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Abstract

To determine whether coronary artery disease (CAD) is associated with abdominal aortic aneurysm (AAA) growth, we performed a meta-analysis of currently available studies. Databases including MEDLINE and EMBASE were searched through October 2015 using PubMed and OVID. Search terms included enlargement, expansion, growth, or progression; rate or rates; and abdominal aortic aneurysm. Studies considered for inclusion met the following criteria: the design was unrestricted; the study population was AAA patients with and without CAD; and outcomes included data regarding AAA growth. For each study, growth rates in both the CAD and non-CAD groups were used to generate standardized mean differences (SMDs) and 95% confidence intervals (CIs). Of 664 potentially relevant publications screened initially, we identified 20 eligible studies including data on a total of 7238 AAA patients. A pooled analysis of all 20 studies demonstrated a statistically significant association of CAD with slower AAA growth rates (i.e. a significantly negative association of CAD with AAA growth) in the fixed-effect model (SMD, −0.06 [–0.0592]; 95% CI, −0.12 [–0.1157] to −0.00 [–0.0027]; p = 0.04). There was minimal between-study heterogeneity (p = 0.16) and a statistically non-significant association of CAD with slower AAA growth rates (i.e. a non-significantly negative association of CAD with AAA growth) in the pooled result from random-effects modeling (SMD, −0.06; 95% CI, −0.13 to 0.01; p = 0.12). In conclusion, CAD may be negatively associated with AAA growth.

Keywords

abdominal aortic aneurysm coronary disease epidemiology meta-analysis

Introduction

The first (published in 2004) meta-analysis by Cornuz et al.¹ of six population-based risk factor studies of abdominal aortic aneurysm (AAA) showed that a history of coronary artery disease (CAD) was a major risk factor or risk indicator for screening-detected AAA (odds ratio [OR], 2.30; 95% confidence interval (CI), 1.92 to 2.75). Another recent (published in 2013) meta-analysis by Li et al.² of three epidemiological studies also showed that CAD was a risk factor for AAA (OR, 1.82; 95% CI, 1.65 to 2.00). Despite the evidence for the positive association of CAD with AAA presence, however, CAD may be not³ or even negatively⁴ associated with AAA growth. Using a large clinical database from the ADAM (Aneurysm Detection and Management) study, Bhak et al.³ found no association between angina or CAD and AAA expansion rates. Unexpectedly, in a retrospective cohort study, Nakayama et al.⁴ demonstrated an inverse association between the existence of CAD and an accelerated AAA expansion rate (i.e. negative association of CAD with AAA growth) in 510 elective repair cases for which at least two follow-up CT scans of AAA were available. These data suggest that atherosclerosis has a minimal role in the continuing expansion that characterizes the natural history of AAA.⁵ Although AAA has been traditionally regarded as a consequence of atherosclerosis, recent findings lend support to the concept that AAA grows through pathologic mechanisms that differ from those responsible for atherosclerotic occlusive disease.^4,6,7 To determine whether CAD is associated with AAA growth, we performed a meta-analysis of currently available studies.

Methods

All studies reporting AAA growth rates were identified using a two-level search strategy. First, databases including MEDLINE and EMBASE were searched through October 2015 using web-based search engines (PubMed and OVID). Second, relevant studies were identified through a manual search of secondary sources including references of initially identified articles and a search of reviews and commentaries. All references were downloaded for consolidation, elimination of duplicates, and further analysis. Search terms included enlargement, expansion, growth, or progression; rate or rates; and abdominal aortic aneurysm.

Studies considered for inclusion met the following criteria: the design was unrestricted; the study population was AAA patients with and without CAD; and outcomes included data regarding AAA growth. Data regarding AAA growth included: (1) growth rates in both the CAD and non-CAD groups; (2) mean differences (MDs) between growth rates in the CAD group and those in the non-CAD group; (3) proportions of CAD patients in both the rapid and slow AAA-growth groups (according to a cut-point defined by the authors of each study); or (4) ORs of rapid (or slow) AAA growth (according to a cut-point defined by the authors of each study) for CAD versus non-CAD patients. Two reviewers (authors) reviewed the eligible studies and determined whether they met the selection criteria. Data regarding detailed inclusion criteria, CAD definition, duration of follow-up, and AAA growth were abstracted (as available) from each individual study. Data were extracted in duplicate by two investigators (authors), and disagreements were resolved by consensus.

