Blood pressure, ocular perfusion pressure, and body mass index in glaucoma patients

Role of excessive weight in intraocular pressure: a systematic review and meta-analysis

OBJECTIVE

This systematic review and meta-analysis aimed to analyse the effect of excess body weight on intraocular pressure (IOP) values.

METHOD AND ANALYSIS

A literature search from PubMed, Medline and ScienceDirect Databases on 18 May 2023 was conducted by three reviewers, then filtered each study based on inclusion and exclusion criteria. For the quality assessment of included studies, the Newcastle-Ottawa Scale was adapted. Meta-analysis was performed using RevMan V.5.4 by entering the IOP values of each group to measure the mean difference.

RESULTS

From 2656 studies, there were 9 studies that matched the criteria and then were included to perform a quantitative meta-analysis. The results showed a mean difference of 0.93 (95% CI: 0.67 to 1.18) of the excessive weight group against the normal weight group. This suggests that there is a significant relationship between excess body weight and increasing values of IOP.

CONCLUSION

It can be concluded that excessive body weight tends to lead to higher IOP, which means that high IOP becomes a major risk factor for glaucoma.

The Correlation Between Body Weight and Intraocular Pressure

INTRODUCTION: Preflight body weight is a strong predictor of visual changes in spaceflight. To understand the effect of body weight on the eye, we examined the effect of increased body mass index on intraocular pressure on Earth.METHODS: We conducted a systematic review to summarize the relationship between weight parameters (including body mass index (BMI) and obesity indices), and intraocular pressure (IOP). Study selection and data extraction were performed in duplicate using EMBASE, MEDLINE, and CENTRAL, from database inception to the second week of April 2020.RESULTS: A total of 66 individual studies were included for qualitative analysis from the 1364 studies eligible for title and abstract screening. A total of 39 studies were available for quantitative analysis. The average BMI was 25.9 (range, 20.148.8) and the average IOP was 14.9 mmHg (range, 11.627.8). The overall pooled RR between BMI and elevated intraocular pressure (IOP) was 1.06 (95 CI%, 1.041.07), meaning for each unit increase in BMI one is 6 more likely of having higher IOP than baseline. Two studies assessed the effects of bariatric surgery, and both showed significant decreases in IOP postoperatively.CONCLUSION: A higher BMI was associated with increased IOP in ground-based studies. IOP also decreased with weight loss. These data support the idea that alterations in body weight affect intraocular pressures. Further research is needed to understand the relationship between body weight, IOP, and microgravity-induced visual changes. This finding may also be useful clinically.Khan S, Kirubarajan A, Lee M, Pitha I, Buckey JC Jr. The correlation between body weight and intraocular pressure. Aerosp Med Hum Perform. 2021; 92(11):886-897.

Obesity, Blood Pressure, and Intraocular Pressure: A Cross-Sectional Study in Italian Children

INTRODUCTION

Several studies in the adult population have shown that obesity is an independent risk factor for elevated intraocular pressure (IOP), whereas data in the paediatric population are sparse and controversial. The purpose of the present study is to investigate the relationship between body mass index (BMI), blood pressure (BP), and IOP in healthy school children.

METHODS

The survey was conducted among a random sample of 8-year-old Italian students. Data were collected on their health status and behaviours related to obesity (physical activity, food and drinking habits, etc.). Physical examinations, conducted at school, included measurements of height, weight, BP, and IOP.

RESULTS

Five hundred and seventy-six subjects were recruited (92.8% response rate); 42.4% were overweight or obese, 58.9% consumed inadequate daily servings of fruit and vegetables, and 87.5% were involved in sedentary activities. Elevated BP/hypertension (HTN) affected 3.6% and high IOP was revealed in 12.5% of the children. In the multivariate analysis, elevated BP/HTN was the only significant determinant of ocular HTN (OR 5.36, 95% CI 1.95-14.73, p = 0.001).

CONCLUSIONS

Our results show that high IOP affects 12.5% of 8-year-old school children and appears to be associated with high BP related to a high BMI.

