June 2011
Volume 52, Issue 7
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Clinical and Epidemiologic Research  |   June 2011
Association of Mean Ocular Perfusion Pressure and Diabetic Retinopathy in Type 2 Diabetes Mellitus: Sankara Nethralaya Diabetic Retinopathy Epidemiology and Molecular Genetic Study (SN-DREAMS, Report 28)
Author Affiliations & Notes
  • Rajiv Raman
    From the Shri Bhagwan Mahavir Vitreoretinal Services, Tamil Nadu, India; and
  • Aditi Gupta
    From the Shri Bhagwan Mahavir Vitreoretinal Services, Tamil Nadu, India; and
  • Vaitheeswaran Kulothungan
    the Department of Preventive Ophthalmology, Tamil Nadu, India.
  • Tarun Sharma
    From the Shri Bhagwan Mahavir Vitreoretinal Services, Tamil Nadu, India; and
  • Corresponding author: Tarun Sharma, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, College Road, Chennai 600 006, Tamil Nadu, India; drtaruns@gmail.com
Investigative Ophthalmology & Visual Science June 2011, Vol.52, 4592-4597. doi:10.1167/iovs.10-6903
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      Rajiv Raman, Aditi Gupta, Vaitheeswaran Kulothungan, Tarun Sharma; Association of Mean Ocular Perfusion Pressure and Diabetic Retinopathy in Type 2 Diabetes Mellitus: Sankara Nethralaya Diabetic Retinopathy Epidemiology and Molecular Genetic Study (SN-DREAMS, Report 28). Invest. Ophthalmol. Vis. Sci. 2011;52(7):4592-4597. doi: 10.1167/iovs.10-6903.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose.: To elucidate the distribution of mean ocular perfusion pressure (MOPP) and to study the relationship between MOPP and diabetic retinopathy (DR) in a south Indian subpopulation with diabetes.

Methods.: This study was a population-based, cross-sectional evaluation of 1368 subjects, aged ≥40 years, with type 2 diabetes. DR was diagnosed on the basis of the modified Klein classification. Systolic and diastolic blood pressure (SBP and DBP) were recorded with a mercury sphygmomanometer. Intraocular pressure (IOP) was assessed by applanation tonometry. MOPP was derived by the formula: MOPP = ⅔[DBP + ⅓(SBP − DBP)] − IOP.

Results.: The mean ± SD for MOPP was 52.6 ± 9.0 mm Hg, higher in the women than in the men (P = 0.046). In comparison to subjects without DR, MOPP was higher in the men with sight-threatening DR (STDR) (P = 0.030) and higher in women with any DR (P = 0.008) and non-STDR (P = 0.006). However, on multivariate analysis after adjustment for all factors, MOPP was found not to be associated with DR (OR = 1.02, 95% CI = 0.99–1.03; P = 0.149), non-STDR (OR = 1.02, 95% CI = 0.99–1.03; P = 0.312), or STDR (OR = 1.02, 95% CI = 0.98–1.05; P = 0.358).

Conclusions.: Univariate analysis revealed very small differences in the association of MOPP and DR in both sexes which are probably of no clinical significance. Multivariate analysis showed no association between MOPP and DR. There seems to be very little evidence of a link between MOPP and DR. It may be more informative to evaluate the association in longitudinal studies.