We conducted a meta-analysis of summary statistics from the individual studies. For each study, growth rates in both the CAD and non-CAD groups were used to generate standardized MDs (SMDs) (between growth rates in the CAD group and those in the non-CAD group) and their 95% CIs, or proportions of CAD patients in both the rapid and slow AAA-growth groups were used to generate ORs (of rapid AAA growth for CAD versus non-CAD patients) and their 95% CIs. Based on an assumption that the underlying continuous measurements in each group follow a logistic distribution (which is a symmetrical distribution similar in shape to the normal distribution but with more data in the distributional tails), and that the variability of the outcomes is the same in both case and control participants,⁸ we re-expressed the OR (of rapid AAA growth for CAD versus non-CAD patients) as SMD (between growth rates in the CAD group and those in the non-CAD group) acording to the simple formula⁹ (the standard error (SE) of the logarithmic OR can be converted to the SE of a SMD by multiplying by the same constant [√3/π = 0.5513]), despite any cut-points. When both univariate-unadjusted and multivariate-adjusted estimates were available, we combined the latter preferentially in pooled analyses. Study-specific estimates were combined using inverse variance-weighted averages of SMDs in both fixed-effect and random-effects models. Between-study heterogeneity was analyzed by means of standard χ² tests. Where no significant statistical heterogeneity was identified, the fixed-effect estimate was used preferentially as the summary measure. Publication bias was assessed graphically using a funnel plot and mathematically using an adjusted rank-correlation test¹⁰ and a linear regression test.¹¹ All analyses were conducted using Review Manager version 5.3 (available from http://tech.cochrane.org/revman) and Comprehensive Meta-Analysis version 3 (Biostat, Englewood, NJ, USA).

Results

As outlined in Figure 1, our search identified 20 eligible studies^{3–5,12–28} reporting the association of CAD and AAA growth, which included data on a total of 7238 AAA patients. The study design and patient characteristics, detailed measurements of AAA diameters, and growth rates of AAA are summarized in Tables 1, 2, and 3, respectively. The study design was a randomized controlled trial in three studies,^3,5,21 a prospective cohort study in 11,^{14–18,20,22,25–28} and a retrospective observational study in six.^{4,12,13,19,23,24} Multivariate-adjusted (including propensity-score matched²³) estimates were available in seven studies.^{3,4,5,16,18,20,23} Despite the noted heterogeneity in design between the studies, there was sufficient similarity between the populations and the hypotheses to merit inclusion of all 20 studies in the quantitative meta-analysis.

Figure 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram for the meta-analysis. Adapted from Moher et al.⁴⁰

Table 1.

Study design and patient characteristics.

Study	Design	Characteristics
		Patient					AAA
		Number	Men (%)	Age (years)	CAD		Initial diameter (mm)	Follow-up	Growth rate (mm/year)
					Definition	Prevalence (%)
Akai, 2015¹²	Retrospective	374	85.8	Median, 74 (IQR, 67–79)	Medical history	23.3	40 (IQR, 35–42)	Median, 66 (IQR, 40–82) months	Median, 2.1 (IQR, 0.7–3.7)
Badger, 2011¹³	Retrospective	150	84.9 (n = 252^a)	Age >70 years, 58.7% (n = 252^a)	Medical history	40.7	39±7 (n = 252^a)	N/A	Mean, 0.9–4.1 in 5 categories of initial size (n = 252^a)
Behr-Rasmussen (VIVA), 2014¹⁴	Prospective	416	100	65–74	Previous acute MI	21.9	40.6±11.8	Mean, 1.78 (range, 0.6–3.0) years	2.55±2.80
Bhak (ADAM), 2015³	Randomized	534	99.6	67.6±6.1	Angina or CAD	39.9	42±6	3.7±2.0 years	Adjusted mean, 2.6 (95% CI, 2.5–2.8)
Brady (UKSAT), 2004⁵	Randomized and non-randomized prospective	1143	77.8 (n = 1743^a)	Mean, 69.4 (range 58–77) (n = 1743^a)	Probable CAD from ECG	16.4	43±7 (n = 1743^a)	1.9±1.3 years (n = 1743^a)	Mean, 2.6 (95% CI, 2.5–2.6) (n = 1743^a)
Burillo, 2015¹⁵	Prospective	122	100	N/A	Previous acute MI	13.9	>30 mm	Mean, 8.1 years	Mean, 2.92
Chang, 1997¹⁶	Prospective	514	73.2	36–92	Medical history of severe CAD and AP, prior MI, and CHF	36.4	25–60	Maximum, 12 years	Mean, 4.9 for <70-year-old patients; 10.2 for ⩾70-year-old patients
Cronin, 2014¹⁷	Prospective	196	80.1	Median, 73 (IQR, 67–77)	Medical history of angina, MI, or coronary revascularization	56.1	Median (IQR) for slow (⩽median) and rapid (>median) growth AAA, respectively, 37 (IQR, 33–46) and 44 (IQR, 41–48)	Median, 2.0 (IQR, 1.0–4.0)	Median, 1.2 (IQR, 0.2–2.0)
De Haro, 2012¹⁸	Prospective	435	84.1	72.1±9.0	Previous MI, positive CAG, or positive isotopic test	32.0	35±2, 40±3, 49±4, and 58±5 according to hs-CRP quartiles	12 months	Median, 4.1
Duellman, 2014¹⁹	Retrospective	141	97.2	N/A	Medical history	85.8	43.0±1.2 (SE) and 62.4±1.9 (SE), respectively, for slow (<3.25 mm/year) and rapid (>3.25 mm/year) growth AAA	Median, 5.06 years	1.08±0.08 (SE) and 51.0±0.55, respectively, for slow (<3.25 mm/year) and rapid (>3.25 mm/year) growth AAA
Ferguson, 2010²⁰	Prospective	652	93.6	Median, 73 (IQR, 70–77)	Clinical interview	46.5	Median, 33.2 (IQR, 31.0–37.0)	Median, 5 (IQR, 3–6) years	Median, 1.1 (IQR, 0.5–2.0)
Karlsson, 2009²¹	Randomized	211	76.5	Median (IQR), 71 (67–74) and 71 (67–76), respectively, for azithromycin and placebo	Medical history of MI	30.8	Median (IQR), 40.0 (37.0–44.0) and 40.0 (36.0–43.5), respectively, for azithromycin and placebo	36 months	Median (IQR), 2.2 (1.2–3.6) and 2.2 (0.10–3.4), respectively, for azithromycin and placebo
Kotze, 2014²²	Prospective	40	90	Median, 74 (range, 60–85)	N/A	32.5	Median, 49.5 (IQR, 43.0–53.0)	12 months	Median, 2.0 (IQR, 0.0–4.0)
Lederle, 2015²³	Retrospective	1122	99.0 (n = 2428^a)	71.2±7.9 (n = 2428^a)	N/A	36.8	40±8 (n = 2428^a)	Median, 2.7 (IQR, 1.4–4.7) years (n = 2428^a)	Mean, 2.0 (95% CI, 1.9–2.3) (n = 2428^a)
Li, 2010²⁴	Retrospective	44	77.3	Median, 78 (IQR, 60–90)	N/A	47.7	Median, 46 (IQR, 32–55)	Median, 20 (IQR, 16–29) months	Median, 6.6 (IQR, 3.6–9.6)
Nakayama, 2012⁴	Retrospective	510	82.7 (n = 665^a)	73.3±7.8 (n = 665^a)	Prior MI, prior PCI/CABG, and/or coronary stenosis identified during preoperative CAG	48.6	N/A (53.5±12.2 at elective repair) (n = 665^a)	N/A	N/A
Parr, 2011²⁵	Prospective	39	69.2	Median, 73 (IQR, 69–77)	Medical history of MI, AP, or coronary revascularization	56.4	Median, 42.1 (IQR, 33.0–47.0)	Median, 1.5 (IQR, 1.1–3.3)	Median, 1.3 (IQR, −0.2–2.7); cm³
Schlösser (SMART), 2008²⁶	Prospective	147	89.1	65±7.7	Medical history of MI	25.9	39±6.8	Median, 3.3 (range, 0.5–11.1)	2.5±3.2
Vega de Céniga, 2006²⁷	Prospective	352	94.6	71 (range, 40–89)	N/A	40.6	36.2±5.61	55.2±37.4 months	2.87±4.39
Vega de Céniga, 2014²⁸	Prospective	96	95.8	70±8.0	Cardiac disease including CAD (medical history of AP, MI and/or PCI, or CABG), VD, and arrhythmia	30.2	39.3±7.5	12 months	2.7±3.2

