Carotid intima-media thickness progression as surrogate marker for cardiovascular risk: Meta-analysis of 119 clinical trials involving 100,667 patients

Peter Willeit; Lena Tschiderer; Elias Allara; Kathrin Reuber; Lisa Seekircher; Lu Gao; Ximing Liao; Eva Lonn; Hertzel C Gerstein; Salim Yusuf; Frank P Brouwers; Folkert W Asselbergs; Wiek van Gilst; Sigmund A Anderssen; Diederick E Grobbee; John J P Kastelein; Frank L J Visseren; George Ntaios; Apostolos I Hatzitolios; Christos Savopoulos; Pythia T Nieuwkerk; Erik Stroes; Matthew Walters; Peter Higgins; Jesse Dawson; Paolo Gresele; Giuseppe Guglielmini; Rino Migliacci; Marat Ezhov; Maya Safarova; Tatyana Balakhonova; Eiichi Sato; Mayuko Amaha; Tsukasa Nakamura; Kostas Kapellas; Lisa M Jamieson; Michael Skilton; James A Blumenthal; Alan Hinderliter; Andrew Sherwood; Patrick J Smith; Michiel A van Agtmael; Peter Reiss; Marit G A van Vonderen; Stefan Kiechl; Gerhard Klingenschmid; Matthias Sitzer; Coen D A Stehouwer; Heiko Uthoff; Zhi-Yong Zou; Ana R Cunha; Mario F Neves; Miles D Witham; Hyun-Woong Park; Moo-Sik Lee; Jang-Ho Bae; Enrique Bernal; Kristian Wachtell; Sverre E Kjeldsen; Michael H Olsen; David Preiss; Naveed Sattar; Edith Beishuizen; Menno V Huisman; Mark A Espeland; Caroline Schmidt; Stefan Agewall; Ercan Ok; Gülay Aşçi; Eric de Groot; Muriel P C Grooteman; Peter J Blankestijn; Michiel L Bots; Michael J Sweeting; Simon G Thompson; Matthias W Lorenz

doi:10.1161/CIRCULATIONAHA.120.046361

. Author manuscript; available in PMC: 2021 Aug 18.

Published in final edited form as: Circulation. 2020 Jun 17;142(7):621–642. doi: 10.1161/CIRCULATIONAHA.120.046361

Carotid intima-media thickness progression as surrogate marker for cardiovascular risk: Meta-analysis of 119 clinical trials involving 100,667 patients

Peter Willeit ^1,^2,^*, Lena Tschiderer ^1,^*, Elias Allara ^2,³, Kathrin Reuber ⁴, Lisa Seekircher ¹, Lu Gao ⁵, Ximing Liao ⁴, Eva Lonn ^6,⁷, Hertzel C Gerstein ^6,⁷, Salim Yusuf ^6,⁷, Frank P Brouwers ⁸, Folkert W Asselbergs ⁹, Wiek van Gilst ¹⁰, Sigmund A Anderssen ¹¹, Diederick E Grobbee ¹², John J P Kastelein ¹³, Frank L J Visseren ¹⁴, George Ntaios ¹⁵, Apostolos I Hatzitolios ¹⁶, Christos Savopoulos ¹⁶, Pythia T Nieuwkerk ¹⁷, Erik Stroes ¹³, Matthew Walters ¹⁸, Peter Higgins ¹⁹, Jesse Dawson ¹⁹, Paolo Gresele ²⁰, Giuseppe Guglielmini ²⁰, Rino Migliacci ²¹, Marat Ezhov ²², Maya Safarova ²³, Tatyana Balakhonova ²⁴, Eiichi Sato ²⁵, Mayuko Amaha ²⁵, Tsukasa Nakamura ²⁵, Kostas Kapellas ²⁶, Lisa M Jamieson ²⁶, Michael Skilton ²⁷, James A Blumenthal ²⁸, Alan Hinderliter ²⁹, Andrew Sherwood ²⁸, Patrick J Smith ²⁸, Michiel A van Agtmael ³⁰, Peter Reiss ^31,³², Marit G A van Vonderen ³³, Stefan Kiechl ^1,³⁴, Gerhard Klingenschmid ¹, Matthias Sitzer ^4,³⁵, Coen D A Stehouwer ³⁶, Heiko Uthoff ³⁷, Zhi-Yong Zou ³⁸, Ana R Cunha ³⁹, Mario F Neves ³⁹, Miles D Witham ⁴⁰, Hyun-Woong Park ⁴¹, Moo-Sik Lee ^41,⁴², Jang-Ho Bae ^43,⁴⁴, Enrique Bernal ⁴⁵, Kristian Wachtell ⁴⁶, Sverre E Kjeldsen ⁴⁶, Michael H Olsen ⁴⁷, David Preiss ⁴⁸, Naveed Sattar ⁴⁹, Edith Beishuizen ⁵⁰, Menno V Huisman ⁵¹, Mark A Espeland ⁵², Caroline Schmidt ⁵³, Stefan Agewall ⁵⁴, Ercan Ok ⁵⁵, Gülay Aşçi ⁵⁵, Eric de Groot ⁵⁶, Muriel P C Grooteman ⁵⁷, Peter J Blankestijn ⁵⁸, Michiel L Bots ¹², Michael J Sweeting ^2,^59,^†, Simon G Thompson ^2,^†, Matthias W Lorenz, on behalf of the PROG-IMT and the Proof-ATHERO Study Groups^4,^†

¹Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria

²Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

³National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK

⁴Department of Neurology, Goethe University, Frankfurt am Main, Germany

⁵MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

⁶Department of Medicine and Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada

⁷Hamilton General Hospital, Hamilton, Ontario, Canada

⁸Department of Cardiology, Haga Teaching Hospital, the Hague, the Netherlands

⁹Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands

¹⁰Department of Experimental Cardiology, University Medical Center Groningen, Groningen, the Netherlands

¹¹Department of Sports Medicine, Norwegian School of Sports Sciences, Oslo, Norway

¹²Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands

¹³Department of Vascular Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands

¹⁴Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands

¹⁵Department of Medicine, University of Thessaly, Larissa, Greece

¹⁶1st Propedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece

¹⁷Department of Medical Psychology, Amsterdam UMC- Location AMC, Amsterdam, the Netherlands

¹⁸School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK

¹⁹Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK

²⁰Division of Internal and Cardiovascular Medicine, Department of Medicine, University of Perugia, Perugia, Italy

²¹Division of Internal Medicine, Cortona Hospital, Cortona, Italy

²²Laboratory of Lipid Disorders, National Medical Research Center of Cardiology, Moscow, Russia

²³Atherosclerosis Department, National Medical Research Center of Cardiology, Moscow, Russia

²⁴Ultrasound Vascular Laboratory, National Medical Research Center of Cardiology, Moscow, Russia

²⁵Division of Nephrology, Shinmatsudo Central General Hospital, Chiba, Japan

²⁶Australian Research Centre for Population Oral Health, University of Adelaide, Adelaide, SA, Australia

²⁷Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, University of Sydney, Sydney, NSW, Australia

²⁸Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA

²⁹Department of Medicine, University of North Carolina, Chapel Hill, NC, USA

³⁰Department of Internal Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands

³¹Department of Global Health, Amsterdam UMC- Location AMC, Amsterdam, the Netherlands

³²Amsterdam Institute for Global Health and Development, University of Amsterdam, Amsterdam, the Netherlands

³³Department of Internal Medicine, Medical Center Leeuwarden, Leeuwarden, the Netherlands

³⁴VASCage GmbH, Research Centre on Vascular Ageing and Stroke, Innsbruck, Austria

³⁵Department of Neurology, Klinikum Herford, Herford, Germany

³⁶Department of Internal Medicine and Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre, Maastricht, the Netherlands

