Imputation of Baseline LDL Cholesterol Concentration in Patients with Familial Hypercholesterolemia on Statins or Ezetimibe.
The prevalence of FH, once considered to be 1 in 500, has been adjusted in light of more precise diagnostic criteria and is now estimated to be 1 in 250 worldwide (3), with a higher prevalence in populations with founder effects, such as that seen in the French-Canadian population.
There are 2 widely used and internationally accepted definitions for FH, namely, those from the Dutch Lipid Clinics Network and the Simon Broome Registry criteria. Each relies on documenting an increased LDL-C in an adult (usually >200 mg/dL or >5.0 mmol/L), together with a family history of increased LDL-C in first-degree relatives, or premature atherosclerotic cardiovascular disease (ASCVD) in a first-degree relative, plus the presence of xanthomas, xanthelasmas, or premature corneal arcus (4-9). A DNA diagnosis may confirm the diagnosis unambiguously but is not required for a clinical diagnosis. Other definitions include that from the Japanese Atherosclerosis Society, which has adapted the Simon-Broome Registry criteria, and also the MedPed definition, which is used infrequently because it relies on strict LDL-C cutpoints imposed on first-, second-, and third-degree relatives, making it impractical in a clinical setting (10, 11). A new Canadian definition for FH has also adapted the Simon Broome Registry criteria, but it offers these in a simplified version (6).
FH is frequently undiagnosed worldwide, except for in countries such as the Netherlands, Norway, Switzerland, Iceland, and the UK (1), where national registries have been implemented. The experience from the Netherlands has shown that if recognized and treated early, patients with FH can have a similar rate of coronary heart disease as the general population (12). A precise diagnosis of FH is important. Two recent studies have shown that the risk of ASCVD in patients with an LDL-C >200 mg/dL (>5.0 mmol/L) is increased approximately 6-fold compared with an individual with an LDL-C of 130 mg/dL (3.4 mmol/L). However, if a patient has a mutation in the LDLR, APOB, or PCSK9 genes known to cause FH, this risk is increased 10- to 22-fold (13, 14). Once a subject is diagnosed with FH, cascade screening, consisting of screening all first-degree relatives, proves to be a cost-efficient way to identify additional affected individuals (15-19). Many international groups have made awareness, screening, and treatment of FH a priority (7, 8, 20-22).
Often, the baseline pretreatment LDL-C is not available because the patient has initiated and continues to receive lipid-lowering therapy, especially statins. Further, the original baseline LDL-C may predate the current assessment by many years and cannot be easily retrieved. Most guidelines consider that a definite diagnosis of FH mandates lipid-lowering therapy and family screening (1,6,21,23,24). Access to inhibitors of PCSK9 and more costly medications, such as mipomersen or lomitapide, may depend on a diagnosis of FH in some countries (25-28).
We have provided an application for computers and smartphones to make the diagnosis of FH based on the Simon Broome Registry criteria, the Dutch Lipid Clinics Network criteria, and the new Canadian FH definition (6) that uses an imputed baseline LDL-C if the patient is on lipid-lowering therapy with either statins and/or ezetimibe. This application can be accessed online (http:// www.circl.ubc.ca/english/web_fh.html) (29). We used a metaanalysis that provides the average LDL-C reduction for each statin at indicated prescribed doses. We used the same data for the fixed 10-mg/day dose of ezetimibe as monotherapy or in combination with statins (30). In the present analysis, we present the validation of the imputation of a baseline LDL-C based on the observed impact of current lipid-lowering therapies with statins and/or ezetimibe in FH patients. Access to this algorithm is also available through the website of FH Canada (www. FHCanada.net) (31).
Materials and Methods
We performed a retrospective analysis on data from 1297 patients with FH from 6 clinics across Canada (Coauthor involved: RAH; LB; JB, PC; RD, AB, JG, IR; DG, DB) in whom a baseline LDL-C was available before the initiation of statins or ezetimibe and in whom an LDL-C was available within 18 months after the initiation of therapy [mean 7.2 (3.8) months]. We selected the 18-month time point to ensure the patients were on a stable dose of medication. We eliminated patients with missing data or noncompliance to treatment (n = 45), patients with an on-treatment LDL-C determined >18 months after initiation of therapy (n = 120), patients on lipid-lowering drugs other than statins or ezetimibe (n = 128), and patients on nonstandard dose of medication (e.g., rosuvastatin, 5 mg 3 times/week; simvastatin, 30 mg/day) (n = 53) (Fig. 1). All data were deidentified. The protocol for the Canadian FH registry was reviewed and accepted by the Research Ethics Board of the McGill University Health Center.