Original cohort.

AAA, abdominal aortic aneurysm; AP, angina pectoris; CABG, coronary artery bypass grafting; CAD, coronary artery disease; CAG, coronary angiography; CHF, congestive heart failure; CI, confidence interval; ECG, electrocardiography; hs-CRP, high-sensitivity C-reactive protein; IQR, interquartile range; MI, myocardial infarction; N/A, not available; PCI, percutaneous coronary intervention; SE, standard error; VD, valvular disease.

Table 2.

Detailed measurements of abdominal aortic aneurysm diameters.

Study	Details of diameter measurement
	Modality	Measurement	Reproducibility	Interval	Number of follow-up scans
Akai, 2015¹²	US/CT	Minor axis	N/A	N/A	N/A
Badger, 2011¹³	US/CT	N/A	N/A	⩾3 months	Median, 5
Behr-Rasmussen (VIVA), 2014¹⁴	US	Maximum systolic longitudinal A-P inner diameter	Interobserver variability, 0.86 mm	Annually	N/A
Bhak (ADAM), 2015³	US/CT	Maximum external diameter in at least 2 planes with US and in any plane but perpendicular to any curvature in aorta with CT	Representative measurement chosen based on assumptions of precision and consistency in instances of multiple measurements	⩾6 months	N/A
Brady (UKSAT), 2004⁵	US	Maximum A-P diameter	±2 mm	6 months for <50-mm AAA; 3 months for ⩾50-mm AAA	5.2±3.1
Burillo, 2015¹⁵	CT	N/A	N/A	Annually	N/A
Chang, 1997¹⁶	US	A-P diameter between leading edges of walls and perpendicular to long axis of aorta	±3 mm	12 months for <40-mm AAA; 6–12 months for >40-mm AAA	Maximum, >20
Cronin, 2014¹⁷	CTA	Maximum diameter assessed by orthogonal measurement from adventitia to adventitia	Inter- and intra-observer mean coefficient of variation, 3.5% and 2.5%, respectively	>6 months apart	2
De Haro, 2012¹⁸	CTA	Maximum transversal internal diameter	N/A	12 months	2
Duellman, 2014¹⁹	US/CT	N/A	N/A	N/A	N/A
Ferguson, 2010²⁰	US	Maximum A-P and transverse diameter	Mean measurement differences [±2SD], 0.4 [–2.62, 3.42] mm	Annual for 30–44-mm AAA; 6 months for 45–50-mm AAA	Median, 6 (IQR, 5–7)
Karlsson, 2009²¹	US	Maximum A-P diameter in both axial and transverse angles	⩾2 measurements	6 months	N/A
Kotze, 2014²²	US	Maximum A-P external wall diameter	N/A	6 months	3
Lederle, 2015²³	US/CT	N/A	N/A	⩾6 months	4.9±3.6
Li, 2010²⁴	CT	Maximum axial and 3-dimensional diameter	N/A	N/A	N/A
Nakayama, 2012⁴	CT	N/A	N/A	N/A	⩾2
Parr, 2011²⁵	CTA	Maximum axial diameter	N/A	N/A	N/A
Schlösser (SMART), 2008²⁶	US	Maximum A-P diameter	N/A	Annual for 30–39-mm AAA; 6 months for 40–55-mm AAA	N/A
Vega de Céniga, 2006²⁷	US for 30–39-mm AAA; contrast CT for 40–49-mm AAA	Maximum A-P and transverse outer diameter	N/A	Annual for 30–39-mm AAA; 6 months for 40–49-mm AAA	N/A
Vega de Céniga, 2014²⁸	US for 30–39-mm AAA; contrast CT for ⩾40-mm AAA	Maximum outer-to-outer transverse, A-P, and lateral diameters, perpendicular to aortic axis	US compare with CT, ±2 mm	Annual for 30–39-mm AAA; 6 months for ⩾40-mm AAA	2–3

AAA, abdominal aortic aneurysm; A-P, anterior-to-posterior; CT, computed tomography; CTA, CT angiography; IQR, interquartile range; N/A, not available; SD, standard deviation; US, ultrasonography.

Table 3.

Growth rates of abdominal aortic aneurysm.

Study	Growth rate (mm/year)
	Calculation	Unadjusted estimate (with univariate analysis)			Adjusted estimate (with multivariate analysis)
