PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease
Abstract
Background
Despite the availability of effective drug therapies that reduce low‐density lipoprotein (LDL)‐cholesterol (LDL‐C), cardiovascular disease (CVD) remains an important cause of mortality and morbidity. Therefore, additional LDL‐C reduction may be warranted, especially for patients who are unresponsive to, or unable to take, existing LDL‐C‐reducing therapies. By inhibiting the proprotein convertase subtilisin/kexin type 9 (PCSK9) enzyme, monoclonal antibodies (PCSK9 inhibitors) may further reduce LDL‐C, potentially reducing CVD risk as well.
Objectives
Primary
To quantify short‐term (24 weeks), medium‐term (one year), and long‐term (five years) effects of PCSK9 inhibitors on lipid parameters and on the incidence of CVD.
Secondary
To quantify the safety of PCSK9 inhibitors, with specific focus on the incidence of type 2 diabetes, cognitive function, and cancer. Additionally, to determine if specific patient subgroups were more or less likely to benefit from the use of PCSK9 inhibitors.
Search methods
We identified studies by systematically searching the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and Web of Science. We also searched Clinicaltrials.gov and the International Clinical Trials Registry Platform and screened the reference lists of included studies. We identified the studies included in this review through electronic literature searches conducted up to May 2016, and added three large trials published in March 2017.
Selection criteria
All parallel‐group and factorial randomised controlled trials (RCTs) with a follow‐up time of at least 24 weeks were eligible.
Data collection and analysis
Two review authors independently reviewed and extracted data. When data were available, we calculated pooled effect estimates.
Main results
We included 20 studies with data on 67,237 participants (median age 61 years; range 52 to 64 years). Twelve trials randomised participants to alirocumab, three trials to bococizumab, one to RG7652, and four to evolocumab. Owing to the small number of trials using agents other than alirocumab, we did not differentiate between types of PCSK9 inhibitors used. We compared PCSK9 inhibitors with placebo (thirteen RCTs), ezetimibe (two RCTs) or ezetimibe and statins (five RCTs).
Compared with placebo, PCSK9 inhibitors decreased LDL‐C by 53.86% (95% confidence interval (CI) 58.64 to 49.08; eight studies; 4782 participants; GRADE: moderate) at 24 weeks; compared with ezetimibe, PCSK9 inhibitors decreased LDL‐C by 30.20% (95% CI 34.18 to 26.23; two studies; 823 participants; GRADE: moderate), and compared with ezetimibe and statins, PCSK9 inhibitors decreased LDL‐C by 39.20% (95% CI 56.15 to 22.26; five studies; 5376 participants; GRADE: moderate).
Compared with placebo, PCSK9 inhibitors decreased the risk of CVD events, with a risk difference (RD) of 0.91% (odds ratio (OR) of 0.86, 95% CI 0.80 to 0.92; eight studies; 59,294 participants; GRADE: moderate). Compared with ezetimibe and statins, PCSK9 inhibitors appeared to have a stronger protective effect on CVD risk, although with considerable uncertainty (RD 1.06%, OR 0.45, 95% CI 0.27 to 0.75; three studies; 4770 participants; GRADE: very low). No data were available for the ezetimibe only comparison. Compared with placebo, PCSK9 probably had little or no effect on mortality (RD 0.03%, OR 1.02, 95% CI 0.91 to 1.14; 12 studies; 60,684 participants; GRADE: moderate). Compared with placebo, PCSK9 inhibitors increased the risk of any adverse events (RD 1.54%, OR 1.08, 95% CI 1.04 to 1.12; 13 studies; 54,204 participants; GRADE: low). Similar effects were observed for the comparison of ezetimibe and statins: RD 3.70%, OR 1.18, 95% CI 1.05 to 1.34; four studies; 5376 participants; GRADE: low. Clinical event data were unavailable for the ezetimibe only comparison.
Authors' conclusions
Over short‐term to medium‐term follow‐up, PCSK9 inhibitors reduced LDL‐C. Studies with medium‐term follow‐up time (longest median follow‐up recorded was 26 months) reported that PCSK9 inhibitors (compared with placebo) decreased CVD risk but may have increased the risk of any adverse events (driven by SPIRE‐1 and ‐2 trials). Available evidence suggests that PCSK9 inhibitor use probably leads to little or no difference in mortality. Evidence on relative efficacy and safety when PCSK9 inhibitors were compared with active treatments was of low to very low quality (GRADE); follow‐up times were short and events were few. Large trials with longer follow‐up are needed to evaluate PCSK9 inhibitors versus active treatments as well as placebo. Owing to the predominant inclusion of high‐risk patients in these studies, applicability of results to primary prevention is limited. Finally, estimated risk differences indicate that PCSK9 inhibitors only modestly change absolute risks (often to less than 1%).
PICOs
Plain language summary
PCSK9 inhibitors for prevention of cardiovascular disease
Research question
Describe the effectiveness and safety of PCSK9 inhibitors for cardiovascular disease prevention.
Background
Despite the availability of effective drug therapies (statins or ezetimibe) that reduce low‐density (LDL) cholesterol (LDL‐C), cardiovascular disease (CVD) remains an important cause of mortality and morbidity. Additional LDL‐C reduction may therefore be warranted, especially for patients who are unresponsive to, or are unable to use, existing LDL‐C reducing therapies. PCSK‐9 inhibition produced by monoclonal antibodies (PCSK9 inhibitors) may further reduce LDL‐C levels and CVD risk.
Study characteristics
Review authors identified 20 studies that evaluated the effects of PCSK9 inhibitors in participants at high risk of CVD; studies were conducted in outpatient clinic settings. Review authors identified the studies included in this review through electronic literature searches conducted up to May 2016, and added three large trials published in March 2017.
Key results
PCSK9 inhibitors constitute a class of drugs that decrease LDL‐C and therefore may decrease the incidence of CVD. We examined the results of 20 studies, which showed beneficial effects on blood cholesterol concentrations of PCSK9 inhibitors at both six months and one year of follow‐up. Although the magnitude of this beneficial effect differed between studies, all showed beneficial effects. In comparisons of PCSK9 inhibitors versus no PCSK9 inhibitors, current evidence suggests that PCSK9 inhibitors decrease CVD incidence without affecting the incidence of all‐cause mortality. In comparisons of PCSK9 inhibitors versus alternative (more established) treatments such as statins or ezetimibe, high‐quality evidence is lacking. Differences in risk between people treated with and without PCKS9 inhibitors suggest the absolute treatment benefit will likely be modest (e.g. < 1% change in risk).
Quality of evidence
Most of the included randomised controlled trials (RCTs) were designed to explore biomarker associations; however, as all trials were industry funded, GRADE assessment revealed that the quality of the evidence was moderate. For associations with clinical endpoints (mortality and CVD), the quality of the evidence was moderate (placebo comparison) to very low (ezetimibe and statin comparisons).
Authors' conclusions
Summary of findings
| PCSK9 inhibitors compared with placebo in addition to statin and/or ezetimibe background care | |||||||
| Patient or population: people at high risk of cardiovascular disease (history of CVD or high LDL‐C despite treatment) | |||||||
| Outcomes | Ilustrative comparative risk or mean (95% CI) | Relative effect (95% CI) | Mean difference (95% CI) | Number of participants | Quality of the evidence | Comments | |
| Assumed risk or mean biomarker using placebo* | Corresponding risk or mean using PCSK9 inhibition† | ||||||
| LDL‐C reduction (LDL‐C) | Mean LDL‐C reduction was ‐6.12 mean percentage change form baseline | Mean LDL‐C reduction in the intervention group was ‐59.98 (‐64.76 to ‐55.19) percentage change form baseline | ‐53.86% (‐58.64 to ‐49.08) in percentage reduction from baseline | 4782 | ⊕⊕⊕⊝ | Negative is beneficial | |
| Cardiovascular disease (CVD) Follow‐up: 6 months to 36 months | Cardiovascular disease risk was 64 per 1000 participants | Cardiovascular disease risk in the intervention group was 55 (51 lower to 59 lower) per 1000 participants | OR 0.86 (0.80 to 0.92) | 59294 | ⊕⊕⊕⊝ | Below 1 is beneficial | |
| All‐cause mortality (mortality) Follow‐up: 6 months to 36 months | All‐cause mortality risk was 18 per 1000 participants | All‐cause mortality risk in the intervention group was 18 (16 lower to 20 higher) per 1000 participants | OR 1.02 (0.91 to 1.14) | 60684 | ⊕⊕⊕⊝ | Below 1 is beneficial | |
| Any adverse events (adverse events) Follow‐up: 6 months to 36 months | Risk of any adverse events was 692 per 1000 participants | Risk of any adverse events in the intervention group was 707 (700 higher to 715 higher) per 1000 participants | OR 1.08 (1.04 to 1.12) | 61038 | ⊕⊕⊝⊝ | Below 1 is beneficial | |
| CI: confidence interval | |||||||
| GRADE Working Group grades of evidence | |||||||
| aUnclear randomisation processes and high risk of other biases. Downgrading one level because of "limitations in the design and implementation of available studies suggesting high likelihood of bias" bResults predominantly determined by 3 large RCTs with a relatively short median follow‐up of 7 months (SPIRE‐1), 12 months (SPIRE‐2), and 26 months (FOURIER). SPIRE‐1/2 trials terminated prematurely owing to an unanticipated drug‐antibody response. Downgrading one level because of "indirectness of evidence" cEffect was driven by the discontinued SPIRE trials. Downgrading one level because of "limitations in the design and implementation of available studies suggesting high likelihood of bias" *Assumed risks or mean LDL‐C was based on the comparison arms of included trials †Corresponding risk or mean was based on the risk difference reported in Table 4 or the mean difference in LDL‐C | |||||||
| PCSK9 Inhibitors compared to ezetimibe. | |||||||
| Patient or population: people at high risk of cardiovascular disease (history of CVD or high LDL‐C despite treatment) | |||||||
| Outcomes | Ilustrative comparative risk or mean (95% CI) | Relative effect (95%CI) | Mean difference (95% CI) | Number of participants | Quality of the evidence | Comments | |
| Assumed risk or mean biomarker using ezetimibe* | Corresponding risk or mean using PCSK9 inhibition† | ||||||
| LDL‐C reduction (LDL‐C) | Mean LDL‐C reduction was ‐6.12 mean percentage change form baseline | Mean LDL‐C reduction in the intervention group was ‐36.32 (‐40.29 to ‐32.34) percentage change from baseline | ‐30.20% (‐34.18 to ‐26.23) in percentage reduction from baseline | 823 | ⊕⊕⊕⊝ | Negative is beneficial | |
| Cardiovascular disease (CVD) | Cardiovascular disease risk was 64 per 1000 participants | Data unavailable | |||||
| All‐cause mortality (mortality) | All‐cause mortality risk was 6 per 1000 participants | Data unavailable | |||||
| Any adverse events (adverse events) | Risk of any adverse events was 692 per 1000 participants | Data unavailable | |||||
| CI: confidence interval | |||||||
| GRADE Working Group grades of evidence | |||||||
| aHigh risk of other biases. Downgrading one level because of "limitations in the design and implementation of available studies suggesting high likelihood of bias" *Assumed risks or mean LDL‐C was based on the comparison arms of included trials †Corresponding risk or mean was based on the risk difference reported in Table 4 or the mean difference in LDL‐C. | |||||||
| PCSK9 inhibitors compared with ezetimibe and statins | |||||||
| Patient or population: people at high risk of cardiovascular disease (history of CVD or high LDL‐C despite treatment) | |||||||
| Outcomes | Ilustrative comparative risk or mean (95% CI) | Relative effect (95%CI) | Mean difference (95% CI) | Number of participants | Quality of the evidence | Comments | |
| Assumed risk or mean biomarker with ezetimibe and statins* | Corresponding risk or mean with PCSK9 inhibition† | ||||||
| LDL‐C reduction (LDL‐C) | Mean LDL‐C reduction was ‐6.12 mean percentage change form baseline | Mean LDL‐C reduction in the intervention group was ‐45.32 (‐62.27 to ‐28.37) percentage change form baseline | ‐39.20% (‐56.15 to ‐22.26) in percentage reduction from baseline | 5376 | ⊕⊕⊕⊝ | Negative is beneficial | |
| Cardiovascular disease (CVD) Follow‐up: 6 months to 11 months | Cardiovascular disease risk was 64 per 1000 participants | Cardiovascular disease risk in the intervention group was 53 (47 lower to 60 lower) per 1000 participants | OR 0.45 (0.27 to 0.75 | 4770 | ⊕⊝⊝⊝ | Below 1 is beneficial | |
| All‐cause mortality (mortality) | All‐cause mortality risk was 6 per 1000 participants | Data unavailable | |||||
| Any adverse events (adverse events) Follow‐up: 6 months to 11 months | Risk of any adverse events was 692 per 1000 participants | Risk of any adverse events in the intervention group was 729 (703 lower to 755 higher) per 1000 participants | OR 1.18 (1.05 to 1.34) | 5376 | ⊕⊕⊝⊝ | Below 1 is beneficial | |
| CI: confidence interval | |||||||
| GRADE Working Group grades of evidence | |||||||
| aMost data were based on OSLER‐1 and/or OSLER‐2, which were open‐label studies. Downgrading one level because of "limitations in the design and implementation of available studies suggesting high likelihood of bias" bITT results were often unavailable; instead data were extracted on the basis of an as treated sample. Downgrading one level because of "limitations in the design and implementation of available studies suggesting high likelihood of bias" cNumber of events was low. Downgrading one level because of " Imprecision of results" *Assumed risks or mean LDL‐C was based on comparison arms of included trials †Corresponding risk or mean was based on the risk difference reported in Table 4 or the mean difference in LDL‐C | |||||||
Background
Description of the condition
Cardiovascular disease (CVD), including fatal and non‐fatal cardiac and vascular diseases, remains a major cause of mortality and morbidity both in the United Kingdom (UK) and globally (Capewell 2008; Kreatsoulas 2010; Krishnamurthi 2013; Moran 2014; Murray 2012; Roger 2011; WHO 2008). Cardiovascular disease imposes a serious personal, financial, and societal burden with estimated direct costs of GBP 14,300,000,000 (i.e. 20% of National Health Service (NHS) funding), indirect costs of GBP 16,200,000,000, and an attributed mortality percentage of 35% in the UK (Capewell 2008). This burden is especially high in patients with familial hypercholesterolaemia (FH) who have loss of function mutation, which affects 1 in 250 individuals of European descent (Benn 2012; Knowles 2014; Nordestgaard 2013). These mutations prevent removal of circulating low‐density lipoprotein cholesterol (LDL‐C), which is one of the most important modifiable risk factors for CVD (Grundy 2004), both in patients with FH and in the general population. Autosomal dominant FH is caused by heterozygous mutations in the low‐density lipoprotein receptor (LDLR: OMIM #143890) (Sudhof 1985), apolipoprotein B (APOB; OMIM #144010) ‐ the major constituent apoprotein of LDL‐C (Garcia 2001; Innerarity 1987; Nordestgaard 2013), or the gene for proprotein convertase subtilisin/kexin type 9 (PCSK9; #603776) (Abifadel 2003). A rare autosomal recessive form of FH is caused by mutations in the gene for the low‐density lipoprotein receptor adaptor protein 1 (LDRRAP1; OMIM #603813). Patients with FH have higher risk of premature coronary heart disease that can be reduced with statin treatment. Polygenic elevation in LDL‐C concentration, which is associated with higher risk of coronary heart disease (CHD), is caused by additive effects of common, largely independently inherited polymorphisms located in more than 50 loci throughout the genome (Willer 2013).
