in a supine position. Systolic blood pressure of the posterior tibial artery or the dorsalis pedis artery was obtained by a handheld Doppler. ABI was calculated as the lowest ankle systolic blood pressure level divided by the right arm brachial systolic blood pressure level. Ankle blood pressure was measured in all patients who entered the study from March 2009 onward (i.e., ABI could be calculated in 50,092 individuals). eGFR was calculated from plasma creatinine, using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation (12). Plasma creatinine measurements were missing in 336 individuals (i.e., eGFR was available in 105,836 individuals).
Biochemical analyses
Nonfasting plasma concentrations of total cholesterol, high-density lipoprotein cholesterol, triglycerides, and glucose were measured using standard enzymatic assays (Thermo Fisher Scientific/Konelab, Helsinki, Finland) and performed in a laboratory with internal (daily) and external (10 times per year) quality assessment and assurance. The plasma creatinine enzymatic assay was isotope dilution mass spectrometry traceable. Lipoprotein(a) total mass was measured using apolipoprotein A isoform‒insensitive turbidimetric assays, either an in-house assay as described elsewhere (13), an assay from DiaSys (DiaSys Diagnostic Systems, Holzheim, Germany) or an assay from Denka Seiken (Denka Seiken, Tokyo, Japan). LDL cholesterol concentration was calculated using the Friedewald equation when plasma triglycerides were ≤4.0 mmol/L (352 mg/dL) and was measured by a direct enzymatic method at higher triglyceride concentrations. Total and LDL cholesterol was multiplied by 1.43 in individuals receiving lipid-lowering medication, corresponding to a mean 30% reduction in LDL cholesterol concentrations (14), as done previously (11, 15). Lipid-lowering medication use was self-reported, with more than 97% accounted for by statins. Statin type and dose were not reported.
Genotyping
LDLR W23X, W66G, W556S, and APOB R3500Q mutations were genotyped by TaqMan assays (Applied Biosystems, Foster City, CA). These four variants account for 39% of FH mutations in the Copenhagen population, and other previously reported mutations have low prevalence (1). Sequencing of randomly selected individuals with each variant was used to verify the TaqMan results.
Covariates
Hypertension was defined as systolic blood pressure level ≥140 mm Hg (≥135 mm Hg for individuals with diabetes), diastolic blood pressure level ≥90 mm Hg (≥85 mm Hg for individuals with diabetes), and/or self-reported use of antihypertensive drugs specifically prescribed for hypertension. BMI was weight in kilograms divided by height in meters squared. DM was defined as a composite of self-reported diabetes, use of antidiabetic medication, nonfasting plasma glucose level >11.0 mmol/L, and/or hospitalization or death due to diabetes (ICD-8: 249 to 250; ICD-10: E10 to E11, E13 to E14). Smoking was defined as current smoking at baseline. Information on current and former smoking, type and amount of tobacco smoked, and menopausal status for women was self-reported. One pack-year was defined as 20 cigarettes or equivalent (smoking cigars, cheroots, pipe) smoked daily for 1 year.
Statistical analyses
Data were analyzed using Stata SE/14.2. We used χ2 analysis to test for distribution of dichotomous variables between the categories of clinical FH. The Cuzick nonparametric test for trend was used to assess differences in continuous variables between categories of clinical FH. For cross-sectional analyses of risks of PAD and CKD by categories of clinical FH, we used logistic regression to estimate ORs. For prospective analyses of risks of PAD, CKD, and MI by categories of clinical FH, we used Cox proportional hazards regression with age as time scale (or follow-up time for risks of MI) to estimate hazard ratios. Both regression models were adjusted for age, birth year, sex, current smoking, pack-years, BMI, lipoprotein(a), hypertension, DM, lipid-lowering medication use, and menopausal status. Birth year was adjusted to accommodate diagnostic and therapeutic changes, including changes in lipid-lowering medication use, over calendar time. Lipoprotein(a) was measured sequentially in 57,522 individuals entering the Copenhagen General Population Study; in addition, because lipoprotein(a) is highly genetically determined, for adjustment only we imputed lipoprotein(a) levels to extend analyses to the complete data set, as done previously (13). Imputation was done using KIV-2, four genetic variants (rs10455872, rs74617384, rs641990, and rs12194138) known to affect lipoprotein(a) levels, age, sex, and diabetes using multivariable chained imputation (mi impute chained in Stata) with fully conditional specification. Because lipoprotein(a) levels were not normally distributed, levels were log-transformed in all analyses.