³⁷Department of Angiology, University Hospital Basel, Basel, Switzerland

³⁸Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, China

³⁹Department of Clinical Medicine, State University of Rio de Janeiro, Rio de Janeiro, Brazil

⁴⁰AGE Research Group, NIHR Newcastle Biomedical Research Centre, Newcastle University and Newcastle-upon-Tyne Hospitals Trust, Newcastle, UK

⁴¹Department of Internal Medicine, Gyeongsang National University Hospital, Daejeon, South Korea

⁴²Department of Preventive Medicine, Konyang University, Jinju, South Korea

⁴³Heart Center, Konyang University Hospital, Daejeon, South Korea

⁴⁴Department of Cardiology, Konyang University College of Medicine, Daejeon, South Korea

⁴⁵Infectious Diseases Unit, Reina Sofia Hospital, Murcia, Spain

⁴⁶Department of Cardiology, Oslo University Hospital, Oslo, Norway

⁴⁷Department of Internal Medicine, Holbaek Hospital, University of Southern Denmark, Odense, Denmark

⁴⁸MRC Population Health Research Unit, Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK

⁴⁹BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK

⁵⁰Department of Internal Medicine, HMC+ (Bronovo), the Hague, the Netherlands

⁵¹Department of Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, the Netherlands

⁵²Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA

⁵³Wallenberg Laboratory for Cardiovascular Research, University of Gothenburg, Gothenburg, Sweden

⁵⁴Oslo University Hospital Ullevål and Institute of Clinical Sciences, University of Oslo, Oslo, Norway

⁵⁵Nephrology Department, Ege University School of Medicine, Bornova-Izmir, Turkey

⁵⁶Imagelabonline & Cardiovascular, Eindhoven and Lunteren, the Netherlands

⁵⁷Department of Nephrology, Amsterdam UMC, Amsterdam, the Netherlands

⁵⁸Department of Nephrology, University Medical Center Utrecht, Utrecht, the Netherlands

⁵⁹Department of Health Sciences, University of Leicester, Leicester, UK

^✉

Corresponding author: Peter Willeit MD MPhil PhD, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria, Tel: +43 512 504-83493, Fax: +43 50 504-23852, peter.willeit@i-med.ac.at

Authors contributed equally to this article.

^†

Authors contributed equally to this article.

PMCID: PMC7115957 EMSID: EMS87368 PMID: 32546049

Abstract

Background

To quantify the association between effects of interventions on carotid intima-media thickness (cIMT) progression and their effects on cardiovascular disease (CVD) risk.

Methods

We systematically collated data from randomized controlled trials. cIMT was assessed as the mean value at the common-carotid-artery; if unavailable, the maximum value at the common-carotid-artery or other cIMT measures were utilized. The primary outcome was a combined CVD endpoint defined as myocardial infarction, stroke, revascularization procedures, or fatal CVD. We estimated intervention effects on cIMT progression and incident CVD for each trial, before relating the two using a Bayesian meta-regression approach.

Results

We analyzed data of 119 randomized controlled trials involving 100,667 patients (mean age 62 years, 42% female). Over an average follow-up of 3.7 years, 12,038 patients developed the combined CVD endpoint. Across all interventions, each 10 μm/year reduction of cIMT progression resulted in a relative risk for CVD of 0.91 (95% credible interval 0.87-0.94), with an additional relative risk for CVD of 0.92 (0.87-0.97) being achieved independent of cIMT progression. Taken together, we estimated that interventions reducing cIMT progression by 10, 20, 30, or 40 μm/year would yield relative risks of 0.84 (0.75-0.93), 0.76 (0.67-0.85), 0.69 (0.59-0.79), or 0.63 (0.52-0.74). Results were similar when grouping trials by type of intervention, time of conduct, time to ultrasound follow-up, availability of individual-participant data, primary vs. secondary prevention trials, type of cIMT measurement, and proportion of female patients.

Conclusions

The extent of intervention effects on cIMT progression predicted the degree of CVD risk reduction. This provides a missing link supporting the usefulness of cIMT progression as a surrogate marker for CVD risk in clinical trials.

Keywords: Intima-media thickness, Cardiovascular disease, Surrogate marker, Clinical trials, Meta-analysis

Introduction

Carotid intima-media thickness (cIMT), the thickness of the intimal and medial layer of the carotid artery wall, can be measured non-invasively using ultrasound imaging and is considered a marker for the early stage of atherosclerosis.¹ Mean values of cIMT in adults range around 650-900 µm and increase – on average – at a rate of 0-40 µm/year.^2,3 A large number of randomized controlled trials (RCTs) have demonstrated that therapeutic interventions may slow progression of cIMT. However, it is uncertain whether effects on cIMT progression translate into reduced risk of cardiovascular disease (CVD) events, that is whether cIMT progression is a valid surrogate marker for CVD.

In 2005, Espeland et al. first proposed cIMT progression as a surrogate marker for CVD risk based on findings in seven statin trials,⁴ but their arguments were based on limited data and most researchers were reluctant to rely on cIMT results alone.⁵ In 2009, ARBITER-6 HALTS was the first RCT to be terminated early based on findings for cIMT progression, showing superiority of extended-release niacin over ezetimibe.⁶ This decision was controversial due to the uncertain validity of the rate of progression of cIMT as a surrogate marker for clinical endpoints.^7,8 Two subsequent literature-based meta-regression analyses on this topic have yielded conflicting results: Goldberger et al. ⁹ observed an association of effects on cIMT progression and risk of myocardial infarction, whereas Costanzo et al. ¹⁰ found no statistically significant association of changes in mean or maximal cIMT with risk of myocardial infarction or stroke. Both of these meta-analyses have been criticized because of methodological flaws.¹¹

To address this uncertainty, we conducted a comprehensive analysis of 119 RCTs involving a total of 100,667 patients. Our aims were to: (i) quantify the reduction in CVD risk associated with reducing cIMT progression by therapeutic intervention; (ii) explore cIMT progression as a surrogate marker for different types of CVD endpoints as well as all-cause mortality; and (iii) investigate differences according to the intervention type, method of cIMT assessment, and other trial characteristics.

Methods

The datasets supporting the conclusions of this article are not made publicly available due to legal restrictions arising from the data distribution policy of the PROG-IMT/Proof-ATHERO collaborations and from the bilateral agreements between the consortium’s coordinating center and participating studies, but they may be requested directly from individual study investigators. Studies that shared individual-participant data have obtained informed consent of the study participants and ethical approval by their respective institutional review boards.

The report of the results of our study adhere to the PRISMA-IPD guidelines (Table I in the Supplement); the objectives and statistical methods in this paper have been described previously¹². We identified relevant RCTs published before 3 February 2020 through systematic searches of ten medical knowledge databases, six clinical trial registries, and reference lists of relevant publications and reviews (Table II in the Supplement). Trials were eligible for inclusion if they: (1) had assigned patients randomly to two or more arms; (2) had applied well-defined inclusion criteria; (3) had measured cIMT at trial baseline and at one or more follow-up visits; and (4) had recorded incident CVD outcomes. We requested anonymized patient-level data from these trials, performed comprehensive plausibility checks, and were able to resolve any data-related queries through direct correspondence with trial investigators. For trials for which patient-level data was unavailable, four authors (PW, LT, EA, MWL) independently extracted the relevant data from the published literature and resolved any discrepancies by consensus.

As a measure of cIMT, we gave preference to assessments of mean values at the common-carotid-artery. If unavailable, we used maximum values at the common-carotid-artery or cIMT at other sections of the carotid artery instead. In trials quantifying cIMT values at different sites (i.e. left or right side, near or far vessel wall, or at different insonation angles), the arithmetic mean of these measurements was used. The primary outcome was a combined CVD endpoint defined as myocardial infarction, stroke, revascularization procedures (e.g. coronary or carotid revascularization), or fatal CVD. For trials without data on cause-specific death, all-cause mortality was included in the primary outcome instead. Table III in the Supplement provides details on the assessment of cIMT progression and primary outcome definition in each trial.