Diabetic retinopathy (DR) is one of the leading causes of blindness today. However, the causes of vascular pathology in this disease are not fully understood. 1 3 Although, chronic hyperglycemia initiates the processes, 4 the factors that link elevated glucose levels to vascular cell dysfunction, capillary dropout, tissue hypoxia, and abnormal angiogenesis, remain poorly described. 2,5,6 Dysfunctional retinal perfusion can explain few aspects of the pathophysiology of DR. 1 Subjects with diabetes have dysfunctional retinal perfusion. 1,2 Although these deficiencies may in part reflect responses to a primary event occurring in the retinal microvasculature, they may independently contribute to the development and progression of this disease. 1 The blood flow in any tissue is generated by perfusion pressure. The circumferential stress in a vessel is directly proportional to the perfusion pressure. 7 A higher perfusion pressure can increase the circumferential stress damage to the vessel wall, leading to a continuing propensity to dilatate with subsequent hyperperfusion. 8 At the same time, high perfusion pressure can reduce retinal perfusion by causing autoregulation of the retinal vasculature, 8 though the diabetic vascular system is known for its abnormal autoregulatory capacity. 9 In any case, both increased and decreased retinal blood flow are detrimental in the development of DR. 2 Moreover, higher perfusion pressure and the resultant stress changes can also increase the net pressure gradient from vessels to tissue, leading to more fluid leaving the retinal capillaries (Starling's forces) 10 and an increased risk of rupture (Laplace's law). 10 Since there is no lymphatic circulation to drain away this excess interstitial fluid, increased leakage will result in retinal edema and diabetic maculopathy and vessel rupture will cause hemorrhages and capillary dropout, manifesting as clinical DR. 11  
The MOPP is expressed as two thirds of the difference between the mean arterial pressure (MAP) and the IOP. Since MOPP is a potentially modifiable factor, knowing its relationship to diabetic retinopathy and maculopathy can be useful in preventing such diabetes complications. 
MOPP has been implicated in the development of DR. 8,12 15 However, the relationship between MOPP and DR, as studied in previous reports remains unclear. Some studies have shown that high ocular perfusion pressure is associated with the progression of DR. 8,12,13 and others have suggested that MOPP decreases as DR worsens. 14 Another study also reported higher MOPP with macular edema. 15  
The present study was conducted to elucidate the distribution of MOPP, the systemic factors associated with MOPP, and its relationship with DR, in a population-based sample of subjects with type 2 diabetes in south India. 
Methods
The details of the study design and methodology are described elsewhere. 16 The study was approved by the Institutional Review Board, and written informed consent was obtained from the subjects according to the Declaration of Helsinki. 17 The study population was selected by multistage systematic random sampling. The sampling was stratified based on socioeconomic criteria. In the first stage of the study, divisions were selected by using computer-generated random numbers. Study subjects were then selected randomly from each selected division. The sample size was calculated based on the assumption that the prevalence of DR in the general population above 40 years of age is 1.3%, as estimated in the Andhra Pradesh Eye Disease Study 18 ; with a relative precision of 25%, a dropout rate of 20%, and a design effect of 2. The estimated sample size was 5830. To meet the target, 600 individuals were enumerated from each of the 10 selected divisions. Of the 5999 subjects enumerated, 5784 (96.42%) responded for first fasting blood sugar estimation. Of 5784, 1816 were subjects with type 2 diabetes and were invited to visit the base hospital for comprehensive evaluation, including a second blood sugar estimation. Of these 1816 subjects, 1563 (85.60%) responded, including 1175 having previously diagnosed diabetes and 388 provisionally diagnosed as having diabetes. Again, 138 subjects were excluded because in two subjects, the age criterion was not met, and in 136, second fasting blood sugar was less than 110 mg/dL. An additional 11 individuals were excluded because their digital fundus photographs were of poor quality, making them ungradable for further analysis. Thus, a total of 1414 subjects with type 2 diabetes was available for the study. 
Thus, in present study, 1680 (28.2%) subjects of 5784 had type 2 diabetes. We have previously reported a temporal trend of increase in prevalence of diabetes in the general population between the NUDS (National Urban Diabetes Study) and our study; from 23.8% in 2000 to 28.2% in 2006. 19 Subjects with type 2 diabetes were identified according to American Diabetes Association criteria, and they underwent a detailed examination at the base hospital. 20  
After 8 hours of overnight fasting, a fasting blood sample was taken to estimate plasma glucose. For those with provisional diabetes, the presence of diabetes was confirmed by re-estimating fasting blood glucose by enzymatic assay; glucose was oxidized by glucose oxidase to produce gluconate and hydrogen peroxide, which was then detected photometrically. Glycosylated hemoglobin fractions were estimated by using a semiautomated analyzer (Micro Laboratory; Merck, Darmstadt, Germany, with a DiaSTAT HbA1c Reagent kit; Bio-Rad, Hercules, CA). 20 The total serum cholesterol (CHOD-POD method), serum triglycerides (CHOD-POD) and HDL (after protein precipitation, CHOD-POD method), were estimated. Hypercholesterolemia was defined as serum total cholesterol levels ≥200 mg/dL, hypertriglyceridemia as a serum triglyceride level ≥150 mg/dL, and serum HDL was designated as low if <40 mg/dL. Microalbuminuria was determined by a semiquantitative procedure (Bayer Clinitek 50 Urine Analyzer; Siemens Healthcare Diagnostics, Deerfield, IL). Subjects were deemed to have microalbuminuria, if urinary albumin excretion (UAE) was between 30 and 300 mg/24 hours and macroalbuminuria if more than 300 mg/24 hours. 21 Generalized obesity and abdominal obesity were defined according to the World Health Organization's (WHO) Asia Pacific guidelines, with a body mass index (BMI) cutoff of ≥23 kg/m2 and waist circumference (WC) cutoffs or ≥90 cm in the men and ≥80 cm in the women. 22  
Definitions
Ocular Perfusion Pressure.
Immediately before the blood sample was collected, the blood pressure was recorded, in the sitting position after 5 minutes of rest, in the right arm to the nearest 2 mm Hg using the mercury sphygmomanometer (Diamond Deluxe BP apparatus; Industrial Electronic and Allied Products, Pune, India), according to a protocol similar to that used in the Multiethnic Study of Atherosclerosis. 23 Two readings were taken, 5 minutes apart. A third measurement was made if the systolic blood pressure (SBP) differed by more than 10 mm Hg or the diastolic blood pressure (DBP) differed by more than 5 mm Hg. 24 The mean between the two closest readings was taken as the blood pressure. Hypertension was defined as SBP ≥140 mm Hg or DBP ≥90 mm Hg. 
The IOP in both the eyes was measured with a Goldmann applanation tonometer (AT 030 Applanation Tonometer; Carl Zeiss, Jena, Germany), with 0.05% proparacaine eye drops as topical anesthesia and 2% fluorescein to stain the tear film. In many recent reports, MOPP has been identified as an independent risk factor for open-angle glaucoma, 25 and glaucoma is documented to have a protective influence against DR (Williams PD. IOVS 2004;45:ARVO E-Abstract 4101). Hence, we further excluded 46 subjects with glaucoma (IOP > 21 mm Hg) from the analysis, so that the direct association between MOPP and DR could be assessed independent of the relationship of either to glaucoma. Thus, 1368 subjects were analyzed in this study. 
MAP is calculated as: MAP = DBP + ⅓(SBP − DBP), where the difference between the systolic and diastolic blood pressures is identified as the pulse pressure. MOPP is calculated from two thirds of the difference between MAP and IOP and two thirds is added to the formula to estimate ophthalmic artery pressure. Hence, MOPP is derived using the following relation 26 : MOPP = ⅔[DBP + ⅓(SBP − DBP)] − IOP. 
Diabetic Retinopathy.
All patients had their fundi photographed with the 45° four-field stereoscopic digital photography (Visucamlite Fundus Camera; Carl Zeiss Meditec) after pupillary dilation. DR was diagnosed based on the modified Klein classification (Modified Early Treatment Diabetic Retinopathy Study scales). 27 For those who showed symptoms of any DR, additional 30° seven-field stereo digital pairs were taken. The prevalence of DR in the general population older than 40 years in our study was found to be 3.5%. 19 DR was divided into mild, moderate, and severe nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR); clinically significant macular edema (CSME), and non-CSME was graded as absent or present. 28 Mild and moderate NPDR was defined as non–sight-threatening diabetic retinopathy (non-STDR) and severe NPDR, PDR, and CSME were defined as sight-threatening DR (STDR). 29 The grading was performed by two independent observers in a masked fashion; the grading agreement was high (k = 0.83). 16  
Statistical Analysis
A computerized database was created for all the records. Statistical software (SPSS for Windows, ver.13.0 SPSS Science, Chicago, IL) was used for the analyses. All the data are expressed as the mean ± SD or as percentage, if categorical. Statistical significance was assumed at P ≤ 0.05. Univariate and multivariate logistic regression analyses were performed to elucidate the risk factors for microangiopathies. The odds ratio (OR), with 95% confidence interval (CI), was calculated for the studied variables. The factors that were used for multivariate analyses included the factors associated with MAP (age, sex, duration of diabetes, age at onset of diabetes, glycosylated hemoglobin, BMI, WC, micro- and macroalbuminuria, serum total cholesterol, serum triglycerides, serum HDL, and anemia) as well as the factors associated with IOP (height and weight of the subject and central corneal thickness). 
Results
Table 1 describes the baseline characteristics of the study population. Of the 1368 subjects, 875 (64%) had systemic hypertension. The mean ± SD for SBP, DBP, MOPP, and IOP, in the overall study population, were 139.0 ± 20.8 mm Hg (median, 140), 82.0 ± 11.4 mm Hg (median, 80), 52.6 ± 9.0 mm Hg (median, 51.4), and 14.80 ± 2.9 mm Hg (median, 14), respectively. The women had higher SBP (P < 0.0001), DBP (P = 0.082), OPP (P = 0.046), and IOP (P = 0.004) than the men. The women had lesser age (P = 0.017), lesser duration of diabetes (P < 0.0001), higher BMI (P < 0.0001), lower WC (P < 0.0001), and higher serum cholesterol (P < 0.0001), than the men. In our study population, 972 (71.1%) subjects were being treated with oral hypoglycemic agents, 66 (4.8%) were being treated with insulin, and 1025 (74.9%) were on exercise and diet control. 
Table 1.
 