To test whether results were different for individuals with a confirmed FH mutation compared with those with clinically diagnosed FH, we also performed separate analyses for those with a confirmed mutation. Cumulative incidences of PAD, CKD, and MI were plotted, and differences across ordered categories of clinical FH and ordered categories of clinical FH and ABI or eGFR were examined using the log-rank test. The definite and probable categories of FH (or definite, probable, and possible categories for risk estimates of MI) were combined to maximize statistical power. Because PAD is part of the DLCN criteria, a sensitivity analysis was performed with PAD excluded from the criteria.
Results
Baseline characteristics by categories of clinical FH are shown in Table 1. In all, 6623 individuals (6.2%) had possible FH and 488 (0.5%) had probable/definite FH. In the probable/definite clinical FH category, 169 individuals (0.16% of total and 35% of those with probable/definite FH) had a confirmed LDLR W23X, W66G, W556S, or APOB R3500Q mutation. Individuals with possible and probable/definite FH were slightly older; had a higher BMI; and were more likely to be smokers, have hypertension and DM, and use lipid-lowering medication than were those with unlikely FH. Individuals with possible and probable/definite FH had higher concentrations of LDL cholesterol, total cholesterol, triglycerides, and lipoprotein(a) and lower concentrations of high-density lipoprotein cholesterol than those with unlikely FH. The prevalence of PAD and CKD, including diagnoses before, at, and after study entry, was higher in individuals with possible and probable/definite FH than in those with unlikely FH, and individuals with possible and probable/definite FH had lower ABI and eGFR than those with unlikely FH.
Baseline Characteristics by Categories of Clinical Familial Hypercholesterolemia
| Dutch Lipid Clinic Network Criteria | ||||
|---|---|---|---|---|
| Unlikely | Possible | Probable/Definite | P for Trend | |
| N (%) | 99,063 (93.3) | 6621 (6.2) | 488 (0.5) | |
| Sex, women (%) | 54,412 (55) | 3625 (55) | 294 (60) | 0.06 |
| Age, y | 58 (48–67) | 60 (53–67) | 59 (51–66) | <0.001 |
| BMI, kg/m2 | 25.5 (23.1–28.3) | 27.1 (24.7–29.9) | 26.3 (23.8–29.6) | <0.001 |
| Current smoking, n (%) | 16,490 (17) | 1503 (23) | 97 (20) | <0.001 |
| Pack-years (current or former smokers) | 15 (6–30) | 21 (9–35) | 16 (6–32) | <0.001 |
| LDL cholesterol, mmol/L | 3.2 (2.7–3.8) | 5.2 (5.0–5.6) | 6.6 (5.3–7.1) | <0.001 |
| mg/dL | 124 (104–147) | 201 (193–215) | 256 (205–277) | <0.001 |
| Total cholesterol, mmol/L | 5.6 (4.9–6.3) | 7.4 (6.4–7.9) | 7.4 (6.6–8.8) | <0.001 |
| mg/dL | 216 (189–242) | 293 (271–313) | 336 (290–372) | <0.001 |
| Triglycerides, mmol/L | 1.4 (0.9–2.0) | 1.8 (1.3–2.6) | 1.7 (1.1–2.5) | <0.001 |
| mg/dL | 123 (79–176) | 158 (114–229) | 150 (97–220) | <0.001 |
| HDL cholesterol, mmol/L | 1.6 (1.3–2.0) | 1.4 (1.2–1.7) | 1.4 (1.2–1.8) | <0.001 |
| mg/dL | 62 (50–77) | 54 (46–66) | 54 (46–69) | <0.001 |