We used the metaanalysis of Hou et al. (30) to determine the mean percent change from baseline for lovastatin (10, 20, 40, and 80 mg), pravastatin (10, 20, and 40 mg), simvastatin (5, 10, 20, 40, and 80 mg), atorvastatin (10, 20, 40, and 80 mg), fluvastatin (20 and 40 mg), rosuvastatin (5, 10, 20, and 40 mg), and ezetimibe (10 mg; all daily doses). The selection criteria were a diagnosis of FH based on the Dutch Lipid Clinics Network (definite or probable) or the Simon Broome Registry criteria (definite or probable), a baseline LDL-C calculated by the Friedewald formula in a patient naive to lipid-lowering therapies and a follow-up evaluation, and on-treatment LDL-C obtained within 18 months. In all cases, secondary causes of severe hypercholesterolemia (e.g., hypothyroidism, liver disease, nephrotic syndrome, or medications) were ruled out. For ezetimibe, most patients were taking an optimal dose of statin, and the follow-up LDL-C was then taken as the new baseline. Some patients were started on statin and ezetimibe simultaneously, and the validation of the imputed LDL-C compared with baseline LDL-C was examined separately. We examined the effects of a patient's sex in the comparison of baseline vs imputed baseline LDL-C.
Data are expressed as mean [+ or -] SE (see figures) or mean [+ or -] SD (see tables). The imputation of the LDL-C was performed by dividing the on-treatment LDL-C by the reciprocal of the expected percent change (Table 1). Patients were then grouped according to the dose and type of statin prescribed. The expected percent change was compared with the observed percent ([+ or -]SE) change for each dose and type of statin or ezetimibe. Because the expected percent change from baseline is based on a metaanalysis of multiple studies, no SE is provided. Therefore, we used a single-sided i-test to determine statistical significance. For each dose of each medication, the mean baseline LDL-C was compared with the mean of imputed baseline LDL-C by 2-tailed paired i-tests. A Bonferroni adjustment for multiple i-tests was made with the level of significance set at P < 0.002 (0.05/23 tests). A correlation between baseline and imputed LDL-C was performed for each dose of each medication by Pearson linear regression, and a correlation coefficient (r) and P value were determined. When available, the association between effect of sex on baseline and imputed LDL-C was determined (n = 788). All statistical analyses were performed using the IBM SPSS Statistics version 22.0.
We obtained data on 1297 patients, and after eliminating those with missing data, nonstandard doses of statins, or medications other than statins or ezetimibe, 951 patients with FH were included in the final analysis (Fig. 1). The mean [+ or -] SD age was 43 (14) years (range, 9-80 years; 50% female). The number of patients on each dose of statin is shown in Table 2. The mean baseline LDL-C for all patients receiving a specific dose of a statin, the on-treatment LDL-C, the observed reduction in LDL-C, and the predicted percent reduction [derived from Table 1 of Hou et al. (30)] are shown in Table 2. We first compared the predicted percent reduction with the observed reduction: Fig. 2 shows the differences between the observed vs expected percent LDL-C reduction for each dose of individual statins. There was a statistically significant difference (P < 0.002) between observed and expected percent LDL-C reduction for ezetimibe only.
The imputed baseline LDL-C was compared with the actual baseline LDL-C by paired 2-tailed i-test (Fig. 3). There were no statistically significant differences (P > 0.002) observed except for ezetimibe (P < 0.001). For lovastatin 80 mg, pravastatin 20 mg, and simvastatin 10, 20, and 40 mg, we observed marginal statistical significance. In the overall group, the mean [+ or -] SE baseline LDL-C was 243.0 (2.2) mg/dL [6.28 (0.06) mmol/L] and the mean [+ or -] SE imputed baseline LDL-C was 244.2 (2.6) mg/dL [6.31 (0.07) mmol/L] (P = 0.48) (Fig. 3, inset). There was no difference in response according to the patient's sex (see Figure 1 in the Data Supplement that accompanies the online version of this article at http://www.clinchem.org/content/vol64/issue2). A DNA mutation in the LDLR, APOB, or PCSK9 genes was identified in 429 of 951 (45%) individuals. Results were similar to those of the whole cohort.