Baseline Characteristics of the Study Population
Table 1.
 
Baseline Characteristics of the Study Population
Overall Men Women P
n 1368 723 645
SBP, mm Hg 139.00 ± 20.8 137.05 ± 20.1 141.30 ± 21.8 <0.0001
DBP, mm Hg 82.00 ± 11.4 81.53 ± 11.1 82.60 ± 11.7 0.082
MOPP, mm Hg 52.60 ± 9.0 52.11 ± 5.7 53.09 ± 9.4 0.046
IOP, mm Hg 14.80 ± 2.9 14.60 ± 2.9 15.00 ± 2.8 0.004
Age, y 56.16 ± 9.98 56.77 ± 10.45 55.48 ± 9.38 0.017
Duration of diabetes, y 5.48 ± 6.18 6.17 ± 6.73 4.71 ± 5.42 <0.0001
Age at onset of diabetes, y 50.37 ± 9.81 50.37 ± 10.34 50.37 ± 9.18 0.999
HbA1c, % 8.18 ± 2.20 8.21 ± 2.18 8.16 ± 2.23 0.647
BMI, kg/m2 25.40 ± 4.11 24.38 ± 3.53 26.53 ± 4.41 <0.0001
WC 91.30 ± 9.84 93.69 ± 9.38 88.62 ± 9.65 <0.0001
Serum total cholesterol, mg/dL 186.66 ± 40.70 181.24 ± 38.30 192.72 ± 42.44 <0.0001
Serum triglycerides, mg/dL 153.16 ± 100.02 158.04 ± 112.91 147.70 ± 83.01 0.056
Serum HDL, mg/dL 1.96 ± 2.66 1.96 ± 3.64 1.96 ± 0.37 0.975
Figure 1 shows the distribution of blood pressure according to sex, MOPP and IOP with increasing age. MOPP and IOP did not show any significant change with increasing age, (P = 0.162 overall, P = 0.706 in the men, P = 0.062 in the women for MOPP; P = 0.546 overall, P = 0.663 in the men, P = 0.228 in the women for IOP). SBP increased (P < 0.0001) and DBP decreased (P < 0.0001) overall, and in either sex, with increasing age. 
Figure 1.
 