| Lipoprotein(a), mg/dL | 9.5 (6.6–27.2) | 13.8 (8.6–49.3) | 17.6 (7.8–43.0) | <0.001 |
| Lipid-lowering medication, n (%) | 10,781 (11) | 1674 (25) | 294 (60) | <0.001 |
| DM, n (%) | 4671 (5) | 429 (6) | 34 (7) | <0.001 |
| Hypertension, n (%) | 52,623 (53) | 4402 (67) | 316 (65) | <0.001 |
| PAD, n (%) | 5825 (6) | 826 (12) | 54 (11) | <0.001 |
| CKD, n (%) | 9987 (10) | 1207 (18) | 89 (18) | <0.001 |
| Myocardial infarction, n (%) | 3581 (4) | 728 (11) | 56 (11) | <0.001 |
| ABI | 1.2 (1.1–1.2) | 1.1 (1.1–1.2) | 1.1 (1.1–1.2) | <0.001 |
| eGFR, mL/min/1.73 m2 | 81 (70–91) | 74 (64–85) | 75 (64–87) | <0.001 |
| Dutch Lipid Clinic Network Criteria | ||||
|---|---|---|---|---|
| Unlikely | Possible | Probable/Definite | P for Trend | |
| N (%) | 99,063 (93.3) | 6621 (6.2) | 488 (0.5) | |
| Sex, women (%) | 54,412 (55) | 3625 (55) | 294 (60) | 0.06 |
| Age, y | 58 (48–67) | 60 (53–67) | 59 (51–66) | <0.001 |
| BMI, kg/m2 | 25.5 (23.1–28.3) | 27.1 (24.7–29.9) | 26.3 (23.8–29.6) | <0.001 |
| Current smoking, n (%) | 16,490 (17) | 1503 (23) | 97 (20) | <0.001 |
| Pack-years (current or former smokers) | 15 (6–30) | 21 (9–35) | 16 (6–32) | <0.001 |
| LDL cholesterol, mmol/L | 3.2 (2.7–3.8) | 5.2 (5.0–5.6) | 6.6 (5.3–7.1) | <0.001 |
| mg/dL | 124 (104–147) | 201 (193–215) | 256 (205–277) | <0.001 |
| Total cholesterol, mmol/L | 5.6 (4.9–6.3) | 7.4 (6.4–7.9) | 7.4 (6.6–8.8) | <0.001 |
| mg/dL | 216 (189–242) | 293 (271–313) | 336 (290–372) | <0.001 |
| Triglycerides, mmol/L | 1.4 (0.9–2.0) | 1.8 (1.3–2.6) | 1.7 (1.1–2.5) | <0.001 |
| mg/dL | 123 (79–176) | 158 (114–229) | 150 (97–220) | <0.001 |
| HDL cholesterol, mmol/L | 1.6 (1.3–2.0) | 1.4 (1.2–1.7) | 1.4 (1.2–1.8) | <0.001 |
| mg/dL | 62 (50–77) | 54 (46–66) | 54 (46–69) | <0.001 |
| Lipoprotein(a), mg/dL | 9.5 (6.6–27.2) | 13.8 (8.6–49.3) | 17.6 (7.8–43.0) | <0.001 |
| Lipid-lowering medication, n (%) | 10,781 (11) | 1674 (25) | 294 (60) | <0.001 |
| DM, n (%) | 4671 (5) | 429 (6) | 34 (7) | <0.001 |
| Hypertension, n (%) | 52,623 (53) | 4402 (67) | 316 (65) | <0.001 |
| PAD, n (%) | 5825 (6) | 826 (12) | 54 (11) | <0.001 |
| CKD, n (%) | 9987 (10) | 1207 (18) | 89 (18) | <0.001 |
| Myocardial infarction, n (%) | 3581 (4) | 728 (11) | 56 (11) | <0.001 |
| ABI | 1.2 (1.1–1.2) | 1.1 (1.1–1.2) | 1.1 (1.1–1.2) | <0.001 |
| eGFR, mL/min/1.73 m2 | 81 (70–91) | 74 (64–85) | 75 (64–87) | <0.001 |
Data are absolute numbers (n, %) for categorical variables and median (interquartile range) for continuous variables. One pack-year was defined as 20 cigarettes or equivalent (cigars, cheroots, pipe) smoked daily for 1 y. Total and LDL cholesterol concentrations were multiplied by 1.43 in individuals receiving lipid-lowering medication, corresponding to a mean 30% reduction in LDL cholesterol concentrations (14). ABI was measured in 50,092 individuals. PAD was defined as an ICD-8 or ICD-10 diagnosis of PAD and/or ABI ≤0.9 and/or intermittent claudication. CKD was defined as an ICD-8 or ICD-10 diagnosis of CKD and/or eGFR <60 mL/min/1.73 m2.
Abbreviation: HDL, high-density lipoprotein.
