Association between metabolic syndrome and the risk of glaucoma: a meta-analysis of observational studies

Abstract

Background

The potential link between metabolic syndrome (MetS) and the risk of glaucoma has been proposed but remains inconclusive. This meta-analysis aimed to systematically evaluate the association between MetS and the risk of glaucoma.

Methods

We conducted a comprehensive search of PubMed, Embase, and Web of Science from inception to August 12, 2024, for observational studies assessing the relationship between MetS and glaucoma risk. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate the association. Heterogeneity was assessed using I² statistics, and a random-effects model was applied.

Results

Nine studies involving 2,258,797 participants were included. The pooled results showed that MetS was significantly associated with an increased risk of glaucoma (OR: 1.34, 95% CI 1.15–1.55, p < 0.001; I² = 75%). Subgroup analyses according to the individual component of MetS suggested that hypertension and hyperglycemia were significantly associated with glaucoma, but not for obesity or dyslipidemia, although the difference among subgroups was not significant (p = 0.05). Further subgroup and meta-regression analyses suggested that the results were not significantly affected by study design, average age, sex, method of glaucoma diagnosis, or glaucoma subtype (primary open-angle glaucoma or normal-tension glaucoma). Sensitivity analysis confirmed the robustness of the findings.

Conclusions

This meta-analysis suggests that MetS is significantly associated with an increased risk of glaucoma. These findings highlight the need for heightened awareness and potential screening strategies for glaucoma in individuals with MetS. Further studies are required to elucidate underlying mechanisms and causality.

Introduction

Glaucoma is a chronic, progressive eye disease that represents one of the leading causes of irreversible blindness worldwide [1, 2]. The global prevalence of glaucoma was estimated to exceed 76 million cases in 2020, and this figure is projected to surpass 112 million by 2040, with a significant impact on public health systems, especially in aging populations [3]. Primary open-angle glaucoma (POAG) is the most common form of the disease, characterized by optic nerve damage and gradual loss of peripheral vision, often progressing to blindness if untreated [4, 5]. Another form, normal-tension glaucoma (NTG), occurs even with normal intraocular pressure, suggesting multifactorial risk factors beyond just elevated intraocular pressure (IOP) [6]. Though current treatments, including medications, laser therapy, and surgery, aim to lower IOP and delay disease progression, they do not fully arrest the damage once it occurs [7]. As such, early detection and preventive strategies are paramount in reducing the burden of glaucoma-related visual impairment [8].

Metabolic syndrome (MetS), a cluster of metabolic abnormalities, has emerged as a significant risk factor for numerous chronic diseases [9, 10]. The syndrome encompasses central obesity, dyslipidemia, hyperglycemia, and hypertension, and is commonly diagnosed using criteria from organizations such as the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) and the International Diabetes Federation (IDF) [11]. According to NCEP-ATP III, MetS is defined by the presence of three or more of the following: waist circumference ≥ 102 cm in men or ≥ 88 cm in women, triglycerides ≥ 150 mg/dL, HDL cholesterol (HDL-C) < 40 mg/dL in men or < 50 mg/dL in women, blood pressure ≥ 130/85 mmHg, and fasting glucose ≥ 100 mg/dL [12]. The IDF criteria emphasize central obesity as an essential component, requiring an increased waist circumference specific to ethnic groups in addition to at least two other abnormalities [13]. MetS is a well-established precursor to type 2 diabetes, cardiovascular disease, and other chronic conditions, including non-alcoholic fatty liver disease and chronic kidney disease [14,15,16]. Given the systemic effects of MetS on vascular health, there is growing interest in its potential contribution to ocular diseases such as glaucoma [17].

The exact mechanisms linking MetS to glaucoma remain uncertain, but several plausible pathways have been proposed. Both MetS and glaucoma share common risk factors, such as aging, hypertension, and obesity [18]. Vascular dysregulation, oxidative stress, and inflammatory processes associated with MetS could contribute to optic nerve damage, further supporting the hypothesis of a link between the two conditions [19, 20]. However, existing research on the association between MetS and glaucoma risk remains inconsistent. Some studies have found a significant association between MetS and an increased risk of developing glaucoma [21,22,23,24,25,26], while others have reported no meaningful relationship [27,28,29]. The heterogeneity in study design, population characteristics, diagnostic criteria for MetS and glaucoma, and the adjustment for confounding factors has led to these conflicting findings. Given the rising prevalence of both MetS and glaucoma, it is crucial to clarify whether MetS is indeed a risk factor for glaucoma to inform future screening and prevention strategies. To address these uncertainties, we conducted a comprehensive meta-analysis of observational studies to systematically evaluate the association between MetS and the risk of glaucoma.