Description of the intervention
Interventions of proven efficacy in reducing cardiovascular events through lowering of LDL‐C include statin drugs targeting 3‐hydroxy‐3‐methyl‐glutaryl‐CoA (HMG‐CoA) reductase and ezetimibe targeting the Niemann‐Pick C1‐like 1 intestinal cholesterol transporter protein (Cannon 2015; CTT 2005a; CTT 2005b; CTT 2012). Cardiovascular risk is reduced but not abolished among patients receiving these medications, suggesting that additional LDL‐C reduction via alternative pathways may result in further reduction in CVD events, especially among patients who have an inadequate response to, or are intolerant of, statins or ezetimibe (Mancini 2011; Marks 2003).
A new pharmacological target for further reduction of LDL‐C is the proprotein convertase subtilisin/kexin type 9 (PCSK9) enzyme. Monoclonal antibodies (mAbs) against the PCSK9 enzyme (PCSK9 inhibitors) are currently being evaluated in phase 3 trials. PCSK9 inhibitors are administered subcutaneously every two or four weeks. Reported mean half‐life times for subcutaneous administration have been six to seven days, with minimal differences due to administration site (abdomen or upper arm) and LDL‐C reaching its lowest level at 15 days (Lunven 2014). The impact, if any, of environmental factors or comedications on PCSK9 mAb efficacy is still mostly unknown (Lunven 2014).
How the intervention might work
PCSK9 is synthesised and secreted by hepatocytes and binds to the LDL‐C receptor (LDLR) on the hepatocyte surface, promoting internalisation and degradation. Reduction in surface LDLR reduces uptake of LDL particles and increases LDL‐C concentration in the blood (Cohen 2005; Cohen 2006). Therefore, inhibitors of PCSK9 are expected to lower LDL‐C. Moreover, inhibition of PCSK9 may further enhance the lipid‐lowering effects of statins, which are thought to be limited by a statin‐induced increase in PCSK9 expression (Catapano 2013).
PCSK9 inhibitors bind to the PCSK9 enzyme with high affinity, disrupting its ability to bind with LDLR. By preventing PCSK9 from binding to LDLR, inhibitors against PCSK9 maintain surface LDLR expression with the aim of reducing LDL‐C serum concentration. This is supported by the finding that variations in the PCSK9 gene are associated with long‐term elevations in LDL‐C and higher risk of CHD (Benn 2010; Chasman 2012). Alternatively, loss of function mutations in PCKS9 that lower LDL‐C levels have also been associated with decreased CHD risk (Cohen 2006). This provides evidence in favour of the PCSK9 enzyme as a valid therapeutic target for prevention of CVD.
Why it is important to do this review
Statins are widely prescribed to reduce LDL‐C levels and CVD risk in patients at increased risk. Patients taking statins reduce their risk of CVD by around 20% to 25% for every 1 mmol/L decrease in LDL‐C (CTT 2005a; CTT 2012), which may be further reduced by taking ezetimibe (Cannon 2015). Given the strong and positive associations, without clear threshold, between LDL‐C and CVD as described in prospective studies (CTT 2005a; CTT 2012), it is expected that further reduction in LDL‐C may lead to further prevention of CVD events. This could be especially important for patients not tolerating statins, those with very high levels of LDL‐C, and those at high cardiovascular risk. Previously, a narrative review of phase 1 and 2 trials found that PCSK9 inhibitors were generally well tolerated (Catapano 2013); however, information on the medium‐term to long‐term safety and efficacy of these drugs has not yet been reviewed. Research on statins seems to suggest the following unintended (safety) endpoints: type 2 diabetes (T2DM), possible weight gain (Sattar 2010; Swerdlow 2014), liver inflammation, and rarely myositis (Collins 2016). It is uncertain if reducing LDL‐C via a different mechanism might be associated with the same or a different set of adverse events. Although a recent meta‐analysis (Navarese 2015), which included the ODYSSEY Long Term trial, showed that PCSK9 inhibitors indeed reduced LDL‐C and cardiovascular‐related mortality, this finding was based mostly on short‐term studies (< 24 weeks) and excluded larger trials with longer follow‐up, such as OSLER‐1 and OSLER‐2 randomised controlled trials (RCTs) and recently published phase 3 trials (FOURIER and SPIRE‐1 and SPIRE‐2) (Sabatine 2015). Furthermore, with recent Food and Drug Administration (FDA) and European Medicines Agency (EMA) approvals of alirocumab (Praluent) and evolocumab (Repatha), these drugs have become available to (selected) patients, and (remaining) questions on long‐term efficacy and safety have become increasingly important to answer. Specifically, the EMA has approved Praluent and Repatha for patients with primary hypercholesterolaemia, and the FDA has approved both drugs for patients with heterozygous familial hypercholesterolaemia or a history of clinical atherosclerotic cardiovascular disease. These recommendations have found their way into the 2016 European Society of Cardiology (ESC)/European Atherosclerosis Society (EAS) Guidelines for the Management of Dyslipidaemias, which recommend consideration of a PCSK9 inhibitor for pharmacological treatment of hypercholesterolaemia "in patients at very high‐risk, with persistent high LDL‐C despite treatment with maximal tolerated statin dose, in combination with ezetimibe or in patients with statin intolerance". The same guidelines recommend that "treatment with a PCSK9 antibody should be considered in FH patients with CVD or at very high‐risk for CHD" (Catapano 2016). Recently, Pfizer discontinued the development of bococizumab, citing lack of long‐term efficacy due to increased immunogenicity over time (Pfizer 2017). Consequently, we considered it timely to conduct a systematic review of RCTs to quantify the long‐term efficacy and safety of inhibitors of PCSK9 for CVD prevention. For this review, CVD is defined as a composite of fatal and non‐fatal cardiac and vascular diseases, including stroke.
Objectives
Primary
To quantify short‐term (24 weeks), medium‐term (one year), and long‐term (five years) effects of PCSK9 inhibitors on lipid parameters and on the incidence of CVD.
Secondary
To quantify the safety of PCSK9 inhibitors, with specific focus on the incidence of type 2 diabetes, cognitive function, and cancer. Additionally, to determine if specific patient subgroups are more or less likely to benefit from the use of PCSK9 inhibitors.
Methods
Criteria for considering studies for this review
Types of studies
We included parallel‐group and factorial RCTs with follow‐up time of at least 24 weeks. Cluster RCTs, cross‐over trials, and non‐randomised studies were ineligible for this review, and we excluded them during title and abstract screening; we note a single cross‐over trial that we have excluded for this reason (Nissen 2016). RCTs were eligible if they were reported as full‐text articles or were published as abstracts, or if they were available only as unpublished data.
Types of participants
RCTs were eligible if they included adults 18 years of age or older, with or without a prior history of CVD. Participants could have normal lipid levels or hypercholesterolaemia. We applied no restriction on comorbidities.
Types of interventions
We included trials if they randomised participants to a PCSK9 inhibitor and to placebo, statins, or ezetimibe, or a combination of these.
Types of outcome measures
Primary outcomes
-
Lipid parameters (total cholesterol, LDL‐C, high‐density lipoprotein cholesterol (HDL‐C), triglycerides, apolipoprotein A1, apolipoprotein B and lipoprotein(a)): mean difference (MD) in mean percentage change from baseline or difference at the end of follow‐up
-
Composite endpoint of CVD, defined as urgent coronary revascularisation, unstable angina pectoris, non‐fatal and fatal myocardial infarction, non‐fatal and fatal stroke, and CHD death
Secondary outcomes
-
All‐cause mortality
-
Any adverse events, including type 2 diabetes (T2DM) and cancer
-
Cognitive function as standardised mean difference (SMD), as mean percentage change from baseline, or as difference between treatment arms at the end of follow‐up
-
Fasting glucose and glycosylated haemoglobin (HbA1c) as mean percentage change from baseline or as difference at the end of follow‐up
-
Quality of life as SMD, as mean percentage change from baseline, or as difference at the end of follow‐up
Search methods for identification of studies
Electronic searches
We identified trials through systematic searches (Lefebvre 2011) of the following databases.
-
Cochrane Central Register of Controlled Trials (CENTRAL; 2016, Issue 4 of 12) in the Cochrane Library.
-
MEDLINE (Ovid, 1946 to April week 4 2016).
-
Embase (Ovid, 1980 to week 19 2016).
-
Web of Science Core Collection (Thomson Reuters, 1970 to 8 May 2016).
Please see Appendix 1 for the search strategies used. We applied the sensitivity‐maximising version of the Cochrane RCT filter (Lefebvre 2011) to MEDLINE and adaptations of it to Embase and Web of Science. We limited searches to records from 2005, as PCSK9 was discovered as a potential target in 2003 (Farnier 2014; Seidah 2003), hence we excluded papers published before 2005. We imposed no language restrictions.
We identified the studies included in this review through electronic literature searches conducted up to May 2016. Through these searches, we identified several ongoing studies, and during the latter stages of finalising the review, we became aware of the publication of three of them in March 2017. We decided to incorporate data from those studies into this version of the review because of their size and impact on review findings.
Additionally, we searched ClinicalTrials.gov (www.ClinicalTrials.gov) and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) Search Portal (apps.who.int/trialsearch/) for relevant RCTs on 18 September 2016.
Searching other resources
We searched the following websites for unpublished studies on 19 September 2016.
-
Food and Drug Administration (FDA) website (http://www.fda.gov/).
-
Pharmaceutical company websites (e.g. Regeneron ‐ http://www.regeneron.com/; Sanofi ‐ http://en.sanofi.com/).
-
ProQuest dissertations and theses (PQDT; http://www.proquest.com/products‐services/pqdt.html).
Additionally, we screened reference lists of included studies for relevant RCTs.
Data collection and analysis
Selection of studies
Two review authors (AFS and LSP) independently screened search results by title and abstract, and subsequently the full text, for potentially relevant studies. A third review author (JPC) resolved disagreements. We distilled multiple reports on a single RCT into a single entry. We have provided a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) flow diagram, as well as details of studies excluded after full text assessment (see Characteristics of excluded studies).
Data extraction and management
Two review authors (AFS and LSP) independently extracted data and resolved differences by returning to the original publication and, if needed, by consulting a third review author (JPC). When appropriate, we extracted data on numbers of events versus no events, means, standard deviations, crude point estimates, or standard error estimates. For continuous endpoints, we extracted data on change from baseline or on differences between study arms at completion of follow‐up. When possible, we focused on estimates adjusted for baseline measurements (Vickers 2001). When reported, we extracted results from an intention‐to‐treat (ITT) analysis. For adverse events, we tried to extract results for per‐protocol or as‐treated populations. When available, we used the study protocol, appendices and design papers as additional sources of information. We combined full‐text screening, data extraction, and data entry using a Microsoft Access database (available from AFS).