		CAD	Non-CAD	Mean difference [95% CI]	CAD	Non-CAD	Mean difference [95% CI]
Akai, 2015¹²	Linear regression	Median, 2.2 (IQR, 1.3–4.0)	Median, 2.0 (IQR, 0.4–3.6)	N/A	Analysis, performed; CAD, not entered
Badger, 2011¹³	Linear regression	Mean, 5.8 (%/year)^a (p=0.93 vs non-CAD)	Mean, 5.2 (%/year)^a	N/A	Analysis, not performed
Behr-Rasmussen (VIVA), 2014¹⁴	Linear regression	2.35±2.39	2.83±2.63	N/A	Analysis, performed; CAD, not entered
Bhak (ADAM), 2015³	Linear mixed-effects model	N/A	N/A	N/A	N/A	N/A	−0.03 [–0.3, 0.3]
Brady (UKSAT), 2004⁵	Linear regression	Mean, 2.46	Mean, 2.54	N/A	N/A	N/A	−0.12 [–0.52, 0.29]^b; −0.06 [–0.48, 0.33]^c,d
Burillo, 2015¹⁵	Linear regression	2.95±2.83	2.85±2.18	N/A	Analysis, not performed
Chang, 1997¹⁶	Slope of best fit line for steady growth; last 2 consecutive measurements for increasing growth	Proportion of CAD patients in slow (⩽10 mm/year) and rapid (>10 mm/year) growth, respectively 33.9% (=153/451) vs 54.0% (=34/63); p=0.0031			OR for rapid growth (>10 mm/year), 1.9056 [1.0958, 3.3139]
Cronin, 2014¹⁷	Weighted to growth per year to adjust for variations in scan intervals	Proportion of CAD patients in slow (⩽median) and rapid (>median) growth, respectively 52.2% (=12/23) vs 63.6% (=14/22)			Analysis, performed; OR for rapid (>median) growth, N/A
De Haro, 2012¹⁸	Difference between diameter at 12 months and baseline size	Proportion of CAD patients in slow (<median) and rapid (>median) growth, respectively 29.2% (=38/130) vs 36.2% (=47/130)			OR for rapid growth (>median), 1.1 [0.7, 1.4]
Duellman, 2014¹⁹	Linear regression	Proportion of CAD patients in slow (<3.25 mm/year) and rapid (>3.25 mm/year) growth, respectively 84.0% (=68/81) vs 88.3% (=53/60)			Analysis, not performed
Ferguson, 2010²⁰	Time-weighted average (taking into account all diameters measured during follow-up)	N/A	N/A	N/A	OR for rapid growth (⩾median), 0.67 [0.46, 0.97]
Karlsson, 2009²¹	Linear regression	Median, 2.2 (IQR, 1.0–3.8)	Median, 2.2 (IQR, 0.1–3.3)	N/A	Analysis, not performed
Kotze, 2014²²	N/A	Median, 0.42% (IQR, 0.00–0.91%)^e	Median, 0.53% (IQR, 0.00–0.93%)^e	N/A	Analysis, performed; CAD, not entered
Lederle, 2015²³	Linear model	N/A	N/A	N/A	N/A	N/A	−0.5 [–1.2, 0.2]
Li, 2010²⁴	Average from baseline and final	Proportion of CAD patients in slow (<4 mm/year) and rapid (⩾4 mm/year) growth, respectively 64.3% (=9/14) vs 40.0% (=12/30)			Analysis, not performed
Nakayama, 2012⁴	N/A	N/A	N/A	N/A	HR for rapid growth (>5 mm/year), 0.55 [0.32, 0.94]
Parr, 2011²⁵	Weighted annual change	Proportion of PAD patients in slow (<5 cm³/year) and rapid (⩾5 cm³/year) volumetric growth, respectively 73.7% (=14/19) vs 40.0% (=8/20)			Analysis, performed; CAD, not entered
Schlösser (SMART), 2008²⁶	Linear regression	N/A	N/A	−0.50 [–1.7, 0.69]	Analysis, performed; CAD, not entered
Vega de Céniga, 2006²⁷	Dividing total growth throughout follow-up by number of years of follow-up	2.09±2.47 for 30–39-mm AAA; 5.02±6.27 for 40–49-mm AAA	2.06±3.62 for 30–39-mm AAA; 4.46±5.67 for 40–49-mm AAA	N/A	Analysis, not performed
Vega de Céniga, 2014²⁸	[Baseline diameter] – [1-year diameter]	OR for slow growth (⩽2 mm/year), 1.05 [0.43, 2.54]			Analysis, performed; CAD, not entered