Short-Term and Long-Term Variability of Intraocular Pressure Measured with an Intraocular Telemetry Sensor in Patients with Glaucoma

PURPOSE

To evaluate the short-term and long-term variability of intraocular pressure (IOP) in eyes with primary open-angle glaucoma.

DESIGN

Prospective study.

PARTICIPANTS

Twenty-two patients previously implanted with a sulcus-based IOP sensor (EyeMate, Implandata GmbH, Germany).

METHODS

Twenty-two patients previously implanted with the EyeMate were requested to obtain at least 4 IOP measurements daily. Data were grouped according to the eye and the medication so that an eye treated with a particular medication was considered as one group, and the same eye treated with a different medication during the observation period was considered as a different group. A day was divided into 7 periods: night, midnight to 5:59 am; early, 6 am to 7:59 am; morning, 8 am to 10:59 am; noon, 11 am to 1:59 pm; afternoon, 2 pm to 5:59 pm; evening, 6 pm to 8:59 pm; and late, 9 pm to 11:59 pm. Short-term variability during a particular period was defined as the variability in IOP measurements obtained during that period on different days within 3 months of each other. Long-term variability was defined as the variability in IOP measurements obtained during a particular period on different days over a period of 1 year or more. Variability was assessed using intraclass correlation coefficients (ICCs).

RESULTS

The mean age of study participants was 67.8 ± 6.8 years and 36.4% were women. The mean follow-up duration of patients was 19.2 ± 21.3 months (median, 9 months; range, 1-58 months). Overall, 92 860 IOP measurements over 15 811 measurement days were obtained and analyzed during the study period. The number of measurements obtained from each eye ranged from 1 daily to 277 daily. Intraclass correlation coefficients for short-term variability among the 7 periods during the day ranged from 0.52 (morning) to 0.66 (early). Long-term ICCs ranged from 0.29 (night) to 0.51 (late).

CONCLUSIONS

Continual IOP monitoring showed that IOP has moderate short-term and high long-term variability in glaucoma patients. These findings demonstrate that single IOP measurements do not characterize day-to-day variations in IOP. Moreover, they show the importance of continual IOP monitoring in glaucoma patients.

Systemic and ocular determinants of mean ocular perfusion pressure in a population-based sample

PURPOSE

To investigate the associations between mean ocular perfusion pressure (MOPP) and several variables including body mass index (BMI), comorbid medical conditions and various ocular parameters in a population-based sample.

STUDY DESIGN

Cross-sectional.

METHODS

Data of 2091 healthy participants from a previous population based cross-sectional study were reviewed. Inclusion criteria were adults ≥40 years of age who were screened on-site for glaucoma. Data on medical history, height, weight, systolic and diastolic blood pressures (SBP and DBP, respectively) were obtained. A basic ocular examination was performed which included intraocular pressure (Tono-Pen XL, Reichert Technologies) and central corneal thickness (Pacline pachymetry;) measurement, slit-lamp examination and non-mydriatic optic disc photography (nonmydα fundus camera, Kowa). MOPP was calculated using the formula [2/3 x (DBP + 1/3(SBP-DBP)]-IOP and low MOPP was defined as MOPP ≤45 mmHg.

RESULTS

Mean age of the subjects was 63.04 ± 9.7 years (range: 44 and 99 years) and the majority were women (74.1%, n = 1549). Mean MOPP values in normal weight (BMI < 25), overweight (BMI = 25-29.9) and in obese individuals were 46.9 ± 9.0 mmHg, 48.6 ± 9.2 mmHg and 50.7 ± 10.0 mmHg, respectively (p < 0.001, in all pairwise comparisons). In the multivariable logistic regression analysis, migraine and IOP elevation (per 1 mmHg increment) were significantly associated with a low MOPP (OR: 2.10 and 1.22, p = 0.008 and < 0.001, respectively). On the other hand, risk of low MOPP was reduced in subjects with hypertension, and with increasing age (per 1-year increment) and BMI (per 1-unit increment) (OR: 0.15, 0.97, and 0.95, respectively, and p < 0.001 for all).

CONCLUSION

Migraine and elevated IOP increase the risk of low MOPP and this may have a causal relationship with impaired optic nerve head blood flow.

