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. 2015 Jul 6;94(1):e35–e42. doi: 10.1111/aos.12786

Ageing effect on flicker‐induced diameter changes in retinal microvessels of healthy individuals

Swathi Seshadri 1, Aniko Ekart 2, Doina Gherghel 1,✉
PMCID: PMC5034828  PMID: 26149453

Abstract

Purpose

To compare flicker‐induced retinal vessel diameter changes in varying age groups with low cardiovascular risk.

Methods

Retinal vascular reactivity to flicker light was assessed by means of dynamic retinal vessel analysis in 57 participants aged 19–30 years, 75 participants aged 31–50 years and 62 participants aged 51–70 years participants. Other assessments included carotid intima–media thickness (c‐IMT), augmentation index (AIx), blood pressure profiles, blood lipid metabolism markers and Framingham risk scores (FRS).

Results

Retinal arterial dilation amplitude (DA) and postflicker percentage constriction (MC%) were significantly decreased in the oldest group compared to the middle‐aged (p = 0.028; p = 0.021) and youngest group (p = 0.003; p = 0.026). The arterial constriction slope (SlopeAC) was also decreased in the oldest group compared to the youngest group (p = 0.027). On the venous side, MC% was decreased in the middle‐aged and oldest groups in comparison with the youngest group (p = 0.015; p = 0.010, respectively). Additionally, men exhibited increased arterial DA (p = 0.007), and percentage dilation (MD%, p < 0.001) in comparison with women, but only in the youngest age group. Both AIx and c‐IMT scores increased with age (both p < 0.001); however, no correlations were found between the observed differences in the measured retinal vascular function and systemic parameters.

Conclusion

In individuals with low cardiovascular risk, there are age‐related differences in flicker‐induced retinal vessel diameter changes throughout the entire functional response curve for arteries and veins. Gender differences mainly affect the arterial dilatory phase and are only present in young individuals.

Keywords: ageing, cardiovascular risk, dynamic retinal vessel analysis, retina, vascular function

Introduction

It is well known that the incidence and prevalence of cardiovascular disease (CVD) increases exponentially with age (McDermott 2007; Rosamond et al. 2008; Nichols et al. 2013). At present, the current identification of at‐risk individuals for primary prevention efforts relies on classical risk factors for CVD such as lipid profiles, smoking and hypertension (Grundy et al. 1999; Committee 2012; Goff et al. 2014). Although some of these variables increase with age, the predictive accuracy of traditional risk estimates that include the aforementioned variables, such as Framingham risk scores (FRS), the Prospective Cardiovascular Mὒnster (PROCAM) score and the European Society of Cardiology Systematic Coronary Risk Evaluation (SCORE) either over‐ or underestimates actual risk in a large number of individuals (Vasan 2006; Koenig 2007; Cohn 2013). Therefore, other measures, such as genetic, inflammatory and coagulation markers, and various tests for subclinical disease have been sought (Helfand et al. 2009; Wang 2011; Ge & Wang 2012). The quantification of vascular and endothelial dysfunction (Deanfield et al. 2007) is a recently emerging early marker for CVD and is usually achieved by employing techniques such as ultrasound flow‐mediated dilation (FMD), pulse wave analysis (PWA), plethysmography and iontophoresis (Ray et al. 2014). These tests can, however, be complex and time‐consuming, and are performed only in highly specialized services. Among the various methods developed to measure microvascular function, dynamic retinal vessel analysis (DVA) features as a noninvasive method that allows for continuous recordings of retinal arterial and venous diameter changes in response to flicker light stimulation. The main advantage of the DVA assessment is that it provides integrated and dynamic data analysis that is specific to each individual. In addition, its output has proven to be modified not only by overt disease but also in the presence of more subtle risk factors for CVD (Pemp et al. 2009; Reimann et al. 2009; Kotliar et al. 2011) including ethnicity (Patel et al. 2011) and impaired glucose tolerance (Patel et al. 2012). Therefore, it is possible to use the assessment of retinal microvascular function as an early marker for vascular and endothelial dysfunction.

Besides pathologies, however, normal ageing as assessed by DVA can also influence retinal microvascular dynamics. Indeed, published data allude to an age‐related decrease in retinal arterial response profiles (Kotliar et al. 2008), as well as to a general decline in overall vessel dilation amplitudes during flicker light stimulation (Nagel et al. 2004; Kneser et al. 2009; Gugleta et al. 2013). Nevertheless, a more complex analysis of the dynamics of both vasodilation and vasoconstriction responses, as well as of the capacity to re‐establish a preflicker diameter after the cessation of stress, is needed for a better understanding of healthy individual vascular dynamics. Therefore, this study, using a more detailed approach for the evaluation of retinal vascular function, seeks to characterize the entire retinal microvascular response to flicker provocation in individuals with low cardiovascular risk belonging to various age groups.

Methods

Study participants

Community‐dwelling volunteers (aged above 18 years) were recruited through local advertisements at the Vascular Research Laboratory and Health Clinics at Aston University (Birmingham, UK). Ethical approval for the study was received from the relevant local and institutional ethics committees. Written informed consent was received from all participants prior to study enrolment, and all study procedures were designed and conducted in accordance with the tenets of the Declaration of Helsinki.