The Pearson correlation coefficient for baseline observed and imputed baseline LDL-C was r = 0.76 (P < 0.001; Fig. 4). Histograms showing the comparison between observed vs expected percent LDL-C reduction (left panels), between baseline vs imputed LDL-C concentrations (middle panel), and the correlations between each dose of statins and ezetimibe (right panels) are shown Figs. 2--8 of the online Data Supplement. The data are presented for individual doses of each statin (lovastatin, 10, 20, 40, and 80 mg; pravastatin, 10, 20, and 40 mg; simvastatin, 5, 10, 20, 40, and 80 mg; atorvastatin, 10, 20, 40, and 80 mg; fluvastatin, 20 and 40 mg; rosuvastatin, 5, 10, 20, and 40 mg; and ezetimibe, 10 mg).
The diagnosis of FH relies on various criteria, each of which includes the concentration of LDL-C before the initiation of lipid-lowering therapy. Clinicians are frequently faced with a patient on lipid-lowering therapy with an on-treatment LDL-C concentration without knowledge of the baseline LDL-C. The reasons for this vary considerably, but inaccessibility of past medical records, changes in care providers, and patients' lack of recall can make the determination of the baseline LDL-C difficult, if not impossible. Current guidelines for the management of increased cholesterol support the use of high-intensity statins (atorvastatin 40-80 mg or rosuvastatin 20-40 mg/day) in high-risk individuals, targeting either a 50% reduction from baseline LDL-C or <70 mg/dL (1.8 mmol/L) (21, 23, 24). In some cases, the LDL-C remains increased despite treatment. This can be because of lack of patient adherence to treatment, a decreased response to the prescribed dose, or an increased baseline LDL-C. Many patients who are compliant to prescribed high-dose statin and who have an on-treatment LDL-C that remains increased may, in fact, have FH.
Here, we provide validation for the calculation of an imputed baseline LDL-C that will help clinicians and raise awareness of the possibility that the patient may have FH, leading to more appropriate and intensive treatment. Importantly, an increased LDL-C should spur cascade screening in the family for the detection of affected individuals. To facilitate both the calculation of the imputed baseline LDL-C and the diagnosis of FH, we provide a web-based and smartphone application, the FH Calculator, part of the updated CardioRisk Calculator available online through either the FH Canada website (http://www.fhcanada.net) (31) or http://www.circl.ubc. ca/english/web_fh.html (29). The "app" is also downloadable from the web free of charge.
National guidelines for the identification and treatment of patients with FH strongly recommend the use of statin therapy and/or ezetimibe to lower LDL-C. This is especially important for patients bearing a mutation in LDLR, PCSK9, or APOB genes, in whom the risk of ASCVD is 10- to 20-fold that of a normolipidemic individual (13, 14).
The data are largely derived from retrospective analyses performed in 6 large specialized lipid clinics across Canada. We used the average response to specific doses of medications, but there is considerable interindividual variability (32). The correlation between baseline and imputed LDL-C (r = 0.76) (Fig. 4) shows that our imputed LDL-C can be overestimated (or, in some cases, underestimated); this can be because of patient adherence to treatment, variability in response, and type of mutations in genes causing FH. These results reflect current clinical practice in specialized clinics. Thus, in our large cohort of well-characterized FH patients, each with directly reported untreated LDL-C concentrations, our algorithm provides an excellent estimate of baseline untreated LDL-C concentrations using current treated concentrations together with information on the type of treatment. In FH in particular, baseline LDL-C concentrations are important for diagnosis and monitoring of adequate patient response to treatment, especially in primary prevention.
Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.
Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:
Employment or Leadership: None declared.
Consultant or Advisory Role: R.A. Hegele, Amgen, Sanofi, Aegerion; G.A. Francis, Akcea, Alexion, Amgen, Sanofi; G.B.J. Mancini, Sanofi. Stock Ownership: None declared.