Baseline Characteristics by Categories of Clinical Familial Hypercholesterolemia
| Dutch Lipid Clinic Network Criteria | ||||
|---|---|---|---|---|
| Unlikely | Possible | Probable/Definite | P for Trend | |
| N (%) | 99,063 (93.3) | 6621 (6.2) | 488 (0.5) | |
| Sex, women (%) | 54,412 (55) | 3625 (55) | 294 (60) | 0.06 |
| Age, y | 58 (48–67) | 60 (53–67) | 59 (51–66) | <0.001 |
| BMI, kg/m2 | 25.5 (23.1–28.3) | 27.1 (24.7–29.9) | 26.3 (23.8–29.6) | <0.001 |
| Current smoking, n (%) | 16,490 (17) | 1503 (23) | 97 (20) | <0.001 |
| Pack-years (current or former smokers) | 15 (6–30) | 21 (9–35) | 16 (6–32) | <0.001 |
| LDL cholesterol, mmol/L | 3.2 (2.7–3.8) | 5.2 (5.0–5.6) | 6.6 (5.3–7.1) | <0.001 |
| mg/dL | 124 (104–147) | 201 (193–215) | 256 (205–277) | <0.001 |
| Total cholesterol, mmol/L | 5.6 (4.9–6.3) | 7.4 (6.4–7.9) | 7.4 (6.6–8.8) | <0.001 |
| mg/dL | 216 (189–242) | 293 (271–313) | 336 (290–372) | <0.001 |
| Triglycerides, mmol/L | 1.4 (0.9–2.0) | 1.8 (1.3–2.6) | 1.7 (1.1–2.5) | <0.001 |
| mg/dL | 123 (79–176) | 158 (114–229) | 150 (97–220) | <0.001 |
| HDL cholesterol, mmol/L | 1.6 (1.3–2.0) | 1.4 (1.2–1.7) | 1.4 (1.2–1.8) | <0.001 |
| mg/dL | 62 (50–77) | 54 (46–66) | 54 (46–69) | <0.001 |
| Lipoprotein(a), mg/dL | 9.5 (6.6–27.2) | 13.8 (8.6–49.3) | 17.6 (7.8–43.0) | <0.001 |
| Lipid-lowering medication, n (%) | 10,781 (11) | 1674 (25) | 294 (60) | <0.001 |
| DM, n (%) | 4671 (5) | 429 (6) | 34 (7) | <0.001 |
| Hypertension, n (%) | 52,623 (53) | 4402 (67) | 316 (65) | <0.001 |
| PAD, n (%) | 5825 (6) | 826 (12) | 54 (11) | <0.001 |
| CKD, n (%) | 9987 (10) | 1207 (18) | 89 (18) | <0.001 |
| Myocardial infarction, n (%) | 3581 (4) | 728 (11) | 56 (11) | <0.001 |
| ABI | 1.2 (1.1–1.2) | 1.1 (1.1–1.2) | 1.1 (1.1–1.2) | <0.001 |
| eGFR, mL/min/1.73 m2 | 81 (70–91) | 74 (64–85) | 75 (64–87) | <0.001 |
| Dutch Lipid Clinic Network Criteria | ||||
|---|---|---|---|---|
| Unlikely | Possible | Probable/Definite | P for Trend | |
| N (%) | 99,063 (93.3) | 6621 (6.2) | 488 (0.5) | |
| Sex, women (%) | 54,412 (55) | 3625 (55) | 294 (60) | 0.06 |
| Age, y | 58 (48–67) | 60 (53–67) | 59 (51–66) | <0.001 |
| BMI, kg/m2 | 25.5 (23.1–28.3) | 27.1 (24.7–29.9) | 26.3 (23.8–29.6) | <0.001 |
| Current smoking, n (%) | 16,490 (17) | 1503 (23) | 97 (20) | <0.001 |
| Pack-years (current or former smokers) | 15 (6–30) | 21 (9–35) | 16 (6–32) | <0.001 |
| LDL cholesterol, mmol/L | 3.2 (2.7–3.8) | 5.2 (5.0–5.6) | 6.6 (5.3–7.1) | <0.001 |
| mg/dL | 124 (104–147) | 201 (193–215) | 256 (205–277) | <0.001 |
| Total cholesterol, mmol/L | 5.6 (4.9–6.3) | 7.4 (6.4–7.9) | 7.4 (6.6–8.8) | <0.001 |
| mg/dL | 216 (189–242) | 293 (271–313) | 336 (290–372) | <0.001 |
| Triglycerides, mmol/L | 1.4 (0.9–2.0) | 1.8 (1.3–2.6) | 1.7 (1.1–2.5) | <0.001 |
| mg/dL | 123 (79–176) | 158 (114–229) | 150 (97–220) | <0.001 |
| HDL cholesterol, mmol/L | 1.6 (1.3–2.0) | 1.4 (1.2–1.7) | 1.4 (1.2–1.8) | <0.001 |
| mg/dL | 62 (50–77) | 54 (46–66) | 54 (46–69) | <0.001 |
| Lipoprotein(a), mg/dL | 9.5 (6.6–27.2) | 13.8 (8.6–49.3) | 17.6 (7.8–43.0) | <0.001 |
| Lipid-lowering medication, n (%) | 10,781 (11) | 1674 (25) | 294 (60) | <0.001 |
| DM, n (%) | 4671 (5) | 429 (6) | 34 (7) | <0.001 |
| Hypertension, n (%) | 52,623 (53) | 4402 (67) | 316 (65) | <0.001 |
| PAD, n (%) | 5825 (6) | 826 (12) | 54 (11) | <0.001 |
| CKD, n (%) | 9987 (10) | 1207 (18) | 89 (18) | <0.001 |
| Myocardial infarction, n (%) | 3581 (4) | 728 (11) | 56 (11) | <0.001 |