Study exclusion criteria were defined as a history or current diagnosis of cardiovascular or cerebrovascular disease including coronary artery disease, heart failure, arrhythmia, stroke, transient ischaemic attacks, peripheral vascular disease, as well as, smoking, hypertension, diabetes, and/or severe dyslipidaemia (defined as plasma triglyceride levels above 6 mmol/l or cholesterol levels above 7 mmol/l). The use of vasoactive medications such as dietary supplements containing vitamins or antioxidants and bronchodilators also served as exclusion criteria. In addition, participants with elevated intraocular pressures (IOP > 21 mmHg), retinal disease, intraocular surgery, neuro‐ophthalmic disease, cataract or other media opacities that may affect the ocular vascular system or prevent retinal vascular examination were also excluded from the study.

General investigations

All study‐related measurements were performed between 8 and 11 AM following a 12‐hr overnight fast, which included refraining from alcohol or caffeine.

Standard anthropometric measures of height and weight were recorded to determine body mass index (BMI = weight/height2). Systolic blood pressure (SBP), diastolic blood pressure (DBP) and heart rate (HR) were measured using an automatic BP monitor (UA‐767; A&D Instruments Ltd, Oxford, UK) to determine mean arterial pressure (MAP = 2/3 DBP + 1/3 SBP). IOP readings were obtained using noncontact tonometry (Pulsair; Keeler Ltd, UK) to determine ocular perfusion pressure (OPP = 2/3 MAP – IOP).

In addition, blood and plasma samples drawn from the antecubital fossa vein were assessed immediately for fasting glucose (GLUC), triglycerides (TG), total cholesterol (CHOL) and high‐density lipoprotein cholesterol (HDL‐c) using the Reflotron Desktop Analyzer (Roche Diagnostics, UK). Low‐density lipoprotein cholesterol (LDL‐c) values were calculated as per the Friedewald equation (Friedewald et al. 1972).

Framingham risk score

The FRS is a widely used gender‐specific algorithm originally developed to estimate CVD risk (Wilson et al. 1987). In this study, FRS was calculated using the current version of the FRS published by an expert panel of the National Heart, Lung and Blood Institute (NHLBI) (2002)and is based on risk factors such as age, gender, CHOL, HDL‐c, SBP, treatment for hypertension, smoking status and diabetes. Risk factors such as age, treatment for hypertension, smoking status and diabetes were identified from self‐report questionnaires, and CHOL, HDL‐c and SBP values were as those determined on the day of study assessment. The scoring algorithm is based on gender‐specific points assigned for each risk factor variable that can be determined using FRS tables, that is point scores by age group; age group and total CHOL; age group and smoking status; HDL‐c level; SBP and treatment status. Ten‐year risk percentage is then calculated by total points (one point, 6%; two points, 8%; three points, 10%; four points, 12%; five points, 16%; six points, 20%; seven points, 25%; 10 points or more, >30%). Absolute CVD risk percentage over 10 years was classified as low risk (<10%), intermediate risk (10–20%) and high risk (>20%) (Ford et al. 2004).

Carotid intima–media thickness

Intima–media thickness measurements of the left and right common carotid arteries were obtained for all participants as described previously (Seshadri et al. 2015) and in accordance with an already published protocol (Salonen et al. 1991).

Pulse wave analysis

Arterial stiffness was assessed by PWA using the validated SphygmoCor device according to an already published protocol (O'Rourke et al. 2001) and as detailed previously (Mroczkowska et al. 2012). The augmentation index (AIx) value calculated by the device software was used as a measure of arterial stiffness (Wilkinson et al. 1998).

Dynamic retinal vessel analysis

Retinal vessel reactivity was measured with the dynamic retinal vessel analyser (IMEDOS GmbH, Jena, Germany) using a previously published protocol (Nagel et al. 2006). All measurements were performed in a quiet, temperature‐controlled room (22°C) following full pupil dilation with 1% tropicamide (Chauvin Pharmaceuticals Ltd, UK). For all participants, measurements were conducted in one unselected eye. A visual fixation target was used to control eye movements and to position the region of interest at the centre of the fundus image. Within this region, a segment approximately 0.5–1 mm and 1–2 disc diameters from the optic nerve head was selected for continual diameter recording, for both the inferior temporal retinal artery and retinal vein. The automated 350‐second flicker stimulation protocol included a 50‐second baseline diameter measurement (under still illumination 25 Hz) followed by three successive cycles of flicker stimulation (opto‐electronically generated at 12.5 Hz) distinguished as 20 seconds of stimulus interrupted by an 80‐second recovery period. The dynamic nature of the vessel response profile was further explored by extracting the raw response data and applying a statistical polynomial regression algorithm (MATLAB; Mathworks, MA, USA) (Mroczkowska et al. 2012). The following vessel reactivity and time‐course parameters were determined for each flicker cycle and then averaged over the three cycles, with the artery and vein regarded separately as follows: the average baseline diameter and range of maximum and minimum baseline vessel diameters (baseline diameter fluctuation, BDF); the maximum vessel dilation diameter during flicker stimulation expressed as a percentage change relative to baseline diameter (MD%) and the time taken in seconds to reach the maximum diameter (tMD); the maximum vessel constriction diameter during the postflicker recovery period expressed as a percentage change relative to baseline diameter (MC%) and the time taken in seconds to reach the maximum vessel constriction diameter (tMC); the overall dilation amplitude (DA) calculated as the difference between MD and MC; and the baseline‐corrected flicker response (BCFR) used to describe the overall dilation amplitude after normalizing for fluctuations in baseline diameters (DA‐BDF). In addition, the arterial (A) and venous (V) dilation slopes (SlopeAD/VD = (MD‐baseline diameter)/tMD) and constriction slopes (SlopeAC/VC = (MC‐MD)/tMC) were also calculated.