Assessment of risk of bias in included studies
We assessed risk of bias using the Cochrane risk of bias tool (Higgins 2011a) on the basis of the following items.
-
Random sequence generation (selection bias).
-
Allocation (selection bias).
-
Blinding of participants and personnel (performance bias).
-
Blinding of outcome assessment (detection bias).
-
Incomplete outcome data (attrition bias).
-
Selective reporting (reporting bias).
-
Other potential sources of bias.
We graded individual items as having "low", "unclear", or "high" risk of bias. We presented "risk of bias" per study and for the outcome LDL‐C (which can be seen more generally as risk of bias for biomarker outcomes).
Assessment of bias in conducting the systematic review
We conducted this Cochrane review according to the published protocol (Schmidt 2015) and reported deviations from it in the Differences between protocol and review section.
Measures of treatment effect
We reported results as mean differences (MDs) for continuous outcomes and as odds ratios (ORs) and risk differences (RDs) for binary endpoints. In the manuscript, we focus on MDs and ORs; however, we include estimates of RDs of meta‐analysed treatment effects because of their relevance for individual patients (Newcombe 2014); given that ORs and RDs represent the same data, we provide only forest plots for OR estimates and report pooled RD (and OR) estimates in Table 1 and Table 2. We calculated confidence intervals (CIs) using the Wald method, assuming a standard normal distribution, or a t‐distribution when appropriate.
| Number of studies | Number of events in the PCSK9 arm | Number of participants in the PCSK9 arm | Number of events in the comparison arm | Number of participants in the comparison arm | Fixed‐effect (95% CI) | Between‐study heterogeneity P value | |
| Placebo comparison | |||||||
| All‐cause mortality | 12 | 580 | 31358 | 558 | 29326 | 1.02 (0.91 to 1.14) | 0.159 |
| Any cardiovascular event | 8 | 1790 | 30355 | 2009 | 28939 | 0.86 (0.80 to 0.92) | 0.803 |
| Any myocardial infarction | 10 | 686 | 30610 | 869 | 29038 | 0.77 (0.69 to 0.85) | 0.674 |
| Any stroke | 8 | 265 | 29828 | 340 | 28672 | 0.76 (0.65 to 0.89) | 0.185 |
| Any adverse event | 13 | 22593 | 31611 | 20435 | 29427 | 1.08 (1.04 to 1.12) | 0.38 |
| Myalgia | 12 | 1249 | 31428 | 1094 | 29363 | 1.07 (0.99 to 1.16) | 0.873 |
| Influenza | 6 | 191 | 2923 | 82 | 1477 | 1.19 (0.91 to 1.55) | 1 |
| Hypertension | 8 | 110 | 3436 | 60 | 1593 | 0.86 (0.62 to 1.18) | 0.741 |
| Cancer | 5 | 83 | 2851 | 46 | 1442 | 0.91 (0.63 to 1.31) | 0.964 |
| Type 2 diabetes | 7 | 956 | 17535 | 911 | 16681 | 1.04 (0.95 to 1.14) | 0.983 |
| Elevated creatinine | 8 | 319 | 30399 | 309 | 28933 | 0.85 (0.73 to 0.99) | 0.419 |
| Neurological events | 5 | 289 | 16036 | 242 | 14919 | 1.04 (0.88 to 1.24) | 0.759 |
| Ezetimibe and statin comparison | |||||||
| All‐cause mortality | |||||||
| Any cardiovascular event | 3 | 29 | 3079 | 33 | 1691 | 0.45 (0.27 to 0.75) | 0.712 |
| Any myocardial infarction | |||||||
| Any stroke | |||||||
| Any adverse event | 5 | 2290 | 3309 | 1347 | 2067 | 1.18 (1.05 to 1.34) | 0.478 |
| Myalgia | 5 | 127 | 3309 | 81 | 2067 | 1.09 (0.81 to 1.48) | 0.715 |
| Influenza | 4 | 113 | 3183 | 51 | 1942 | 1.28 (0.91 to 1.80) | 0.45 |
| Hypertension | 3 | 6 | 207 | 12 | 453 | 1.10 (0.41 to 2.96) | 0.893 |
| Cancer | |||||||
| Type 2 diabetes | 4 | 35 | 3183 | 22 | 1942 | 1.10 (0.63 to 1.93) | 0.057 |
| Elevated creatinine | 5 | 20 | 3183 | 29 | 1942 | 0.51 (0.28 to 0.92) | 0.969 |
| Neurological events | 2 | 5 | 207 | 9 | 453 | 1.22 (0.40 to 3.69) | 1 |
| Number of studies | Number of events in the PCSK9 arm | Number of participants in the PCSK9 arm | Number of events in the comparison arm | Number of participants in the comparison arm | Fixed‐effect (95% CI) | Between‐study heterogeneity P value | |
| Placebo comparison | |||||||
| All‐cause mortality | 12 | 580 | 31358 | 558 | 29326 | 0.000 (‐0.002 to 0.002) | 0.781 |
| Any cardiovascular event | 8 | 1790 | 30355 | 2009 | 28939 | ‐0.009 (‐0.013 to ‐0.005) | 0.005 |
| Any myocardial infarction | 10 | 686 | 30610 | 869 | 29038 | ‐0.007 (‐0.009 to ‐0.004) | < 0.001 |
| Any stroke | 8 | 265 | 29828 | 340 | 28672 | ‐0.003 (‐0.004 to ‐0.001) | 0.409 |
| Any adverse event | 13 | 22593 | 31611 | 20435 | 29427 | 0.015 (0.008 to 0.023) | < 0.001 |
| Myalgia | 12 | 1249 | 31428 | 1094 | 29363 | 0.002 (‐0.001 to 0.006) | 0.979 |
| Influenza | 6 | 191 | 2923 | 82 | 1477 | 0.010 (‐0.005 to 0.025) | 0.513 |
| Hypertension | 8 | 110 | 3436 | 60 | 1593 | ‐0.005 (‐0.016 to 0.006) | 1 |
| Cancer | 5 | 83 | 2851 | 46 | 1442 | ‐0.003 (‐0.013 to 0.008) | 0.892 |
| Type 2 diabetes | 7 | 956 | 17535 | 911 | 16681 | 0.002 (‐0.03 to 0.07) | 0.73 |
| Elevated creatinine | 8 | 319 | 30399 | 309 | 28933 | ‐0.002 (‐0.003 to ‐0.000) | < 0.001 |
| Neurological events | 5 | 289 | 16036 | 242 | 14919 | 0.001 (‐0.002 to 0.004) | 0.923 |
| Ezetimibe and statin comparison | |||||||
| All‐cause mortality | |||||||
| Any cardiovascular event | 3 | 29 | 3079 | 33 | 1691 | ‐0.011 (‐0.017 to ‐0.004) | 1 |
| Any myocardial infarction | |||||||
| Any stroke | |||||||
| Any adverse event | 5 | 2290 | 3309 | 1347 | 2067 | 0.037 (0.011 to 0.063) | 0.862 |
| Myalgia | 5 | 127 | 3309 | 81 | 2067 | 0.003 (‐0.007 to 0.014) | 0.901 |
| Influenza | 4 | 113 | 3183 | 51 | 1942 | 0.009 (‐0.002 to 0.019) | 1 |
| Hypertension | 3 | 6 | 207 | 12 | 453 | 0.002 (‐0.020 to 0.025) | 0.922 |
| Cancer | |||||||
| Type 2 diabetes | 4 | 35 | 3183 | 22 | 1942 | 0.001 (‐0.007 to 0.008) | 0.027 |
| Elevated creatinine | 5 | 20 | 3183 | 29 | 1942 | ‐0.006 (‐0.012 to ‐0.000) | 0.984 |
| Neurological events | 2 | 5 | 207 | 9 | 453 | 0.004 (‐0.019 to 0.028) | 1 |
Unit of analysis issues
This Cochrane review focused exclusively on parallel‐group designed RCTs, hence we had no unit of analysis issues.
Dealing with missing data
We contacted trial authors to request missing data.
Assessment of heterogeneity
We measured between‐study heterogeneity by using the I² statistic with a one‐sided confidence interval (with a z‐value of ‐1.96) and tested it using a Q test. For binary endpoints, we measured between‐study heterogeneity by using Tau² and tested it using a likelihood ratio test.
Originally, we intended to refrain from meta‐analysis if heterogeneity was greater than 50%. Although we observed a large amount of heterogeneity in the biomarker estimates, we nevertheless meta‐analysed the data. We made this decision because we believed that between‐study differences in treatment effects did not preclude a clinically relevant combination of data (see results and discussion).
Assessment of reporting biases
Fewer than 10 trials reported on the same comparator groups (see data synthesis and results), hence we did not assess reporting bias.
Data synthesis
Before meta‐analysing results, we grouped trials together on the basis of comparator treatment(s) received, including placebo, ezetimibe, and ezetimibe or statin. Trials comparing PCSK9 mAbs against statins only, or comparing mAbs types, were unavailable.
We combined study‐specific estimates in R (R Development Core Team 2014) and combined continuous data using the inverse variance method for fixed‐effect meta‐analysis. For binary data, we reconstructed individual participant data on the basis of cell counts, and we combined results using generalised linear models (GLMs) with a random intercept for study (Bradburn 2007; Sweeting 2004). For continuous data, we reported both fixed‐effect and random‐effects estimates, and for binary endpoints, we reported only fixed‐effect estimates, because owing to data sparseness, random‐effects models were unreliable.
In the case of multiple treatment or comparator arms, we pooled estimates across arms to facilitate a comparison between inhibitors and comparison therapy. Alternatively, we could have compared results from a single intervention arm versus multiple comparator groups (or vice versa), but this would have resulted in correlated effect estimates with erroneously small P values (i.e. increased type 1 errors).
'Summary of findings' table
We created 'Summary of findings' tables (using the GRADE approach to assess the quality of evidence; Grade Working Group 2004) for each comparison group separately and (on the basis of the protocol) included outcomes, LDL‐C, CVD events, adverse events, and mortality. We calculated risk under the intervention using estimated mean differences or risk differences; we included odds ratios in the table but did not use them to calculate (reduced) risk under treatment. Given that all studies provided participants with a combination of statins or ezetimibe, we estimated the mean or risk under the comparison treatment using the entire sample of trials. We changed column names to reflect this approach.
Subgroup analysis and investigation of heterogeneity
We assessed potential sources of between‐study heterogeneity in PCSK9 inhibitor effects on LDL‐C (at six months) using the following subgroup analyses: gender, age, history of CHD, diabetes at baseline, baseline LDL‐C level, and familial or non‐familial hypercholesterolaemia. We calculated interaction effects within study (Altman 2003) and meta‐analysed them, preventing bias due to study‐specific factors (Schmidt 2014b). We explored these subgroup analyses separately for RCTs comparing PCSK9 mAbs against placebo or against ezetimibe. Study authors did not report subgroup effects in sufficient detail for RCTs comparing PCSK9 mAbs against ezetimibe or statin for inclusion in the analysis.
Additionally (owing to the limited number of RCTs, only for trials using a placebo comparator), we employed meta‐regression (weighted for inverse variance weights; Thompson 2002) to explore whether between‐study heterogeneity was related to baseline characteristics described before, with the addition of ethnicity and proportion of missing LDL‐C measurements.
Sensitivity analysis
We attempted the following sensitivity analyses.
-
We stratified trials by allocated dose of PCSK9 mAb. Owing to the limited number of trials, we did this only for the placebo comparison and the endpoints of LDL‐C and apolipoprotein B at six months.
-
We intended to explore the influence of perceived risk of bias by grouping studies that had a low perceived risk of bias on all items (see Characteristics of included studies) and comparing six‐month LDL‐C reduction in studies that did not have low risk of bias on all items (higher‐risk group). However, none of the trials had low risk of bias on all items, hence we did not perform this sensitivity analysis.
-
We also intended to explore differences due to type of PCSK9 mAb, but we had already explored this by stratifying doses for placebo trials. For remaining comparison groups, RCTs were too few for meaningful exploration of this.
Please note that in our published protocol, we originally set out to perform these sensitivity analyses for CVD and mortality as well; however, owing to the limited number of events, we were not able to perform these analyses. Simillarly, we aimed to explore the impact of missing data by stratifying RCTs on missing 0% to 5%, 6% to 10%, and more than 10% of LDL‐C, CVD, or mortality data. However, owing to data sparseness, we did this only for the LDL‐C endpoint and used a meta‐regression analysis instead.
Reaching conclusions
We based our conclusions on findings derived from quantitative synthesis of included studies.
Results
Description of studies
Results of the search
The search yielded 1066 hits, which we supplemented by 11 additional records obtained by cross‐referencing trial registry sites and other sources (see Figure 1 for a flow diagram). After screening titles and abstracts, we retrieved 42 full‐text articles and excluded 25 of these. We included 19 references describing 20 studies. Most studies had multiple publications (e.g. conference abstracts) that we distilled into a single entry. For the ODYSSEY trials, we extracted additional information from a recent FDA report (FDA 2015).