Statistical analysis

We conducted analyses according to a pre-specified analysis plan. For factorial trials, we analyzed the intervention contrast anticipated to have the greatest effect on CVD risk. For trials with more than two trial arms, we compared the arm that was – based on prior trials – anticipated to have the greatest effect to the arm anticipated to have the least effect (or no effect in case of placebo). For all trials, the latter group was used as reference.

The principal analysis consisted of three steps. First, we quantified intervention effects on cIMT progression. For each trial for which patient-level data was available, we used a linear mixed model to estimate the difference in yearly cIMT progression between trial arms. The model included fixed effects for assigned treatment, time in study, and the interaction of the two, plus an intercept and time variable allowed to vary randomly at the patient level. For each trial for which literature-based data was available (i.e. tabular data extracted from the trials’ publications), we annualized differences in cIMT progression and calculated standard errors from P values, if necessary.

Second, we quantified intervention effects on the CVD outcome. For each trial with patient-level data, we fitted a Cox proportional-hazards model to estimate the log hazard ratio and its standard error comparing the trial arms. If estimates were inestimable due to a low event number, we applied an augmentation procedure to allow incorporation of the trial in the meta-analysis.¹³ For each trial with literature-based data, we calculated the log risk ratio and its standard error based on the number of events and patients in each trial arm. For trials in which one arm had zero events, the number of events and non-events were each augmented by +0.5 in both trial arms. Hazard ratios and risk ratios are collectively described as measures of relative risk (RR).

Third, to test whether effects on CVD risk depended on effects on cIMT progression, we used a Bayesian meta-regression approach that models both effects simultaneously, while taking into account the estimated precisions in these two effects.¹⁴ The principal analysis involved (i) a model with an intercept of zero (i.e. forcing the regression line through the origin and thereby assuming that all the effects on CVD risk operate through cIMT progression) and (ii) a model with a non-zero intercept (i.e. allowing for an effect on CVD risk independent of cIMT progression). The meta-regression also took into account the within-study correlation of the two effects, which was estimated using bootstrapping in the trials with patient-level data and >30 events.¹⁵ For other trials, an overall correlation coefficient pooled using random-effects meta-analysis was used instead. Further details on methods for assessing surrogacy are provided in the Methods in the Supplement.

Subsidiary analyses evaluated surrogacy for individual disease endpoints and in trials grouped by intervention type, time of conduct, time to ultrasound follow-up, availability of individual-participant data, primary vs. secondary prevention trials, type of cIMT measure, and proportion of female patients. A Bayesian approach was taken for estimation of the meta-regression model parameters and for prediction (for details, see the Methods in the Supplement). Analyses were performed using Stata 15, R 2.5.1 and JAGS 4.3.0. PW had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Results

Among 10,260 articles screened, we identified 119 trials involving 100,667 patients that met the pre-specified inclusion criteria (Figure I in the Supplement). 103 trials (87%) had two arms, seven had three arms, one had four arms, seven had a 2x2 factorial design, and one had a 3x2 factorial design (Table 1). The trials employed antidiabetic (18 trials), antihypertensive (19 trials), dietary/vitamin (20 trials), lipid-lowering (33 trials), and/or other interventions (37 trials). Mean age at baseline was 62 years (standard deviation 8); 42% were female. Over an average follow-up duration of 3.7 years, 12,038 patients developed the primary CVD endpoint. The median proportion of patients with repeat cIMT measurements across trials was 90%. Seven large cardiovascular outcome trials had measured cIMT only in a subset of patients (Table 1). Mean cIMT measured at the common-carotid-artery was available in 91 trials, maximum cIMT at the common-carotid-artery in 49 trials, and other cIMT measures in 11 trials. Across contributing trials, the mean rate of cIMT progression was +9.1 µm/year (95% confidence interval: 7.1 to 11.1) in control arms and +1.0 µm/year (-0.6 to 2.7) in interventions arms. Across all contributing trials, the RR for CVD with intervention was 0.88 (0.83-0.92).

Table 1. Key features of the trials included in this report.