Statistical analysis

All data are reported as mean (SD) unless otherwise indicated. The Shapiro–Wilk test was used to determine the distribution of the data. Univariate associations were determined using Pearson's (normally distributed data) or Spearman's method (non‐normally distributed data), and forward stepwise regression analyses were performed to test the influences of BMI, BP, circulating markers, c‐IMT and AIx on the measured variables. Differences between groups were subsequently assessed using one‐way analysis of variance (anova) or analysis of covariance (ancova), followed by Tukey's post hoc analysis as appropriate. p‐values of <0.05 were considered significant, except in certain cases where a stricter p‐value of <0.01 was adopted to correct for multiple comparisons. All analyses were performed using the commercially available Statistica® software (Version 9; StatSoft Inc., Tulsa, OK, USA).

Results

A total of 236 volunteers were initially screened for study inclusion of which 42 individuals were excluded based on having moderate or high FRS (>10%). The remaining 194 participants with low FRS (<10%) were included in the final analysis and classified into one of three age groups (Group 1: 19–30 years; Group 2: 31–50 years; and Group 3: 51–70 years). The number of participants in each group was similar (Group 1: 57; Group 2; 75; Group 3; 62, chi‐square test: p = 0.295), as was the distribution of male (M) and female (F) participants within each group (Group 1: M = 27, F = 30; Group 2: M = 42, F = 33; Group 3: M = 33, F = 29, chi‐square test p = 0.612).

Clinical characteristics

Table 1 summarizes the clinical characteristics of the study population. There was a significant difference between groups in age (p < 0.001), BMI (p = 0.002), SBP (p = 0.002), DBP (p = 0.001), HR (p < 0.001), MAP (p = 0.001), IOP (p < 0.001), CHOL (p = 0.002), HDL‐c (p = 0.007), LDL‐c (p < 0.001), FRS (p < 0.001), c‐IMT scores (p < 0.001) and AIx (p < 0.001), but not in OPP (p = 0.089), GLUC (p = 0.102) or TG levels (p = 0.161). Post hoc comparisons revealed that FRS and c‐IMT scores significantly increased with age, with each group differing significantly from the other (all p < 0.001). In addition, in comparison with the youngest group, BMI, DBP, IOP and LDL‐c were higher in the middle‐aged (p = 0.023, p = 0.013, p = 0.019, and p = 0.006, respectively) and older (p = 0.002, p = 0.001, p < 0.001 and p = 0.001, respectively) groups. SBP and AIx were also higher in the oldest group compared to the youngest (p = 0.009 and p < 0.001, respectively) and middle‐aged groups (p = 0.003 and p = 0.010, respectively). Finally, with regard to HR, MAP, CHOL and HDL‐c, the middle‐aged group did not differ significantly from the youngest or oldest group (all p > 0.05); however, HR (p < 0.001) and HDL‐c (p = 0.009) were lower, and MAP (p < 0.001) and CHOL (p = 0.001) were higher in the oldest age group in comparison with the youngest group.

Table 1.

Summary of systemic characteristicsb

Characteristic Group (1) (19–30 years) Group (2) (31–50 years) Group (3) (51–70 years) p‐value Significant difference by group
No. of participants 57 75 62 0.295 –
Gender distribution 27M: 30F 42M: 33F 33M: 29F 0.612 –
Age (years) 26 (3) 40 (6) 56 (5) <0.001a 1 < 2 <  3
BMIc 24.11 (3.84) 26.00 (3.74) 26.69 (4.69) 0.002a 1 < 2, 3; 2 = 3
SBP (mmHg) 116 (13) 117 (12) 123 (13) 0.002a 1, 2 < 3; 1 = 2
DBP (mmHg) 71 (9) 76 (11) 77 (10) 0.001a 1 < 2, 3; 2 = 3
HR (bpm) 71 (11) 67 (8) 64 (8) <0.001a 1 > 3; 2 = 1, 3
MAPe 85.94 (9.33) 89.63 (10.67) 92.92 (10.26) 0.001a 1 < 3; 2 = 1, 3
IOP (mmHg) 13 (2) 14 (3) 15 (2) <0.001a 1 < 2, 3; 2 = 3
OPPd 44.69 (6.06) 45.97 (7.08) 47.44 (7.09) 0.089 –
GLUC (mmol/L) 4.80 (0.74) 4.92 (0.68) 5.09 (0.78) 0.102 –
TG (mmol/L) 1.04 (0.47) 1.22 (0.65) 1.18 (0.50) 0.161 –
CHOL (mmol/L) 4.18 (0.77) 4.49 (0.89) 4.75 (0.97) 0.002a 1 < 3; 2 = 1, 3
HDL‐c (mmol/L) 1.44 (0.50) 1.38 (0.41) 1.22 (0.38) 0.007a 1 > 3; 2 = 1, 3
LDL‐c (mmol/L) 2.25 (0.75) 2.71 (0.86) 2.82 (0.91) <0.001a 1 < 2, 3; 2 = 3
FRS % 0.74 (0.48) 3.41 (2.41) 8.25 (2.71) <0.001a 1 < 2 <  3
c‐IMT (mm) 0.46 (0.01) 0.56 (0.01) 0.63 (0.02) <0.001a 1 < 2 <  3
AIx 10 (9) 15 (12) 22 (12) <0.001a 1, 2 < 3; 1 = 2
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BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure; HR = heart rate; MAP = mean arterial pressure; IOP = intraocular pressure; OPP = ocular perfusion pressure; GLUC = glucose; TG = triglycerides; CHOL total cholesterol; HDL‐c = high‐density lipoprotein cholesterol; LDL‐c = low‐density lipoprotein cholesterol; FRS = Framingham risk score; c‐IMT = carotid intima–media thickness; AIx = augmentation index.

a

p < 0.05 was considered a significant difference.

b

Data are presented as mean (SD) unless otherwise indicated.

c

Calculated as weight in kilograms divided by height in metres squared.

d

Calculated as MAP = 2/3 DBP + 1/3 SBP.

e

Calculated as OPP = 2/3 MAP – IOP.