| ABI | 1.2 (1.1–1.2) | 1.1 (1.1–1.2) | 1.1 (1.1–1.2) | <0.001 |
| eGFR, mL/min/1.73 m2 | 81 (70–91) | 74 (64–85) | 75 (64–87) | <0.001 |
Data are absolute numbers (n, %) for categorical variables and median (interquartile range) for continuous variables. One pack-year was defined as 20 cigarettes or equivalent (cigars, cheroots, pipe) smoked daily for 1 y. Total and LDL cholesterol concentrations were multiplied by 1.43 in individuals receiving lipid-lowering medication, corresponding to a mean 30% reduction in LDL cholesterol concentrations (14). ABI was measured in 50,092 individuals. PAD was defined as an ICD-8 or ICD-10 diagnosis of PAD and/or ABI ≤0.9 and/or intermittent claudication. CKD was defined as an ICD-8 or ICD-10 diagnosis of CKD and/or eGFR <60 mL/min/1.73 m2.
Abbreviation: HDL, high-density lipoprotein.
Clinical FH and risk of PAD and CKD
In cross-sectional analyses including diagnoses before, at, and after study entry, risk of PAD was higher for individuals with possible and probable/definite FH than with those with unlikely FH, with ORs of 1.84 (95% CI, 1.70 to 2.00) and 1.36 (95% CI, 1.00 to 1.84), respectively (Fig. 1, upper panel). For CKD, stepwise higher risks were seen by categories of clinical FH, with ORs of 1.92 (95% CI, 1.78 to 2.07) in possible FH and 2.42 (95% CI, 1.86 to 3.15) in probable/definite FH compared with unlikely FH. Individuals with a confirmed LDLR W23X, W66G, W556S, or APOB R3500Q mutation had risk estimates similar to those for individuals with clinical FH, with ORs of 1.32 (95% CI, 0.75 to 2.33) for PAD and 1.99 (95% CI, 1.22 to 3.26) for CKD compared with those with unlikely FH.
Cross-sectional (upper panel) and prospective (lower panel) risks of PAD and CKD as a function of categories of clinical FH based on modified DLCN criteria and for individuals with a confirmed FH mutation. Cross-sectional endpoints before, at, and after study entry for PAD were a composite of ICD-8 and ICD-10 diagnoses and/or intermittent claudication and/or ABI ≤0.9 and for CKD were ICD-8 and ICD-10 diagnoses and/or eGFR <60 mL/min/1.73 m2. Prospective endpoints were based on ICD-8 and ICD-10 diagnoses only because information on ABI, intermittent claudication, and eGFR was obtained only at baseline. Individuals with an ICD-8 or ICD-10 diagnosis at baseline were excluded from the prospective analyses. ORs and hazard ratios were adjusted for sex, age, birth year, menopause, current smoking, pack-years, BMI, lipoprotein(a), hypertension, DM, and lipid-lowering medication use. N, number.
In prospective analyses excluding individuals with endpoints before and at study entry and compared with individuals with unlikely FH, the hazard ratio of PAD was 1.47 (95% CI, 1.28 to 1.69) for those with possible FH and 1.64 (95% CI, 1.0 to 2.56) for individuals with probable/definite FH (Fig. 1, lower panel). Corresponding hazard ratios of CKD were 1.04 (95% CI, 0.84 to 1.28) for possible FH and 0.79 (95% CI, 0.33 to 1.90) for probable/definite FH. Individuals with a confirmed mutation had risk estimates similar to those of individuals with clinical FH, with hazard ratios of 1.56 (95% CI, 0.70 to 3.48) for PAD and 0.93 (95% CI, 0.23 to 3.73) for CKD compared with those with unlikely FH. Exclusion of information on peripheral vascular disease from the diagnostic criteria of clinical FH showed similar results (compare Supplemental Fig. 1 with Fig. 1).