					Type of intervention^*									CVD risk		cIMT progression

Trial	Years of baseline	Country	Access to IPD	No. of trial arms	Antidiabetic	Antihypertensive	Dietary / vitamins	Lipid-lowering	Other	No. of patients	Type of population	Mean age (SD), years	% female	Median follow-up, years	No. of events	Maximum follow-up, years	% with cIMT data	Mean CCA-IMT	Max CCA-IMT	Other cIMT
ACAPS^16,17	1989-1990	USA	●	2x2	-	-	-	●	●	919	Elevated CVD risk	62 (8)	48	5.0	18	6.0	100	-	●	-
ACT NOW^18,19	2004-2006	USA	-	2	●	-	-	-	-	602	Dysglycemia	52 (10)	58	2.2^†	13	4.0	63	●	-	-
ALLO-IMT²⁰	2009-2010	UK	●	2	-	-	-	-	●	80	Pre-existing CVD	68 (10)	43	1.0	11	1.2	100	●	●	-
AMAR²¹	2004-2005	Russia	-	2	-	-	●	-	-	257	Elevated CVD risk	61 (9)	0	2.0^‡	21	2.0	76	●	-	-
ARBITER²²	1999-2001	USA	-	2	-	-	-	●	-	161	Elevated CVD risk	60 (12)	29	1.0^‡	6	1.0	86	●	●	-
ARBITER 2²³	2001-2003	USA	-	2	-	-	-	●	-	167	Pre-existing CVD	67 (10)	9	1.0^‡	10	1.0	89	●	-	-
ARBITER 6-HALTS^6,24,25	2006-2009	USA	-	2	-	-	-	●	-	363	Pre-existing CVD	65 (10)	20	1.2^‡	11	1.2	57	●	●	-
ARTSTIFF²⁶	2008-2011	International	-	3	-	●	-	-	-	133	Hypertension	53 (10)	37	1.0^‡	0	1.0	87	●	-	-
ASAP-FINLAND^27–29	1994-1995	Finland	-	2	-	-	●	-	-	520	Hyperlipidemia	60 (6)	51	6.0^‡	22	6.0	85	●	-	-
ASAP-NL^30,31	1997-1998	Netherlands	-	2	-	-	-	●	-	330	Hyperlipidemia	49 (11)	61	2.0^‡	5	2.0	85	●	-	-
ASFAST³²	1998-2000	International	-	2	-	-	●	-	-	315	Kidney disease	56 (13)	32	3.3^†	73	3.6	77	-	●	-
ATIC^33,34	2001-2002	Netherlands	-	2	-	-	-	-	●	93	Kidney disease	53 (12)	43	2.0^‡	4	1.5	80	●	-	-
Ahn et al.³⁵	2005-2006	Korea	-	2	-	-	-	-	●	130	Pre-existing CVD	64 (11)	38	2.0^‡	18	2.0	73	-	-	●
Andrews et al.^36,37	2011-2015	USA	-	2	-	-	-	-	●	80	Kidney disease	57 (12)	20	0.2^‡	1	0.2	79	●	-	-
BCAPS³⁸	1994-1996	Sweden	-	2x2	-	●	-	●	-	793	Elevated CVD risk	62 (5)	54	3.0^†	18	3.0	99	●	-	-
BKREGISTRY-II³⁹	2000-2003	Korea	●	2	-	-	-	●	-	205	Pre-existing CVD	60 (10)	32	0.5	3	1.1	59	●	-	-
BVAIT⁴⁰	2000-2006	USA	-	2	-	-	●	-	-	506	General population	61 (10)	39	3.1^†	20	2.5	97	●	-	-
CAIUS⁴¹	1991-1992	Italy	-	2	-	-	-	●	-	305	Hyperlipidemia	55 (6)	47	3.0^‡	5	3.0	100	-	●	-
CAMERA⁴²	2009-2011	UK	●	2	●	-	-	-	-	173	Pre-existing CVD	63 (8)	23	1.5	12	2.3	100	●	-	-
CAPPA⁴³	2009	Korea	-	2	-	-	-	-	●	420	Dysglycemia	60 (9)	50	3.0^‡	6	3.0	99	●	●	-
CAPTIVATE⁴⁴	2004-2005	International	-	2	-	-	-	●	-	892	Hyperlipidemia	55 (9)	39	2.0^‡	32	1.0	99	-	-	●
CERDIA⁴⁵	1999-2001	Netherlands	●	2	-	-	-	●	-	250	Dysglycemia	58 (11)	53	2.1	14	2.5	99	●	●	-
CHICAGO⁴⁶	2003-2005	USA	-	2	●	-	-	-	-	462	Dysglycemia	60 (8)	37	1.4^‡	13	1.4	78	●	●	-
CIMT phase 1^47,48	2008-2009	Denmark	-	2	●	-	-	-	-	412	Dysglycemia	61 (9)	32	1.5^‡	20	1.5	100	●	●	-
CLAS^49–51	1980-1984	USA	-	2	-	-	-	●	-	162	Pre-existing CVD	54 (5)	0	7.0^†	82	4.0	48	●	-	-
CONTRAST^52,53	2004-2009	Netherlands	●	2	-	-	-	-	●	714	Kidney disease	64 (14)	38	2.4	173	3.1	20	●	●	-
Cao et al.⁵⁴	2008-2011	China	-	2	-	-	●	-	-	287	Elevated CVD risk	71 (13)	53	2.0^‡	36	2.0	100	-	-	●
DAPC^55,56	2004-2006	International	-	2	-	-	-	-	●	329	Dysglycemia	64 (7)	48	2.0^‡	3	2.0	90	●	●	-
DAPHNE⁵⁷	NR	Netherlands	-	2	-	●	-	-	-	80	Pre-existing CVD	59 (7)	0	3.0^‡	16	3.0	100	-	-	●
DOIT⁵⁸	1997-1999	Norway	-	2	-	-	●	-	-	561	Elevated CVD risk	70 (5)	0	3.0^‡	63	3.0	83	●	-	-
EGE STUDY^59,60	2005-2006	Turkey	●	2x2	-	-	-	-	●	644	Kidney disease	59 (14)	46	3.0	60	3.0	100	●	-	-
ELITE (early MP)^61,62	2005-2008	USA	-	2	-	-	-	-	●	271	General population	55 (4)	100	5.0	1	5.0	92	●	-	-
ELITE (late MP)^61,62	2005-2008	USA	-	2	-	-	-	-	●	372	General population	65 (6)	100	5.0	5	5.0	94	●	-	-
ELSA⁶³	NR	International	-	2	-	●	-	-	-	2334	Hypertension	56 (7)	45	4.0^‡	60	4.0	87	-	-	●
ELVA⁶⁴	NR	Sweden	-	2	-	●	-	-	-	129	Hyperlipidemia	60 (10)	49	3.0^‡	4	3.0	71	●	-	-
ENCORE^65,66	2003-2008	USA	●	3	-	-	●	-	-	144	Elevated CVD risk	52 (10)	67	0.4	1	1.1	98	●	-	-
ENHANCE⁶⁷	2002-2004	International	●	2	-	-	-	●	-	720	Hyperlipidemia	47 (9)	49	2.0	52	2.3	100	●	●	-
EPAT⁶⁸	1994-1998	USA	-	2	-	-	-	-	●	222	Hyperlipidemia	61 (7)	100	2.0^‡	7	2.0	90	●	-	-
FIELD^69,70	1998-2000	International	-	2	-	-	-	●	-	9795	Dysglycemia	62 (7)	37	6.0^‡	1295	5.0	2	-	●	-
FIRST^71,72	2008-2010	USA	-	2	-	-	-	●	-	682	Pre-existing CVD	61 (9)	32	2.1^‡	30	2.0	84	-	●	-
FRANCIS^73,74	2011-2012	Netherlands	-	2	-	-	-	-	●	320	Elevated CVD risk	53 (11)	70	5.0^‡	9	5.0	100	●	-	-
GRACE⁷⁵	2003-2005	International	●	2x2	●	-	●	-	-	1189	Dysglycemia	63 (8)	36	5.8	374	5.1	100	●	●	-
Gresele et al.⁷⁶	2003-2005	International	●	2	-	-	-	-	●	442	Pre-existing CVD	67 (9)	21	0.6	8	0.6	57	●	●	-
HART⁷⁷	1999-2000	International	●	2	-	-	●	-	-	925	Pre-existing CVD	69 (7)	24	5.0	152	5.6	100	●	●	-
HERS^78,79	1993-1994	USA	-	2	-	-	-	-	●	2763	General population	67 (7)	100	4.1^†	552	4.7	16	-	●	-
HYRIM⁸⁰	1997-1999	Norway	●	2x2	-	-	-	●	●	568	Hypertension	57 (9)	0	4.1	47	4.6	99	-	●	-
INSIGHT^81–83	1994-1996	France	-	2	-	●	-	-	-	6321	Elevated CVD risk	65 (7)	54	3.5^†	347	4.0	5	●	-	-
J-STARS^84–88	2004-2009	Japan	-	2	-	-	-	●	-	1589	Pre-existing CVD	66 (8)	31	4.9^†	290	5.0	50	●	-	-
JART⁸⁹	2008-2010	Japan	-	2	-	-	-	●	-	348	Hyperlipidemia	64 (9)	51	2.0^‡	9	2.0	40	●	●	-
KAPS⁹⁰	1984-1989	Finland	-	2	-	-	-	●	-	447	Hyperlipidemia	57 (4)	0	3.0^‡	28	3.0	95	-	●	-
KEEPS⁹¹	2005-2008	USA	-	3	-	-	-	-	●	727	General population	53 (3)	100	4.0^‡	1	4.0	100	●	-	-
KIMVASC⁹²	2011-2012	UK	●	2	-	-	●	-	-	80	Pre-existing CVD	77 (5)	45	0.5	1	0.5	99	●	-	-
Katakami et al.⁹³	1998	Japan	-	3	●	-	-	-	-	159	Dysglycemia	61 (9)	51	3.3^†	0	3.3	74	-	-	●
Koyasu et al.⁹⁴	2006-2008	Japan	-	2	●	-	-	-	-	90	Pre-existing CVD	66 (8)	9	1.0^‡	0	1.0	90	-	●	-
LAARS95	NR	International	-	2	-	●	-	-	-	280	Hypertension	59 (9)	50	2.0^‡	0	2.0	72	●	-	-
LIFE-ICARUS⁹⁶	1996-1997	International	●	2	-	●	-	-	-	83	Hypertension	67 (6)	27	4.9	8	3.1	98	●	-	-
LIPID^97–100	1990-1992	International	-	2	-	-	-	●	-	9014	Pre-existing CVD	61 (8)	17	6.1^†	3229	4.0	4	●	-	-
Luijendijk et al.^101,102	2007-2009	Netherlands	-	2	-	-	-	●	-	155	Pre-existing CVD	36 (12)	38	3.3^†	0	4.4	100	●	-	-