Retinal vessel diameter

Group differences in flicker‐induced retinal arterial and venous diameter changes (DVA) are summarized in Table 2. All reported values are based on data averaged across the three flicker cycles, with the artery and vein regarded separately.

Table 2.

Retinal arterial and venous vascular function parameters (DVA)

Parameter Mean (SD) p‐value Significant difference by group
Group (1) (19–30 years) Group (2) (31–50 years) Group (3) (51–70 years)
Arteries
Baseline 99.89 (0.76) 99.97 (0.20) 99.98 (0.14) 0.488 –
BDF 6.06 (3.29) 5.93 (2.59) 5.54 (2.78) 0.093 –
DAb 7.04 (3.61) 6.59 (2.72) 5.36 (2.36) 0.003a 1, 2 > 3; 1 = 2
BCFRc 1.05 (3.03) 0.90 (2.65) 0.03 (2.36) 0.083 –
MD% 4.38 (3.00) 4.09 (2.22) 3.82 (2.04) 0.036 –
MC% −2.67 (2.32) −2.41 (1.67) −1.37 (1.77) <0.001a 1, 2 > 3; 1 = 2
tMD (sec) 22 (9) 20 (8) 21 (7) 0.105 –
tMC (sec) 24 (9) 28 (9) 29 (8) 0.041 –
SlopeAD d 0.23 (0.15) 0.27 (0.16) 0.28 (0.41) 0.063 –
SlopeAC e −0.42 (0.35) −0.27 (0.57) −0.23 (0.20) <0.001a 1 > 3; 2 = 1, 3
Veins
Baseline 99.89 (0.76) 99.98 (0.13) 99.96 (0.20) 0.490 ‐
BDF 4.83 (2.78) 3.99 (1.63) 4.64 (2.82) 0.114 ‐
DAb 5.80 (3.33) 5.25 (2.53) 5.51 (2.78) 0.557 ‐
BCFRc 1.05 (2.67) 1.30 (2.29) 0.92 (2.55) 0.097 ‐
MD% 4.31 (2.19) 4.59 (2.43) 4.46 (2.74) 0.794 ‐
MC% −1.61 (1.70) −0.81 (1.10) −0.75 (1.16) 0.002a 1 > 2, 3; 2 = 3
tMD (sec) 23 (8) 21 (6) 22 (7) 0.129 ‐
tMC (sec) 28 (9) 30 (7) 29 (7) 0.390 ‐
SlopeVD d 0.23 (0.15) 0.25 (0.14) 0.26 (0.17) 0.391 ‐
SlopeVC e −0.25 (0.17) −0.19 (0.15) −0.22 (0.16) 0.087 ‐
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DVA = dynamic retinal vessel analysis; baseline, baseline diameter; BDF = baseline diameter fluctuation; DA = dilation amplitude; BCFR = baseline‐corrected flicker response; MD% = percentage change in diameter from baseline to maximum; MC% = percentage constriction below baseline; tMD = reaction time to maximum dilation diameter; tMC = reaction time to maximum constriction diameter from maximum dilation diameter; SlopeAD/VD = slope of arterial/venous dilation; SlopeAC/VC = slope of arterial/venous constriction. Unless otherwise indicated, all values are expressed in arbitrary units, which approximately correspond to micrometres (μ m) in a normal Gullstrand eye.

a

p < 0.01 was considered a significant difference.

b

Calculated as MD – MC.

c

Calculated as DA – BDF (Nagel et al. 2004).

d

Calculated as (MD – baseline)/tMD (Mroczkowska et al. 2012).

e

Calculated as (MC – MD)/tMC (Mroczkowska et al. 2012).

Arterial response

After controlling for influential covariates identified in multivariate analysis, there were no significant group differences in baseline diameter, BDF, BCFR, MD%, tMD, tMC and SlopeAD (all ancova p > 0.01, Table 2). There were, however, significant group differences in arterial DA (p = 0.003), MC% (p < 0.001) and SlopeAC (p < 0.001) (Table 2). Post hoc comparisons showed DA and MC% to be significantly decreased in the oldest age group compared to the middle‐aged (p = 0.028 and p = 0.021, respectively) and youngest groups (p = 0.003 and p = 0.026, respectively). Additionally, SlopeAC was decreased in the oldest age group compared to the youngest group (p = 0.027), with the middle‐aged group not differing significantly from the youngest (p = 0.525) or oldest groups (p = 0.216) (Fig. 1A).

Figure 1.

Figure 1

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Comparison of retinal (A) arterial and (B) venous response profiles across age groups. AU, arbitrary units; MD, maximum dilation diameter during flicker; DA, dilation amplitude (MD‐MC), MC, maximum constriction diameter postflicker; MC%, percentage constriction below baseline; SlopeAC, arterial constriction slope (MC‐MD/time taken to reach MC).