Distribution of blood pressure, MOPP, and IOP according to sex in the study population with type 2 diabetes. MOPP, mean ocular perfusion pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; IOP, intraocular pressure.
Figure 1.
 
Distribution of blood pressure, MOPP, and IOP according to sex in the study population with type 2 diabetes. MOPP, mean ocular perfusion pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; IOP, intraocular pressure.
Table 2 shows the multiple logistic regression analysis for the systemic factors associated with MOPP. MOPP data were divided into four quartiles: Higher MOPP (fourth quartile) was significantly associated with age (OR = 1.03, 95% CI = 1.01–1.05), the age at onset of diabetes >40 years (OR = 1.03, 95% CI = 1.01–1.05), micro- and macroalbuminuria (OR = 2.48, 95% CI = 1.62–3.82), hypertriglyceridemia (OR = 1.52, 95% CI = 1.07–2.15), and the weight of the subject (OR = 1.08, 95% CI = 1.06–1.11). 
Table 2.
 
Systemic Factors Associated with MOPP in the Study Population
Table 2.
 
Systemic Factors Associated with MOPP in the Study Population
MOPP Range (mm Hg) P *
26.2–46.4 46.5–51.4 51.5–57.5 57.6–90.2
n 357 328 343 340
Age, y 1.00 (Reference) 1.00 (0.99–1.02) 1.01 (0.99–1.03) 1.03 (1.01–1.05) 0.035
Sex, female 1.00 (Reference) 1.34 (0.85–2.09) 1.37 (0.86–2.18) 0.87 (0.54–1.40) 0.054
Duration of diabetes, y 1.00 (Reference) 0.99 (0.96–1.02) 1.01 (0.98–1.04) 0.97 (0.94–1.00) 0.389
Age at onset of diabetes, y 1.00 (Reference) 1.00 (0.99–1.02) 1.00 (0.98–1.02) 1.03 (1.01–1.05) 0.028
HbA1c > 7 1.00 (Reference) 1.47 (1.05–2.06) 1.29 (0.92–1.80) 1.05 (0.74–1.50) 0.588
Generalized obesity defined by BMI 1.00 (Reference) 0.85 (0.40–1.81) 0.49 (0.21–1.18) 0.90 (0.40–2.02) <0.0001
Abdominal obesity defined by WC 1.00 (Reference) 1.24 (0.76–2.02) 1.31 (0.78–2.21) 0.95 (0.54–1.66) <0.0001
Micro and macroalbuminuria 1.00 (Reference) 1.13 (0.73–1.75) 1.12 (0.71–1.75) 2.48 (1.62–3.82) <0.0001
Hypercholesterolemia 1.00 (Reference) 0.97 (0.69–1.37) 0.94 (0.67–1.33) 1.23 (0.86–1.77) 0.068
Hypertriglyceridemia 1.00 (Reference) 0.96 (0.68–1.35) 1.10 (0.79–1.54) 1.52 (1.07–2.15) 0.011
Low serum HDL 1.00 (Reference) 0.52 (0.37–0.73) 0.48 (0.34–0.66) 0.55 (0.38–0.78) 0.001
Presence of anemia 1.00 (Reference) 0.76 (0.46–1.24) 1.00 (0.62–1.61) 0.96 (0.58–1.59) 0.866
Weight 1.00 (Reference) 1.03 (1.00–1.05) 1.04 (1.02–1.09) 1.08 (1.06–1.11) <0.0001
Height 1.00 (Reference) 0.98 (0.95–1.01) 0.96 (0.93–0.98) 0.97 (0.94–0.99) 0.618
Central corneal thickness 1.00 (Reference) 1.00 (0.99–1.00) 1.00 (0.97–1.00) 0.99 (0.99–1.00) 0.617
Table 3 shows the univariate association of MOPP with DR and diabetic maculopathy. When compared with subjects with diabetes, and without DR, MOPP was higher in subjects with any DR (P = 0.042). When evaluated separately in men and women, in comparison to subjects without DR, MOPP was higher in men with STDR (P = 0.03) and higher in women with any DR (P = 0.008) and non-STDR (P = 0.006). In comparison to subjects with diabetes and without diabetic maculopathy, MOPP was higher in men with CSME (P = 0.044). 
Table 3.
 
Association of MOPP with DR and Diabetic Maculopathy
Table 3.
 