Derived from the geometric mean of proportionate increases estimated from regression of logarithmically transformed sizes.

Adjusted for age, sex, baseline diameter, and curvature in growth pattern.

Adjusted for age, sex, baseline diameter, curvature in growth pattern, and other variables in group except those available on the trial patients only.

Combined in a meta-analysis.

Expansion at 12 months divided by aneurysm size at baseline.

CAD, coronary artery disease; CI, confidence interval; HR, hazard ratio; IQR, interquartile range; N/A, not available; OR, odds ratio; PAD, peripheral artery disease.

Of the 20 studies, only one¹⁶ demonstrated a statistically significant association of CAD with greater AAA growth rates, whereas three^4,20,25 indicated a statistically significant association of CAD with slower AAA growth rates. A pooled analysis of all 20 studies demonstrated a statistically significant association of CAD with slower AAA growth rates (i.e. a significantly negative association of CAD with AAA growth) in the fixed-effect model (SMD, −0.06 [–0.0592]; 95% CI, −0.12 [–0.1157] to −0.00 [–0.0027]; p = 0.04; Figure 2). There was minimal between-study heterogeneity (p = 0.16) and a statistically non-significant association of CAD with slower AAA growth rates (i.e. a non-significantly negative association of CAD with AAA growth) in the pooled result from random-effects modeling (SMD, −0.06; 95% CI, −0.13 to 0.01; p = 0.12). To assess publication bias we generated a funnel plot of the effect size (SMD) versus the precision (reciprocal of SE) for each study (Figure 3). There was no evidence of significant publication bias (2-tailed p with continuity correction = 0.65 by the adjusted rank-correlation test;¹⁰ 2-tailed p = 0.84 by the linear regression test¹¹).

Figure 2.

Forest plot of standardized mean differences of abdominal aortic aneurysm growth rates between patients with and without coronary artery disease (CAD). (ADAM, Aneurysm Detection and Management; CI, confidence interval; IV, inverse variance; SMART, Second Manifestations of ARTerial disease; UKSAT, UK Small Aneurysm Trial; VIVA, Viborg Vascular.)

Figure 3.

Funnel plot of the precision (reciprocal of standard error) by the standardized mean difference of abdominal aortic aneurysm growth rates between patients with and without coronary artery disease.

Discussion

The results of our analysis suggest a negative association of CAD with AAA growth. Most individual studies were unable to demonstrate a statistically significant benefit to CAD, likely due to systematic underpowering (as reflected in the wide CIs and lack of statistical significance) of these studies in the design phase and primarily due to the small number of enrolled patients.