Study flow diagram.
Included studies
PCSK9 inhibitors; settings and participants
Investigators collected a combined sample of 68,341 participants in these 20 trials; of these, 1104 participants were included twice ‐ once in OSLER‐1, and once in the meta‐analysis of OSLER‐1 and OSLER‐2 (OLSER‐2 was unavailable separately). Of 67,237 unique participants, 20,210 were women (30%; of 67,130 participants for whom gender was reported; see Characteristics of included studies), 6984 did not have a history of CVD (11%; of 61,382 participants with reported CVD history), 2513 had FH (7%; of 33,707 with reported FH status), 25,536 had a T2DM diagnosis at baseline (39%; of 65,740 participants with recorded T2DM status). We note that the three FH studies focused exclusively on participants with FH (self‐identified). Caucasians were the predominant ethnic group included in these studies (86%). All trials included participants treated in outpatient care settings.
All included studies were industry‐sponsored, multi‐centre trials; most focused on alirocumab (REGN727, SAR256553), three explored bococizumab (RM316, PF‐04950615; Ballantyne 2015;SPIRE 1/2), one examined RG7652 (Equator), and four studied evolocumab (AMG145). The evolocumab trials (Descartes; OSLER‐1; OSLER 1/2) are closely related in the sense that, after completing the Descartes study, participants were offered enrolment in the OSLER‐2 study. The OSLER‐2 has been published only in combination with OSLER‐1 (which similarly limited enrolment to participants who first completed a 12‐week "parent" trial). Given that the OSLER‐2 trial has not been published separately, we included meta‐analysis results of OSLER‐1 and OSLER‐2, but we also used OLSER‐1 data for outcomes not reported in the meta‐analysis of OLSER‐1 and OSLER‐2 trials.
Comparison group
Investigators in 13 trials randomised participants to placebo or PCSK9 inhibitors (Ballantyne 2015; Descartes; Equator; FOURIER; ODYSSEY CHOICE I; ODYSSEY CHOICE II; ODYSSEY COMBO I; ODYSSEY FH I; ODYSSEY FH II; ODYSSEY HIGH FH; ODYSSEY Long Term; SPIRE 1/2, with SPIRE1/2 counted as two studies) as add‐on to background therapy, which could consist of ezetimibe, statins, and other interventions (see Characteristics of included studies). They randomised participants enrolled in ODYSSEY COMBO II and ODYSSEY MONO to alirocumab or ezetimibe. Finally, the remaining five studies (OSLER‐1; OSLER 1/2; ODYSSEY ALTERNATIVE; ODYSSEY OPTIONS I; ODYSSEY OPTIONS II) compared participants receiving a PCSK9 inhibitor with those receiving ezetimibe or statins, or usual care involving both ezetimibe and statins. Note that the OPTIONS I and OPTIONS II trials compared alirocumab with ezetimibe and atorvastatin, atorvastatin, or rosuvastatin. As described in the Methods section, to prevent erroneously small P values (due to use of the same alirocumab arm twice), we combined multiple arms of comparison groups and estimated effects of alirocumab versus ezetimibe and statin.
Researchers administered PCSK9 inhibitors every two weeks, every four weeks, or every eight weeks; for the sake of comparison, we calculated the two weeks' equivalence dosage (see Characteristics of included studies), which ranged from 50 mg to 210 mg every two weeks. In most studies (except ODYSSEY FH II, ODYSSEY HIGH FH, DESCARTES, OSLER‐1, and ODYSSEY LONG TERM), participants received different dosages of PCSK9, often depending on a predefined up‐titration criterion such as LDL‐C reduction or history of CVD; to account for these within‐study differences in dosage by stratified analyses (see methods and results), we grouped studies (when needed) by using a dosage range instead of a single dosage.
Excluded studies
We excluded 25 trials (Characteristics of excluded studies), predominantly owing to follow‐up time less than 24 weeks (see main objectives), or because trials described a meta‐analysis while providing little to no detail on individual studies (which were already included separately). Besides these excluded trials, we identified seven ongoing trials (Characteristics of ongoing studies) that fit our inclusion criteria; of these, two trials (ODYSSEY Outcomes; TAUSSIG) focus on long‐term effects on clinical outcomes, and one describes the six SPIRE biomarker trials and is pending classification.
Risk of bias in included studies
We have provided per study risk of bias with rationale in the Characteristics of included studies table. All studies described used a randomised trial design; we have discussed risk of bias for biomarker endpoints in the following sections and have summarised this information in Figure 2 and Figure 3, See section on "Detection and attrition bias of the association with clinical endpoints" for risk of bias reflecting clinical endpoints.
Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.
Risk of bias summary: review authors' judgements about each risk of bias item for each included study.
Allocation
Six trials (Equator; ODYSSEY ALTERNATIVE; ODYSSEY CHOICE I; ODYSSEY CHOICE II; ODYSSEY COMBO I; ODYSSEY MONO) did not provide sufficient detail on how randomisation was achieved (unclear risk of bias). The remaining studies typically used a voice‐based or Internet‐based centralised response system, and we perceived them to have low risk of bias.
We ensured allocation concealment by using centralised allocation and in some cases permuted blocks. Five RCTs (Ballantyne 2015; Equator; ODYSSEY CHOICE I; ODYSSEY CHOICE II; ODYSSEY COMBO I) did not sufficiently report on this item, and we perceived them as having unclear risk of bias (we contacted study authors but they did not respond).
Blinding
Owing to the open‐label design, the OSLER‐1 and OSLER‐2 studies are at high risk of performance bias. It seems plausible that knowledge of drug exposure may influence choices on lifestyle or additional clinical care, which may distort difference in biomarkers and clinical events across treatment arms.
Most studies assessed biomarkers in a central laboratory, making detection bias unlikely; one possible exception is the Equator study, which did not describe how biomarkers were assessed.
Incomplete outcome data
Loss due to follow‐up (attrition bias) was typically low (arbitrarily defined as < 5%), except in the Descartes, Equator, ODYSSEY ALTERNATIVE, ODYSSEY COMBO I, ODYSSEY Long Term, and OSLER‐1 trials, and in meta‐analysis of OSLER 1/2. Most studies used advanced analytics, such as mixed‐effects models or (multiple) imputations, to ameliorate loss due to follow‐up (even if this was minor) and to ensure the ITT analysis. However, information on both performance of these methods and appropriateness of assumptions underlying these methods was missing.
Selective reporting
We compared endpoints described in study protocols and on clinicaltrials.gov versus endpoints reported in the primary publication, and general found good agreement. We assigned seven trials (contributing 2901 participants) an unclear risk of bias grade because the full publication was unavailable, hence we could not fully compare results.
Other potential sources of bias
In accordance with guidance provided by Cochrane (Lundh 2017), we assigned high risk of bias to all industry‐funded trials.
Detection and attrition bias of association with clinical endpoints
Most studies reported clinical endpoints based on the safety sample, typically defined as the sample that received at least one dose of the allocated study drug, and not the sample randomised. Especially worrisome were the Descartes, Equator, ODYSSEY ALTERNATIVE, ODYSSEY COMBO I, ODYSSEY Long Term, and OSLER‐1 trials, which, as described, had considerable attrition. Positive exceptions were the SPIRE‐1, SPIRE‐2, and FOURIER trials, which were specifically designed to explore clinical endpoints, used the ITT sample, and report small numbers of participants lost to follow‐up. Although potential lack of blinding seems unlikely to bias biomarker measurements, it may pose a considerable source of bias for detection of clinical endpoints. Of particular concern are the OSLER‐1 and OSLER‐2 studies, which were open‐label trials (high risk of bias); however, other studies did not always adequately explain how clinical endpoints were detected and how detection bias was prevented (unclear risk of bias; see Characteristics of included studies).
Effects of interventions
See: Summary of findings for the main comparison Summary of findings for PCSK9 compared with placebo; Summary of findings 2 Summary of findings for PCSK9 compared with ezetimibe; Summary of findings 3 Summary of findings for PCSK9 compared with ezetimibe and statins
See 'Summary of findings' tables for the following.
-
PCSK9 mAb against placebo (summary of findings Table for the main comparison).
-
PCSK9 mAb against ezetimibe and statins (summary of findings Table 2).
-
PCSK9 mAb against ezetimibe (summary of findings Table 3).
Biomarker effects in comparison of PCSK9 mAb against placebo at six months
At six months follow‐up, the effect of PCSK9 inhibitors on LDL‐C compared with placebo was noted as ‐53.86% (95% CI ‐58.64 to ‐49.08; eight studies; 4782 participants; GRADE: moderate) reduction from baseline (Figure 4) (see Table 3 and Appendix 2 for remaining forest plots). Review authors observed similar reductions for triglycerides (‐11.39%, 95% CI ‐17.04 to ‐5.74); total cholesterol (‐31.41%, 95% CI ‐43.65 to ‐19.16); apolipoprotein B (‐41.93%, 95% CI ‐49.76 to ‐34.10); lipoprotein(a) (‐19.80%, 95% CI ‐25.43 to ‐14.17); and non‐HDL‐C (‐47.17%, 95% CI ‐53.92 to ‐40.42) (see Table 3). Treatment effect estimates on HDL‐C and apolipoprotein A1 at six months were as follows: 6.00, 95% CI 4.31 to 7.69; and 3.50%, 95% CI 2.37 to 4.64, respectively. Findings of two studies reveal that the association with HbA1c, as absolute change from baseline, was 0.01% (95% CI ‐0.06 to 0.08).
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in LDL‐C at six months.
| Number of studies | Number of PCSK9 participants | Number of comparator arm participants | Fixed‐effect (95% CI) | Random‐effects (95% CI) | Between‐study heterogeneity P value | |
| Placebo comparison | ||||||
| LDL‐C % change | 8 | 3255 | 1527 | ‐57.62 (‐59.37 to ‐55.87) | ‐53.86 (‐58.64 to ‐49.08) | < 0.001 |
| HDL‐C % change | 5 | 2324 | 1175 | 5.48 (4.37 to 6.59) | 6.00 (4.31 to 7.69) | 0.19 |
| Triglycerides % change | 5 | 2324 | 1175 | ‐14.62 (‐16.74 to ‐12.50) | ‐11.39 (‐17.04 to ‐5.74) | < 0.001 |
| Total cholesterol % change | 2 | 1762 | 895 | ‐35.79 (‐37.36 to ‐34.23) | ‐31.41 (‐43.65 to ‐19.16) | < 0.001 |
| Apolipoprotein A1 % change | 3 | 2043 | 1033 | 3.49 (2.38 to 4.60) | 3.50 (2.37 to 4.64) | 0.36 |
| Apolipoprotein B % change | 6 | 2507 | 1239 | ‐47.79 (‐49.51 to ‐46.08) | ‐41.93 (‐49.76 to ‐34.10) | < 0.001 |
| Lipoprotein(a) % change | 4 | 2252 | 1140 | ‐22.43 (‐24.30 to ‐20.56) | ‐19.80 (‐25.43 to ‐14.17) | < 0.001 |
| Non‐HDL‐C % change | 4 | 2252 | 1140 | ‐50.03 (‐51.73 to ‐48.33) | ‐47.17 (‐53.92 to ‐40.42) | < 0.001 |
| HbA1c absolute change | 2 | 490 | 245 | 0.00 (‐0.04 to 0.05) | 0.01 (‐0.06 to 0.08) | 0.151 |
| Ezetimibe and statin comparison | ||||||
| LDL‐C % change | 5 | 3309 | 2067 | ‐52.17 (‐53.91 to ‐50.43) | ‐39.20 (‐56.15 to ‐22.26) | < 0.001 |
| HDL‐C % change | 3 | 333 | 578 | 7.53 (5.54 to 9.51) | 6.42 (1.31 to 11.52) | 0.002 |
| Triglycerides % change | 2 | 229 | 327 | ‐3.47 (‐8.26 to 1.32) | ‐3.47 (‐8.26 to 1.32) | 0.46 |
| Total cholesterol % change | ||||||
| Apolipoprotein A1 % change | ||||||
| Apolipoprotein B % change | 3 | 333 | 578 | ‐26.86 (‐29.50 to ‐24.22) | ‐26.72 (‐30.26 to ‐23.19) | 0.169 |
| Lipoprotein(a) % change | 2 | 207 | 453 | ‐19.51 (‐24.48 to ‐14.53) | ‐19.51 (‐24.48 to ‐14.53) | 0.60 |
| Non‐HDL‐C % change | 2 | 207 | 453 | ‐28.19 (‐32.79 to ‐23.59) | ‐28.19 (‐32.79 to ‐23.59) | 0.65 |
| HbA1c absolute change | ||||||
| Ezetimibe comparison | ||||||
| LDL‐C % change | 2 | 531 | 292 | ‐30.20 (‐34.18 to ‐26.23) | ‐30.20 (‐34.18 to ‐26.23) | 0.71 |
| HDL‐C % change | 2 | 531 | 292 | 7.40 (5.11 to 9.70) | 7.01 (3.70 to 10.32) | 0.22 |
| Triglycerides % change | 2 | 531 | 292 | ‐0.43 (‐4.90 to 4.03) | ‐0.43 (‐4.90 to 4.03) | 0.89 |
| Total cholesterol % change | 2 | 531 | 292 | ‐15.51 (‐18.18 to ‐12.83) | ‐15.84 (‐19.37 to ‐12.30) | 0.24 |
| Apolipoprotein A1 % change | 2 | 531 | 292 | 6.13 (4.34 to 7.91) | 6.13 (4.34 to 7.91) | 0.68 |
| Apolipoprotein B % change | 2 | 531 | 292 | ‐23.18 (‐26.28 to ‐20.08) | ‐23.18 (‐26.28 to ‐20.08) | 0.37 |
| Lipoprotein(a) % change | 2 | 531 | 292 | ‐18.70 (‐23.03 to ‐14.37) | ‐13.69 (‐30.60 to 3.21) | 0.003 |
| Non‐HDL‐C % change | 2 | 531 | 292 | ‐23.45 (‐27.07 to ‐19.83) | ‐23.45 (‐27.07 to ‐19.83) | 0.57 |
| HbA1c absolute change | ||||||
Biomarker effects in comparison of PCSK9 mAb against ezetimibe and statins at six months
Compared with those given ezetimibe and statins, participants receiving PCSK9 inhibitors showed a reduction (percentage change from baseline) of ‐39.20% in LDL‐C (95% CI ‐56.15 to ‐22.26; five studies; 5376 participants; GRADE: moderate) (Figure 5); ‐3.47% (95% CI ‐8.26 to 1.32) in triglycerides; ‐26.72% (95% CI ‐30.26 to ‐23.19) in apolipoprotein B; ‐19.51% (95% CI ‐24.48 to ‐14.53) in lipoprotein(a); ‐28.19% (95% CI ‐32.79 to ‐23.59) in non‐HDL‐C, and 6.42% (95% CI 1.31 to 11.52) in HDL‐C (see Table 3 and Appendix 2 for remaining forest plots). No information was available on total cholesterol, apolipoprotein A1, or HbA1c.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in LDL‐C at six months.