MARS^103,104	1985-1989	USA	-	2	-	-	-	●	-	270	Hyperlipidemia	58 (7)	9	2.2^†	54	4.0	27	●	-	-
MAVET¹⁰⁵	1994-1995	Australia	-	2	-	-	●	-	-	409	Elevated CVD risk	64 (6)	55	4.0^‡	6	4.0	81	-	●	-
MECANO^106,107	2005-2006	Netherlands	-	2	-	-	-	-	●	185	Kidney disease	51 (13)	36	1.5^‡	6	2.0	88	●	-	-
MEDICLAS^108,109	2003-2005	Netherlands	●	2	-	-	-	-	●	48	Elevated CVD risk	42 (10)	0	3.0	1	3.2	77	●	-	-
METEOR¹¹⁰	2002-2004	International	-	2	-	-	-	●	-	984	Elevated CVD risk	57 (6)	40	2.0^‡	3	2.0	89	●	●	-
MG600¹¹¹	2010-2011	Brazil	●	2	-	-	●	-	-	35	Hypertension	55 (7)	100	0.5	0	0.5	100	●	●	-
MIDAS¹¹²	NR	USA	-	2	-	●	-	-	-	883	Hypertension	59 (9)	22	3.0^‡	47	3.0	100	-	●	-
MITEC^113,114	2000-2002	France	-	2	-	●	-	-	-	209	Elevated CVD risk	60 (8)	36	3.0^‡	0	3.0	41	●	-	-
Makimura et al.¹¹⁵	2008-2010	USA	-	2	-	-	-	-	●	60	Elevated CVD risk	41 (2)	35	1.0^‡	0	1.0	97	●	-	-
Masia et al.¹¹⁶	2006-2007	Spain	●	2	-	-	-	-	●	68	Elevated CVD risk	52 (11)	10	6.0	4	6.9	99	●	●	-
Mitsuhashi et al.¹¹⁷	NR	Japan	-	2	-	-	-	-	●	62	Dysglycemia	63 (7)	35	2.6^†	1	2.6	100	-	-	●
Mortazavi et al.¹¹⁸	NR	Iran	-	2	-	-	●	-	-	54	Kidney disease	57 (12)	50	0.5^‡	1	0.5	96	●	-	-
NTPP¹¹⁹	2005-2010	Japan	-	2	-	-	-	●	-	123	Elevated CVD risk	59 (9)	54	3.0^‡	0	3.0	79	●	●	-
Nakamura et al. II¹²⁰	2001	Japan	●	2	-	-	-	-	●	50	Kidney disease	53 (7)	40	6.9	8	4.1	100	●	●	-
Ntaios et al.¹²¹	2005	Greece	●	2	-	-	●	-	-	103	Elevated CVD risk	73 (5)	45	1.5	18	1.5	100	●	-	-
OPAL^122,123	1997-1999	International	●	3	-	-	-	-	●	866	General population	59 (7)	100	3.1	9	3.7	100	●	●	-
PART-2¹²⁴	NR	New Zealand	-	2	-	●	-	-	-	617	Pre-existing CVD	61 (8)	18	4.7^†	150	4.0	87	●	-	-
PEACE¹²⁵	2007-2008	Japan	-	2	-	-	-	●	-	303	Hyperlipidemia	66 (9)	43	1.0^‡	2	1.0	74	●	●	-
PERFORM^126,127	2006-2008	International	-	2	-	-	-	-	●	19120	Pre-existing CVD	67 (8)	37	2.4^†	2910	3.0	5	●	-	-
PERIOCARDIO¹²⁸	2010-2012	Australia	●	2	-	-	-	-	●	273	Elevated CVD risk	41 (10)	42	1.0	3	1.4	99	●	●	-
PHOREA¹²⁹	1995-1996	Germany	-	3	-	-	-	-	●	321	General population	59 (4)	100	0.9^‡	1	0.9	54	-	●	-
PHYLLIS^130,131	1995-1997	Italy	-	4	-	●	-	●	-	508	Elevated CVD risk	58 (7)	60	2.6^†	6	2.6	82	-	●	-
PLAC II^132–134	1987-1990	USA	-	2	-	-	-	●	-	151	Elevated CVD risk	63 (NR)	15	3.0^‡	14	3.0	100	-	●	-
PPAR¹³⁵	2002-2003	International	-	2	●	-	-	-	-	200	Elevated CVD risk	59 (10)	20	1.0^‡	17	1.0	100	-	-	●
PREDIMED^136,137	2008-2009	Spain	-	3	-	-	●	-	-	7447	Elevated CVD risk	67 (6)	57	4.8	288	2.4	2	●	●	-
PREVEND IT^138–141	1998-1999	Netherlands	●	2x2	-	●	-	●	-	864	Kidney disease	51 (12)	35	3.9	102	4.7	94	●	-	-
PREVENT^142,143	1992-1997	International	-	2	-	●	-	-	-	825	Elevated CVD risk	57 (10)	20	3.0^‡	196	3.0	46	-	●	●
PROBE^144,145	2002-2003	Japan	-	2	●	-	-	-	-	587	Dysglycemia	58 (NR)	37	4.0^‡	14	3.3	30	●	●	-
RADIANCE I^146,147	2003-2004	International	●	2	-	-	-	●	-	904	Hyperlipidemia	46 (13)	51	2.0	44	2.3	98	●	●	-
RADIANCE II^147,148	2004-2006	International	●	2	-	-	-	●	-	752	Hyperlipidemia	57 (8)	36	2.0	37	2.4	98	●	●	-
RAS¹⁴⁹	2002-2003	Sweden	-	2	●	-	-	-	-	557	Elevated CVD risk	67 (6)	54	1.0^‡	5	1.0	80	●	-	-
REGRESS^150,151	1989-1991	Netherlands	-	2	-	-	-	●	-	885	Elevated CVD risk	56 (8)	0	2.0^‡	148	2.0	29	●	-	-
REMOVAL^152,153	2011-2014	International	-	2	●	-	-	-	-	428	Dysglycemia	56 (9)	41	3.0^‡	17	3.0	99	●	●	-
RIS¹⁵⁴	1987-1989	Sweden	●	2	-	-	-	-	●	164	Elevated CVD risk	66 (5)	0	5.9	47	7.3	99	●	●	-
SANDS^155–157	2003-2004	USA	-	2	-	-	-	-	●	499	Elevated CVD risk	56 (9)	66	3.0^‡	18	3.0	100	●	-	-
SCIMO^158,159	1992-1994	Germany	-	2	-	-	●	-	-	223	Elevated CVD risk	58 (9)	20	2.0^‡	55	2.0	77	-	●	-
SECURE¹⁶⁰	1994-1995	Canada	●	3x2	-	●	●	-	-	731	Elevated CVD risk	66 (7)	24	4.4	103	5.3	100	-	●	-
SEKONA¹⁶¹	2004-2005	Germany	-	2	-	-	-	-	●	600	Elevated CVD risk	49 (6)	11	3.0^‡	110	3.0	66	●	-	-
SENDCAP¹⁶²	1990-1993	UK	-	2	-	-	-	●	-	164	Dysglycemia	51 (8)	29	3.0^‡	4	3.0	77	-	●	-
SPEAD-A^163,164	2011-2013	Japan	-	2	●	-	-	-	-	341	Dysglycemia	65 (9)	42	2.0^‡	4	2.0	94	●	●	-
SPIKE^165–167	2012	Japan	-	2	●	-	-	-	-	282	Dysglycemia	64 (7)	40	2.0^‡	6	2.0	97	●	●	-
STARR¹⁶⁸	2001-2003	International	●	2x2	●	●	-	-	-	1320	Dysglycemia	53 (11)	55	4.2	30	4.5	100	●	●	-
STOP-NIDDM^169,170	1996-1998	Germany	-	2	●	-	-	-	-	1429	Dysglycemia	55 (8)	51	3.3^†	47	3.9	8	●	-	-
Safarova et al.¹⁷¹	2007-2009	Russia	●	2	-	-	-	●	-	60	Pre-existing CVD	55 (6)	0	3.0	40	2.8	100	●	-	-
Sander et al. (Cp neg)^172,173	1995-1998	Germany	-	2	-	-	-	-	●	147	Pre-existing CVD	64 (12)	44	3.0^‡	9	2.0	100	●	-	-
Sander et al. (Cp pos)^172,173	1995-1998	Germany	-	2	-	-	-	-	●	125	Pre-existing CVD	65 (14)	43	3.0^‡	19	2.0	100	●	-	-
Spring et al.¹⁷⁴	NR	Switzerland	-	2	-	-	-	●	-	100	Pre-existing CVD	67 (11)	22	0.5^‡	2	0.5	89	●	-	-
Stanley et al.¹⁷⁵	2011-2013	USA	-	2	-	-	-	-	●	50	Elevated CVD risk	51 (7)	16	0.5^‡	1	0.5	86	●	-	-
Stanton et al.¹⁷⁶	NR	UK	-	2	-	●	-	-	-	69	Hypertension	48 (11)	41	1.0^‡	1	1.0	80	●	-	-
TART¹⁷⁷	1997-1998	USA	-	2	●	-	-	-	-	299	Dysglycemia	52 (9)	66	2.0	12	2.0	92	●	-	-
TEAAM¹⁷⁸	2004-2009	USA	-	2	-	-	-	-	●	308	General population	68 (5)	0	3.0^‡	16	3.0	99	●	-	-
TRIPOD¹⁷⁹	1995-1998	USA	-	2	●	-	-	-	-	266	Dysglycemia	34 (7)	100	2.9	0	4.0	72	●	-	-
Tasic et al.¹⁸⁰	NR	Serbia	-	2	-	●	-	-	-	40	Hypertension	64 (9)	35	0.8^‡	6	0.8	100	●	-	-
VEAPS¹⁸¹	1996-1999	USA	-	2	-	-	●	-	-	353	Hyperlipidemia	56 (9)	52	3.0^†	18	3.0	94	●	-	-
VHAS^182,183	NR	Italy	-	2	-	●	-	-	-	1414	Hypertension	54 (7)	51	2.0^‡	33	4.0	27	-	-	●
VIP¹⁸⁴	2005-2007	Netherlands	-	2	-	-	-	-	●	119	Kidney disease	53 (12)	33	3.0^‡	10	3.0	86	●	-	-
VITAL¹⁸⁵	2002-2004	Netherlands	●	2	-	-	-	-	●	199	Elevated CVD risk	49 (12)	41	1.5	12	2.5	99	●	-	-
WISH¹⁸⁶	2004-2007	USA	-	2	-	-	●	-	-	350	General population	61 (7)	100	2.7	1	3.0	93	●	-	-
Yang et al.¹⁸⁷	2013-2017	China	-	2	-	-	-	-	●	119	Elevated CVD risk	54 (11)	72	0.5^‡	0	0.5	100	-	-	●
Yun et al.¹⁸⁸	2010-2013	China	-	2	●	-	-	-	-	135	Pre-existing CVD	62 (5)	40	2.3^†	23	4.5	93	●	-	-
Zou et al.¹⁸⁹	2010	China	-	2	-	-	●	-	-	96	Elevated CVD risk	57 (5)	59	1.0^‡	0	1.0	89	●	-	-
Total: 119 trials	1980-2017		30		18	19	20	33	37	100667		62 (8)	41.9	3.7	12038	3.5	90	91	49	11