Association of MOPP with DR and Diabetic Maculopathy
MOPP (mm Hg)
Overall Men Women
n Mean ± SD P* n Mean ± SD P* n Mean ± SD P*
Retinopathy
No DR 1124 52.3 ± 8.9 Ref 571 51.9 ± 8.6 Ref 553 52.7 ± 9.1 Ref
Any DR 244 53.6 ± 9.6 0.042 152 52.6 ± 9.1 0.379 92 55.2 ± 10.4 0.008
Non-STDR 200 53.4 ± 9.6 0.112 123 51.9 ± 8.6 1.00 77 55.8 ± 10.7 0.006
STDR 44 54.4 ± 9.7 0.126 29 55.5 ± 10.5 0.03 15 52.3 ± 7.9 0.866
Maculopathy
No maculopathy 164 53.7 ± 9.4 Ref 103 52.2 ± 8.2 Ref 61 55.2 ± 10.8 Ref
Non-CSME 64 53.5 ± 10.5 0.889 40 52.6 ± 11.1 0.814 24 55.0 ± 9.5 0.937
CSME 16 53.4 ± 8.8 0.903 9 58.0 ± 8.2 0.044 7 47.6 ± 5.7 0.073
Table 4 shows the multivariate regression analysis performed keeping non-DR, non-STDR, and STDR as the dependent variables, with MOPP unadjusted at first and then sequentially adjusted for various factors. As is evident, age- and sex-adjusted MOPP (DR: OR = 1.01, 95% CI = 0.99–1.03, P = 0.102; non-STDR: OR = 1.01, 95% CI = 0.99–1.02, P = 0.343; and STDR: OR = 1.03, 95% CI = 0.99–1.06, P = 0.093) was found to have no significant association with DR, non-STDR, and STDR when compared with non-DR. Similarly, MOPP, when adjusted for all factors, was found not to be associated with DR (OR = 1.02, 95% CI = 0.99–1.03; P = 0.149), non-STDR (OR = 1.02, 95% CI = 0.99–1.03; P = 0.312), and STDR (OR = 1.02, 95% CI = 0.98–1.05; P = 0.358). When compared with subjects with no maculopathy, MOPP adjusted for all variables had no association with non-CSME and CSME (results not shown in the table). 
Table 4.
 
Multivariate Logistic Regression Analysis of MOPP with DR with Sequential Adjustment of Risk Factors
Table 4.
 