Honoraria: R.A. Hegele, Amgen, Aegerion, Sanofi, Akcea, Valeant, Boston Heart; G.A. Francis, Alexion, Amgen, Sanofi; G.B.J. Mancini, Sanofi.
Research Funding: Funding from Amgen, Sanofi, and Pfizer to the institution.
Expert Testimony: None declared.
Patents: None declared.
Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, or final approval of manuscript.
Acknowledgment: The authors thank all clinic personnel who helped with data collection.
(1.) Nordestgaard BG, Chapman MJ, Humphries SE, Ginsberg HN, Masana L, DescampsOS, et al. Familial hypercholesterolemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society. Eur Heart J 2013; 34:3478-90a.
(2.) Talmud PJ, Shah S, Whittall R, Futema M, Howard P, Cooper JA, et al. Use of low-density lipoprotein cholesterol gene score to distinguish patients with polygenic and monogenicfamilial hypercholesterolaemia: a case-control study. Lancet 2013;381:1293-301.
(3.) Akioyamen LE, Genest J, Shan SD, Reel RL, Albaum JB, Chu M, Tu JV. Estimating the prevalence of heterozygous familial hypercholesterolemia: a systematic review and meta-analysis. BMJ Open 2017;7:e016461.
(4.) Civeira F, International Panel on Management of Familial Hypercholesterolemia. Guidelines for the diagnosis and management of heterozygous familial hypercholesterolemia. Atherosclerosis 2004;173:55-68.
(5.) Ito MK, McGowan MP, Moriarty PM, National Lipid Association Expert Panel on Familial Hypercholesterolemia. Management of familial hypercholesterolemias in adult patients: recommendations from the National Lipid Association Expert Panel on Familial Hypercholesterolemia. J Clin Lipidol 2011;5:S38-45.
(6.) Primary P, Genest J, Hegele RA, Bergeron J, Brophy J, Carpentier A, et al. Canadian Cardiovascular Society position statement on familial hypercholesterolemia. Can J Cardiol 2014;30:1471-81.
(7.) Watts GF, Gidding S, Wierzbicki AS, Toth PP, Alonso R, Brown WV, et al. Integrated guidance on the care of familial hypercholesterolemia from the International FH Foundation. J Clin Lipidol 2014;8:148-72.
(8.) Gidding SS, Champagne MA, de Ferranti SD, Defesche J, Ito MK, Knowles JW, et al. The agenda for familial hypercholesterolemia: a scientific statement from the American Heart Association. Circulation 2015;132: 2167-92.
(9.) Henderson R, O'Kane M, McGilligan V, Watterson S. The genetics and screening of familial hypercholesterolaemia. J Biomed Sci 2016;23:39.
(10.) Harada-Shiba M, Arai H, Oikawa S, Ohta T, Okada T, Okamura T, et al. Guidelines for the management of familial hypercholesterolemia. J Atheroscler Thromb 2012;19:1043-60.
(11.) Williams RR, Hunt SC, Schumacher MC, Hegele RA, Leppert MF, Ludwig EH, Hopkins PN. Diagnosing Heterozygous familial hypercholesterolemia using new practical criteria validated by molecular genetics. Am J Cardiol 1993;72:171-6.
(12.) Versmissen J, Oosterveer DM, Yazdanpanah M, Defesche JC, Basart DC, Liem AH, et al. Efficacy of statins in familial hypercholesterolaemia: a long term cohort study. BMJ 2008;337:a2423.
(13.) Khera AV, Won HH, Peloso GM, Lawson KS, Bartz TM, Deng X, et al. Diagnostic yield and clinical utility of sequencing familial hypercholesterolemia genes in patients with severe hypercholesterolemia. J Am Coll Cardiol 2016;67:2578-89.
(14.) Abul-Husn NS, Manickam K,Jones LK, Wright EA, Hartzel DN, Gonzaga-Jauregui C, et al. Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science 2016;354:aaf7000.
(15.) Wonderling D, Umans-Eckenhausen MA, Marks D, Defesche JC, Kastelein JJ, Thorogood M. Cost-effectiveness analysis of the genetic screening program for familial hypercholesterolemia in the Netherlands. Semin Vasc Med 2004;4:97-104.
