Abstract
purpose. To describe the relationship of retinal vascular caliber with cardiovascular risk factors in an Asian population.
methods. The Singapore Malay Eye Study is a population-based, cross-sectional survey that included 3280 (78.7% response) persons aged 40 to 80 years. Retinal vascular caliber was measured from digital retinal photographs using a validated standardized protocol. Data on major cardiovascular risk factors were collected from all participants.
results. Of the 3019 participants with retinal vascular caliber data available, the mean retinal arteriolar caliber (CRAE) was 139.5 ± 15.7 μm (SD), and mean venular caliber (CRVE) was 219.3 ± 22.2 μm (SD). Smaller retinal arteriolar caliber was associated with higher current mean blood pressure and male sex (P < 0.001 for both). Larger retinal venular caliber was associated with younger age, current cigarette smoking, greater body mass index, higher glycosylated hemoglobin level, and lower HDL cholesterol (P = 0.012 for glycosylated hemoglobin level and P < 0.001 for other risk factors). The association of retinal arteriolar narrowing and blood pressure was stronger in younger people than in older people and in men than in women (P for interaction < 0.001 for both).
conclusions. In this Asian population, smaller retinal arteriolar caliber was associated with hypertension and larger retinal venular caliber with cigarette smoking, dyslipidemia, hyperglycemia, and higher body mass index. The pattern of these associations is similar to that in white populations.
The retina offers a unique opportunity for the direct, noninvasive study of the human microvasculature and its relationship to cardiovascular disease risk factors (e.g., hypertension and diabetes).
1 2 Recent developments in retinal imaging techniques have made quantitative measurement of retinal vascular caliber possible. Using such techniques, many large population-based studies have described the relationship of various vascular risk factors and cardiovascular outcomes (e.g., stroke, heart disease) with retinal vascular caliber.
3 4 5 6 7 8 9 10 11 Findings in these studies have indicated an important role of microvascular disease in the pathogenesis of major cardiovascular diseases. These data also support potentially novel therapeutic agents that specifically target the microcirculation
12 and are useful for physicians when considering treatment strategies for cardiovascular diseases. For example, physicians may consider choosing antihypertensive medications that have direct beneficial effects on microvascular structure, which could thereby reduce damage to the microvasculature beyond the reduction resulting from lowered blood pressure (e.g., angiotensin converting enzyme inhibitors).
1 12 13
However, most studies have been conducted exclusively in whites
3 4 7 8 9 14 or predominantly white populations,
5 with only one study in an exclusively Asian population.
6 There are well-recognized ethnic differences in the relationship between cardiovascular risk factors and large vessel atherosclerosis.
15 In contrast, there are limited data on associations of cardiovascular risk factors with microvascular disease in nonwhite populations.
6 Furthermore, few studies have examined the potential age and sex interactions of these relationships. Some have suggested that the role of microvascular disease may be more prominent in middle-aged persons (age, 43–69 years) than in older people (age, ≥70 years).
16 For example, in the Blue Mountains Eye Study, the association of higher blood pressure with arteriolar narrowing diminished with increasing age,
10 suggesting that aging may lead to local changes in vessel wall elasticity and reactivity that prevent the microcirculation from responding to changes brought on by blood pressure. However, whether the microvascular response to other cardiovascular risk factors also diminishes with aging is unknown. There is also a suggestion that retinal arteriolar caliber may be narrower in men than in women,
5 which could be due to sex-specific effects of cardiovascular risk factors (e.g., diabetes),
17 sex hormonal influence, or sex-related structural and functional differences in the vasculature.
18
The purposes of this study were to describe the distribution of retinal vascular caliber and its associations with cardiovascular risk factors in an Asian population. We also examined potential age and sex interactions of retinal vascular caliber with blood pressure.