Although patients with AAA frequently have atherosclerotic diseases such as CAD and peripheral artery disease (PAD), it is unknown whether this association between AAA and atherosclerosis is causal or simply due to common risk factors. One possibility is that an AAA develops as a pathological response to aortic atherosclerosis, a theory first suggested more than half a century ago, when the term ‘atherosclerotic aneurysms’ was commonly used, but still prevalent today.⁷ Environmental and genetic risk factors lead to development of aortic atherosclerosis according to the following two theories.⁷ First, resultant positive remodeling, intimal thrombosis, and release of proinflammatory cytokines stimulate secondary matrix degradation and adventitial inflammation, which promotes AAA development. Second, environmental and genetic risk factors directly stimulate aortic medial degradation and adventitial inflammation, leading to AAA formation, which secondarily stimulates intimal atherosclerosis. More likely, both pathways act to some extent, with the relative proportion varying from patient to patient depending on the risk profile.⁷ Some differences between atherosclerosis and AAA, however, have been documented. First, diabetes is a negative or neutral risk factor for AAA but an important risk factor for atherosclerosis, and, additionally, male gender and smoking are much more dominant risk factors for AAA than atherosclerosis.^7,29 Second, there are a number of disparate markers for AAA and atherosclerosis;⁷ for example, low-density lipoprotein has no clear association with AAA but is an important risk factor for atherosclerosis.³⁰ Third, some recognized genetic determinants of atherosclerosis have no consistent association with AAA;⁷ for example, apolipoprotein E single-nucleotide polymorphisms.³¹ Fourth, marked elastin fragmentation and adventitial chronic inflammation are mainly restricted to AAA.^7,29 Fifth, there are examples of differential effects of interventions on AAA and atherosclerosis progression in rodent models;⁷ for example, tumor necrosis factor³² and matrix metalloproteinase-12 deficiency,³³ and peroxisome proliferator-activated receptor ligation.³⁴ Sixth, despite a significant association between carotid artery total plaque area and history of CAD with AAA prevalence, there is no association between carotid artery total plaque area and aortic diameter within the AAA range (i.e. there is no consistent correlation between atheroma extent and AAA severity).^7,35 The lack of a consistent dose–response relationship between atherosclerosis and AAA diameter suggests that atherosclerosis may not be a causal event in AAA but develops in parallel with or secondary to aneurysmal dilatation.³⁵ Finally, there may be different local responses to atherosclerosis in the abdominal aorta. On the one hand, plaque deposition associated with localized dilation, thinning of the media, and loss of medial elastic lamellae may predispose that segment of aorta to subsequent aneurysm formation.⁶ On the other hand, plaque deposits without media thinning, without loss of elastic lamellae, and without artery wall dilation may predispose the aorta, in the event of continuing plaque accumulation, to the development of lumen stenosis.⁶

Despite the positive association of CAD with AAA presence,^1,2 the reason why CAD is negatively associated with AAA growth is unclear. Diabetes, a major risk factor of CAD, is independently and negatively associated with not only AAA presence,^36,37 but also AAA growth.^38,39 The RESCAN meta-analysis³⁹ of 6268 individual patient data from 10 studies (of the available 18 studies) demonstrated that diabetics had growth rates that were on average 0.51 (SE, 0.10) mm/year slower than those of non-diabetics after adjustment for all demographics, medical history, and drug history. Our recent meta-analysis³⁹ of 19 studies also demonstrated statistically significant slower growth rates in diabetic patients than in non-diabetic patients (unadjusted SMD, −0.32; 95% CI, −0.40 to −0.24; p < 0.00001; adjusted SMD, −0.29; 95% CI, −0.417 to −0.18; p < 0.00001). On the other hand, PAD as well as CAD, a major comorbidity of diabetes, is also positively associated with AAA presence.^1,2 The meta-analysis by Cornuz et al.¹ of eight population-based risk factor studies of AAA showed that a history of PAD was a major risk factor or risk indicator for screening-detected AAA (OR, 2.48; 95% CI, 2.10 to 2.92). The meta-analysis by Li et al.² of three epidemiological studies also showed that claudication was a risk factor for AAA (OR, 3.00; 95% CI, 1.74 to 5.19). Although the association of PAD with AAA growth has been debated, PAD may be not³ or even negatively¹⁴ associated with AAA growth despite its positive association with AAA presence.^1,2 Bhak et al.³ found no independent association between claudication and AAA expansion rates (MD, −0.1 mm/year; 95% CI, −0.6 to 0.3 mm/year; p = 0.52). Whereas, in a longitudinal study from the UKSAT (UK Small Aneurysm Trial), Brady et al.⁵ indicated that a lower ankle/brachial pressure index (indicating more severe PAD) was associated with slower AAA growth (i.e. negatively associated with AAA growth) (MD per 0.2-unit increase in the index, 0.22 mm/year; 95% CI, 0.10 to 0.34 mm/year). Further investigations would be required to elucidate why CAD is negatively associated with AAA growth despite its positive association with AAA presence.