(16.) Nherera L, Marks D, Minhas R, Thorogood M, Humphries SE. Probabilistic cost-effectiveness analysis of cascade screening for familial hypercholesterolaemia using alternative diagnostic and identification strategies. Heart 2011;97:1175-81.
(17.) Ademi Z, Watts GF, Pang J, Sijbrands EJ, van Bockxmeer FM, O'Leary P, et al. Cascade screening based on genetic testing is cost-effective: evidence for the implementation of models of care for familial hypercholesterolemia. J Clin Lipidol 2014;8:390-400.
(18.) Henneman L, McBride CM, Cornel MC, Duquette D, Qureshi N. Screening for familial hypercholesterolemia in children: what can we learn from adult screening programs? Healthcare (Basel) 2015;3:1018-30.
(19.) Wald DS, Bestwick JP, Morris JK, Whyte K, Jenkins L, Wald NJ. Child-parent familial hypercholesterolemia screening in primary care. N Engl J Med 2016;375: 1628-37.
(20.) Santos RD, Gidding SS, Hegele RA, Cuchel MA, Barter PJ, Watts GF, et al. Defining severe familial hypercholesterolaemia and the implications for clinical management: a consensus statement from the International Atherosclerosis Society Severe Familial Hypercholesterolemia Panel. Lancet Diabetes Endocrinol 2016;4:850-61.
(21.) Catapano AL, Graham I, De Backer G, Wiklund O, Chapman MJ, Drexel H, et al. 2016 ESC/EAS guidelines for the management of dyslipidaemias. Eur Heart J 2016; 37:2999-3058.
(22.) Vallejo-Vaz AJ, Kondapally Seshasai SR, Cole D, Hovingh GK, Kastelein JJ, Mata P, et al. Familial hypercholesterolaemia: a global call to arms. Atherosclerosis 2015;243:257-9.
(23.) Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014;129:S1-45.
(24.) Anderson TJ, Gregoire J, Pearson GJ, Barry AR, Couture P, Dawes M, et al. 2016 Canadian Cardiovascular Society guidelines for the management of dyslipidemia for the prevention of cardiovascular disease in the adult. Can J Cardiol 2016;32:1263-82.
(25.) Najam O, Ray KK. Familial hypercholesterolemia: a review of the natural history, diagnosis, and management. Cardiol Ther 2015;4:25-38.
(26.) Hegele RA. Improving the monitoring and care of patients with familial hypercholesterolemia. J Am Coll Cardiol 2016;67:1286-8.
(27.) Alkindi M, Siminovitch KA, Gupta M, Genest J. Monoclonal antibodies for the treatment of hypercholesterolemia: targeting PCSK9. Can J Cardiol 2016;32:1552-60.
(28.) Knowles JW, Howard WB, Karayan L, Baum SJ, Wilemon KA, Ballantyne CM, Myers KD. Access to nonstatin lipid-lowering therapies in patients at high risk of atherosclerotic cardiovascular disease. Circulation 2017; 135:2204-6.
(29.) Cardiorisk calculator, familial hypercholesterolemia calculator. http://www.circl.ubc.ca/english/web_fh. html.(Accessed July 3,2017).
(30.) Hou R, Goldberg AC. Lowering low-density lipoprotein cholesterol: statins, ezetimibe, bile acid sequestrants, and combinations: comparative efficacy and safety. Endocrinol Metab Clin North Am 2009; 38:79-97.
(31.) Familial hypercholesterolemia Canada registry. FH Canada website. (Accessed July 3,2017).
(32.) Boekholdt SM, Hovingh GK, Mora S, Arsenault BJ, Amarenco P, Pedersen TR, et al. Very low levels of atherogenic lipoproteins and the risk for cardiovascular events: a meta-analysis of statin trials. J Am Coll Cardiol 2014;64:485-94.