Retinal vascular caliber (CRAE and CRVE) was analyzed as continuous variables. We used analysis of covariance (ANCOVA) to estimate mean retinal vascular caliber associated with the presence versus absence of categorical variables (e.g., diabetes) or increasing quartiles of continuous variables (e.g., glucose levels) adjusted for age and sex. Tests for trend were determined by treating categorical risk factors (e.g., quartiles of glucose) as continuous ordinal variables in multiple linear regression models. We analyzed age both continuously and categorically (age groups: 40–49, 50–59, 60–69, and 70–80 years).
We constructed two multivariate models. First, in model 1, we constructed a model for CRAE initially including variables that were significantly associated with CRAE in age and sex analyses. Final variables were selected based on a stepwise backward approach, adjusting for age. We then constructed a similar model for CRVE based on variables significantly associated with CRVE in age and sex analyses. Second, in model 2, we preformed supplementary analysis by additional adjustment for the fellow retinal vessel caliber (i.e., CRVE was included as an independent variable in the model for CRAE, and vice versa), as previously described by Liew et al.
26 to control for potential confounding from fellow vessel diameter. We calculated sequential
R 2 to indicate the contribution of each independent variable to the model. Data presented here are based on results from model 1, as results from model 2 were largely similar.
Finally, we investigated the potential interaction of risk factors with age and sex. Where interactions were statistically significant (P < 0.01), stratified analyses were performed by subgroup. All analyses were conducted with commercial software (Stata ver. 9.0; Stata Corp., College Station, TX).
In this population-based study among Asians, we demonstrated two distinct patterns of association for retinal arteriolar and venular caliber. Retinal arteriolar caliber is strongly related to blood pressure, and retinal venular caliber is related to cigarette smoking, lower HDL cholesterol level, higher HbA1c level, and greater BMI. Our current findings are consistent with previous reports on the patterns of association of retinal vascular caliber with blood pressure and cardiovascular risk factors from predominantly white persons
3 4 5 8 9 14 and from a Japanese population, the only other reported study of an Asian ethnic group.
6
The association of retinal arteriolar narrowing and hypertension is well documented.
1 2 In our study, the observed magnitude of the association between elevated blood pressure and smaller arteriolar caliber was similar to that demonstrated in other population-based studies of white persons.
3 4 5 6 28 29 Besides blood pressure, we found few associations with arteriolar caliber. The association of current cigarette smoking with larger arteriolar caliber became nonsignificant after further adjustment for the fellow vascular caliber, suggesting the possibility of a confounding effect of retinal venular caliber on this association.
26
We have shown that larger retinal venular caliber predicts incident stroke and coronary heart disease.
9 The relationship of larger retinal venular caliber to current cigarette smoking, lower HDL cholesterol level, higher glycosylated hemoglobin, and higher BMI, all major cardiovascular risk factors, is consistent with previous data in white and black populations
5 14 30 and supports the notion that larger venular caliber is a biomarker of adverse vascular risk in persons with diabetes and prediabetes.
31 32 33 The finding that lower income was associated with larger venular caliber, independent of other risk factors, is interesting. Lower income, reflecting lower socioeconomic status, has been shown to affect health behavior and lifestyle patterns and is considered a surrogate for cardiovascular risk factors and other exposures.
34 35
We have described an interaction between age, blood pressure, and retinal arteriolar caliber. The weaker association of higher blood pressure and narrower retinal arteriolar caliber in the older compared with the younger participants is consistent with previous observations among white persons in the Beaver Dam and Blue Mountains Eye studies.
3 10 In addition, in persons with lower blood pressure, we showed that increasing age was associated with smaller arteriolar caliber; however, in persons with higher blood pressure, increasing age was independently associated with wider arteriolar caliber. This arteriolar “ballooning” effect associated with increasing blood pressure in older individuals has been described previously
28 and may be related to cumulative fibrotic or atrophic arteriolar wall changes from both aging (and its related conditions) and blood pressure, with subsequent passive dilation.
36
Our finding that retinal arteriolar caliber is wider in women than in men is similar to reports of some previous studies in whites and other ethnicities,
4 5 although this has not been a consistent finding.