The cumulative incidence of PAD as a function of age increased from individuals with unlikely FH through those with possible FH to individuals with probable/definite FH (P across ordered categories <0.001) (Fig. 2, upper panel). At age 80 years, the cumulative incidences of PAD were 10% for unlikely FH, 15% for possible FH, and 24% for probable/definite FH. The cumulative incidences of CKD as a function of age were similar for categories of clinical FH and unlikely FH (P = 0.80) (Fig. 2, lower panel). At age 80 years, the cumulative incidences of CKD were 5% for unlikely FH, 6% for possible FH, and 5% for probable/definite FH.
Cumulative incidences of PAD (upper panel) and CKD (lower panel) as a function of age and by categories of clinical FH. N, number.
ABI and eGFR associate with high risk of MI in clinical FH
The risk of MI increased stepwise by categories of clinical FH and ABI or eGFR (P for trend <0.001) (Fig. 3). Compared with individuals with unlikely FH and ABI >0.9, the multivariable adjusted hazard ratios of MI were 4.60 (95% CI, 2.36 to 8.97) in those with possible/probable/definite FH and ABI ≤0.9. Compared with individuals with unlikely FH and eGFR ≥60 mL/min/1.73 m2, the corresponding value was 2.19 (95% CI, 1.71 to 2.82) in those with possible/probable/definite FH and eGFR <60 mL/min/1.73 m2.
Prospective risk of myocardial infarction as a function of categories of clinical FH and ABI above/at and below 0.9 or eGFR at and above/below 60 mL/min/1.73 m2. A diagnosis of clinical FH included individuals with possible/probable/definite FH. No FH = unlikely FH. Hazard ratios were adjusted for sex, age, birth y, menopause, current smoking, pack-years, BMI, lipoprotein(a), hypertension, DM, and lipid-lowering medication use. ABI was available in 50,092 individuals. Individuals with an event at baseline were excluded from the prospective analysis. N, number.
The cumulative incidence of MI as a function of follow-up time increased from individuals with unlikely FH and ABI >0.9, through possible/probable/definite FH and ABI >0.9 and unlikely FH and ABI ≤0.9, to possible/probable/definite FH and ABI ≤0.9 (P across ordered categories <0.001) (Fig. 4, upper panel). After 5 years of follow-up, the cumulative incidence of MI was 1% in those with unlikely FH and ABI >0.9; 2% in those with possible/probable/definite FH and ABI >0.9; 4% in those with unlikely FH and ABI ≤0.9; and 9% in those with possible/probable/definite FH and ABI ≤0.9.
Cumulative incidences of myocardial infarction as a function of follow-up time (corresponding to a screening setting) and by categories of clinical FH and ABI above/at and below 0.9 (upper panel) or eGFR at and above/below 60 mL/min/1.73 m2 (lower panel). A diagnosis of clinical FH included individuals with possible/probable/definite FH. No FH = unlikely FH. N, number.
For eGFR, the cumulative incidences of MI as a function of follow-up time increased from individuals with unlikely FH and eGFR ≥60 mL/min/1.73 m2, through possible/probable/definite FH and eGFR ≥60 mL/min/1.73 m2 and unlikely FH and eGFR <60 mL/min/1.73 m2, to possible/probable/definite FH and eGFR <60 mL/min/1.73 m2 (P < 0.001) (Fig. 4, lower panel). After 5 years of follow-up, the cumulative incidence of MI was 1% in individuals with unlikely FH and eGFR ≥60 mL/min/1.73 m2; 2% in those with possible/probable/definite FH and eGFR ≥60 mL/min/1.73 m2; 3% in those with unlikely FH and eGFR <60 mL/min/1.73 m2; and 4% in those with possible/probable/definite FH and eGFR <60 mL/min/1.73 m2.
Discussion
In this prospective cohort from the general population, we have shown that individuals with clinical FH have increased risk of PAD and CKD and that low ABI and eGFR are associated with high risk of MI even after adjusting for sex, age, birth year, menopause, current smoking, pack-years, BMI, lipoprotein(a), hypertension, DM, and lipid-lowering treatment. Although it may be argued that this finding is not surprising, the risk of PAD and CKD and its association with risk of MI in individuals with clinical FH has not, to our knowledge, previously been shown.