Limitations

Our analysis must be viewed in the context of its limitations. First, our results may be influenced by a publication bias. This risk was minimized through an exhaustive search of the available literature. By not adding the search terms coronary artery/arterial/heart disease, ischemic/ischaemic heart disease, myocardial infarction, angina, and acute coronary syndrome (adding these terms indeed led to retrieving a limited number of studies), we initially identified 83 potential eligible studies reporting AAA growth and then selected 20 studies providing the association of CAD with AAA growth. Although the statistical tests did not indicate publication bias, there is clearly limited power to detect such bias, given the small number of studies examined. Second, all data currently available for inclusion were abstracted from epidemiologic studies which commonly lacked blinding in accessing data. Additionally, retrospective studies included the possibility of missed information. Interpretation of our results was complicated by the heterogeneity among the studies, probably due to differences in study designs, populations, definitions of AAA and CAD, measurements of AAA diameter, adjustment factors, and durations of follow-up. Finally, the risk of obtaining a spurious explanation for variable effects could have appeared higher because of the many characteristics that differed among the studies included and the relatively small number of cases.³⁶ Thus, we preferred to use data adjusted for local confounders at each study level (when the information was available). Multivariate-adjusted (including propensity-score matched) estimates were available in seven^{3,4,5,16,18,20,23} of the 20 studies included in the present meta-analysis. Although cardiovascular medications were adjusted for in five^3,4,18,20,23 of these seven studies, the potential for confounding by other factors associated with treatment of CAD (such as smoking cessation counseling/quit rates) still remains.

Conclusion

In conclusion, we found that, based on a meta-analysis, CAD may be negatively associated with AAA growth. Further investigations would be required to elucidate why CAD is negatively associated with AAA growth despite its positive association with AAA presence.

Footnotes

Acknowledgements

The ALICE (All-Literature Investigation of Cardiovascular Evidence) Group is composed of 15 doctors who currently belong (Hisato Takagi, MD, PhD; Mitsuyoshi Matsumoto, MD; Shohei Mitta, MD; Norikazu Kawai, MD; Takuya Umemoto, MD, PhD) or have belonged (Hiroki Ogura, MD; Yusuke Mizuno, MD; Hirotaka Yamamoto, MD; Taku Watanabe, MD; Masao Niwa, MD; Shin-nosuke Goto, MD; Masafumi Matsui, MD; Takayoshi Kato, MD; Seishiro Sekino, MD; Yukihiro Matsuno, MD, PhD) to the Department of Cardiovascular Surgery, Shizuoka Medical Center, Shizuoka, Japan since March in 2004.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

References

Cornuz

Sidoti Pinto

Tevaearai

. Risk factors for asymptomatic abdominal aortic aneurysm: Systematic review and meta-analysis of population-based screening studies. Eur J Public Health 2004; 14: 343–349.

Zhao

Zhang

. Prevalence and trends of the abdominal aortic aneurysms epidemic in general population–a meta-analysis. PLoS One 2013; 8: e81260.

Bhak

Wininger

Johnson

. Aneurysm Detection and Management (ADAM) Study Group. Factors associated with small abdominal aortic aneurysm expansion rate. JAMA Surg 2015; 150: 44–50.

Nakayama

Morita

Miyata

. Inverse association between the existence of coronary artery disease and progression of abdominal aortic aneurysm. Atherosclerosis 2012; 222: 278–283.

Brady

Thompson

Fowkes

. UK Small Aneurysm Trial Participants. Abdominal aortic aneurysm expansion: Risk factors and time intervals for surveillance. Circulation 2004; 110: 16–21.

Zarins

Glagov

Aneurysmal and occlusive atherosclerosis of the human abdominal aorta. J Vasc Surg 2001; 33: 91–96.

Golledge

Norman

PE.

Atherosclerosis and abdominal aortic aneurysm: Cause, response, or common risk factors?

Arterioscler Thromb Vasc Biol 2010; 30: 1075–1077.

Deeks

Higgins

Altman

DG.

Chapter 9: Analysing data and undertaking meta-analyses. In: Higgins

JPT

Green

(eds) Cochrane Handbook for Systematic Reviews of Interventions, Version 5.1.0 (updated March 2011). The Cochrane Collaboration. Available from: www.cochrane-handbook.org (2011, accessed 1 November 2015).

Chinn

A simple method for converting an odds ratio to effect size for use in meta-analysis. Stat Med 2000; 19: 3127–3131.

10.

Begg

Mazumdar

Operating characteristics of a rank correlation test for publication bias. Biometrics 1994; 50: 1088–1101.

11.

Egger

Davey Smith

Schneider

. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997; 315: 629–634.

12.

Akai

Watanabe

Hoshina

. Family history of aortic aneurysm is an independent risk factor for more rapid growth of small abdominal aortic aneurysms in Japan. J Vasc Surg 2015; 61: 287–290.

13.

Badger

Jones

McClements

. Surveillance strategies according to the rate of growth of small abdominal aortic aneurysms. Vasc Med 2011; 16: 415–421.

14.

Behr-Rasmussen

Grøndal

Bramsen

. Mural thrombus and the progression of abdominal aortic aneurysms: A large population-based prospective cohort study. Eur J Vasc Endovasc Surg 2014; 48: 301–307.

15.

Burillo

Lindholt

Molina-Sánchez

. ApoA-I/HDL-C levels are inversely associated with abdominal aortic aneurysm progression. Thromb Haemost 2015; 113: 1335–1346.

16.

Chang

Stein

Liu

. Risk factors associated with rapid growth of small abdominal aortic aneurysms. Surgery 1997; 121: 117–122.

17.