Biomarker effects in comparison of PCSK9 mAb against ezetimibe at six months
Two trials (ODYSSEY COMBO II; ODYSSEY MONO) evaluated PCSK9 mAb against ezetimibe and reported the following effects (percentage change from baseline) on biomarkers: ‐30.20% (95% CI ‐34.18 to ‐26.23; two studies; 823 participants; GRADE: moderate) for LDL‐C (Figure 6); ‐0.43% (95% CI ‐4.90 to 4.03) for triglycerides; ‐15.84% (95% CI ‐19.37 to ‐12.30) for total cholesterol; ‐13.69% (95% CI ‐30.60 to 3.21) for lipoprotein(a); ‐23.18% (95% CI ‐26.28 to ‐20.08) for apolipoprotein B; ‐23.45% (95% CI ‐27.07 to ‐19.83) for non‐HDL‐C; 7.01% (95% CI 3.70 to 10.32) for HDL‐C; and 6.13% (95% CI 4.34 to 7.91) for apolipoprotein A1. Information on HbA1c was unavailable.
Association of PCSK9 inhibitors compared with ezetimibe with mean percentage change from baseline in LDL‐C at six months.
Biomarker effects of PCSK9 mAb after one year
At one year, effect estimates of PCSK9 inhibitors versus placebo were available for six trials (Descartes; FOURIER; ODYSSEY COMBO I; ODYSSEY Long Term; SPIRE 1/2, with SPIRE1/2 counted as two studies) and generally showed similar effect estimates as for six months: ‐52.87% (95% CI ‐60.03 to ‐45.72) for LDL‐C; ‐28.47% (95% CI ‐38.85 to ‐18.10) for total cholesterol; ‐12.53% (95% CI ‐15.45 to ‐9.61) for triglycerides; ‐43.51% (95% CI ‐48.88 to ‐38.13) for apolipoprotein B; 3.00% (95% CI 1.31 to 4.69) for apolipoprotein A1; ‐43.46% (95% CI ‐57.45 to ‐29.47) for non‐HDL‐C; and 6.06% (95% CI 4.30 to 7.82) for HDL‐C. Associations with glucose and HbA1c were 1.80 mg/dL (95% CI 0.61 to 2.99) and 0.02% (95% CI ‐0.01 to 0.05). Results for other biomarkers were unavailable.
The meta‐analysis (OSLER 1/2) provided estimates at one year for PCSK9 mAbs compared with ezetimibe and statins, again reporting similar effect estimates as before (see Table 4 and Appendix 2 for remaining forest plots). Studies comparing PCSK9 inhibitors against ezetimibe did not follow participants up to one year.
| Number of studies | Number of PCSK9 participants | Number of comparator arm participants | Fixed‐effect (95% CI) | Random‐effects (95% CI) | Between‐study heterogeneity P value | |
| Placebo comparison | ||||||
| LDL‐C % change | 6 | 29865 | 28694 | ‐52.80 (‐53.46 to 52.14) | ‐52.87 (‐60.03 to ‐45.72) | < 0.001 |
| HDL‐C % change | 4 | 14528 | 14127 | 5.55 (5.07 to 6.03) | 6.06 (4.30 to 7.82) | 0.102 |
| Triglycerides % change | 3 | 14319 | 14020 | ‐12.53 (‐15.45 to ‐9.61) | ‐12.53 (‐15.45 to ‐9.61) | 0.679 |
| Total cholesterol % change | 2 | 808 | 409 | ‐31.33 (‐33.80 to ‐28.86) | ‐28.47 (‐38.85 to ‐18.10) | < 0.001 |
| Apolipoprotein A1 % change | 1 | 599 | 302 | 3.00 (1.31 to 4.69) | ||
| Apolipoprotein B % change | 4 | 14528 | 14127 | ‐47.18 (‐48.29 to ‐48.29) | ‐43.51 (‐48.88 to ‐38.13) | < 0.001 |
| Lipoprotein(a) % change | ||||||
| Non‐HDL‐C % change | 2 | 808 | 409 | ‐47.16 (‐50.77 to ‐43.55) | ‐43.46 (‐57.45 to ‐29.47) | 0.001 |
| Glucose (mg/dL) absolute change* | 2 | 13720 | 13718 | 1.80 (0.61 to 2.99) | ||
| HbA1c absolute change* | 2 | 13720 | 13718 | 0.02 (‐0.01 to 0.05) | ||
| Ezetimibe and statin comparison | ||||||
| LDL‐C % change | 1 | 2976 | 1489 | ‐58.40 (‐60.40 to ‐56.40) | ||
| HDL‐C % change | 1 | 736 | 368 | 5.40 (3.09 to 7.71) | ||
| Triglycerides % change | 1 | 736 | 368 | ‐10.00 (‐13.59 to ‐6.41) | ||
| Total cholesterol % change | ||||||
| Apolipoprotein A1 % change | 1 | 736 | 368 | 4.30 (2.61 to 5.99) | ||
| Apolipoprotein B % change | 1 | 736 | 368 | ‐38.80 (‐41.18 to ‐36.42) | ||
| Lipoprotein(a) % change | 1 | 736 | 368 | ‐20.80 (‐23.95 to ‐17.65) | ||
| Non‐HDL‐C % change | 1 | 736 | 368 | ‐44.00 (‐46.77 to ‐41.23) | ||
| Glucose (mg/dL) absolute change* | ||||||
| HbA1c absolute change | ||||||
*On the basis of the combined analysis of SPIRE‐1 and SPIRE‐2, study‐specific estimates were unavailable, hence no random‐effects or between‐study heterogeneity estimates could be calculated
Exploration of between‐study heterogeneity
Generally, between‐study heterogeneity (measured as I²) in treatment response was high. To explore this, we performed the following subgroup analyses on LDL‐C and apolipoprotein B.
Grouping studies with similar PCSK9 dosages (Included studies) compared with placebo at six months follow‐up resulted in mean percentage changes in LDL‐C of ‐54.37% (95% CI ‐59.14 to ‐49.60) for bi‐weekly 75 to 150 mg mAbs; ‐51.95% (95% CI ‐63.73 to ‐40.17) for bi‐weekly 150 mg mAbs; and ‐54.00% (95% CI ‐77.46 to ‐30.54) for bi‐weekly 50 to 200 mg mAbs compared with the overall effect in all RCTs combined of ‐53.86% (95% CI ‐58.64 to ‐49.08) (see Figure 7). Mean percentage changes in apolipoprotein B were ‐40.99% (95% CI ‐50.78 to ‐31.20) for biweekly 75 to 150 mg mAbs; ‐41.74% (95% CI ‐55.22 to ‐28.26) for biweekly 150 mg mAbs; and ‐45.50% (95% CI ‐65.27 to ‐25.73) for biweekly 50 to 200 mg mAbs compared with the overall effect in all RCTs combined of ‐41.93% (95% CI ‐49.76 to ‐34.10) (see Figure 8). Between‐study heterogeneity persisted despite grouping of RCTs administering similar dosages and reporting no clear dose‐response effect (increasing effectiveness).
Sensitivity analyses grouping RCTs by PCSK9 dose compared with placebo on 6 months LDL‐C mean percentage change from baseline.
Sensitivity analyses grouping RCTs by PCSK9 dose compared with placebo on 6 months apolipoprotein B mean percentage change from baseline.
To further explore sources of between‐study heterogeneity, we meta‐analysed reported subgroup effect estimates on PCSK9 mAbs compared with placebo on six months mean percentage change in LDL‐C (Figure 9). These analyses suggested that some between‐study heterogeneity may be explained by more pronounced effects in participants who were 65 years of age or younger, had a body mass index (BMI) of 30 or greater, or had a history of T2DM. High baseline levels of LDL‐C and total PCSK9 seemed to be related to treatment response but were available for only a single trial (ODYSSEY Long Term). We performed similar analyses for trials comparing PCSK9 inhibitors versus ezetimibe, but with a maximum sample size of two RCTs, results were imprecise (Figure 10). Finally, using meta‐regression (Figure 11), we found that the proportion of Caucasians and the proportion of participants for whom follow‐up LDL‐C measurements were missing were related, and effects of PCSK9 inhibitors on mean percentage change in LDL‐C were increased. However, these estimates became non‐significant after correction for unexplained between‐study heterogeneity based on a random‐effects model.
Subgroup and interaction effects of six months mean percentage change in LDL‐C for PCSK9 trials using a placebo comparison arm.
Subgroup and interaction effects of six months mean percentage change in LDL‐C for PCSK9 trials using a ezetimibe comparison arm.
Meta‐regression of PCSK9 mAbs compared with placebo at six months mean percentage change in LDL‐C. The long dashed line represents the fixed effect, the long‐short dashed line random effects, circle diameter is proportionate to the inverse of the variance (i.e. equal to study precision).
PCSK9 effects on clinical endpoints in comparison with placebo
Owing to the fact that original publications did not report treatment effect estimates with clinical endpoints over time, results on clinical endpoints (summarised in Table 1 and Table 2) are based on the maximum follow‐up available.
Odds ratio estimates of PCSK9 inhibitors compared with placebo with intended effects were as follows: OR 1.02 (95% CI 0.91 to 1.14; 12 studies; 60,684 participants; GRADE: moderate) for all‐cause mortality; OR 0.86 (95% CI 0.80 to 0.92; eight studies; 59,294 participants; GRADE: moderate) for any CVD event (Figure 12); OR 0.77 (95% CI 0.69 to 0.85) for any myocardial infarction (MI); and OR 0.76 (95% CI 0.65 to 0.89) for any stroke. Treatment effect estimates of unintended effects were as follows: OR 1.08 (95% CI 1.04 to 1.12; 13 studies; 61,038 participants; GRADE: low) for any adverse events (Figure 13); OR 1.07 (95% CI 0.99 to 1.16) for myalgia; OR 1.19 (95% CI 0.91 to 1.55) for influenza; OR 0.86 (95% CI 0.62 to 1.18) for hypertension; OR 0.91 (95% CI 0.63 to 1.31) for any cancer diagnosis; OR 1.04 (95% CI 0.95 to 1.14) for T2DM; OR 0.85 (95% CI 0.73 to 0.99) for elevated creatinine; and OR 1.04 (95% CI 0.88 to 1.24) for neurological events. Exclusion of terminated SPIRE‐1/2 ‐ bococizumab ‐ trials from any adverse events and myalgia meta‐analyses resulted in attenuated effect estimates of OR 1.01 (95% CI 0.96 to 1.06) and OR 1.17 (95% CI 0.87 to 1.56). Evaluation of these treatment effect estimates on the RD scale revealed that the effect of PCSK9 inhibitors on the risk of an event was typically modest, with changes in risk often less than 1% (see Table 2).
Association of PCSK9 inhibitors compared with placebo with the incidence of any CVD.
Association of PCSK9 inhibitors compared with placebo with the incidence of any adverse events.