Previous cross-sectional studies have reported a prevalence of PAD between 1% and 31% in patients with FH (16–19). In an angiography study screening 117 heterozygous patients with FH for atherosclerotic lesions, 33% had renal artery atherosclerosis, 56% had iliac atherosclerosis, 68% had abdominal aortic sclerosis, and 60% had CAD (20). Our findings together with the previously mentioned reports and with randomized clinical trials showing that LDL cholesterol‒lowering therapy improved both coronary and peripheral vascular outcomes in individuals who have hyperlipidemia with and without FH (21–24) support that FH is a systemic atherogenic disease. The reported variation in prevalence of PAD in FH may be due to a number of factors. First, the severity of atherosclerosis is proportional to cumulative LDL cholesterol exposure (3, 25). Accordingly, differences in median age between studies may be one explanation, together with methodological differences such as definitions of end points. Second, although our study included individuals from the general population, other studies included cohorts of patients with FH, in whom the clinical phenotype may be more severe because of ascertainment bias (26). Third, severity, extent, and anatomical topography of atherosclerotic lesions seem to vary between individuals despite similar LDL cholesterol exposure (3, 27). This may be due to differences in genotype, as well as differences in the presence of other major risk factors of atherosclerotic disease, such as sex, smoking, diabetes, hypertension, and high concentrations of triglyceride-rich lipoproteins (3, 7). Fourth, specific risk factors may be more important for the development of disease at specific anatomical sites. In epidemiological studies, smoking has been identified as an important risk factor for lower extremity vascular disease, as has DM (7, 28). Differences in blood flow, mechanical load, and arterial microenvironment may also contribute to anatomical differences in atherosclerotic plaque formation. In a recent study that included partly the same individuals from the Copenhagen general population, Beheshti et al. (29) found no association between clinical or genetic FH, or high LDL cholesterol per se, and risk of ischemic stroke, even though LDL cholesterol traditionally has been seen as a risk factor for ischemic stroke and despite the fact that statins reduced ischemic stroke risk in clinical intervention trials (30). The risk of stroke instead seemed to be driven by the presence of previous ischemic heart disease, and the authors speculated that high LDL cholesterol level leads to ischemic heart disease producing mural thrombi from MI and heart failure—mural thrombi that will dislocate and cause ischemic stroke (29). Alternatively, individuals with ischemic heart disease may have a higher burden of other major atherosclerotic risk factors in addition to FH, such as high concentrations of triglyceride-rich lipoproteins.
The prevalence of CKD has not previously been determined in a large FH population. In the current general population study, 18% of individuals with clinical FH had CKD according to ICD-8 and ICD-10 diagnoses and/or eGFR measurements compared with 10% of those with unlikely FH. This is lower than the rate reported in angiographically characterized patients with FH, where mild to moderate renal artery stenosis was observed in 39 of 80 patients (33%), suggesting that atherosclerotic lesions are present in the renal arteries before any potential decline in renal function (20).
In our study, we found an association between FH and CKD in a cross-sectional analysis, in which the endpoint was a composite of CKD ICD-8 and ICD-10 diagnoses (n = 1993) and/or an eGFR <60 mL/min/1.73 m2 (n = 10,221), but not in a prospective analysis based solely on incident ICD-8 and ICD-10 diagnoses of CKD (n = 1382). This may be due to clinical underdiagnosis of CKD and thus lack of power in the prospective analysis. We could not include eGFR prospectively because plasma creatinine was measured only at baseline.
Because PAD defines a pure vascular disease, with ABI as a diagnostic marker, our findings of an association between FH and high risk of PAD as well as low ABI as a predictor of cardiovascular risk in FH are in line with the present evidence of LDL cholesterol as a causal factor in the pathogenesis of atherosclerosis (25). In contrast, CKD covers a variety of kidney abnormalities of different etiologies, with eGFR being the gold standard but unspecific measure of decline in kidney function (12). DM and hypertension are both risk factors and/or major causes of CKD, and CKD is associated with dyslipidemia and increased cardiovascular risk (31). The potential roles of LDL cholesterol and atherosclerosis as causal risk factors of CKD have not been determined. In randomized clinical trials, statins reduced the risk of cardiovascular events in individuals with CKD but seemed to have no or little effect on the progression of kidney disease (32). In contrast, in a study of 4040 patients with renovascular disease and reduced kidney function, statins were associated with significant reductions in risk of dialysis and hospitalization (33). In a study of 100 patients who had FH without hypertension and with normal glucose tolerance, lowering of LDL cholesterol with a statin was associated with improved kidney function. Interestingly, improvement was observed not only in terms of creatinine clearance but also for microalbuminuria, a marker of glomerular endothelial dysfunction (34). This suggests that high LDL cholesterol concentration is a contributing factor in the progression of kidney disease, even in the absence of hypertension and DM, which might be due to renal atherosclerosis. Whether the improvement in microalbuminuria represents a direct effect of LDL cholesterol on glomerular function or is a secondary effect of improved renal microvascular circulation is unclear. Further studies are needed to elucidate the potential causal effects of LDL cholesterol in kidney disease.