Isabelle Ruel,  Sumayah Aljenedil,  Iman Sadri,  Emilie de Varennes,  Robert A. Hegele,  Patrick Couture,  Jean Bergeron,  Eric Wanneh,  Alexis Baass, [4,5,6] Robert Dufour,  Daniel Gaudet,  Diane Brisson,  Liam R. Brunham, [9,10,11] Gordon A. Francis, [9,10,11] Lubomira Cermakova,  James M. Brophy,  Arnold Ryomoto,  G.B. John Mancini,  and Jacques Genest  *
 Research Institute of the McGill University Health Centre, Royal Victoria Hospital, Montreal, QC, Canada;  Departments of Medicine and Biochemistry, Schulich School of Medicine and Robarts Research Institute, Western University, London, ON, Canada;  Lipid Research Centre, CHU de Quebec-Universite Laval, Quebec City, QC, Canada;  Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, QC, Canada;  Nutrition, Metabolism, and Atherosclerosis Clinic, Institut de recherches cliniques de Montreal, QC, Canada;  Division of Medical Biochemistry, Department of Med icine, McGill University, QC, Canada;  Department of Nutrition, Universite de Montreal, Montreal, QC, Canada;  Lipidology Unit, Community Genomic Medicine Centre and ECOGENE-21, Department of Medicine, Universite de Montreal, Saguenay, QC, Canada;  Healthy HeartProgram Prevention Clinic, St. Paul's Hospital, Vancouver, BC, Canada;  Department of Medicine, University of British Columbia, Vancouver, BC, Canada;  Centre for Heart Lung Innovation, Providence Health Care Research Institute, University of British Columbia, Vancouver, BC, Canada;  Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada;  McGill University, Royal Victoria Hospital, Montreal, QC, Canada;  Department of Medicine, Division of Cardiology, University of British Columbia, Vancouver, BC, Canada.
* Address correspondence to this author at: Institutde recherche du centre universitaire de sante McGill, 1001 boul. Decarie Bloc E, Office EM12212, Montreal, QC, H4A 3J1, Canada. Fax +514-933-6418; e-mail email@example.com.
Received July 11,2017; accepted August 31,2017.
Previously published online at DOI: 10.1373/clinchem.2017.279422
[C] 2017 American Association for Clinical Chemistry
 Nonstandard abbreviations: FH, familial hypercholesterolemia; LDL-C, LDL cholesterol; ASCVD, atherosclerotic cardiovascular disease.
 Human genes: LDLR, LDL receptor gene; APOB, apolipoprotein B gene; PCSK9, proprotein convertase subtilisin/kexin type 9 gene.
Caption: Fig. 1. Study flow chart.
Caption: Fig. 2. Histogram of observed percent reduction ([+ or -] SE) vs expected percent reduction from Hou et al. (30) for all statins and all doses.
Caption: Fig. 3. Histogram of baseline vs imputed baseline LDL-C ([+ or -] SE) for all doses of statins and ezetimibe. Inset: overall comparison of observed baseline vs imputed baseline LDL-C.
Caption: Fig. 4. Scatterplot of all data (n = 951 patients), including ezetimibe, of observed baseline vs imputed baseline LDL-C. The dotted lines indicate the 95% CIs for the regression line.