PCSK9 effects on clinical endpoints in comparison with ezetimibe and statins
Odds ratio estimates of PCSK9 inhibitors compared with ezetimibe and statins with intended effects were as follows: OR 0.45 (95% CI 0.27 to 0.75; three studies; 4770 participants; GRADE: very low) for any CVD event (Figure 14 data on all‐cause mortality and any MI were unavailable. Treatment effect estimates with unintended effects were as follows: OR 1.18 (95% CI 1.05 to 1.34; five studies; 5376 participants; GRADE: low) for any adverse events (Figure 15); OR 1.09 (95% CI 0.81 to 1.48) for myalgia; OR 1.28 (95% CI 0.91 to 1.80) for influenza; OR 1.10 (95% CI 0.41 to 2.96) for hypertension; OR 1.10 (95% CI 0.63 to 1.93) for T2DM, OR 0.51 (95% CI 0.28 to 0.92) for elevated creatinine; and OR 1.22 (95% CI 0.40 to 3.69) for neurological events. Data for any stroke and for cancer were unavailable.
Association of PCSK9 inhibitors compared with ezetimibe and statins with the incidence of any CVD.
Association of PCSK9 inhibitors compared with ezetimibe and statins with the incidence of any adverse events.
Evaluation of these estimates on the RD scale revealed that effects of PCSK9 inhibitors on risks of an event were typically modest; changes in risk often were less than 1% (see Table 2).
Outcomes and comparisons without data
See respective sections for details on missing outcome data that were unavailable for some comparisons. Clinical outcome data were insufficiently available to perform a meta‐analysis for comparison with ezetimibe. Data on quality of life were unavailable for all studies. Although the substudy of the FOURIER ‐ EBBINGHAUB ‐ presented little or no effect on cognitive function, these data had not been published in sufficient detail to be included here. Regardless of the publication status of the EBBINGHAUB trial, data on cognitive function were not published by other trials, hence we decided (post hoc) to extract data on neurological events.
Discussion
Summary of main results
In this systematic review and meta‐analysis, we showed that randomised trials evaluating PCSK9 inhibitors (primarily against placebo) had a beneficial profile in terms of cardiovascular risk factors that most likely explain their protective effects on cardiovascular events.
In terms of cardiovascular biomarkers, treatment with PCSK9 inhibitors was characterised by a decrease in low‐density lipoprotein cholesterol (LDL‐C), apolipoprotein B, non‐high‐density lipoprotein cholesterol (HDL‐C), triglycerides, and lipoprotein(a), and a modest increase in HDL‐C and apolipoprotein A1. Investigators reported some differences in biomarker response depending on the use of placebo or active comparisons.
Although we observed high between‐study heterogeneity for biomarker outcomes, most study authors agreed on direction of effect and deemed that differences in magnitude were similar enough to provide clinically relevant treatment effect estimates. We did not observe a dose‐response effect of PCSK9 inhibitors on LDL‐C or apolipoprotein B when comparing trials with similar PCSK9 monoclonal antibody (mAb) dosages. A dose‐response effect may have been due to the crude categorisation used by review authors and/or to grouping of studies by different comparator drugs or by other differences in study‐specific factors.
Trials published to date comparing PCSK9 inhibitors against placebo showed potentially little to no effect on all‐cause mortality; nevertheless, PCSK9 inhibitors showed protective effects on cardiovascular disease (CVD) events, myocardial infarction (MI), and any stroke. Treatment with PCSK9 inhibitors was associated with a modest increase in the risk of any adverse events (odds ratio (OR) 1.08, 95% confidence interval (CI) 1.04 to 1.12), largely driven by the SPIRE‐1 and SPIRE‐2 trials, which used an agent that was discontinued owing to immunogenicity. When looking at specific adverse events (extracted in this systematic review), we found that compared with placebo, PCSK9 inhibitors did not show significant association with type 2 diabetes (T2DM), cancers, or neurocognitive events, possibly as the result of limited follow‐up duration. It is important to note that recent phase 3 trials (FOURIER, SPIRE‐1, and SPIRE‐2) did not report on cancer. Regarding minor adverse events, PCSK9 inhibitors showed potentially increased risk of myalgia and influenza, with the former becoming non‐significant after the SPIRE‐1/2 trials were excluded. Study authors reported that they observed a protective effect with PCSK9 inhibitors, which decreased the risk of elevated creatinine (compared with placebo and active treatments). Trials comparing PCSK9 inhibitors against ezetimibe and statins described a more pronounced protective effect on CVD risk when compared with placebo; this discrepancy is likely related to the lower quality of evidence. Researchers provided no data on all‐cause mortality, stroke, or MI. Information on clinical endpoints was unavailable for the ezetimibe only comparison.
Estimation of the same associations on a risk difference scale (Table 4) revealed that PCSK9 inhibitors only modestly changed the outcome risk, often with less than 1% change in risk.
Overall completeness and applicability of evidence
Given selection criteria and study designs reported by published trials evaluating PCSK9 inhibitors, we consider it important to highlight situations that may limit the applicability of existing evidence.
First, most of the evidence was obtained from people with established atherosclerotic CVD or at high risk of cardiovascular events; therefore evidence regarding the use of PCSK9 inhibitors for primary prevention remains controversial. Second, information on clinical endpoints for the placebo comparison was based on the large sample size in the FOURIER and SPIRE‐1 and SPIRE‐2 trials. Although these trials were large, median follow‐up was less than three years, hence information on long‐term efficacy and safety is absent. For the other comparisons, follow‐up was shorter and events were fewer, prohibiting any strong recommendations at this time. Third, information on the safety of PCSK9 inhibitors did not reveal an increase in risk of cancer or T2DM. However, the largest trials to date (FOURIER and SPIRE‐1 and SPIRE‐2) did not provide cancer data, and again, follow‐up time was very modest, leaving questions on long‐term effectiveness and risk unanswered. Three recent genetic studies with large sample size and long‐term follow‐up showed that variation in the PCSK9 locus was associated with increased glucose and T2DM (Ference 2016; Lotta 2016; Schmidt 2017). Lack of significant association with T2DM may be due to the relatively small number of T2DM events collected to date (< 2000) as a comparison; the association of statins with T2DM was discovered only after more than 4000 events were reported (Swerdlow 2014).
Quality of the evidence
Although all available data were derived from industry‐sponsored randomised controlled trials (RCTs), most trials seemed to have low risk of bias. Exceptions are the open‐label OSLER trials, which were at high risk of performance bias. Another important potential source of bias was attrition bias, whereby some RCTs included missing observations for more than 5% of enrolled participants. Most trials tried to minimise this bias by using advanced analytics that explicitly (multiple imputation) or implicitly (mixed‐effects models) imputed these missing observations, thus ensuring that all comparison were made on an intention‐to‐treat (ITT) basis. The appropriateness of these models (and their underlying assumptions) was not reported, hence these imputation algorithms may have failed to correct for potential attribution bias. For the placebo comparison, however, the large number of participants in the FOURIER and SPIRE‐1/2 trials had very low attrition rates and generally were perceived to have low risk of bias.
The quality of evidence was high for the biomarker endpoints in comparison with placebo or ezetimibe. For the comparison of PCSK9 mAbs against ezetimibe and statins, we graded quality as moderate owing to inclusion of the open‐label OSLER trials. Despite the GRADE (Grade Working Group 2004) recommendation to downgrade evidence associated with high between‐study heterogeneity, we decided against this approach because most studies (i.e. LDL‐C outcomes) reported the same direction of effects. Heterogeneity reflected a difference in magnitude ‐ not in direction of effect (confirmed by clinical experts JPC and ADH). Furthermore, use of random‐effects models resulted in point estimates and confidence intervals that are free of bias (Thompson 1999), even in the presence of heterogeneity. Although we believe that this between‐study heterogeneity should not be reflected in our GRADE score, it does reflect a potential need for personalised medicine (Schmidt 2016).
For intended effect and clinical outcomes (i.e. CVD, all‐cause mortality, and MI) with PCSK9 inhibitors compared with placebo, we graded the quality of the evidence as moderate. Results were derived from three trials with large sample sizes (FOURIER, SPIRE‐1 and ‐2), two of which used the terminated bococizumab drug. Furthermore, median follow‐up was less than three years, hence long‐term effectiveness and safety remain uncertain, potentially influencing the absence of an effect on all‐cause mortality or other outcomes with longer lag time. We graded the quality of the evidence as very low in the PCSK9 mAb‐to‐ezetimibe and statin comparison, again owing to inclusion of the open‐label OSLER trials. Bias due to unblinded allocation may explain the likely overly large effect of PCSK9 inhibitors against ezetimibe and statins on CVD events (OR 0.45, 95 CI% 0.27 to 0.75) versus PCSK9 mAb against placebo (OR 0.86, 95% CI 0.0.80 to 0.92).
Given the reported antibody drug response, inclusion of the discontinued bococizumab trials may seem controversial. However, owing to the limited large sample size of trials with modestly long follow‐up, we decided to include these data. Side effects may differ between PCSK9 inhibitors, for example, the potential myalgia effect in the placebo comparison seemed more pronounced in the SPIRE‐1 and SPIRE‐2 trials than in the FOURIER trials (evolocumab). Owing to the limited number of adequately sampled trials, we could not perform formal analyses.
Potential biases in the review process
The meta‐analysis presented may show some weaknesses. First, meta‐analyses explored a large number of endpoints, increasing the probability of a false positive finding. We did not correct for multiple testing because we sought to inform ongoing trials, which can act as an independent and final arbiter. Second, despite our best efforts, we may have failed to identify certain PCSK9 inhibitor trials. Given that we are unaware of the results of any unidentified RCTs, this seems unlikely to bias our results but will obviously reduce sample size. Third, although we set out to report effect estimates with clinical endpoints, similar to biomarker endpoints, at six months, one year, and five years of follow‐up, we found that this was impossible owing to the limited sample size and the fact that the original RCT did not present data in sufficient detail. Fourth, we did not present data by type of PCSK9 inhibitor because of the limited sample size and the focus of trials on alirocumab, making such an analysis uninformative at the moment. Fifth, effect estimates of PCSK9 compared with ezetimibe and statin may be further biased by the limited number of events influencing both point estimates and confidence intervals (Bradburn 2007; Sweeting 2004). We tried to deal with this potential source of bias by re‐creating individual participant data (based on reported cell frequencies) and by estimating a combined effect by using a mixed‐effect model with random intercept (and slope) for study indicator. Nevertheless, we found that random‐effects models (mixed‐effect model with random intercept and slope) often did not converge, hence we did not report these estimates. Given the large sample size included in FOURIER and SPIRE‐1 and SPIRE‐2 trials for the placebo comparison, sparse data are less of an issue for effect estimates on major CVD events but remain inconclusive for rarer CVD and non‐CVD events such as haemorrhagic stroke, cancers, and T2DM. Furthermore, although the FOURIER and SPIRE trials collected data on a large number of participants, investigators provided relatively short follow‐up times, leaving open the question of long‐term efficacy and safety.
Agreements and disagreements with other studies or reviews
We are aware of two previous systematic reviews and meta‐analyses on PCSK9 inhibitors (Navarese 2015; Zhang 2015); both included a large number of RCTs with short follow‐up of 12 weeks, which we excluded here, as well as several longer‐term follow‐up studies that we included.
The meta‐analysis of Zhang 2015 revealed a protective effect on mortality of alirocumab versus placebo (OR 0.43, 95% CI 0.19 to 0.96) and of alirocumab versus ezetimibe (OR 0.48, 95% CI 0.16 to 1.45); these effects are different from the more neutral effect that we observed (OR 1.02, 95% CI 0.91 to 1.14). This difference may have occurred because Zhang 2015 relied predominantly on short‐term follow‐up studies, limiting the number of events per study, and this likely biased effect estimates.
Similar to this review, Zhang found a protective effect of alirocumab on elevated creatine kinase versus placebo (OR 0.72, 95% CI 0.52 to 1.01) and versus ezetimibe (OR 0.75, 95% CI 0.46 to 1.24). Review authors found a similar protective effect of elevated creatine kinase for evolucumab (vs placebo or ezetimibe), as well as protective effects of elevated alanine aminotransferase and aspartate aminotransferase (no information was reported on mortality for evolucumab). Contrary to our meta‐analyses, Zhang 2015 found a non‐significant decrease in influenza for both alirocumab and evolucumab. Results may be concordant with a null effect, as in both Zhang's review and ours, these associations did not reach significance at an alpha of 0.05. Alternatively, Zhang included trials with only a few weeks of follow‐up, potentially excluding the annual influenza season, and the shorter duration of exposure conveys less risk.
Navarese 2015 reported a protective effect of PCSK9 inhibitors (vs all types of comparators) for all‐cause mortality (OR 0.45, 95% CI 0.23 to 0.86) and a decreased incidence of increased serum creatine kinase levels (OR 0.72, 95% CI 0.54 to 0.96), as well as protective effects for cardiovascular mortality and MI (OR 0.50, 95% CI 0.23 to 1.10; OR 0.49, 95% CI 0.26 to 0.93, respectively). As with the Zhang 2015 study, results were based on many short‐term trials with very few events per study, hence the caveats described before continue to hold.