37 We also found a stronger effect of blood pressure on retinal arteriolar narrowing in men than in women. We speculate that these findings reflect a protective effect of sex hormones on the microcirculation, consistent with a similar effect of estrogen on macrovascular disease.
38 We should note, however, that previous studies lend little support to an effect of hormone replacement therapy or female reproductive parameters on retinal arteriolar caliber in elderly white women.
39 40
Taken together, the range of cardiovascular risk factors considered in our analysis explained only 8.7% and 4.2%, respectively, of the variance in retinal arteriolar caliber and venular caliber. This suggests that many unmeasured and unknown factors (e.g., genetic factors and inflammatory markers) affect retinal vascular caliber.
26 41
The strengths of our study include its population-based sample, the use of standardized retinal vascular caliber measurement from digital photographs, and detailed information on a range of risk factors and potential confounders. The findings in our study should be interpreted within the context of several potential limitations. First, the cross-sectional nature of our study and others does not provide temporal information or support for the associations. There are current plans for a prospective study. Second, blood glucose, HbA1c, and lipids were measured from nonfasting blood samples. Third, retinal vascular caliber measured using our imaging software may be influenced by the pulse cycle, although this variation is likely to bias associations toward the null.
42 We obtained estimates of central retinal vascular caliber using the improved Knudtson formulas, which have been shown to be more robust than the previous Parr-Hubbard formulas, and this may partially explain some differences between our findings and those of other studies in which the Parr-Hubbard formulas were used.
22 Finally, some variables (e.g., alcohol consumption, smoking, and education level) were obtained from self-reported questionnaires based on validated questionnaires used in other population-based studies, such as the Blue Mountains Eye Study, and are unlikely to be a major source of bias.
In conclusion, findings from this large Asian Malay population confirm observations in white, blacks, and Japanese populations that there are two distinct patterns of association for retinal arteriolar and venular caliber. Retinal arteriolar caliber is strongly related to blood pressure. Retinal venular caliber, in contrast, is related to cigarette smoking, lower HDL cholesterol, higher HbA1c levels, and greater BMI. Stronger effects of blood pressure on retinal arteriolar caliber were seen in men compared with women and in younger compared with older people. These findings add to the growing body of literature on the effects of blood pressure and cardiovascular factors on retinal vascular changes in diverse populations.
Supported by the National Medical Research Council Grants 0796/2003, 0863/2004 and CSI/0002/2005; Biomedical Research Council Grant No 501/1/25-5; and the Singapore Tissue Network and the Ministry of Health, Singapore.
Submitted for publication November 11, 2007; revised January 6, 2008; accepted March 13, 2008.
Disclosure:
C. Sun, None;
G. Liew, None;
J.J. Wang, None;
P. Mitchell, None;
S.M. Saw, None;
T. Aung, None;
E.S. Tai, None;
T.Y. Wong, None
The publication costs of this article were defrayed in part by page charge payment. This article must therefore be marked “
advertisement” in accordance with 18 U.S.C. §1734 solely to indicate this fact.
Corresponding author: Tien Yin Wong, Centre for Eye Research Australia, University of Melbourne, 32 Gisborne Street, Victoria 3002, Australia;
[email protected].