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Table V in the Supplement provides full names of the contributing trials. *Table III in the Supplement provides detailed information on the interventions in each trial. †Mean. ‡Maximum. Abbreviations: CCA-IMT=common-carotid-artery intima-media thickness. cIMT=carotid intima-media thickness. CVD=cardiovascular disease. IPD=individual-participant data. NR=not reported. SD=standard deviation.

Results of the principal analysis are provided in Figure 1. Across all interventions, in the model assuming an intercept of zero, each 10 μm/year reduction of cIMT progression was associated with a RR for CVD of 0.88 (95% credible interval [CI] 0.85-0.91). In the model allowing for a non-zero intercept, the RR for CVD was 0.91 (0.87-0.94) per 10 μm/year slower cIMT progression, with a further RR of 0.92 (0.87-0.97) achieved independent of cIMT progression. Based on the non-zero intercept model, the proportion of variance in the CVD outcome explained by cIMT progression was 98% albeit with a wide 95% CI (71-100%). Taken together, we estimated that interventions that reduce cIMT progression by 10, 20, 30, or 40 µm/year would yield RRs of 0.84 (0.75-0.93), 0.76 (0.67-0.85), 0.69 (0.59-0.79), or 0.63 (0.52-0.74).

The intercept of the primary model was 0.92 (95% CI 0.87-0.97). Each bubble represents a trial. Trials with point estimates outside of this area are indicated with the symbol x. The areas of the bubbles are proportional to the inverse variance of the log relative risk for the primary CVD endpoint. The shaded areas around lines-of-fit are 95% prediction intervals. For purpose of presentation, the graph area was limited to -80 to 80 μm/year on the horizontal axis and 0.25 to 4 on the vertical axis. Abbreviations: CI=credible interval. cIMT=carotid intima-media thickness. CVD=cardiovascular disease. RR=relative risk.

Due to presence of effects on CVD risk unexplained by cIMT progression, subsequent analyses focused on the non-zero intercept model. In outcome-specific analyses (Figure 2), RRs per 10 µm/year slower cIMT progression were 0.88 (0.82-0.94) for myocardial infarction, 0.92 (0.86-1.00) for stroke, 0.90 (0.83-0.98) for revascularization procedures, 0.91 (0.83-1.01) for fatal CVD, and 0.96 (0.89-1.04) for all-cause mortality. There was no evidence for differences in the RR for CVD associated with slower cIMT progression nor in the intercept across trials grouped by intervention type (Figure 3 and Figure 4). Similarly, there was no evidence for differences in these RRs in trials grouped by time of conduct, time to ultrasound follow-up, availability of individual-participant data, primary vs. secondary prevention trials, type of cIMT measurements, and proportion of female patients (Figure 4, P values for heterogeneity >0.05). In a sensitivity analysis that omitted trials with extreme effect sizes (i.e. cIMT progression changes >80 µm/year or RR for CVD <0.25 or >4.0), the RR for CVD per 10 µm/year slower cIMT progression was 0.91 (0.87-0.95). Results were also highly robust across leave-one-out cross-validation analyses (Figure II in the Supplement). Trial-specific estimates are provided in Table IV in the Supplement.

*The RRs for intercepts are the effects achieved independent of cIMT progression. Abbreviations: CI=credible interval. cIMT=carotid intima-media thickness. CVD=cardiovascular disease. RR=relative risk.

The RRs for intercepts as well as P values for heterogeneity of intercept and slope are provided in Figure 4. The areas of the bubbles are proportional to the inverse variance of the log relative risk for the primary CVD endpoint. For purpose of presentation, the graph area was limited to -80 to 80 μm/year on the horizontal axis and 0.25 to 4 on the vertical axis. Trials with point estimates outside of this area are indicated with the symbol x. Abbreviations: cIMT=carotid intima-media thickness. CVD=cardiovascular disease. RR=relative risk.

Abbreviations: CCA-IMT=intima-media thickness of the common-carotid-artery. CI=credible interval. cIMT=carotid intima-media thickness. IPD=individual-participant data. RR=relative risk. *P values for heterogeneity. §The RRs for intercepts are the effects achieved independent of cIMT progression.^||Numbers of trials across some subgroups do not sum up to 119 because of missing information or contribution of trials to multiple subgroups.