Finally, we are aware that a recent network meta‐analysis (Lipinski 2016) indirectly comparing mAb versus placebo showed an OR for neurocognitive adverse events of 2.34 (95% CI 1.11 to 4.93). This association was not observed in the current meta‐analyses, which directly compared PCSK9 inhibitors versus placebo and therefore were less susceptible to bias.
Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.
Risk of bias summary: review authors' judgements about each risk of bias item for each included study.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in LDL‐C at six months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in LDL‐C at six months.
Association of PCSK9 inhibitors compared with ezetimibe with mean percentage change from baseline in LDL‐C at six months.
Sensitivity analyses grouping RCTs by PCSK9 dose compared with placebo on 6 months LDL‐C mean percentage change from baseline.
Sensitivity analyses grouping RCTs by PCSK9 dose compared with placebo on 6 months apolipoprotein B mean percentage change from baseline.
Subgroup and interaction effects of six months mean percentage change in LDL‐C for PCSK9 trials using a placebo comparison arm.
Subgroup and interaction effects of six months mean percentage change in LDL‐C for PCSK9 trials using a ezetimibe comparison arm.
Meta‐regression of PCSK9 mAbs compared with placebo at six months mean percentage change in LDL‐C. The long dashed line represents the fixed effect, the long‐short dashed line random effects, circle diameter is proportionate to the inverse of the variance (i.e. equal to study precision).
Association of PCSK9 inhibitors compared with placebo with the incidence of any CVD.
Association of PCSK9 inhibitors compared with placebo with the incidence of any adverse events.
Association of PCSK9 inhibitors compared with ezetimibe and statins with the incidence of any CVD.
Association of PCSK9 inhibitors compared with ezetimibe and statins with the incidence of any adverse events.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in HDL‐C at six months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in triglycerides at six months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in total cholesterol at six months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in apolipoprotein A1 at six months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in apolipoprotein B at six months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in lipoprotein(a) at six months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in non‐HDL‐C at six months.
Association of PCSK9 inhibitors compared with placebo with mean absolute change from baseline in HbA1c at six months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in HDL‐C at six months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in triglycerides at six months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in apolipoprotein B at six months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in lipoprotein(a) at six months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in non‐HDL‐C at six months.
Association of PCSK9 inhibitors compared with ezetimibe with mean percentage change from baseline in HDL‐C at six months.
Association of PCSK9 inhibitors compared with ezetimibe with mean percentage change from baseline in triglycerides at six months.
Association of PCSK9 inhibitors compared with ezetimibe with mean percentage change from baseline in total cholesterol at six months.
Association of PCSK9 inhibitors compared with ezetimibe with mean percentage change from baseline in apolipoprotein A1 at six months.
Association of PCSK9 inhibitors compared with ezetimibe with mean percentage change from baseline in apolipoprotein B at six months.
Association of PCSK9 inhibitors compared with ezetimibe with mean percentage change from baseline in lipoprotein(a) at six months.
Association of PCSK9 inhibitors compared with ezetimibe with mean percentage change from baseline in non‐HDL‐C at six months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in LDL‐C at 12 months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in HDL‐C at 12 months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in triglycerides at 12 months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in total cholesterol at 12 months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in apolipoprotein A1 at 12 months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in apolipoprotein B at 12 months.
Association of PCSK9 inhibitors compared with placebo with mean percentage change from baseline in non‐HDL‐C at 12 months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in LDL‐C at 12 months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in HDL‐C at 12 months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in triglycerides at 12 months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in apolipoprotein A1 at 12 months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in apolipoprotein B at 12 months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in lipoprotein(a) at 12 months.
Association of PCSK9 inhibitors compared with ezetimibe and statins with mean percentage change from baseline in non‐HDL‐C at 12 months.
Association of PCSK9 inhibitors compared with placebo with the incidence of all‐cause mortality.
Association of PCSK9 inhibitors compared with placebo with the incidence of any MI.
Association of PCSK9 inhibitors compared with placebo with the incidence of any stroke.
Association of PCSK9 inhibitors compared with placebo with the incidence of myalgia.
Association of PCSK9 inhibitors compared with placebo with the incidence of influenza.
Association of PCSK9 inhibitors compared with placebo with the incidence of hypertension.
Association of PCSK9 inhibitors compared with placebo with the incidence of any cancer diagnosis.
Association of PCSK9 inhibitors compared with placebo with the incidence of type 2 diabetes.
Association of PCSK9 inhibitors compared with placebo with the incidence of elevated creatine.
Association of PCSK9 inhibitors compared with placebo with the incidence of neurological events.
Association of PCSK9 inhibitors compared with ezetimibe and statins with the incidence of myalgia.
Association of PCSK9 inhibitors compared with ezetimibe and statins with the incidence of influenza.
Association of PCSK9 inhibitors compared with ezetimibe and statins with the incidence of hypertension.
Association of PCSK9 inhibitors compared with ezetimibe and statins with the incidence of type 2 diabetes.
Association of PCSK9 inhibitors compared with ezetimibe and statins with the incidence of elevated creatinine.
Association of PCSK9 inhibitors compared with ezetimibe and statins with the incidence neurological events
| PCSK9 inhibitors compared with placebo in addition to statin and/or ezetimibe background care | |||||||
| Patient or population: people at high risk of cardiovascular disease (history of CVD or high LDL‐C despite treatment) | |||||||
| Outcomes | Ilustrative comparative risk or mean (95% CI) | Relative effect (95% CI) | Mean difference (95% CI) | Number of participants | Quality of the evidence | Comments | |
| Assumed risk or mean biomarker using placebo* | Corresponding risk or mean using PCSK9 inhibition† | ||||||
| LDL‐C reduction (LDL‐C) | Mean LDL‐C reduction was ‐6.12 mean percentage change form baseline | Mean LDL‐C reduction in the intervention group was ‐59.98 (‐64.76 to ‐55.19) percentage change form baseline | ‐53.86% (‐58.64 to ‐49.08) in percentage reduction from baseline | 4782 | ⊕⊕⊕⊝ | Negative is beneficial | |
| Cardiovascular disease (CVD) Follow‐up: 6 months to 36 months | Cardiovascular disease risk was 64 per 1000 participants | Cardiovascular disease risk in the intervention group was 55 (51 lower to 59 lower) per 1000 participants | OR 0.86 (0.80 to 0.92) | 59294 | ⊕⊕⊕⊝ | Below 1 is beneficial | |
| All‐cause mortality (mortality) Follow‐up: 6 months to 36 months | All‐cause mortality risk was 18 per 1000 participants | All‐cause mortality risk in the intervention group was 18 (16 lower to 20 higher) per 1000 participants | OR 1.02 (0.91 to 1.14) | 60684 | ⊕⊕⊕⊝ | Below 1 is beneficial | |
| Any adverse events (adverse events) Follow‐up: 6 months to 36 months | Risk of any adverse events was 692 per 1000 participants | Risk of any adverse events in the intervention group was 707 (700 higher to 715 higher) per 1000 participants | OR 1.08 (1.04 to 1.12) | 61038 | ⊕⊕⊝⊝ | Below 1 is beneficial | |
| CI: confidence interval | |||||||
| GRADE Working Group grades of evidence | |||||||
| aUnclear randomisation processes and high risk of other biases. Downgrading one level because of "limitations in the design and implementation of available studies suggesting high likelihood of bias" bResults predominantly determined by 3 large RCTs with a relatively short median follow‐up of 7 months (SPIRE‐1), 12 months (SPIRE‐2), and 26 months (FOURIER). SPIRE‐1/2 trials terminated prematurely owing to an unanticipated drug‐antibody response. Downgrading one level because of "indirectness of evidence" cEffect was driven by the discontinued SPIRE trials. Downgrading one level because of "limitations in the design and implementation of available studies suggesting high likelihood of bias" *Assumed risks or mean LDL‐C was based on the comparison arms of included trials †Corresponding risk or mean was based on the risk difference reported in Table 4 or the mean difference in LDL‐C | |||||||
| PCSK9 Inhibitors compared to ezetimibe. | |||||||
| Patient or population: people at high risk of cardiovascular disease (history of CVD or high LDL‐C despite treatment) | |||||||
| Outcomes | Ilustrative comparative risk or mean (95% CI) | Relative effect (95%CI) | Mean difference (95% CI) | Number of participants | Quality of the evidence | Comments | |
| Assumed risk or mean biomarker using ezetimibe* | Corresponding risk or mean using PCSK9 inhibition† | ||||||
| LDL‐C reduction (LDL‐C) | Mean LDL‐C reduction was ‐6.12 mean percentage change form baseline | Mean LDL‐C reduction in the intervention group was ‐36.32 (‐40.29 to ‐32.34) percentage change from baseline | ‐30.20% (‐34.18 to ‐26.23) in percentage reduction from baseline | 823 | ⊕⊕⊕⊝ | Negative is beneficial | |
| Cardiovascular disease (CVD) | Cardiovascular disease risk was 64 per 1000 participants | Data unavailable | |||||
| All‐cause mortality (mortality) | All‐cause mortality risk was 6 per 1000 participants | Data unavailable | |||||
| Any adverse events (adverse events) | Risk of any adverse events was 692 per 1000 participants | Data unavailable | |||||
| CI: confidence interval | |||||||
| GRADE Working Group grades of evidence | |||||||
| aHigh risk of other biases. Downgrading one level because of "limitations in the design and implementation of available studies suggesting high likelihood of bias" *Assumed risks or mean LDL‐C was based on the comparison arms of included trials †Corresponding risk or mean was based on the risk difference reported in Table 4 or the mean difference in LDL‐C. | |||||||
| PCSK9 inhibitors compared with ezetimibe and statins | |||||||