Ocular blood flow as a clinical observation: Value, limitations and data analysis

Alterations in ocular blood flow have been identified as important risk factors for the onset and progression of numerous diseases of the eye. In particular, several population-based and longitudinal-based studies have provided compelling evidence of hemodynamic biomarkers as independent risk factors for ocular disease throughout several different geographic regions. Despite this evidence, the relative contribution of blood flow to ocular physiology and pathology in synergy with other risk factors and comorbidities (e.g., age, gender, race, diabetes and hypertension) remains uncertain. There is currently no gold standard for assessing all relevant vascular beds in the eye, and the heterogeneous vascular biomarkers derived from multiple ocular imaging technologies are non-interchangeable and difficult to interpret as a whole. As a result of these disease complexities and imaging limitations, standard statistical methods often yield inconsistent results across studies and are unable to quantify or explain a patient's overall risk for ocular disease. Combining mathematical modeling with artificial intelligence holds great promise for advancing data analysis in ophthalmology and enabling individualized risk assessment from diverse, multi-input clinical and demographic biomarkers. Mechanism-driven mathematical modeling makes virtual laboratories available to investigate pathogenic mechanisms, advance diagnostic ability and improve disease management. Artificial intelligence provides a novel method for utilizing a vast amount of data from a wide range of patient types to diagnose and monitor ocular disease. This article reviews the state of the art and major unanswered questions related to ocular vascular anatomy and physiology, ocular imaging techniques, clinical findings in glaucoma and other eye diseases, and mechanistic modeling predictions, while laying a path for integrating clinical observations with mathematical models and artificial intelligence. Viable alternatives for integrated data analysis are proposed that aim to overcome the limitations of standard statistical approaches and enable individually tailored precision medicine in ophthalmology.

The effect of bariatric surgery on intraocular pressure

PURPOSE

Study purpose was to investigate the effects of bariatric surgery on intraocular pressure (IOP) and other ophthalmic parameters in a prospective observational follow-up study.

METHODS

Ophthalmic examination was performed on 22 obese women before and 6 months after bariatric surgery. A control group of 15 non-obese age-matched women were studied twice 6 months apart. IOP was measured with the Goldmann applanation tonometer (GAT) and the Pascal dynamic contour tonometer (PDCT). None of the subjects had glaucoma.

RESULTS

Average weight loss 6 months after bariatric surgery was 25 ±8 kg, (p < 0.05). Visual acuity (VA), pachymetry and systolic as well as diastolic blood pressures did not differ between the obese and control groups and no change between the visits was detected. At baseline, IOP was significantly higher in the obese group than in the controls (16.6 ± 3.0 mmHg GAT and 18.1 ± 2.2 mmHg PDCT compared with 14.3 ± 1.5 mmHg GAT and 16.5 ± 1.9 mmHg PDCT respectively). After bariatric surgery, IOP was significantly lower (15.2 ± 2.7 mmHg GAT and 16.5 ± 2.0 mmHg PDCT, p < 0.05) and no significant difference was detected between the operated and control groups. In the whole data at baseline, IOP correlated with weight, body mass index, waist circumference, body fat per cent and systolic blood pressure values.

CONCLUSION

Intraocular pressure (IOP) is significantly higher in obese women than in non-obese age-matched controls. Obese subjects had a decrease in IOP after bariatric surgery with no change in IOP in the control group. This change in obese group may be related to weight loss. Different measures of obesity correlated with IOP at baseline.

IOP, IOP Transient Impulse, Ocular Perfusion Pressure, and Mean Arterial Pressure Relationships in Nonhuman Primates Instrumented With Telemetry

Purpose

To characterize relationships between intraocular pressure (IOP), mean arterial pressure (MAP), ocular perfusion pressure (OPP), IOP transient impulse, and IOP baseline impulse using continuous telemetry in nonhuman primates.