Table 1. Characteristics of the SiMES Study Population
Table 1. Characteristics of the SiMES Study Population
| All Persons (n = 3019) | Males (n = 1455) | Females (n = 1564) | P * |
Age (y) | 57.7 (10.6) | 58.3 (11.0) | 57.1 (10.3) | 0.001 |
Systolic blood pressure (mm Hg) | 145.9 (23.1) | 143.9 (21.8) | 147.8 (24.2) | <0.001 |
Diastolic blood pressure (mm Hg) | 79.6 (11.1) | 81.3 (10.8) | 78.0 (11.1) | <0.001 |
Serum glucose (mmol/dL) | 6.8 (3.7) | 6.7 (3.6) | 6.8 (3.7) | 0.47 |
Body mass index (kg/m2) | 26.4 (5.1) | 25.3 (4.4) | 27.5 (5.5) | <0.001 |
HDL cholesterol (mg/dL) | 1.4 (0.3) | 1.2 (0.3) | 1.5 (0.3) | <0.001 |
Serum creatinine (mg/dL) | 91.9 (48.4) | 105.6 (47.3) | 79.1 (45.8) | <0.001 |
Sex, male | 1455 (48.2) | — | — | — |
Education, primary/lower ≤6 y | 2221 (73.6) | 997 (68.8) | 1224 (78.4) | <0.001 |
Hypertension | 2024 (67.0) | 928 (63.8) | 1096 (70.1) | <0.001 |
Diabetes | 690 (22.9) | 303 (20.8) | 387 (24.7) | 0.001 |
Cigarette smoker, current | 624 (20.7) | 583 (40.2) | 41 (2.6) | <0.001 |
Alcohol consumption, ever | 52 (1.7) | 45 (3.1) | 7 (0.5) | <0.001 |
Myocardial infarction | 190 (6.3) | 131 (9.0) | 59 (3.8) | <0.001 |
Stroke | 70 (2.3) | 33 (2.3) | 37 (2.4) | 0.60 |
Income ≤USD2040 | 2908 (96.3) | 1380 (94.9) | 1528 (97.7) | <0.001 |
House 1–2 room public flat | 430 (14.3) | 177 (12.2) | 253 (16.2) | <0.001 |
Table 2. Relationship of Cardiovascular Risk Factors with Retinal Arteriolar and Venular Caliber
Table 2. Relationship of Cardiovascular Risk Factors with Retinal Arteriolar and Venular Caliber
| Arterial Caliber (CRAE) | | | Venular Caliber (CRVE) | | |
| n | M (SE), μm* | P , † | n | M (SE), μm* | P , † |
Age | | | | | | |
40–49 y | 805 | 141.0 (0.55) | 0.001 | 805 | 223.0 (0.78) | <0.001 |
50–59 y | 939 | 139.4 (0.51) | | 939 | 219.9 (0.72) | |
60–69 y | 723 | 139.0 (0.58) | | 723 | 217.3 (0.82) | |
70–80 y | 552 | 138.3 (0.66) | | 552 | 215.4 (0.94) | |
Sex | | | | | | |
Male | 1455 | 137.5 (0.41) | <0.001 | 1455 | 219.40 (0.58) | 0.78 |
Female | 1564 | 141.4 (0.39) | | 1564 | 219.16 (0.56) | |
Hypertension | | | | | | |
Absent | 994 | 144.4 (0.52) | <0.001 | 994 | 220.0 (0.75) | 0.29 |
Present | 2024 | 137.2 (0.35) | | 2024 | 218.9 (0.51) | |
Income (monthly) | | | | | | |
≤USD2040 | 2908 | 139.6 (0.29) | 0.12 | 2908 | 219.5 (0.41) | 0.01 |
>USD2040 | 111 | 137.2 (1.50) | | 111 | 214.0 (2.12) | |
Systolic BP (mm Hg) | | | | | | |
1st quartile, <129.0 | 756 | 144.82 (0.58) | <0.001 | 756 | 219.73 (0.84) | 0.28 |
2nd quartile, 129.0–143.5 | 764 | 140.93 (0.55) | | 764 | 220.07 (0.80) | |
3rd quartile, 144.0–161.5 | 759 | 136.84 (0.55) | | 759 | 218.23 (0.80) | |