Venous response

There was an overall significant difference in venous MC% across groups (anova p = 0.002) with post hoc comparisons showing MC% to be similarly decreased in the middle‐aged (p = 0.015) and older (p = 0.010) groups in comparison with the youngest group (Fig. 1B). After controlling for influential covariates, no significant group differences in any of the other measured venous DVA parameters were identified (ancova, all p > 0.05, Table 2).

Gender comparisons

Arterial MD% was significantly higher in men compared to women with regard to the study population as a whole (M: 4.42 ± 2.51 versus F: 3.84 ± 2.27, p = 0.011, Fig. 2). Within‐group gender comparisons in the measured retinal arterial DVA parameters are displayed in Table 3. Arterial DA (p = 0.007) and MD% (p < 0.001) were significantly higher in men compared to women belonging to the youngest age group (Fig. 3, Table 3) but not between men and women in the middle‐aged and oldest groups (all p > 0.01). There were no significant differences between men and women in any of the other measured arterial DVA parameters for the study population or within groups (all p > 0.01, Table 3). There were also no significant gender differences in any of the measured venous DVA parameters (all p > 0.01, data not shown).

Figure 2.

Figure 2

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Comparison of the retinal arterial response profile between men and women in the study population. AU, arbitrary units; MD, maximum dilation diameter; MD%, percentage change in diameter from baseline to maximum during flicker; MC, maximum constriction diameter postflicker.

Table 3.

Group comparisons of retinal arterial vascular function parameters (DVA)

Parameterb Mean (SD)
Group 1 (19–30 years) Group 2 (31–50 years) Group 3 (51–70 years)
M F p M F p M F p
Baseline 99.79 (1.07) 100.00 (0.01) 0.288 99.99 (0.07) 99.95 (0.27) 0.414 99.97 (0.19) 100.00 (0.01) 0.306
BDF 6.95 (3.50) 5.36 (2.77) 0.059 5.99 (2.40) 5.92 (2.48) 0.895 5.05 (1.57) 6.02 (3.09) 0.137
DA 8.21 (4.01) 5.72 (2.75) 0.007a 6.38 (2.69) 7.01 (2.82) 0.298 5.53 (2.27) 5.43 (2.64) 0.872
BCFR 1.26 (3.44) 0.48 (2.64) 0.336 0.46 (2.94) 1.09 (2.16) 0.277 0.50 (2.15) −0.37 (2.55) 0.120
MD% 5.85 (3.29) 3.06 (1.80) <0.001a 3.93 (2.08) 4.61 (2.53) 0.175 3.97 (1.96) 3.71 (2.14) 0.592
MC% −2.37 (2.82) −2.66 (1.88) 0.654 −2.45 (1.55) −2.41 (1.68) 0.895 −1.56 (1.72) −1.72 (1.73) 0.687
tMD (sec) 23 (10) 21 (7) 0.222 19 (7) 22 (8) 0.080 20 (7) 21 (8) 0.316
tMC (sec) 23 (10) 26 (8) 0.148 29 (7) 25 (9) 0.027 29 (8) 27 (8) 0.232
SlopeAD 0.29 (0.18) 0.20 (0.17) 0.071 0.27 (0.16) 0.27 (0.17) 0.991 0.28 (0.31) 0.31 (0.49) 0.757
SlopeAC −0.50 (0.28) −0.35 (0.37) 0.081 −0.26 (0.13) −0.50 (0.72) 0.022 −0.24 (0.14) −0.29 (0.19) 0.289
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DVA = dynamic retinal vessel analysis; M = men; F = women; Baseline = baseline diameter; BDF = baseline diameter fluctuation; DA = dilation amplitude; BCFR = baseline‐corrected flicker response; MD% = percentage change in diameter from baseline to maximum; MC% = percentage constriction below baseline; tMD = reaction time to maximum dilation diameter; tMC = reaction time to maximum constriction diameter from maximum dilation diameter; SlopeAD/VD = slope of arterial/venous dilation; SlopeAC/VC = slope of arterial/venous constriction. Unless otherwise indicated, all values are expressed in arbitrary units, which approximately correspond to micrometres (μ m) in a normal Gullstrand eye.

a

p < 0.01 was considered a significant difference.

b

See Table 2 footnotes b through e for calculations of DA, BCFR, SlopeAD and SlopeAC.

Figure 3.

Figure 3

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Comparison of the retinal arterial response profile between men and women in the youngest age group (19–30 years). AU, arbitrary units; MD%, percentage change in diameter from baseline to maximum during flicker; DA, dilation amplitude; MD, maximum dilation diameter during flicker; MC, maximum constriction diameter postflicker.

Discussion

In the present study, we used a specific computational model to evaluate the entire dynamic response of retinal microvessels after flicker stimulation in a sample of individuals with low CVD risk belonging to various age groups. Our results show that independent of systemic influences, older healthy individuals displayed abnormal dilatory and constrictory responses to flicker light stimulation in retinal arteries and veins. Additionally, in younger individuals, gender had an influence on retinal arterial dilation. This effect was, however, lost in the middle‐aged and oldest groups.