Discussion

In this large-scale meta-analysis involving data from 119 RCTs and 100,667 patients, we showed that interventions reducing cIMT progression are also likely to reduce CVD event rates (summarized in Figure 5). Specifically, a 10 µm/year slower cIMT progression was associated with a RR of 0.91 (95% CI 0.87-0.94) for the principal outcome of CVD, with the differences in RR for CVD largely explained by the differences in cIMT progression. The same model also indicated a non-zero intercept, overall and for different types of interventions, highlighting that a small but significant proportion of the intervention effect acted independently of cIMT progression. By estimating CVD risk reductions according to specific reductions in cIMT progression, we provide guidance to future trials in the cardiovascular field.⁵ Results were robust for a range of disease endpoints and across clinically important trial characteristics, including type of intervention or type of cIMT measurement.

Abbreviations: CI=credible interval. cIMT=carotid intima-media thickness. CVD=cardiovascular disease. RCTs= randomized controlled trials.

Exploring the association between cIMT and CVD risk has some history. cIMT measured at a single time-point is associated with incident CVD and provides incremental predictive value over and beyond conventional CVD risk factors.^190–192 For cIMT progression over time, our earlier analyses of observational studies within the PROG-IMT collaboration indicated no statistically significant association with subsequent CVD risk in individuals of the general population,² patients with diabetes mellitus,¹⁹³ or patients at high CVD risk¹⁹⁴. This null association could be explained by the challenges of precisely estimating cIMT progression in individuals over time. In contrast, our present report focuses on groups of patients in RCTs and is therefore better suited to provide answers about the surrogate value of cIMT progression: averaging across patients improves the signal-to-noise ratio, confounders are expected to be balanced due to randomization, trial cohorts might be more homogeneous, and cIMT protocols may be of higher quality in clinical trial settings.

Prior RCT data on cIMT progression as a surrogate marker for CVD risk are limited. Because most RCTs reporting both cIMT and endpoints (with few exceptions^{63,70,97,127,170}) have not been designed as CVD outcome trials and as a range of intervention effect sizes is needed for meaningful results, meta-analysis is the method of choice to investigate this question.¹⁹⁵ Three such pooled analyses had been undertaken before. Espeland et al. demonstrated that statin treatment reduced cIMT progression and CVD risk in a concordant manner.⁴ In a meta-analysis involving 28 RCTs of different intervention types, Goldberger et al. observed an association between reduced cIMT progression and lower risk for non-fatal myocardial infarction, but noted marked between-trials heterogeneity.⁹ A meta-analysis by Costanzo et al. involving 41 RCTs demonstrated no statistically significant relationship between slower cIMT progression and risk of cardiovascular outcomes.¹⁰ Compared to these earlier reports, our meta-analysis stands out by (i) exclusively conducting within-trial comparison (thereby upholding the principle of randomization); (ii) increasing statistical power by involving >5 times as many patients as the previously largest report¹⁰; (iii) enhancing validity by accessing patient-level data of 28 trials; and (iv) using modern statistical methods that incorporate uncertainties both around the intervention effects on cIMT progression and CVD risk as well as their within-trial correlation.

What do we know about the suitability of cIMT progression as a surrogate marker for CVD risk? Ultrasound-based cIMT measurement fulfills several requirements of a surrogate marker,¹⁹⁶ including (i) high correlation with thickness of the vessel wall measured in histological samples¹⁹⁷; (ii) acceptable reproducibility¹⁹⁸, which was further enhanced by clear recommendations for measurement and technical improvements¹⁹⁹; (iii) close correlation with risk factors and prevalent CVD^190–192; (iv) established correlation with atherosclerosis in other vascular beds¹⁹⁶; (v) association with occurrence of clinical events^190–192; (vi) the ability to change over time^2,193; and (vii) the possibility to influence cIMT with interventions²⁰⁰. In the present analysis, we have provided evidence for the last missing requirement not credibly proven by earlier studies, namely that a change in cIMT progression is related to the change in risk of CVD events.

Importantly, using cIMT progression as a surrogate endpoint in future RCTs may facilitate and speed up development and licensing of new therapies. To illustrate this point, we conducted a sample size calculation for a hypothetical future trial. For this calculation, we assumed 80% power, several parameters similar to our individual-participant data (i.e. 2-year cumulative incidence of CVD 6.57%, a standard deviation of cIMT 178 µm, and a correlation between baseline and follow-up cIMT 0.79), no losses to follow-up, and a perfect relationship between treatment effects on cIMT progression and those on the CVD outcome. To have 80% power to detect a hazard ratio of 0.84, a future 2-year CVD outcome trial would require 8,600 patients in each trial arm. In comparison, a future 2-year cIMT progression trial would require 470 patients per trial arm to detect a 10 µm/year reduction in cIMT progression (corresponding to the above hazard ratio) at 2-years, also with a power of 80%. Consequently, a cIMT trial would only require 5.5% of the sample size of a comparable CVD endpoint trial.

In addition to demonstrating the association between intervention effects on cIMT and intervention effects on CVD risk, we found that the regression line had a small but significant non-zero intercept, in the overall analysis and in all subgroups of trials investigated. The non-zero intercept – which indicates that a small proportion of the intervention effect on CVD risk bypasses cIMT – may be explained by “pleiotropic” effects; meaning that the intervention influences the clinical endpoint via multiple pathways. While effects of interventions on the extent of atherosclerosis may be captured by cIMT progression, any effects on other pathophysiological mechanisms related to CVD events, such as endogenous thrombogenesis and fibrinolysis,¹ may bypass cIMT progression and thereby lead to a non-zero intercept. Alternative pathways have been described for many major cardiovascular substance groups, including lipid-lowering medications (e.g. statins,^1,201,202 fibrates,²⁰³ niacin,²⁰⁴ resins,²⁰⁵ and omega-3 fatty acids²⁰⁶), antidiabetic medications (e.g. AMPK activators,²⁰⁷ thiazolidinediones,²⁰⁷ DPP-4 inhibitors,^207,208 GLP-1 receptor agonists,^207,208 SGLT-2 inhibitors²⁰⁸), or antihypertensive medications (e.g. beta-blockers,²⁰⁹ calcium channel-inhibitors,^210,211 angiotensin-II antagonists,²¹² ACE inhibitors²¹²). Nevertheless, this finding does not negate the main result that an intervention effect on cIMT predicts the effect on CVD risk.

A major strength of our study is that we systematically collated and analyzed worldwide data on cIMT progression and CVD outcomes published up to February 2020. Access to patient-level data allowed us to include hitherto unpublished data and thereby reduce publication bias. Supplementing our analysis with published data enhanced generalizability and statistical power. Strengths of our meta-regression analysis include that it upholds randomization within trials, allows for between-trials heterogeneity, makes no distributional assumption about the true intervention effects on cIMT progression across trials (unlike standard bivariate random-effects meta-analysis), and improved precision by incorporating within-trial correlations of intervention effects on cIMT progression and CVD risk.

Our analysis also has limitations. First, our principal analysis combined trials of varying types of interventions. While we conducted a sensitivity analysis by medication class, further research is required to precisely quantify the differences in the surrogate value of cIMT by intervention type. Second, our analysis involved a broad range of types of trial populations. While sensitivity analysis revealed no evidence for differential effects in the setting of primary vs. secondary prevention trials, further study is needed on specific trial populations, such as patients with diabetes or chronic kidney disease. Third, the definition of the primary combined CVD endpoint varied across the included trials. However, the differences were relatively minor (see Table III in the Supplement), so we are confident that this does not constitute a major source of systematic bias. Finally, while ultrasound scanning protocols may have differed across contributing trials – in particular before consensus guidelines were available²¹³, there was no evidence for effect modification by type of cIMT measure or baseline years of the trials.

Conclusions

In conclusion, effects of interventions on cIMT progression and on CVD risk are associated, endorsing the usefulness of cIMT progression as a surrogate marker in clinical trials. Using cIMT progression as a surrogate marker may be a useful tool to guide future development for cardiovascular drugs.

Supplementary Material

Supplemental Material

EMS87368-supplement-Supplemental_Material_.pdf^{(3.5MB, pdf)}

Clinical Perspective.