Table 1. Expected percent reduction in LDL-C according to dose and statin and ezetimibe. (a) Mean reduction by dose: percent change from baseline (divide LDL-C by this factor) Medication 5 mg 10 mg 20 mg Rosuvastatin -40 (0.60) -46 (0.54) -52 (0.48) Atorvastatin -- -37(0.63) -43 (0.57) Simvastatin -26(0.74) -30 (0.70) -38 (0.62) Lovastatin -- -21 (0.79) -27 (0.73) Pravastatin -- -20(0.80) -24 (0.76) Fluvastatin -- -- -22 (0.78) Ezetimibe alone -- -20(0.80) -- Ezetimibe 10 mg -20(0.80) -20 (0.80) -20 (0.80) added to a statin Mean reduction by dose: percent change from baseline (divide LDL-C by this factor) Medication 40 mg 80 mg Rosuvastatin -55 (0.45) -- Atorvastatin -48 (0.52) -51 (0.49) Simvastatin -41 (0.59) -47 (0.53) Lovastatin -31 (0.69) -40 (0.60) Pravastatin -30 (0.70) -36 (0.64) Fluvastatin -25 (0.75) -35 (0.65) Ezetimibe alone -- -- Ezetimibe 10 mg -20 (0.80) -20 (0.80) added to a statin (a) Data derived from Hou et al. (30). Table 2. Baseline and imputed baseline LDL-C. (a) Statin, Baseline On Rx LDL-C, dose/mg Number LDL-C, mg/dL mg/dL Lovastatin, 10 9 265.6 [+ or -] 89.2 199.8 [+ or -] 81.7 Lovastatin, 20 97 255.0 [+ or -] 61.4 182.9 [+ or -] 48.7 Lovastatin, 40 63 258.9 [+ or -] 60.3 176.0 [+ or -] 41.9 Lovastatin, 80 17 317.1 [+ or -] 83.7 218.2 [+ or -] 50.3 Pravastatin, 10 18 229.2 [+ or -] 52.4 174.5 [+ or -] 42.5 Pravastatin, 20 28 258.4 [+ or -] 44.8 180.3 [+ or -] 37.9 Pravastatin, 40 22 279.1 [+ or -] 71.0 183.3 [+ or -] 56.9 Simvastatin, 5 7 271.5 [+ or -] 101.1 208.8 [+ or -] 93.7 Simvastatin, 10 72 246.2 [+ or -] 49.1 179.1 [+ or -] 44.9 Simvastatin, 20 97 267.6 [+ or -] 53.0 172.3 [+ or -] 38.8 Simvastatin, 40 42 280.1 [+ or -] 41.2 185.4 [+ or -] 40.6 Simvastatin, 80 6 282.2 [+ or -] 49.4 142.6 [+ or -] 20.2 Atorvastatin, 10 37 231.4 [+ or -] 51.0 154.3 [+ or -] 42.5 Atorvastatin, 20 58 252.6 [+ or -] 53.4 146.5 [+ or -] 34.0 Atorvastatin, 40 52 289.9 [+ or -] 68.1 157.4 [+ or -] 47.0 Atorvastatin, 80 23 290.8 [+ or -] 70.8 131.2 [+ or -] 31.9 Fluvastatin, 20 10 226.4 [+ or -] 51.0 171.9 [+ or -] 39.1 Fluvastatin, 40 7 234.8 [+ or -] 35.3 157.4 [+ or -] 32.9 Rosuvastatin, 5 34 221.4 [+ or -] 47.1 138.8 [+ or -] 37.0 Rosuvastatin, 10 46 236.9 [+ or -] 50.3 130.1 [+ or -] 41.1 Rosuvastatin, 20 26 273.6 [+ or -] 61.9 139.8 [+ or -] 44.8 Rosuvastatin, 40 6 274.7 [+ or -] 57.1 140.9 [+ or -] 35.0 Ezetimibe, 10 172 169.5 [+ or -] 43.6 123.5 [+ or -] 34.2 Statin, Observed Expected dose/mg reduction, % reduction (30), % Lovastatin, 10 24.2 [+ or -] 14.6 21 Lovastatin, 20 27.6 [+ or -] 12.6 27 Lovastatin, 40 30.8 [+ or -] 13.8 31 Lovastatin, 80 28.9 [+ or -] 17.3 40 Pravastatin, 10 22.7 [+ or -] 13.4 20 Pravastatin, 20 29.4 [+ or -] 13.7 24 Pravastatin, 40 33.4 [+ or -] 16.8 30 Simvastatin, 5 24.1 [+ or -] 15.5 26 Simvastatin, 10 27.3 [+ or -] 11.3 30 Simvastatin, 20 34.9 [+ or -] 11.8 38 Simvastatin, 40 32.9 [+ or -] 15.7 41 Simvastatin, 80 48.9 [+ or -] 6.9 47 Atorvastatin, 10 32.7 [+ or -] 14.8 37 Atorvastatin, 20 40.5 [+ or -] 12.9 43 Atorvastatin, 