It is known that decreased vessel distensibility and focal narrowing occur in ageing vessels independently of other arteriosclerotic risk factors such as hypertension (Van Bortel & Spek 1998; Hubbard et al. 1999; Wong et al. 2003). Despite various adaptations to vascular structural remodelling and changes in viscoelastic properties that occur with ageing, there could still be individual limitations in functional vascular reserves that may only be evident as responses to provocative stressors. In line with previous research (Nagel et al. 2004; Kneser et al. 2009), this study shows an age‐related decline in retinal vasoregulative capacity in both dilatory and constrictory phases that was independent of any systemic influences. It was previously hypothesized that in ageing vessels, this vascular adaptive response may be attributed to a resetting of vessels’ average working points within which the points of maximum dilation and constriction tend to occur (Kneser et al. 2009). The cause of this shift in vessel behaviour remains unclear; however, a possible contender is a high level of oxidative stress that occurs with ageing and is a known cause of senescent endothelial dysfunction (Heo et al. 2011). Indeed, we have already demonstrated that in otherwise healthy individuals with low to moderate cardiovascular risk, retinal microvascular dilation and constriction responses to stress levels are influenced by systemic antioxidant capacity (Seshadri et al. 2015). Although the levels of antioxidant molecules were not determined in this study, it can be hypothesized that similar interactions take place in individuals with similar CVD risk.

Other factors such as age‐related vascular stiffness can also be involved. An understanding of microcirculatory responses with regard to systemic haemodynamic parameters is important as the combination of age‐related arterial stiffening and ensuing hypertension (O'Rourke 2007) can offset the stiffness gradient between the heart and periphery and augment pressure pulsatility penetrating into the microvasculature. Indeed, in our study groups, the AIx, a measure of peripheral arterial stiffness, was higher in the older group than in middle‐aged or younger individuals. In normal microvessels, low resistance protects them from intense pulsations and flow fluctuations; nevertheless, it is well known that this balance can be modified by age‐related stiffening of the vascular wall, resulting in microcirculatory damage (Barodka et al. 2011). Although the age‐related decline in the retinal vessels’ dilation and constriction phases demonstrated in our study occurred independently of systemic factors, it cannot, however, be excluded that local microvascular stiffness played some role in our findings.

In previous studies, we have already documented that individuals with various levels of cardiovascular risk exhibit abnormal retinal and venous responses to flicker‐induced provocation (Mroczkowska et al. 2012, 2013; Patel et al. 2012; Qin et al. 2014). In the present study, in a sample with low CVD risk, decreases in the postflicker constrictory phase of retinal veins were apparent in the middle‐aged and older individuals only. The role of the venular circulation in CVD has previously received limited attention until unexpected associations implicated retinal venular dilation, rather than arteriolar narrowing, as a stronger predictor of adverse vascular phenomena (Wong et al. 2006; Wang et al. 2007; Wong & Mitchell 2007). These studies have since stimulated an increased interest in retinal venular physiology although it remains unclear whether venous changes detected by the DVA represent separate causal pathways of endothelial dysfunction, or are epiphenomena of the arterial response. In such a context, it could be possible that the observed decrease in postflicker venous diameters returns reflects a compensatory adaptation following sustained arterial dilation during flicker. Further investigation is required to understand the relevance of an impaired venous constriction postflicker; however, it could be hypothesized that a change in venous calibre associated with either structural or endothelial irregularities could also be used as a marker for ageing and associative cardiovascular risk.

The dynamic behaviour of retinal microvessels also appears to be affected by more than just the ageing functional state of the endothelium. In the present study, gender differences in the retinal vasoregulative response were lost with ageing. Sex hormones influence both vascular tone and blood flow in various organs and tissues, including retinal vessels (Ogueta et al. 1999). Taking into consideration the above, gender differences in vascular tonus and blood flow are to be expected due to changes in hormonal status across the lifespan of individuals. Indeed, in the present study, we have observed an overall gender difference in arterial MD%, however, with younger men exhibiting higher MD% values than age‐matched women. To our knowledge, this is the first study to observe gender differences in DVA measurements in healthy individuals, and as oestrogens upregulate NO production and suppress the effect of vasoconstrictors such as endothelin‐1 (Kauser & Rubanyi 1997), our results are somewhat unexpected. As the retinal vascular response to flickering light is also a neurovascular coupling driven response (Riva et al. 2005) and sex hormones can exert effects on other cells in the neurovascular unit such as neurons and astrocytes (Yang et al. 2005), it is possible that the gender differences in flicker‐induced retinal diameter changes do not truly match those assessed by other methods that measure resting blood flow and vascular tone. Nevertheless, the expected ageing‐related blunt with regard to gender differences in vascular reactivity was still apparent in our results. More work is, however, necessary to clarify the mechanism of gender differences in flicker‐induced retinal diameter changes as measured by DVA.

Conclusion

In conclusion, this study demonstrates that age and gender are variables to be considered when assessing the retinal vascular response to flicker stimulation. In addition, the entire retinal microvascular response to flicker provocation, as well as the age and gender of each individual, should be considered when assessing possible pathological changes associated with vascular disease by means of the DVA.

*The copyright line was changed on 19 August 2015 after original publication.