4th quartile, >161.5 | 736 | 135.46 (0.59) | | 736 | 218.97 (0.85) | |
Diastolic BP (mm Hg) | | | | | | |
1st quartile, <71.5 | 797 | 144.05 (0.54) | <0.001 | 797 | 220.19 (0.79) | 0.32 |
2nd quartile, 71.5–78.0 | 723 | 141.35 (0.56) | | 723 | 218.81 (0.82) | |
3rd quartile, 78.5–86.5 | 757 | 138.19 (0.55) | | 757 | 218.94 (0.80) | |
4th quartile, >86.5 | 738 | 134.28 (0.56) | | 738 | 218.99 (0.81) | |
Diabetes | | | | | | |
Absent | 2329 | 139.1 (0.32) | 0.004 | 2329 | 218.7 (0.45) | 0.01 |
Present | 690 | 141.1 (0.60) | | 690 | 221.1 (0.85) | |
Serum glucose (mg/dL) | | | | | | |
1st quartile, <4.8 | 802 | 140.0 (0.55) | 0.74 | 802 | 217.6 (0.78) | 0.005 |
2nd quartile, 4.8–5.4 | 667 | 138.9 (0.60) | | 667 | 219.9 (0.85) | |
3rd quartile, 5.4–7.1 | 729 | 138.2 (0.58) | | 729 | 218.1 (0.81) | |
4th quartile, ≥7.1 | 704 | 140.6 (0.59) | | 704 | 221.5 (0.83) | |
HbA1c (%) | | | | | | |
1st quartile, <5.7 | 845 | 140.6 (0.54) | 0.43 | 845 | 217.7 (0.76) | 0.001 |
2nd quartile, 5.7–5.9 | 644 | 139.2 (0.61) | | 644 | 219.1 (0.86) | |
3rd quartile, 6.0–6.6 | 754 | 137.7 (0.57) | | 754 | 218.0 (0.80) | |
4th quartile, >6.6 | 707 | 140.6 (0.59) | | 707 | 222.3 (0.83) | |
Cigarette smoking | | | | | | |
Past/never | 2387 | 138.9 (0.33) | <0.001 | 2387 | 218.1 (0.46) | <0.001 |
Current | 624 | 142.1 (0.70) | | 624 | 223.7 (0.99) | |
BMI (kg/m2) | | | | | | |
1st quartile, <23.1 | 751 | 140.9 (0.57) | <0.001 | 751 | 216.9 (0.80) | 0.009 |
2nd quartile, 23.1–26.0 | 750 | 140.3 (0.57) | | 750 | 220.8 (0.80) | |
3rd quartile, 26.0–29.4 | 750 | 139.3 (0.57) | | 750 | 218.7 (0.80) | |
4th quartile, ≥29.4 | 750 | 137.8 (0.58) | | 750 | 220.8 (0.82) | |
Total cholesterol, mg/dL | | | | | | |
1st quartile, <4.9 | 746 | 142.1 (0.57) | 0.002 | 746 | 218.7 (0.81) | 0.12 |
2nd quartile, 4.9–5.5 | 742 | 139.3 (0.57) | | 742 | 218.2 (0.81) | |
3rd quartile, 5.5–6.3 | 738 | 139.0 (0.57) | | 738 | 219.7 (0.81) | |
4th quartile, ≥6.3 | 738 | 138.6 (0.57) | | 738 | 220.1 (0.81) | |
HDL cholesterol, mg/dL | | | | | | |
1st quartile, <1.1 | 743 | 139.6 (0.59) | 0.77 | 743 | 222.2 (0.83) | <0.001 |
2nd quartile, 1.1–1.3 | 746 | 139.3 (0.57) | | 746 | 219.5 (0.81) | |
3rd quartile, 1.3–1.5 | 736 | 140.2 (0.57) | | 736 | 218.9 (0.81) | |
4th quartile, ≥1.5 | 738 | 139.1 (0.59) | | 738 | 215.9 (0.81) | |
Serum creatinine, mg/dL | | | | | | |
1st quartile, <4.8 | 741 | 140.8 (0.64) | 0.005 | 741 | 219.1 (0.91) | 0.66 |
2nd quartile, 4.8–5.4 | 730 | 139.9 (0.58) | | 730 | 220.2 (0.83) | |
3rd quartile, 5.4–7.1 | 741 | 138.8 (0.60) | | 741 | 218.8 (0.85) | |