Methods

The authors adhered to PRISMA 2020 guidelines [30, 31] and the Cochrane Handbook for Systematic Reviews and Meta-analyses [32] in conducting this meta-analysis, covering study design, data collection, statistical analysis, and result interpretation.

Literature search

To locate studies pertinent to this meta-analysis, we queried PubMed, Embase, and Web of Science with an extensive array of search terms, which included: (1) “metabolic syndrome” OR “insulin resistance syndrome” OR “syndrome X”; and (2) “glaucoma” OR “ocular hypertension”. The search was limited to research involving human subjects and included only studies published as full-text articles in English within peer-reviewed journals. Additionally, we manually reviewed the references of relevant original and review articles to identify further pertinent studies. The literature was assessed from the inception of the databases up to August 12, 2024.

Inclusion and exclusion criteria

The inclusion criteria for potential studies were defined according to the PICOS framework:

P (Population): Adult population (aged 18 years or older).

I (Exposure): Participants with MetS, which was diagnosed according to the criteria used in the primary studies.

C (Comparison): Participants without MetS.

O (Outcome): Incidence or prevalence of glaucoma, which was compared between those with and without MetS. The diagnosis of glaucoma was also consistent with the methods and criteria used in the primary studies.

S (Study Design): Observational studies, including cohort, case-control, and cross-sectional studies.

Exclusion criteria included reviews, editorials, meta-analyses, preclinical studies, studies that did not evaluate MetS as exposure, or studies that did not report the outcome related to the prevalence or the incidence of glaucoma. In cases of overlapping populations, the study with the largest sample size was selected for inclusion in the meta-analysis.

Study quality evaluation and data extraction

The literature search, study identification, quality assessment, and data extraction were conducted independently by two authors, with any disagreements resolved by consulting the corresponding author. Study quality was evaluated using the Newcastle-Ottawa Scale (NOS) [33], which assesses selection, control of confounders, and outcome measurement and analysis, with scores ranging from 1 to 9, where 9 signifies the highest quality. Data collected for analysis included study details (author, year, country, and design), participant characteristics (source, sample size, age, and sex), the criteria for the diagnosis of MetS and number of subjects with MetS, follow-up periods for longitudinal studies, methods used to validate the diagnosis of glaucoma, type of glaucoma reported, numbers of patients with glaucoma, and variables adjusted when analyzing the association between MetS and the risk of glaucoma was reported.

Statistical analyses

The association between MetS and glaucoma was analyzed using odds ratios (ORs) and 95% confidence intervals (CI), comparing between subjects with and without MetS. OR values and their standard errors were calculated from 95% CIs or p-values and logarithmically transformed for variance stabilization. To assess heterogeneity, we used the Cochrane Q test and I² statistics [34], with I² > 50% indicating considerable heterogeneity. A random-effects model was applied to integrate the results, accounting for study variability [32]. Sensitivity analyses were conducted by excluding individual studies to test the robustness of the findings. A subgroup analysis was performed to explore the association between individual component of MetS and glaucoma. Further subgroup analyses were performed to explore the effects of factors such as study design, average age, sex, methods for confirming the diagnosis of glaucoma, types of glaucoma (such as primary open-angle glaucoma [POAG], primary angle-closure glaucoma [PACG], and normal-tension glaucoma [NTG] etc.), and NOS scores. Subgroups were defined using the median values of continuous variables. In addition, a univariate meta-regression analysis was performed to investigate the results could be significantly modified by study characteristics such as proportion of men, prevalence of patients with MetS in each study, proportion of patients with glaucoma in each study, and study quality scores in NOS. Publication bias was evaluated using funnel plots and visual inspection for asymmetry, supplemented by Egger’s regression test [35]. Analyses were performed using RevMan (Version 5.1; Cochrane Collaboration, Oxford, UK) and Stata software (version 12.0; Stata Corporation, College Station, TX).

Results

Study inclusion

The study inclusion process is illustrated in Fig. 1. Initially, 435 potentially relevant records were identified from the three databases, with 159 excluded due to duplication. A subsequent screening of titles and abstracts led to the exclusion of 249 studies, primarily because they did not align with the meta-analysis’s objectives. The full texts of the remaining 27 records were reviewed by two independent authors, resulting in the exclusion of 18 studies for reasons detailed in Fig. 1. Ultimately, nine observational studies were deemed appropriate for the quantitative analysis [21,22,23,24,25,26,27,28,29].