References

  1. Barodka VM, Joshi BL, Berkowitz DE, Hogue CW Jr & Nyhan D (2011): Review article: implications of vascular aging. Anest Analg 112: 1048–1060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Cohn JN (2013): Identifying the risk and preventing the consequences of cardiovascular disease. Heart Lung Circ 22: 512–516. [DOI] [PubMed] [Google Scholar]
  3. Committee UNS (2012): The handbook for vascular risk assessment, risk reduction and risk management. Leicester, UK: University of Leicester. [Google Scholar]
  4. Deanfield JE, Halcox JP & Rabelink TJ (2007): Endothelial function and dysfunction: testing and clinical relevance. Circulation 115: 1285–1295. [DOI] [PubMed] [Google Scholar]
  5. Ford ES, Giles WH & Mokdad AH (2004): The distribution of 10‐Year risk for coronary heart disease among US adults: findings from the National Health and Nutrition Examination Survey III. J Am Coll Cardiol 43: 1791–1796. [DOI] [PubMed] [Google Scholar]
  6. Friedewald WT, Levy RI & Fredrickson DS (1972): Estimation of the concentration of low‐density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18: 499–502. [PubMed] [Google Scholar]
  7. Ge Y & Wang TJ (2012): Identifying novel biomarkers for cardiovascular disease risk prediction. J Intern Med 272: 430–439. [DOI] [PubMed] [Google Scholar]
  8. Goff DC Jr, Lloyd‐Jones DM, Bennett G et al. (2014): 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 129: S49–S73. [DOI] [PubMed] [Google Scholar]
  9. Grundy SM, Pasternak R, Greenland P, Smith S Jr & Fuster V (1999): Assessment of cardiovascular risk by use of multiple‐risk‐factor assessment equations: a statement for healthcare professionals from the American Heart Association and the American College of Cardiology. Circulation 100: 1481–1492. [DOI] [PubMed] [Google Scholar]
  10. Gugleta K, Turksever C, Polunina A & Orgul S (2013): Effect of ageing on the retinal vascular responsiveness to flicker light in glaucoma patients and in ocular hypertension. Br J Ophthalmol 97: 848–851. [DOI] [PubMed] [Google Scholar]
  11. Helfand M, Buckley DI, Freeman M, Fu R, Rogers K, Fleming C & Humphrey LL (2009): Emerging risk factors for coronary heart disease: a summary of systematic reviews conducted for the U.S. Preventive Services Task Force. Ann Intern Med 151: 496–507. [DOI] [PubMed] [Google Scholar]
  12. Heo KS, Fujiwara K & Abe J (2011): Disturbed‐flow‐mediated vascular reactive oxygen species induce endothelial dysfunction. Circ J 75: 2722–2730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hubbard LD, Brothers RJ, King WN et al. (1999): Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. Ophthalmology 106: 2269–2280. [DOI] [PubMed] [Google Scholar]
  14. Kauser K & Rubanyi GM (1997): Potential cellular signaling mechanisms mediating upregulation of endothelial nitric oxide production by estrogen. J Vasc Res 34: 229–236. [DOI] [PubMed] [Google Scholar]
  15. Kneser M, Kohlmann T, Pokorny J & Tost F (2009): Age related decline of microvascular regulation measured in healthy individuals by retinal dynamic vessel analysis. Med Sci Monit 15: CR436–CR441. [PubMed] [Google Scholar]
  16. Koenig W (2007): Cardiovascular biomarkers: added value with an integrated approach? Circulation 116: 3–5. [DOI] [PubMed] [Google Scholar]
  17. Kotliar KE, Mucke B, Vilser W, Schilling R & Lanzl IM (2008): Effect of aging on retinal artery blood column diameter measured along the vessel axis. Invest Ophthalmol Vis Sci 49: 2094–2102. [DOI] [PubMed] [Google Scholar]
  18. Kotliar KE, Lanzl IM, Schmidt‐Trucksass A, Sitnikova D, Ali M, Blume K, Halle M & Hanssen H (2011): Dynamic retinal vessel response to flicker in obesity: a methodological approach. Microvasc Res 81: 123–128. [DOI] [PubMed] [Google Scholar]
  19. McDermott MM (2007): The international pandemic of chronic cardiovascular disease. JAMA: J Am Med Assoc 297: 1253–1255. [DOI] [PubMed] [Google Scholar]
  20. Mroczkowska S, Ekart A, Suung V et al. (2012): Coexistence of macro‐ and micro‐vascular abnormalities in newly diagnosed normal tension glaucoma patients. Acta Ophthalmol 90: e553–e559. [DOI] [PubMed] [Google Scholar]
  21. Mroczkowska S, Benavente‐Perez A, Negi A, Sung V, Patel SR & Gherghel D (2013): Primary open‐angle glaucoma vs normal‐tension glaucoma: the vascular perspective. JAMA Ophthalmol 131: 36–43. [DOI] [PubMed] [Google Scholar]
  22. Nagel E, Vilser W & Lanzl I (2004): Age, blood pressure, and vessel diameter as factors influencing the arterial retinal flicker response. Invest Ophthalmol Vis Sci 45: 1486–1492. [DOI] [PubMed] [Google Scholar]
  23. Nagel E, Vilser W, Fink A & Riemer T (2006): Variance of retinal vessel diameter response to flicker light. A methodical clinical study. Der Ophthalmologe: Zeitschrift der Deutschen Ophthalmologischen Gesellschaft 103: 114–119. [DOI] [PubMed] [Google Scholar]
  24. National Cholesterol Education Program Expert Panel on Detection E & A Treatment of High Blood Cholesterol in (2002): Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 106: 3143–3421. [PubMed] [Google Scholar]