4th quartile, ≥7.1 | 691 | 138.2 (0.64) | | 691 | 218.8 (0.91) | |
Table 3. Relationship of Cardiovascular Risk Factors for Retinal Arteriolar And Venular Caliber, Model 1
Table 3. Relationship of Cardiovascular Risk Factors for Retinal Arteriolar And Venular Caliber, Model 1
| Unit Change | Arteriolar Caliber (CRAE)* | | | Venular Caliber (CRVE), † | | |
| | β (SE) | P | R 2 | β (SE) | P | R 2 |
Risk Factors | | | | | | | |
Age | Per 10-y increase | 0.15 (0.29) | 0.60 | 0.005 | −2.86 (0.42) | <0.001 | 0.017 |
Sex | Male vs. female | −4.80 (0.63) | <0.001 | 0.015 | — | — | — |
MABP | Per SD increase (14 mm Hg) | −4.01 (0.28) | <0.001 | 0.063 | — | — | — |
Cigarette smoking | Current vs. never/past | 2.89 (0.78) | <0.001 | 0.004 | 4.00 (1.05) | <0.001 | 0.005 |
BMI | Per SD increase (5.1 kg/m2) | — | — | — | 1.23 (0.42) | 0.003 | 0.004 |
HbA1c level | Per SD increase (1.5%) | — | — | — | 1.41 (0.41) | 0.001 | 0.005 |
HDL cholesterol | Per SD increase (0.33 mg/dL) | — | — | — | −1.71 (0.42) | <0.001 | 0.009 |
Income | <USD2040 vs. ≥USD2040 | | | | 5.58 (2.13) | 0.009 | 0.002 |
Total R 2 | | | | 0.087 | | | 0.042 |
Table 4. Interaction between Age and Sex with MABP in Relation to Retinal Arterial Caliber (CRAE)
Table 4. Interaction between Age and Sex with MABP in Relation to Retinal Arterial Caliber (CRAE)
| n | <100* (n = 1505) | 100–109* (n = 686) | 110–119* (n = 492) | >120* (n = 336) | P Trend, † | β (SE) for MABP, † | |
Age group | | | | | | | | |
40–49 | 805 | 143.78 (0.61) | 138.55 (1.12) | 134.11 (1.44) | 126.98 (2.06) | <0.001 | −5.26 (0.54) | Age–MABP interaction P < 0.001 |
50–59 | 939 | 143.79 (0.69) | 137.10 (0.95) | 134.57 (1.26) | 131.00 (1.46) | <0.001 | −4.44 (0.47) | |
60–69 | 723 | 142.48 (0.86) | 138.41 (1.21) | 137.68 (1.21) | 132.38 (1.50) | <0.001 | −3.04 (0.51) | |
70–80 | 552 | 140.59 (1.23) | 137.15 (1.47) | 136.74 (1.68) | 137.32 (1.87) | 0.12 | −1.27 (0.67) | |
| P trend, † | 0.03 | 0.59 | 0.05 | <0.001 | | | |
| β (SE) for increasing age, † | −0.79 (0.37) | −0.31 (0.57) | 1.28 (0.66) | 3.14 (0.91) | | | |
Sex | | | | | | | | |
Male | 1455 | 140.78 (0.61) | 135.73 (0.88) | 132.22 (0.99) | 127.36 (1.35) | <0.001 | −4.35 (0.39) | Sex–MABP interaction P < 0.001 |
Female | 1564 | 144.88 (0.56) | 139.91 (0.89) | 138.80 (1.03) | 134.89 (1.35) | <0.001 | −2.83 (0.37) | |
| P trend, † | <0.001 | 0.001 | <0.001 | 0.001 | | | |
| β (SE) for males vs. females, † | −4.10 (0.89) | −4.19 (1.31) | −6.58 (1.47) | −7.53 (2.01) | | | |
The authors thank the staff and participants in the SiMES study for their important contributions.
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