What Is New?

We analyzed data of 119 randomized controlled trials that involved 100,667 patients and 12,038 incident cardiovascular disease events.
We used a Bayesian meta-regression approach to evaluate progression of carotid intima-media thickness as a surrogate marker for cardiovascular events.
Our analysis revealed a statistically significant association between treatment effects on progression of carotid intima-media thickness and treatment effects on cardiovascular disease risk.

What Are the Clinical Implications?

Our paper provides the key missing link supporting the usefulness of carotid intima-media thickness progression as a surrogate marker for cardiovascular disease risk in clinical trials.
Using progression of carotid intima-media thickness as a surrogate endpoint in future randomized controlled trials may facilitate and speed up the development and licensing of new therapies.

Funding Sources

This work was supported by the Austrian Science Fund (FWF) [P 32488]; the Dr.-Johannes-and-Hertha-Tuba Foundation; the German Research Foundation [DFG Lo 1569/2-1 and DFG Lo 1569/2-3]; and the excellence initiative “Competence Centers for Excellent Technologies” (COMET) of the Austrian Research Promotion Agency (FFG) “Research Center of Excellence in Vascular Ageing: Tyrol, VASCage” [K-Project No. 843536], funded by Bundesministerium für Verkehr, Innovation und Technologie (BMVIT), Bundesministerium für Bildung, Wissenschaft und Forschung (BMWFW), Wirtschaftsagentur Wien, and Standortagentur Tirol.

Non-standard Abbreviations and Acronyms

CI: credible interval
cIMT: Carotid intima-media thickness
CVD: cardiovascular disease
RCT: randomized controlled trial
RR: relative risk

Footnotes

Conflict of Interest Disclosures

P. Willeit reports grants from the German Research Foundation DFG, the Austrian Science Fund FWF, the Austrian Research Promotion Agency FFG and the Dr.-Johannes-and-Hertha-Tuba Foundation during the conduct of the study. L. Tschiderer reports grants from the Dr.-Johannes-and-Hertha-Tuba Foundation during the conduct of the study and non-financial support from Sanofi outside the submitted work. E. Allara was supported by a National Institute for Health Research PhD studentship (NIHR BTRU-2014-10024) during the conduction of this study and reports support from EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart grant n° 116074 outside the submitted work. L. Seekircher reports non-financial support from Sanofi outside the submitted work. H.C. Gerstein reports grants from Sanofi, Eli Lilly, Astra Zeneca, Boehringer Ingelheim, Novo Nordisk, Merck, and Abbott, and personal fees from Sanofi, Eli Lilly, Astra Zeneca, Boehringer Ingelheim, Abbott, Novo Nordisk, Merck, Jannsen, Kowa Research Institute, and Cirius outside the submitted work. E. Stroes reports Lecturing/ad-boards fees paid to institution by Amgen, Sanofi-Regeneron, Novartis, Athera, Mylan unrelated to the present work. K. Kapellas reports grants from the National Health and Medical Research Council during the conduct of the study. M. Skilton reports grants from the National Health and Medical Research Council of Australia during the conduct of the study. M.G.A. van Vonderen reports grants from Abbott International and Boehringer Ingelheim during the conduct of the study. S. Kiechl reports grants from the Austrian Promotion Agency FFG outside the submitted work. G. Klingenschmid reports non-financial support from Sanofi and Pfizer outside the submitted work. S.E. Kjeldsen reports personal fees from Bayer, Merck KGaA, MSD, Sanofi, and Takeda outside the submitted work. M.H. Olsen reports grants from the Novo Nordic Foundation outside the submitted work. N. Sattar reports personal fees from Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Janssen, NAPP Pharmaceuticals, Novo Nordisk, and Sanofi, and grants from Boehringer Ingelheim outside the submitted work. M.P.C. Grooteman reports grants from the Dutch Kidney Foundation, Fresenius Medical Care Netherlands BV, Gambro Sweden, the Twiss Fund, and ZON MW during the conduct of the study. P.J. Blankestijn reports grants from the European Commission and other financial activities from Medtronic, Baxter, and Braun outside the submitted work. M.L. Bots reports grants from AstraZeneca outside the submitted work. M.J. Sweeting reports grants from the German Research Foundation during the conduct of the study. S.G. Thompson reports grants from the UK Medical Research Council, the British Heart Foundation, and the German Research Foundation DFG during the conduct of the study. M.W. Lorenz reports grants from the German Research Foundation DFG during the conduct of the study. Other authors have no conflicts of interests.

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PERMALINK

Carotid intima-media thickness progression as surrogate marker for cardiovascular risk: Meta-analysis of 119 clinical trials involving 100,667 patients

Peter Willeit, MD, MPhil, PhD

Lena Tschiderer, DI, BSc, PhD

Elias Allara, MD

Kathrin Reuber, MSc

Lisa Seekircher, DI, Mag., BSc

Lu Gao, BSc, MSc

Ximing Liao, BSc, MSc, PhD

Eva Lonn, MD, MSc, FRCPC, FACC

Hertzel C Gerstein, MD, MSc, FRCPC

Salim Yusuf, MD, DPhil, MRCP

Frank P Brouwers, MD, PhD

Folkert W Asselbergs, MD, PhD

Wiek van Gilst, PhD

Sigmund A Anderssen, PhD

Diederick E Grobbee, MD, PhD

John J P Kastelein, MD, PhD, FESC

Frank L J Visseren, MD

George Ntaios, MD, MSc, PhD, FESO

Apostolos I Hatzitolios, MD, PhD, FESH

Christos Savopoulos, MD, PhD

Pythia T Nieuwkerk, PhD

Erik Stroes, MD, PhD

Matthew Walters, MD, FRCP

Peter Higgins, MD, MRCP

Jesse Dawson, MD, FRCP, FESO

Paolo Gresele, MD, PhD

Giuseppe Guglielmini, PhD

Rino Migliacci, MD, PhD

Marat Ezhov, MD, PhD

Maya Safarova, MD

Tatyana Balakhonova, MD, PhD

Eiichi Sato, MD

Mayuko Amaha, MD

Tsukasa Nakamura, MD, PhD

Kostas Kapellas, PhD

Lisa M Jamieson, PhD

Michael Skilton, PhD

James A Blumenthal, PhD

Alan Hinderliter, MD

Andrew Sherwood, PhD

Patrick J Smith, PhD, MPH

Michiel A van Agtmael, MD, PhD

Peter Reiss, MD, PhD

Marit G A van Vonderen, MD, PhD

Stefan Kiechl, MD

Gerhard Klingenschmid, MD

Matthias Sitzer, MD

Coen D A Stehouwer, MD, PhD, FESC

Heiko Uthoff, MD, PD

Zhi-Yong Zou, MD

Ana R Cunha, PhD

Mario F Neves, MD, PhD

Miles D Witham, BMBCh, PhD

Hyun-Woong Park, MD

Moo-Sik Lee, MD, PhD

Jang-Ho Bae, MD, FACC

Enrique Bernal, MD, PhD

Kristian Wachtell, MD, PhD, DrMedSci

Sverre E Kjeldsen, MD, PhD

Michael H Olsen, MD, PhD, DMSc

David Preiss, PhD, FRCPath, MRCP

Naveed Sattar, MD, PhD

Edith Beishuizen, MD

Menno V Huisman, MD, PhD

Mark A Espeland, PhD

Caroline Schmidt, PhD

Stefan Agewall, MD, PhD

Ercan Ok, MD

Gülay Aşçi, MD

Eric de Groot, MD, PhD

Muriel P C Grooteman, MD, PhD

Peter J Blankestijn, MD

Michiel L Bots, MD, PhD

Michael J Sweeting, PhD

Simon G Thompson, DSc

Matthias W Lorenz, MD, PD

Abstract

Background