Multivariate Logistic Regression Analysis of MOPP with DR with Sequential Adjustment of Risk Factors
Association of MOPP with DR, non-STDR, and STDR No DR vs. Any DR P No DR vs. Non-STDR P No DR vs. STDR P
OR (95% CI) OR (95% CI) OR (95% CI)
MOPP, unadjusted 1.01 (0.99–1.03) 0.134 1.01 (0.99–1.02) 0.403 1.02 (0.99–1.06) 0.111
Adjusted for sex 1.01 (0.99–1.03) 0.094 1.01 (0.99–1.02) 0.333 1.03 (0.99–1.06) 0.088
Adjusted for age, sex 1.01 (0.99–1.03) 0.102 1.01 (0.99–1.02) 0.343 1.03 (0.99–1.06) 0.093
Adjusted for age, sex, duration of DM 1.02 (1.01–1.03) 0.032 1.01 (0.99–1.03) 0.185 1.03 (1.00–1.07) 0.051
Adjusted for age, sex, duration, BMI 1.03 (1.01–1.04) 0.003 1.02 (1.00–1.03) 0.047 1.04 (1.01–1.07) 0.018
Adjusted for age, sex, duration, BMI, HbA1c 1.03 (1.01–1.04) 0.003 1.02 (1.00–1.03) 0.05 1.04 (1.01–1.07) 0.020
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol 1.03 (1.01–1.04) 0.003 1.02 (1.00–1.04) 0.047 1.04 (1.01–1.07) 0.021
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol, HDL 1.03 (1.01–1.04) 0.003 1.02 (1.00–1.03) 0.047 1.04 (1.00–1.07) 0.028
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol, HDL, TG 1.03 (1.01–1.04) 0.003 1.02 (1.00–1.04) 0.045 1.04 (1.00–1.07) 0.028
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol, HDL, TG, albuminuria 1.02 (1.00–1.03) 0.048 1.02 (0.99–1.03) 0.101 1.02 (0.98–1.05) 0.368
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol, HDL, TG, albuminuria, WC 1.02 (1.00–1.03) 0.048 1.02 (0.99–1.03) 0.101 1.02 (0.98–1.05) 0.38
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol, HDL, TG, albuminuria, WC, onset of DM 1.02 (0.99–1.03) 0.149 1.02 (0.99–1.03) 0.312 1.02 (0.98–1.05) 0.358
Discussion
We report the MOPP among subjects with type 2 diabetes and elucidate the systemic factors associated with MOPP and its association with DR. In the present study, the women had higher MOPP than the men. Also, SBP was found to be higher among the women than the men. Recently, Zheng at al. 25 reported higher MOPP, higher DBP, but lower SBP in men than in women. The higher SBP in the women in the present study may be explained by the higher BMI 22 and the poor health-seeking behavior of women on the Indian subcontinent. 30  
There was no significant trend of MOPP with age, as seen in Figure 1. We found a very small association of MOPP with the age at onset of diabetes and weight of the subject, which may not be clinically significant. We also found that MOPP is associated with the presence of nephropathy and serum lipids (triglycerides). There was no association with glycemic control and duration of diabetes. This lack of association is interesting, as poor glycemic control and longer duration of diabetes are the most important risk factors for DR. Kohner 31 observed that retinal hyperperfusion was worse in those with poor diabetic control. Konno et al. 32 observed a transition from decreasing retinal blood flow to increasing retinal blood flow in patients with longer duration of type 1 diabetes and suggested that there is a net decrease in the resistance to blood flow with longer duration of diabetes. Thus, longer duration of diabetes and poor glycemic control may be associated with increased retinal perfusion and a decrease in the resistance to flow. However, MOPP is calculated from IOP and measured brachial blood pressure, and it is unlikely that retinal changes can explain any change or lack of change in MOPP. Moreover, blood flow changes are related to the complex pathologic alterations that occur in the diabetic retina and are not yet fully understood. 32  
On evaluating the relationship between MOPP and DR in both sexes, we found that in the men, higher MOPP was associated with STDR and CSME; whereas in the women, it was associated with any DR and non-STDR. However, the size of the differences found (in mm Hg) was very small, and most had only borderline statistical significance, which disappeared on multivariate analysis. Moss et al. 12 reported that a higher MOPP predicted higher incidence and progression of DR in younger-onset patients. Patel et al. 8 reported a higher MOPP in the DR group when compared with the nondiabetic control subjects and a higher MOPP in the PDR group than in the no DR group. Another report also showed that low MOPP appeared to protect against DR. 13 Langham et al. 14 suggested that ophthalmic arterial blood pressure and MOPP decreases with worsening retinopathy. However, they did not report calculations of MOPP in their series of 33 patients, and their suggestion was based on the indirect evidence of a decrease in the choroidal blood flow with severity of retinopathy in diabetes. Roy and Klein 15 observed that after adjustment for the duration of diabetes, patients with a higher MOPP were, on an average, twice as likely to have macular edema and severe hard exudates than were those with lower MOPP. 
Thus, there is contradictory evidence in literature regarding the relationship of MOPP with DR. However, none of these reports studied the sex-based association, which might explain these differences. A recent study evaluated the distribution of MOPP and its association with open-angle glaucoma and found that the association was stronger in women than in men. 25 This was attributed to the more frequent occurrence of vascular dysregulation among women in white populations. In the present study, as depicted in Table 1, female subjects had a higher BMI than did male subjects. Earlier, we reported an increased prevalence of obesity, defined by BMI, in women than in men. 22 Increasing evidence for the role played by adipocytokines like adiponectin, 33 leptin, 34,35 hepatocyte growth factor, 36 and zinc-α2-glycoprotein 37 in pathogenesis of DR and the reports of inverse association between sex-hormone–binding globulin and insulin levels in men and women 38,39 prompted us to evaluate the sex-based differences between MOPP and DR. However, we did not find any significant association of MOPP with DR, non-STDR, and STDR in multivariate analyses. 
Although the direct relationship of MOPP and DR has not been studied in many reports, there are many studies that have evaluated the association of retinal blood flow with DR. An overview of retinal perfusion abnormalities in the different stages of DR shows contradictory findings. 2 One of the first hints of altered retinal blood flow in patients with diabetes mellitus came from Kohner et al., 40,41 who reported increased retinal blood flow in patients with absent or mild, but not with moderate or severe, DR. The lack of increased blood flow in those with severe DR was explained on the basis that the blood vessels were so diseased that they could not respond even to a strong autoregulatory stimulus. Increased retinal blood flow in the early stages of DR and decreased retinal blood flow in PDR was observed in subsequent reports. 42,43 By contrast, Blair et al. 44 did not observe an alteration of mean retinal circulation time in early retinal damage but reported prolongation in PDR. Yoshida et al. 45 reported increasing blood flow with the progression of background DR. In type 1 diabetic patients, Bursell et al. 46 reported an increased mean circulation time indicative of reduced retinal blood flow in subjects with no apparent DR and a sequential decrease in mean circulation time with advancing NPDR. 47 Another report observed a shift from decreased to increased retinal blood flow with progression of the disease in type 1 diabetes. 33 Patel et al. 8 reported an increase in the total retinal blood flow with progression of DR, showing highest values in patients with PDR. However, evidence from a variety of other studies showed that retinal vasodilatation occurred before the clinical onset of DR in patients with diabetes, 2,48 and increased ocular blood flow was observed in patients with background DR. 2,49  
Thus, despite the contradictory results, there is evidence that both increased and decreased retinal blood flow are detrimental in the development of DR. 2 However, it should be considered that alterations in MOPP cannot be directly correlated to alterations in retinal blood flow. In the late stages of DR, the nature of ocular perfusion abnormalities appears to depend strongly on glycemic control as well as on specific pathologic features. 2  
The limitations of our study include its cross-sectional design and its focus on patients with type 2 diabetes only. Whether our results can be extrapolated to patients with type 1 diabetes is unclear. Another limitation is the unavailability of data on how many were treated for high IOP or high blood pressure. Since this information is not available, the treatment is a possible confounder in this study. Also, we did not study the retinal blood flow in our study population. It will be interesting to study the retinal blood flow and MOPP in both sexes. 
In univariate analyses, we found very small sex-related differences in MOPP (of 1 mm of Hg, which is approximately 2% of the mean), which are probably of no clinical significance. On multivariate logistic regression analyses of MOPP with DR with sequential adjustment of risk factors, we did not find any significant association between MOPP and DR. However, the present study evaluated the MOPP association at a single time point, rather than prospectively. Hence, future prospective studies may provide more information about the association between the MOPP and DR, if any, although the clinical significance of such an association seems little only. 
Conclusion
We report no association of MOPP with DR, non-STDR, and STDR. There seems to be very little evidence for a link between MOPP and DR, at least a link that is significant. Since DR is a multifactorial disease and perfusion pressure and vascular factors are likely to be involved in its pathophysiology, it may be more informative to evaluate these associations in prospective studies. 
Footnotes
 Supported by the RD Tata Trust, Mumbai, India.
Footnotes
 Disclosure: R. Raman, None; A. Gupta, None; V. Kulothungan, None; T. Sharma, None
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Figure 1.
 