Fig. 1

Flowchart of database search and study inclusion

Overview of study characteristics

Table 1 presents the summarized characteristics of the included studies. Overall, one prospective cohort study [25], one retrospective cohort study [21], six cross-sectional studies [22, 24, 26,27,28,29], and another case-control study [23] were included. These studies were reported from 2009 to 2023, and conducted in the United States, Korea, Iran, and Singapore. Community adult populations were included eight studies [21, 22, 24,25,26,27,28,29], and another case-control study included PAOG patients and healthy control [23]. Overall, 2,258,797 participants were included in this meta-analysis, with the mean ages from 51.9 to 61.0 years. The NCEP-ATP III criteria were used in all the included studies for the diagnosis of MetS. Accordingly, 427,360 (18.9%) of the participants had MetS. The diagnosis of glaucoma was based on the results of ophthalmic examination in five studies [22,23,24, 26, 27], via self-reported glaucoma diagnosed by a doctor in one study [28], and as evidenced by the International Classification of Diseases (ICD) codes in another three studies [21, 25, 29]. The outcome of overall glaucoma was reported in three studies [27,28,29], and the subtype of POAG and NTG were reported in five [21, 23, 25,26,27] and two studies [22, 24], respectively. Overall, 66,688 of the included subjects were diagnosed as glaucoma (3.0%). Multivariate analysis was performed in all of the included studies when the association between MetS and glaucoma was evaluated, with the adjustment of age, sex, and other potential confounding factors to a varying degree. The NOS scores of the included studies were seven or eight, suggesting an overall good study quality (Table 2).

Table 1 Characteristics of the included studies

Table 2 Study quality evaluation via the Newcastle-Ottawa Scale

Results of meta-analysis and sensitivity analysis

Since one of the included studies reported the outcome by gender separately [25], these datasets were included independently into the meta-analysis, making 10 datasets available for the overall meta-analysis. Overall, the pooled results of the 10 datasets from the nine observational studies [21,22,23,24,25,26,27,28,29] showed that compared to participants without MetS, adults with MetS were significantly associated with an increased risk of glaucoma (OR: 1.34, 95% CI 1.15–1.55, p < 0.001; I² = 75%; Fig. 2A). Sensitivity analysis by excluding one dataset at a time did not significantly change the results (OR: 1.27–1.40, p all < 0.05).

Fig. 2

Forest plots representing the meta-analysis of the association between MetS and glaucoma in adult population; A overall meta-analysis; B subgroup analysis according to the individual component of MetS

Results of the subgroup and meta-regression analyses

The results of subgroup analysis according to the individual component of MetS indicated that hypertension and hyperglycemia were both associated with an increased risk of glaucoma, but the association with glaucoma was not significant for obesity, hypertriglycemia, or a low HDL-C. However, the difference among the subgroups were not statistically significant (p = 0.05; Fig. 2B). Further subgroup analyses indicated that the association between MetS and glaucoma was not significantly different between cohort and cross-sectional/case control studies (p for subgroup difference = 0.08; Fig. 3A). In addition, similar results were observed in patients with mean age < and ≥ 55 years, and in participants with the proportion of men < and ≥ 48% (p for subgroup difference = 0.57 and 0.92, Figs. 3B and 4A). A similar results was observed for studies with glaucoma validated by ophthalmologic examination and by ICD codes/self-reported diagnosis (p for subgroup difference = 0.47, Fig. 4B). The subgroup analysis according to the subtype of glaucoma retrieved a similar association between MetS with POAG and NTG (p for subgroup difference = 0.23, Fig. 5A). Subsequently, consistent results were obtained for studies with NOS score of seven and eight (p for subgroup difference = 0.26, Fig. 5B). Finally, results of meta-regression analysis showed that none of the observed variables, such as mean age, proportion of men, prevalence of patients with MetS in each study, proportion of patients with glaucoma in each study, or the NOS could significantly modify the results of the meta-analysis (p all > 0.05; Table 3).

Fig. 3

Forest plots representing the subgroup analyses of the association between MetS and glaucoma in adult population; A subgroup analysis according to study design; and B subgroup analysis according to the mean age of the participants

Fig. 4

Forest plots representing the subgroup analyses of the association between MetS and glaucoma in adult population; A subgroup analysis according to the proportion of men of the studied population; and B subgroup analysis according to the methods for validating the diagnosis of glaucoma

Fig. 5

Forest plots representing the subgroup analyses of the association between MetS and glaucoma in adult population; A subgroup analysis according to the subtype of glaucoma; and B subgroup analysis according to the NOS scores of the included studies

Table 3 Results of univariate meta-regression analysis

Publication bias

Upon visual inspection, the funnel plots for meta-analysis of the association between MetS and glaucoma appear symmetrical, indicating a low likelihood of publication bias (Fig. 6). Additionally, Egger’s regression test results (p = 0.35) also support this conclusion by suggesting a low risk of publication bias.