Cronin

Liu

Bradshaw

. Visceral adiposity is not associated with abdominal aortic aneurysm presence and growth. Vasc Med 2014; 19: 272–280.

18.

De Haro

Acin

Bleda

. Prediction of asymptomatic abdominal aortic aneurysm expansion by means of rate of variation of C-reactive protein plasma levels. J Vasc Surg 2012; 56: 45–52.

19.

Duellman

Warren

Matsumura

. Analysis of multiple genetic polymorphisms in aggressive-growing and slow-growing abdominal aortic aneurysms. J Vasc Surg 2014; 60: 613–621.e3.

20.

Ferguson

Clancy

Bourke

. Association of statin prescription with small abdominal aortic aneurysm progression. Am Heart J 2010; 159: 307–313.

21.

Karlsson

Gnarpe

Bergqvist

. The effect of azithromycin and Chlamydophilia pneumonia infection on expansion of small abdominal aortic aneurysms—A prospective randomized double-blind trial. J Vasc Surg 2009; 50: 23–29.

22.

Kotze

Rudd

Ganeshan

. CT signal heterogeneity of abdominal aortic aneurysm as a possible predictive biomarker for expansion. Atherosclerosis 2014; 233: 510–517.

23.

Lederle

Noorbaloochi

Nugent

. Multicentre study of abdominal aortic aneurysm measurement and enlargement. Br J Surg 2015; 102: 1480–1487.

24.

Sadat

U-King-Im

. Association between aneurysm shoulder stress and abdominal aortic aneurysm expansion: A longitudinal follow-up study. Circulation 2010; 122: 1815–1822.

25.

Parr

McCann

Bradshaw

. Thrombus volume is associated with cardiovascular events and aneurysm growth in patients who have abdominal aortic aneurysms. J Vasc Surg 2011; 53: 28–35.

26.

Schlösser

Tangelder

Verhagen

. SMART study group. Growth predictors and prognosis of small abdominal aortic aneurysms. J Vasc Surg 2008; 47: 1127–1133.

27.

Vega

Céniga

Gómez

Estallo

. Growth rate and associated factors in small abdominal aortic aneurysms. Eur J Vasc Endovasc Surg 2006; 31: 231–236.

28.

Vega

Céniga

Esteban

Barba

. Assessment of biomarkers and predictive model for short-term prospective abdominal aortic aneurysm growth—A pilot study. Ann Vasc Surg 2014; 28: 1642–1648.

29.

Golledge

Muller

Daugherty

. Abdominal aortic aneurysm: Pathogenesis and implications for management. Arterioscler Thromb Vasc Biol 2006; 26: 2605–2613.

30.

Golledge

van Bockxmeer

Jamrozik

. Association between serum lipoproteins and abdominal aortic aneurysm. Am J Cardiol 2010; 105: 1480–1484.

31.

Golledge

Biros

Cooper

. Apolipoprotein E genotype is associated with serum C-reactive protein but not abdominal aortic aneurysm. Atherosclerosis 2010; 209: 487–491.

32.

Xanthoulea

Thelen

Pöttgens

. Absence of p55 TNF receptor reduces atherosclerosis, but has no major effect on angiotensin II induced aneurysms in LDL receptor deficient mice. PLoS One 2009; 4: e6113.

33.

Luttun

Lutgens

Manderveld

. Loss of matrix metalloproteinase-9 or matrix metalloproteinase-12 protects apolipoprotein E-deficient mice against atherosclerotic media destruction but differentially affects plaque growth. Circulation 2004; 109: 1408–1414.

34.

Golledge

Cullen

Rush

. Peroxisome proliferator-activated receptor ligands reduce aortic dilatation in a mouse model of aortic aneurysm. Atherosclerosis 2010; 210: 51–56.

35.

Johnsen

Forsdahl

Singh

. Atherosclerosis in abdominal aortic aneurysms: A causal event or a process running in parallel? The Tromsø study. Arterioscler Thromb Vasc Biol 2010; 30: 1263–1268.

36.

De Rango

Farchioni

Fiorucci

. Diabetes and abdominal aortic aneurysms. Eur J Vasc Endovasc Surg 2014; 47: 243–261.

37.

Takagi

Umemoto

A contemporary meta-analysis of the association of diabetes with abdominal aortic aneurysm. Int Angiol 2015; 34: 375–382.

38.

Sweeting

Thompson

Brown

. RESCAN collaborators. Meta-analysis of individual patient data to examine factors affecting growth and rupture of small abdominal aortic aneurysms. Br J Surg 2012; 99: 655–665.

39.

Takagi

Umemoto

. Diabetes and abdominal aortic aneurysm growth. Angiology. Epub ahead of print 25 August 2015. DOI: 10.1177/0003319715602414.

40.

Moher

Liberati

Tetzlaff

. (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7): e1000097. doi:10.1371/journal.pmed.1000097

Coronary artery disease and abdominal aortic aneurysm growth

Abstract

Keywords

Introduction

Methods

Results

Discussion

Limitations

Conclusion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

References