| Patient or population: people at high risk of cardiovascular disease (history of CVD or high LDL‐C despite treatment) | |||||||
| Outcomes | Ilustrative comparative risk or mean (95% CI) | Relative effect (95%CI) | Mean difference (95% CI) | Number of participants | Quality of the evidence | Comments | |
| Assumed risk or mean biomarker with ezetimibe and statins* | Corresponding risk or mean with PCSK9 inhibition† | ||||||
| LDL‐C reduction (LDL‐C) | Mean LDL‐C reduction was ‐6.12 mean percentage change form baseline | Mean LDL‐C reduction in the intervention group was ‐45.32 (‐62.27 to ‐28.37) percentage change form baseline | ‐39.20% (‐56.15 to ‐22.26) in percentage reduction from baseline | 5376 | ⊕⊕⊕⊝ | Negative is beneficial | |
| Cardiovascular disease (CVD) Follow‐up: 6 months to 11 months | Cardiovascular disease risk was 64 per 1000 participants | Cardiovascular disease risk in the intervention group was 53 (47 lower to 60 lower) per 1000 participants | OR 0.45 (0.27 to 0.75 | 4770 | ⊕⊝⊝⊝ | Below 1 is beneficial | |
| All‐cause mortality (mortality) | All‐cause mortality risk was 6 per 1000 participants | Data unavailable | |||||
| Any adverse events (adverse events) Follow‐up: 6 months to 11 months | Risk of any adverse events was 692 per 1000 participants | Risk of any adverse events in the intervention group was 729 (703 lower to 755 higher) per 1000 participants | OR 1.18 (1.05 to 1.34) | 5376 | ⊕⊕⊝⊝ | Below 1 is beneficial | |
| CI: confidence interval | |||||||
| GRADE Working Group grades of evidence | |||||||
| aMost data were based on OSLER‐1 and/or OSLER‐2, which were open‐label studies. Downgrading one level because of "limitations in the design and implementation of available studies suggesting high likelihood of bias" bITT results were often unavailable; instead data were extracted on the basis of an as treated sample. Downgrading one level because of "limitations in the design and implementation of available studies suggesting high likelihood of bias" cNumber of events was low. Downgrading one level because of " Imprecision of results" *Assumed risks or mean LDL‐C was based on comparison arms of included trials †Corresponding risk or mean was based on the risk difference reported in Table 4 or the mean difference in LDL‐C | |||||||
| Number of studies | Number of events in the PCSK9 arm | Number of participants in the PCSK9 arm | Number of events in the comparison arm | Number of participants in the comparison arm | Fixed‐effect (95% CI) | Between‐study heterogeneity P value | |
| Placebo comparison | |||||||
| All‐cause mortality | 12 | 580 | 31358 | 558 | 29326 | 1.02 (0.91 to 1.14) | 0.159 |
| Any cardiovascular event | 8 | 1790 | 30355 | 2009 | 28939 | 0.86 (0.80 to 0.92) | 0.803 |
| Any myocardial infarction | 10 | 686 | 30610 | 869 | 29038 | 0.77 (0.69 to 0.85) | 0.674 |
| Any stroke | 8 | 265 | 29828 | 340 | 28672 | 0.76 (0.65 to 0.89) | 0.185 |
| Any adverse event | 13 | 22593 | 31611 | 20435 | 29427 | 1.08 (1.04 to 1.12) | 0.38 |
| Myalgia | 12 | 1249 | 31428 | 1094 | 29363 | 1.07 (0.99 to 1.16) | 0.873 |
| Influenza | 6 | 191 | 2923 | 82 | 1477 | 1.19 (0.91 to 1.55) | 1 |
| Hypertension | 8 | 110 | 3436 | 60 | 1593 | 0.86 (0.62 to 1.18) | 0.741 |
| Cancer | 5 | 83 | 2851 | 46 | 1442 | 0.91 (0.63 to 1.31) | 0.964 |
| Type 2 diabetes | 7 | 956 | 17535 | 911 | 16681 | 1.04 (0.95 to 1.14) | 0.983 |
| Elevated creatinine | 8 | 319 | 30399 | 309 | 28933 | 0.85 (0.73 to 0.99) | 0.419 |
| Neurological events | 5 | 289 | 16036 | 242 | 14919 | 1.04 (0.88 to 1.24) | 0.759 |
| Ezetimibe and statin comparison | |||||||
| All‐cause mortality | |||||||
| Any cardiovascular event | 3 | 29 | 3079 | 33 | 1691 | 0.45 (0.27 to 0.75) | 0.712 |
| Any myocardial infarction | |||||||
| Any stroke | |||||||
| Any adverse event | 5 | 2290 | 3309 | 1347 | 2067 | 1.18 (1.05 to 1.34) | 0.478 |
| Myalgia | 5 | 127 | 3309 | 81 | 2067 | 1.09 (0.81 to 1.48) | 0.715 |
| Influenza | 4 | 113 | 3183 | 51 | 1942 | 1.28 (0.91 to 1.80) | 0.45 |
| Hypertension | 3 | 6 | 207 | 12 | 453 | 1.10 (0.41 to 2.96) | 0.893 |
| Cancer | |||||||
| Type 2 diabetes | 4 | 35 | 3183 | 22 | 1942 | 1.10 (0.63 to 1.93) | 0.057 |
| Elevated creatinine | 5 | 20 | 3183 | 29 | 1942 | 0.51 (0.28 to 0.92) | 0.969 |
| Neurological events | 2 | 5 | 207 | 9 | 453 | 1.22 (0.40 to 3.69) | 1 |
| Number of studies | Number of events in the PCSK9 arm | Number of participants in the PCSK9 arm | Number of events in the comparison arm | Number of participants in the comparison arm | Fixed‐effect (95% CI) | Between‐study heterogeneity P value | |
| Placebo comparison | |||||||
| All‐cause mortality | 12 | 580 | 31358 | 558 | 29326 | 0.000 (‐0.002 to 0.002) | 0.781 |
| Any cardiovascular event | 8 | 1790 | 30355 | 2009 | 28939 | ‐0.009 (‐0.013 to ‐0.005) | 0.005 |
| Any myocardial infarction | 10 | 686 | 30610 | 869 | 29038 | ‐0.007 (‐0.009 to ‐0.004) | < 0.001 |
| Any stroke | 8 | 265 | 29828 | 340 | 28672 | ‐0.003 (‐0.004 to ‐0.001) | 0.409 |
| Any adverse event | 13 | 22593 | 31611 | 20435 | 29427 | 0.015 (0.008 to 0.023) | < 0.001 |
| Myalgia | 12 | 1249 | 31428 | 1094 | 29363 | 0.002 (‐0.001 to 0.006) | 0.979 |
| Influenza | 6 | 191 | 2923 | 82 | 1477 | 0.010 (‐0.005 to 0.025) | 0.513 |
| Hypertension | 8 | 110 | 3436 | 60 | 1593 | ‐0.005 (‐0.016 to 0.006) | 1 |
| Cancer | 5 | 83 | 2851 | 46 | 1442 | ‐0.003 (‐0.013 to 0.008) | 0.892 |
| Type 2 diabetes | 7 | 956 | 17535 | 911 | 16681 | 0.002 (‐0.03 to 0.07) | 0.73 |
| Elevated creatinine | 8 | 319 | 30399 | 309 | 28933 | ‐0.002 (‐0.003 to ‐0.000) | < 0.001 |
| Neurological events | 5 | 289 | 16036 | 242 | 14919 | 0.001 (‐0.002 to 0.004) | 0.923 |
| Ezetimibe and statin comparison | |||||||
| All‐cause mortality | |||||||
| Any cardiovascular event | 3 | 29 | 3079 | 33 | 1691 | ‐0.011 (‐0.017 to ‐0.004) | 1 |
| Any myocardial infarction | |||||||
| Any stroke | |||||||
| Any adverse event | 5 | 2290 | 3309 | 1347 | 2067 | 0.037 (0.011 to 0.063) | 0.862 |
| Myalgia | 5 | 127 | 3309 | 81 | 2067 | 0.003 (‐0.007 to 0.014) | 0.901 |
| Influenza | 4 | 113 | 3183 | 51 | 1942 | 0.009 (‐0.002 to 0.019) | 1 |
| Hypertension | 3 | 6 | 207 | 12 | 453 | 0.002 (‐0.020 to 0.025) | 0.922 |
| Cancer | |||||||
| Type 2 diabetes | 4 | 35 | 3183 | 22 | 1942 | 0.001 (‐0.007 to 0.008) | 0.027 |
| Elevated creatinine | 5 | 20 | 3183 | 29 | 1942 | ‐0.006 (‐0.012 to ‐0.000) | 0.984 |
| Neurological events | 2 | 5 | 207 | 9 | 453 | 0.004 (‐0.019 to 0.028) | 1 |
| Number of studies | Number of PCSK9 participants | Number of comparator arm participants | Fixed‐effect (95% CI) | Random‐effects (95% CI) | Between‐study heterogeneity P value | |
| Placebo comparison | ||||||
| LDL‐C % change | 8 | 3255 | 1527 | ‐57.62 (‐59.37 to ‐55.87) | ‐53.86 (‐58.64 to ‐49.08) | < 0.001 |
| HDL‐C % change | 5 | 2324 | 1175 | 5.48 (4.37 to 6.59) | 6.00 (4.31 to 7.69) | 0.19 |
| Triglycerides % change | 5 | 2324 | 1175 | ‐14.62 (‐16.74 to ‐12.50) | ‐11.39 (‐17.04 to ‐5.74) | < 0.001 |
| Total cholesterol % change | 2 | 1762 | 895 | ‐35.79 (‐37.36 to ‐34.23) | ‐31.41 (‐43.65 to ‐19.16) | < 0.001 |
| Apolipoprotein A1 % change | 3 | 2043 | 1033 | 3.49 (2.38 to 4.60) | 3.50 (2.37 to 4.64) | 0.36 |
| Apolipoprotein B % change | 6 | 2507 | 1239 | ‐47.79 (‐49.51 to ‐46.08) | ‐41.93 (‐49.76 to ‐34.10) | < 0.001 |
| Lipoprotein(a) % change | 4 | 2252 | 1140 | ‐22.43 (‐24.30 to ‐20.56) | ‐19.80 (‐25.43 to ‐14.17) | < 0.001 |
| Non‐HDL‐C % change | 4 | 2252 | 1140 | ‐50.03 (‐51.73 to ‐48.33) | ‐47.17 (‐53.92 to ‐40.42) | < 0.001 |
| HbA1c absolute change | 2 | 490 | 245 | 0.00 (‐0.04 to 0.05) | 0.01 (‐0.06 to 0.08) | 0.151 |
| Ezetimibe and statin comparison | ||||||
| LDL‐C % change | 5 | 3309 | 2067 | ‐52.17 (‐53.91 to ‐50.43) | ‐39.20 (‐56.15 to ‐22.26) | < 0.001 |
| HDL‐C % change | 3 | 333 | 578 | 7.53 (5.54 to 9.51) | 6.42 (1.31 to 11.52) | 0.002 |
| Triglycerides % change | 2 | 229 | 327 | ‐3.47 (‐8.26 to 1.32) | ‐3.47 (‐8.26 to 1.32) | 0.46 |
| Total cholesterol % change | ||||||
| Apolipoprotein A1 % change | ||||||
| Apolipoprotein B % change | 3 | 333 | 578 | ‐26.86 (‐29.50 to ‐24.22) | ‐26.72 (‐30.26 to ‐23.19) | 0.169 |
| Lipoprotein(a) % change | 2 | 207 | 453 | ‐19.51 (‐24.48 to ‐14.53) | ‐19.51 (‐24.48 to ‐14.53) | 0.60 |
| Non‐HDL‐C % change | 2 | 207 | 453 | ‐28.19 (‐32.79 to ‐23.59) | ‐28.19 (‐32.79 to ‐23.59) | 0.65 |
| HbA1c absolute change | ||||||
| Ezetimibe comparison | ||||||
| LDL‐C % change | 2 | 531 | 292 | ‐30.20 (‐34.18 to ‐26.23) | ‐30.20 (‐34.18 to ‐26.23) | 0.71 |
| HDL‐C % change | 2 | 531 | 292 | 7.40 (5.11 to 9.70) | 7.01 (3.70 to 10.32) | 0.22 |
| Triglycerides % change | 2 | 531 | 292 | ‐0.43 (‐4.90 to 4.03) | ‐0.43 (‐4.90 to 4.03) | 0.89 |
| Total cholesterol % change | 2 | 531 | 292 | ‐15.51 (‐18.18 to ‐12.83) | ‐15.84 (‐19.37 to ‐12.30) | 0.24 |
| Apolipoprotein A1 % change | 2 | 531 | 292 | 6.13 (4.34 to 7.91) | 6.13 (4.34 to 7.91) | 0.68 |
| Apolipoprotein B % change | 2 | 531 | 292 | ‐23.18 (‐26.28 to ‐20.08) | ‐23.18 (‐26.28 to ‐20.08) | 0.37 |
| Lipoprotein(a) % change | 2 | 531 | 292 | ‐18.70 (‐23.03 to ‐14.37) | ‐13.69 (‐30.60 to 3.21) | 0.003 |
| Non‐HDL‐C % change | 2 | 531 | 292 | ‐23.45 (‐27.07 to ‐19.83) | ‐23.45 (‐27.07 to ‐19.83) | 0.57 |
| HbA1c absolute change | ||||||
| Number of studies | Number of PCSK9 participants | Number of comparator arm participants | Fixed‐effect (95% CI) | Random‐effects (95% CI) | Between‐study heterogeneity P value | |
| Placebo comparison | ||||||
| LDL‐C % change | 6 | 29865 | 28694 | ‐52.80 (‐53.46 to 52.14) | ‐52.87 (‐60.03 to ‐45.72) | < 0.001 |
| HDL‐C % change | 4 | 14528 | 14127 | 5.55 (5.07 to 6.03) | 6.06 (4.30 to 7.82) | 0.102 |
| Triglycerides % change | 3 | 14319 | 14020 | ‐12.53 (‐15.45 to ‐9.61) | ‐12.53 (‐15.45 to ‐9.61) | 0.679 |
| Total cholesterol % change | 2 | 808 | 409 | ‐31.33 (‐33.80 to ‐28.86) | ‐28.47 (‐38.85 to ‐18.10) | < 0.001 |
| Apolipoprotein A1 % change | 1 | 599 | 302 | 3.00 (1.31 to 4.69) | ||
| Apolipoprotein B % change | 4 | 14528 | 14127 | ‐47.18 (‐48.29 to ‐48.29) | ‐43.51 (‐48.88 to ‐38.13) | < 0.001 |
| Lipoprotein(a) % change | ||||||
| Non‐HDL‐C % change | 2 | 808 | 409 | ‐47.16 (‐50.77 to ‐43.55) | ‐43.46 (‐57.45 to ‐29.47) | 0.001 |
| Glucose (mg/dL) absolute change* | 2 | 13720 | 13718 | 1.80 (0.61 to 2.99) | ||
| HbA1c absolute change* | 2 | 13720 | 13718 | 0.02 (‐0.01 to 0.05) | ||
| Ezetimibe and statin comparison | ||||||
| LDL‐C % change | 1 | 2976 | 1489 | ‐58.40 (‐60.40 to ‐56.40) | ||
| HDL‐C % change | 1 | 736 | 368 | 5.40 (3.09 to 7.71) | ||
| Triglycerides % change | 1 | 736 | 368 | ‐10.00 (‐13.59 to ‐6.41) | ||
| Total cholesterol % change | ||||||
| Apolipoprotein A1 % change | 1 | 736 | 368 | 4.30 (2.61 to 5.99) | ||
| Apolipoprotein B % change | 1 | 736 | 368 | ‐38.80 (‐41.18 to ‐36.42) | ||
| Lipoprotein(a) % change | 1 | 736 | 368 | ‐20.80 (‐23.95 to ‐17.65) | ||
| Non‐HDL‐C % change | 1 | 736 | 368 | ‐44.00 (‐46.77 to ‐41.23) | ||
| Glucose (mg/dL) absolute change* | ||||||
| HbA1c absolute change | ||||||
| *On the basis of the combined analysis of SPIRE‐1 and SPIRE‐2, study‐specific estimates were unavailable, hence no random‐effects or between‐study heterogeneity estimates could be calculated | ||||||