  25. Nichols M, Townsend N, Scarborough P & Rayner M (2013): European Cardiovascular Disease Statistics 4th edition 2012: EuroHeart II. Eur Heart J 34: 3007. [DOI] [PubMed] [Google Scholar]
  26. Ogueta SB, Schwartz SD, Yamashita CK & Farber DB (1999): Estrogen receptor in the human eye: influence of gender and age on gene expression. Invest Ophthalmol Vis Sci 40: 1906–1911. [PubMed] [Google Scholar]
  27. O'Rourke MF (2007): Arterial aging: pathophysiological principles. Vasc Med 12: 329–341. [DOI] [PubMed] [Google Scholar]
  28. O'Rourke MF, Pauca A & Jiang XJ (2001): Pulse wave analysis. Br J Clin Pharmacol 51: 507–522. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Patel SR, Bellary S, Qin L, Gill PS, Taheri S, Heitmar R, Gibson JM & Gherghel D (2011): Abnormal retinal vascular function and lipid levels in a sample of healthy UK South Asians. Br J Ophthalmol 95: 1573–1576. [DOI] [PubMed] [Google Scholar]
  30. Patel SR, Bellary S, Qin L, Balanos GM, McIntyre D & Gherghel D (2012): Abnormal retinal vascular reactivity in individuals with impaired glucose tolerance: a preliminary study. Invest Ophthalmol Vis Sci 53: 5102–5108. [DOI] [PubMed] [Google Scholar]
  31. Pemp B, Weigert G, Karl K, Petzl U, Wolzt M, Schmetterer L & Garhofer G (2009): Correlation of flicker‐induced and flow‐mediated vasodilatation in patients with endothelial dysfunction and healthy volunteers. Diabetes Care 32: 1536–1541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Qin L, Mroczkowska SA, Ekart A, Patel SR, Gibson JM & Gherghel D (2014): Patients with early age‐related macular degeneration exhibit signs of macro‐ and micro‐vascular disease and abnormal blood glutathione levels. Graefes Arch Clin Exp Ophthalmol 252: 23–30. [DOI] [PubMed] [Google Scholar]
  33. Ray S, Miglio C, Eden T & Del RD (2014): Assessment of vascular and endothelial dysfunction in nutritional studies. Nutr Metab Cardiovasc Dis 24: 940–946. [DOI] [PubMed] [Google Scholar]
  34. Reimann M, Prieur S, Lippold B, Bornstein SR, Reichmann H, Julius U & Ziemssen T (2009): Retinal vessel analysis in hypercholesterolemic patients before and after LDL apheresis. Atheroscler Suppl 10: 39–43. [DOI] [PubMed] [Google Scholar]
  35. Riva CE, Logean E & Falsini B (2005): Visually evoked hemodynamical response and assessment of neurovascular coupling in the optic nerve and retina. Prog Retin Eye Res 24: 183–215. [DOI] [PubMed] [Google Scholar]
  36. Rosamond W, Flegal K, Furie K et al. (2008): Heart disease and stroke statistics‐2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 117: e25–e146. [DOI] [PubMed] [Google Scholar]
  37. Salonen R, Haapanen A & Salonen JT (1991): Measurement of intima‐media thickness of common carotid arteries with high‐resolution B‐mode ultrasonography: inter‐ and intra‐observer variability. Ultrasound Med Biol 17: 225–230. [DOI] [PubMed] [Google Scholar]
  38. Seshadri S, Mroczkowska S, Qin L, Patel S, Ekart A & Gherghel D (2015): Systemic circulatory influences on retinal microvascular function in middle‐age individuals with low to moderate cardiovascular risk. Acta Ophthalmol 93: e266–e274. [DOI] [PubMed] [Google Scholar]
  39. Van Bortel LM & Spek JJ (1998): Influence of aging on arterial compliance. J Hum Hypertens 12: 583–586. [DOI] [PubMed] [Google Scholar]
  40. Vasan RS (2006): Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation 113: 2335–2362. [DOI] [PubMed] [Google Scholar]
  41. Wang TJ (2011): Assessing the role of circulating, genetic, and imaging biomarkers in cardiovascular risk prediction. Circulation 123: 551–565. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Wang JJ, Liew G, Klein R et al. (2007): Retinal vessel diameter and cardiovascular mortality: pooled data analysis from two older populations. Eur Heart J 28: 1984–1992. [DOI] [PubMed] [Google Scholar]
  43. Wilkinson IB, Fuchs SA, Jansen IM, Spratt JC, Murray GD, Cockcroft JR & Webb DJ (1998): Reproducibility of pulse wave velocity and augmentation index measured by pulse wave analysis. J Hypertens 16: 2079–2084. [DOI] [PubMed] [Google Scholar]
  44. Wilson PW, Castelli WP & Kannel WB (1987): Coronary risk prediction in adults (the Framingham Heart Study). Am J Cardiol 59: 91G–94G. [DOI] [PubMed] [Google Scholar]
  45. Wong TY & Mitchell P (2007): The eye in hypertension. Lancet 369: 425–435. [DOI] [PubMed] [Google Scholar]
  46. Wong TY, Klein R, Klein BE, Meuer SM & Hubbard LD (2003): Retinal vessel diameters and their associations with age and blood pressure. Invest Ophthalmol Vis Sci 44: 4644–4650. [DOI] [PubMed] [Google Scholar]
  47. Wong TY, Kamineni A, Klein R, Sharrett AR, Klein BE, Siscovick DS, Cushman M & Duncan BB (2006): Quantitative retinal venular caliber and risk of cardiovascular disease in older persons: the cardiovascular health study. Arch Intern Med 166: 2388–2394. [DOI] [PubMed] [Google Scholar]
  48. Yang SH, Liu R, Perez EJ, Wang X & Simpkins JW (2005): Estrogens as protectants of the neurovascular unit against ischemic stroke. Curr Drug Targets CNS Neurol Disord 4: 169–177. [DOI] [PubMed] [Google Scholar]

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