Methods

We used our validated implantable telemetry system to wirelessly record bilateral IOP and arterial BP at 500 Hz in 7 eyes of 4 male rhesus macaques, aged 4 to 5 years. IOP, MAP, OPP, IOP transient impulse, and IOP baseline impulse were averaged into 1-hour periods over 20 days for each NHP. IOP transient impulse was defined as the portion of total IOP due to transient IOP fluctuations <0.5 seconds duration alone and IOP baseline impulse as the remaining area under the IOP versus time curve. OPP was defined as arterial BP-IOP (calculated continuously), and MAP was the hourly average of the continuous BP curve. Relationships between the variables were analyzed for each 24-hour period using either multivariate linear regression or Spearman Correlation Coefficients as appropriate.

Results

Over twenty 24-hour periods, IOP transient impulse and OPP showed significant positive relationship in all eyes, which was driven largely by the data during waking hours. There was no significant relationship between IOP and MAP, IOP transient impulse and MAP, or IOP baseline impulse and IOP transient impulse.

Conclusions

There are significant positive relationships between the frequency and/or size of transient IOP fluctuations (IOP transient impulse) and OPP. A possible explanation of this finding is that higher OPP, as well as a greater number of blinks and saccades (the primary sources of IOP transients), are associated with increased activity.

Effect of Head Position and Weight Loss on Intraocular Pressure in Obese Subjects

PURPOSE

To measure the influence of body weight on postural intraocular pressure (IOP) changes and to determine if significant weight loss effects IOP.

PATIENTS AND METHODS

In this prospective case-control study 25 morbidly obese subjects scheduled for bariatric surgery and 25 age-matched and sex-matched normal weight controls were recruited. Subjects had tonometry performed in 7 positions with the order randomized: sitting with the neck in neutral position, neck flexion at 30 degrees, extension at 30 degrees, supine, right, and left lateral decubitus, and with the head and upper body elevated at 30 degrees. The obese subjects were reassessed 1 to 2 years after bariatric surgery.

RESULTS

Mean IOP in the obese group was significantly higher than the control group across all positions by a mean of 2.5±0.4 mm Hg (P<0.02). There was no significant difference in the magnitude of postural IOP change between obese and control subjects. In total, 19 obese subjects completed follow-up after bariatric surgery. Mean weight loss was 49.1±17.2 kg, 36% of total body weight. Mean IOP was significantly lower after bariatric surgery by 1.6±0.5 mm Hg (P<0.001). Linear regression demonstrated that every 10% body weight loss was correlated with 1.4 mm Hg decrease in IOP (r=-0.46).

CONCLUSIONS

Obesity is associated with increased IOP compared with normal weight controls, but not with the magnitude of postural IOP change across different seated and supine positions. Significant weight loss after bariatric surgery is weakly associated with IOP lowering. The relationship between IOP, glaucoma, and obesity deserves further study.

The Association between Adiposity and the Risk of Glaucoma: A Meta-Analysis

Purpose

This meta-analysis was conducted to determine the potential association between adiposity and glaucoma incidence.

Materials and Methods

A comprehensive literature search was performed in PubMed and ISI Web of Science. A meta-analysis was conducted using STATA software.

Results

Fifteen eligible studies involving 2,445,980 individuals were included to investigate the association between adiposity and glaucoma incidence. The relative risks (RRs) were pooled with 95% confidence intervals (CI) by using a random-effects model. The pooled RR between adiposity and elevated intraocular pressure (IOP) was 1.73 (95% CI, 1.18–2.54), whereas that between adiposity and open-angle glaucoma (OAG) was 0.97 (95% CI, 0.83–1.13). The pooled RR between abdominal adiposity and glaucoma was 1.28 (95% CI, 1.15–1.41), whereas that between general adiposity and glaucoma was 1.09 (95% CI, 0.87–1.37). Results of subgroup analysis by sex indicated the association between adiposity and glaucoma in the female group (RR, 1.31; 95% CI, 1.05–1.64), but not in the male group (RR, 1.11; 95% CI, 0.77–1.60). The pooled RR of cohort studies and cross-sectional studies were 1.00 (95% CI, 0.84–1.20) and 1.22 (95% CI, 0.89–1.66), respectively.

Conclusions

Adiposity has a higher risk of elevated IOP, and abdominal adiposity has a positive association with glaucoma, especially in female patients.