40 45.3 [+ or -] 12.0 48 Atorvastatin, 80 53.0 [+ or -] 13.4 51 Fluvastatin, 20 21.8 [+ or -] 18.4 22 Fluvastatin, 40 32.8 [+ or -] 10.5 25 Rosuvastatin, 5 37.0 [+ or -] 11.6 40 Rosuvastatin, 10 45.4 [+ or -]11.7 46 Rosuvastatin, 20 48.9 [+ or -] 10.8 52 Rosuvastatin, 40 45.5 [+ or -] 23.1 55 Ezetimibe, 10 26.3 [+ or -] 13.7 20 Pearson correlation Statin, Imputed dose/mg LDL-C, mg/dL P r P value (b) value value Lovastatin, 10 252.9 [+ or -] 103.4 0.50 0.85 0.004 Lovastatin, 20 250.5 [+ or -] 66.7 0.32 0.76 <0.001 Lovastatin, 40 255.1 [+ or -] 60.7 0.59 0.58 <0.001 Lovastatin, 80 363.6 [+ or -] 83.8 0.05 0.40 0.107 Pravastatin, 10 218.1 [+ or -] 53.2 0.32 0.62 0.006 Pravastatin, 20 237.3 [+ or -] 49.9 0.03 0.49 0.007 Pravastatin, 40 261.8 [+ or -] 81.3 0.30 0.51 0.015 Simvastatin, 5 282.2 [+ or -] 126.7 0.56 0.94 0.002 Simvastatin, 10 255.8 [+ or -] 64.1 0.04 0.79 <0.001 Simvastatin, 20 277.9 [+ or -] 62.6 0.04 0.66 <0.001 Simvastatin, 40 314.3 [+ or -] 68.8 0.003 0.27 0.09 Simvastatin, 80 269.1 [+ or -] 38.2 0.43 0.67 0.15 Atorvastatin, 10 244.9 [+ or -] 67.5 0.10 0.70 <0.001 Atorvastatin, 20 257.1 [+ or -] 59.7 0.49 0.62 <0.001 Atorvastatin, 40 302.6 [+ or -] 90.4 0.16 0.71 <0.001 Atorvastatin, 80 267.7 [+ or -] 65.1 0.14 0.43 0.04 Fluvastatin, 20 220.4 [+ or -] 50.1 0.76 0.30 0.40 Fluvastatin, 40 209.9 [+ or -] 43.9 0.10 0.66 0.11 Rosuvastatin, 5 231.4 [+ or -] 61.6 0.20 0.70 <0.001 Rosuvastatin, 10 241.0 [+ or -] 76.1 0.58 0.76 <0.001 Rosuvastatin, 20 291.3 [+ or -] 93.3 0.18 0.71 <0.001 Rosuvastatin, 40 313.1 [+ or -] 77.9 0.45 -0.42 0.41 Ezetimibe, 10 154.3 [+ or -] 42.7 <0.001 0.69 <0.001 (a) For each statin (lovastatin, 10,20,40, and 80 mg-day), pravastatin (10,20, and 40 mg-day), simvastatin (5,10,20,40, and 80 mg-day), atorvastatin (10,20,40, and 80 mg-day), fluvastatin (20 and 40 mg-day), rosuvastatin (5,10,20, and 40 mg-day), and ezetimibe (10 mg-day), the number of subjects, baseline and on-treatment, the observed reduction for each dose of statins and ezetimibe, the expeded redudion, and the imputed baseline LDL-C are shown. (b) P value is for paired f-tests between observed baseline LDL-C and imputed baseline LDL-C. The nominal level of significance for multiple testing is set at P< 0.002. The Pearson linear correlation coefficient (r) and P value for baseline LDL-C and imputed baseline LDL-C are also shown. Results are expressed in mean [+ or -] SD. For SI units, please see Table 1 in the online Data Supplement.
|Printer friendly Cite/link Email Feedback|
|Title Annotation:||Lipids, Lipoproteins, and Cardiovascular Risk Factors|
|Author:||Ruel, Isabelle; Aljenedil, Sumayah; Sadri, Iman; de Varennes, Emilie; Hegele, Robert A.; Couture, Pa|
|Date:||Feb 1, 2018|
|Previous Article:||Human Toxicity Caused by Indole and Indazole Carboxylate Synthetic Cannabinoid Receptor Agonists: From Horizon Scanning to Notification.|
|Next Article:||New Insights into Cardiac and Vascular Natriuretic Peptides: Findings from Young Adults Born with Very Low Birth Weight.|