Distribution of blood pressure, MOPP, and IOP according to sex in the study population with type 2 diabetes. MOPP, mean ocular perfusion pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; IOP, intraocular pressure.
Figure 1.
 
Distribution of blood pressure, MOPP, and IOP according to sex in the study population with type 2 diabetes. MOPP, mean ocular perfusion pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; IOP, intraocular pressure.
Table 1.
 
Baseline Characteristics of the Study Population
Table 1.
 
Baseline Characteristics of the Study Population
Overall Men Women P
n 1368 723 645
SBP, mm Hg 139.00 ± 20.8 137.05 ± 20.1 141.30 ± 21.8 <0.0001
DBP, mm Hg 82.00 ± 11.4 81.53 ± 11.1 82.60 ± 11.7 0.082
MOPP, mm Hg 52.60 ± 9.0 52.11 ± 5.7 53.09 ± 9.4 0.046
IOP, mm Hg 14.80 ± 2.9 14.60 ± 2.9 15.00 ± 2.8 0.004
Age, y 56.16 ± 9.98 56.77 ± 10.45 55.48 ± 9.38 0.017
Duration of diabetes, y 5.48 ± 6.18 6.17 ± 6.73 4.71 ± 5.42 <0.0001
Age at onset of diabetes, y 50.37 ± 9.81 50.37 ± 10.34 50.37 ± 9.18 0.999
HbA1c, % 8.18 ± 2.20 8.21 ± 2.18 8.16 ± 2.23 0.647
BMI, kg/m2 25.40 ± 4.11 24.38 ± 3.53 26.53 ± 4.41 <0.0001
WC 91.30 ± 9.84 93.69 ± 9.38 88.62 ± 9.65 <0.0001
Serum total cholesterol, mg/dL 186.66 ± 40.70 181.24 ± 38.30 192.72 ± 42.44 <0.0001
Serum triglycerides, mg/dL 153.16 ± 100.02 158.04 ± 112.91 147.70 ± 83.01 0.056
Serum HDL, mg/dL 1.96 ± 2.66 1.96 ± 3.64 1.96 ± 0.37 0.975
Table 2.
 
Systemic Factors Associated with MOPP in the Study Population
Table 2.
 
Systemic Factors Associated with MOPP in the Study Population
MOPP Range (mm Hg) P *
26.2–46.4 46.5–51.4 51.5–57.5 57.6–90.2
n 357 328 343 340
Age, y 1.00 (Reference) 1.00 (0.99–1.02) 1.01 (0.99–1.03) 1.03 (1.01–1.05) 0.035
Sex, female 1.00 (Reference) 1.34 (0.85–2.09) 1.37 (0.86–2.18) 0.87 (0.54–1.40) 0.054
Duration of diabetes, y 1.00 (Reference) 0.99 (0.96–1.02) 1.01 (0.98–1.04) 0.97 (0.94–1.00) 0.389
Age at onset of diabetes, y 1.00 (Reference) 1.00 (0.99–1.02) 1.00 (0.98–1.02) 1.03 (1.01–1.05) 0.028
HbA1c > 7 1.00 (Reference) 1.47 (1.05–2.06) 1.29 (0.92–1.80) 1.05 (0.74–1.50) 0.588
Generalized obesity defined by BMI 1.00 (Reference) 0.85 (0.40–1.81) 0.49 (0.21–1.18) 0.90 (0.40–2.02) <0.0001
Abdominal obesity defined by WC 1.00 (Reference) 1.24 (0.76–2.02) 1.31 (0.78–2.21) 0.95 (0.54–1.66) <0.0001
Micro and macroalbuminuria 1.00 (Reference) 1.13 (0.73–1.75) 1.12 (0.71–1.75) 2.48 (1.62–3.82) <0.0001
Hypercholesterolemia 1.00 (Reference) 0.97 (0.69–1.37) 0.94 (0.67–1.33) 1.23 (0.86–1.77) 0.068
Hypertriglyceridemia 1.00 (Reference) 0.96 (0.68–1.35) 1.10 (0.79–1.54) 1.52 (1.07–2.15) 0.011
Low serum HDL 1.00 (Reference) 0.52 (0.37–0.73) 0.48 (0.34–0.66) 0.55 (0.38–0.78) 0.001
Presence of anemia 1.00 (Reference) 0.76 (0.46–1.24) 1.00 (0.62–1.61) 0.96 (0.58–1.59) 0.866
Weight 1.00 (Reference) 1.03 (1.00–1.05) 1.04 (1.02–1.09) 1.08 (1.06–1.11) <0.0001
Height 1.00 (Reference) 0.98 (0.95–1.01) 0.96 (0.93–0.98) 0.97 (0.94–0.99) 0.618
Central corneal thickness 1.00 (Reference) 1.00 (0.99–1.00) 1.00 (0.97–1.00) 0.99 (0.99–1.00) 0.617
Table 3.
 