Our study has some limitations. Information regarding LDL cholesterol concentrations in children and family members and the presence of tendon xanthomas and corneal arcus was not recorded and could not be included in the diagnostic criteria. This may have led to a slight underdiagnosis of FH in our population. Also, because peripheral vascular disease is part of the DLCN criteria for FH, the cross-sectional risk of PAD may rather be seen as a measure of the prevalence of PAD by DLCN criteria at baseline. However, we observed results similar to the main analyses when peripheral vascular disease was excluded from the criteria. In the prospective analysis, individuals with PAD diagnoses at baseline were excluded. Lipid-lowering medication use was recorded in all individuals, with 97% accounted for by statins. However, we did not have information regarding type or dose of lipid-lowering therapy or changes in lipid-lowering therapy over time. To account for diagnostic and therapeutic changes over time, including changes in lipid-lowering therapy, all prospective analyses were adjusted for birth year. To our knowledge, this adjustment is the best method available, but it may not be sufficient. Another limitation of the study is that eGFR was calculated from a single measurement of plasma creatinine, which may lead to some misclassification of kidney disease. Unfortunately, we did not have access to albuminuria or proteinuria measurements. Genetic FH diagnosis was confirmed in 169 individuals (0.16%) in the studied population. Separate analyses for this group showed results similar to those for individuals with clinical FH compared with individuals with unlikely FH. However, these analyses were underpowered because of the low number of individuals and events. In contrast, the large number of individuals with unlikely and possible FH were likely responsible for the low P values, even for covariates for which the differences in frequency or medians between the clinical FH categories were small, such as age.
The strengths of our study include showing that ABI and eGFR are associated with high risk of MI in individuals with clinical FH, independent of sex, age, birth year, menopause, current smoking, pack-years, BMI, lipoprotein(a), hypertension, DM, and lipid-lowering treatment. In addition, the study is based on a well-characterized population with complete follow-up, including >7000 individuals with clinical FH. The current findings have a number of clinical implications, which are currently not reflected in guidelines for the management and treatment of FH (3, 9).
Diagnosis of clinical FH should include screening for signs and symptoms of PAD and CKD.
Assessment of total cardiovascular risk in individuals with FH should include evaluation of ABI and eGFR, also in the absence of symptoms of PAD or CKD. As for preexisting coronary heart disease and diabetes (risk factors that are included in present guidelines), abnormal ABI or eGFR values may be used to identify those at very high risk of MI who should receive high-intensity lipid-lowering treatment, aiming at LDL cholesterol concentrations below 1.8 mmol/L (70 mg/dL).
Risk factors of CKD (i.e., hypertension and DM) should be treated, and use of potentially nephrotoxic drugs should be limited in patients with FH.
In conclusion, the current study shows that a clinical diagnosis of possible and probable/definite FH based on the DLCN criteria is associated with an increased risk of PAD and CKD. Furthermore, reduced ABI and eGFR values are associated with increased risk of MI in individuals with clinical FH in the general population. This suggests that individuals with FH should be routinely screened for PAD and CKD and that ABI and eGFR may be used as prognostic tools in the management and treatment of FH.
Abbreviations:
- ABI
ankle-brachial index
- BMI
body mass index
- CAD
coronary artery disease
- CKD
chronic kidney disease
- DLCN
Dutch Lipid Clinic Network
- DM
diabetes mellitus
- eGFR
estimated glomerular filtration rate
- FH
familial hypercholesterolemia
- ICD-8
International Classification of Diseases, Eighth Revision
- ICD-10
International Classification of Diseases, 10th Revision
- LDL
low-density lipoprotein
- PAD
peripheral arterial disease
Acknowledgments
We thank the participants and staff of the Copenhagen General Population Study for their important contributions.
Financial Support: This work was supported by the Danish Council of Independent Research (to F. E.).
Disclosure Summary: The authors have nothing to disclose.