Association of MOPP with DR and Diabetic Maculopathy
Table 3.
 
Association of MOPP with DR and Diabetic Maculopathy
MOPP (mm Hg)
Overall Men Women
n Mean ± SD P* n Mean ± SD P* n Mean ± SD P*
Retinopathy
No DR 1124 52.3 ± 8.9 Ref 571 51.9 ± 8.6 Ref 553 52.7 ± 9.1 Ref
Any DR 244 53.6 ± 9.6 0.042 152 52.6 ± 9.1 0.379 92 55.2 ± 10.4 0.008
Non-STDR 200 53.4 ± 9.6 0.112 123 51.9 ± 8.6 1.00 77 55.8 ± 10.7 0.006
STDR 44 54.4 ± 9.7 0.126 29 55.5 ± 10.5 0.03 15 52.3 ± 7.9 0.866
Maculopathy
No maculopathy 164 53.7 ± 9.4 Ref 103 52.2 ± 8.2 Ref 61 55.2 ± 10.8 Ref
Non-CSME 64 53.5 ± 10.5 0.889 40 52.6 ± 11.1 0.814 24 55.0 ± 9.5 0.937
CSME 16 53.4 ± 8.8 0.903 9 58.0 ± 8.2 0.044 7 47.6 ± 5.7 0.073
Table 4.
 
Multivariate Logistic Regression Analysis of MOPP with DR with Sequential Adjustment of Risk Factors
Table 4.
 
Multivariate Logistic Regression Analysis of MOPP with DR with Sequential Adjustment of Risk Factors
Association of MOPP with DR, non-STDR, and STDR No DR vs. Any DR P No DR vs. Non-STDR P No DR vs. STDR P
OR (95% CI) OR (95% CI) OR (95% CI)
MOPP, unadjusted 1.01 (0.99–1.03) 0.134 1.01 (0.99–1.02) 0.403 1.02 (0.99–1.06) 0.111
Adjusted for sex 1.01 (0.99–1.03) 0.094 1.01 (0.99–1.02) 0.333 1.03 (0.99–1.06) 0.088
Adjusted for age, sex 1.01 (0.99–1.03) 0.102 1.01 (0.99–1.02) 0.343 1.03 (0.99–1.06) 0.093
Adjusted for age, sex, duration of DM 1.02 (1.01–1.03) 0.032 1.01 (0.99–1.03) 0.185 1.03 (1.00–1.07) 0.051
Adjusted for age, sex, duration, BMI 1.03 (1.01–1.04) 0.003 1.02 (1.00–1.03) 0.047 1.04 (1.01–1.07) 0.018
Adjusted for age, sex, duration, BMI, HbA1c 1.03 (1.01–1.04) 0.003 1.02 (1.00–1.03) 0.05 1.04 (1.01–1.07) 0.020
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol 1.03 (1.01–1.04) 0.003 1.02 (1.00–1.04) 0.047 1.04 (1.01–1.07) 0.021
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol, HDL 1.03 (1.01–1.04) 0.003 1.02 (1.00–1.03) 0.047 1.04 (1.00–1.07) 0.028
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol, HDL, TG 1.03 (1.01–1.04) 0.003 1.02 (1.00–1.04) 0.045 1.04 (1.00–1.07) 0.028
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol, HDL, TG, albuminuria 1.02 (1.00–1.03) 0.048 1.02 (0.99–1.03) 0.101 1.02 (0.98–1.05) 0.368
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol, HDL, TG, albuminuria, WC 1.02 (1.00–1.03) 0.048 1.02 (0.99–1.03) 0.101 1.02 (0.98–1.05) 0.38
Adjusted for age, sex, duration, BMI, HbA1c, cholesterol, HDL, TG, albuminuria, WC, onset of DM 1.02 (0.99–1.03) 0.149 1.02 (0.99–1.03) 0.312 1.02 (0.98–1.05) 0.358
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