Fig. 6

Funnel plots for the meta-analysis of the association between MetS and glaucoma in adult population

Discussion

This meta-analysis, which included nine observational studies involving over 2.2 million participants, revealed a significant association between MetS and an increased risk of glaucoma. Our pooled results demonstrated that individuals with MetS had a 34% higher likelihood of developing glaucoma compared to those without MetS. Sensitivity analysis confirmed the robustness of these findings, as the exclusion of individual studies did not alter the overall effect size. Subgroup analysis suggested that hypertension and hyperglycemia were significantly associated with glaucoma, but not for obesity or dyslipidemia, although the difference among subgroups was not significant. The association remained consistent across various subgroups, including different study designs, age groups, sexes, methods of glaucoma diagnosis, and types of glaucoma. These findings provide important insights into the potential role of MetS in the pathogenesis of glaucoma, supporting the notion that systemic metabolic disturbances may contribute to the development of this blinding disease.

The association between MetS and glaucoma may be explained by several underlying pathophysiological mechanisms. MetS is characterized by a cluster of metabolic abnormalities, including central obesity, insulin resistance, hypertension, dyslipidemia, and hyperglycemia [36] Each of these components has been individually implicated in the development of glaucoma. For example, obesity and insulin resistance may lead to increased intraocular pressure (IOP), a key risk factor for glaucoma, by elevating episcleral venous pressure and impairing aqueous humor outflow [37, 38] Hyperglycemia and dyslipidemia can contribute to vascular dysfunction, which may compromise ocular blood flow and result in optic nerve damage [39]. Hypertension, another hallmark of MetS, may exacerbate oxidative stress and inflammation in the optic nerve, accelerating glaucomatous neurodegeneration [40, 41]. These metabolic perturbations, when present together as MetS, may synergistically increase the risk of glaucoma by promoting IOP elevation, vascular insufficiency, and neurodegeneration. Interestingly, recent studies suggest that MetS was independently associated with reduced retinal nerve fiber layer thickness [42] and localized retinal nerve fiber layer defects [43], suggesting the possible role of MetS in the development of glaucoma.

The results of subgroup analysis according to the components of MetS suggested that hypertension and hyperglycemia were both associated with an increased risk of glaucoma, while the association with glaucoma was not significant for obesity, hypertriglycemia, or a low HDL-C. However, since limited number of datasets were available for each component of MetS, and the differences among the subgroups were not statistically significant, these results should be interpreted with caution. The influences of individual components of MetS on the risk of glaucoma should be validated in future studies. In addition, our subgroup analyses revealed no significant differences in the association between MetS and glaucoma across different study designs and age groups. This suggests that the link between MetS and glaucoma is consistent across diverse populations. Additionally, no differences were observed between studies that diagnosed glaucoma via ophthalmologic examination and those that relied on ICD codes or self-reported diagnoses, further supporting the reliability of our findings. Importantly, the association between MetS and both POAG and NTG was similar, indicating that MetS may be a risk factor for multiple subtypes of glaucoma. This broad applicability highlights the potential impact of metabolic health on glaucoma risk and emphasizes the need for a holistic approach to managing glaucoma patients with metabolic comorbidities.

Despite the robustness of our findings, this meta-analysis has several limitations. First, all included studies diagnosed MetS using the NCEP-ATPIII criteria, which may limit the generalizability of our results to populations diagnosed with other MetS criteria. Second, no studies specifically reported on PACG, so the association between MetS and this subtype could not be assessed. Third, significant heterogeneity was observed in the overall analysis, likely due to variations in study design, population characteristics, and methods of glaucoma diagnosis. Although subgroup analyses partially explained this heterogeneity, unmeasured or unadjusted confounding factors, such as lifestyle habits, genetic predispositions, and treatment histories, may still have influenced the results. Additionally, while multivariate analyses were performed in all included studies to adjust for potential confounders, the extent of adjustment varied, and residual confounding could not be entirely ruled out.

From a clinical perspective, these findings underscore the importance of metabolic health in glaucoma prevention and management. Given the rising prevalence of MetS globally, especially in aging populations [44] clinicians should consider metabolic risk factors when assessing patients for glaucoma. Regular screening for MetS components, such as hypertension, dyslipidemia, and hyperglycemia, may help identify individuals at higher risk of developing glaucoma. Conversely, glaucoma patients with MetS may benefit from more intensive monitoring of their IOP and optic nerve function, as metabolic disturbances could accelerate disease progression [45] Moreover, the findings suggest that improving metabolic health through lifestyle interventions or pharmacological treatment may reduce glaucoma risk, though further studies are needed to confirm this hypothesis.

Future research should focus on several key areas. First, more longitudinal studies are needed to clarify the temporal relationship between MetS and glaucoma development, as the majority of studies included in this meta-analysis were cross-sectional. Additionally, future studies should investigate the impact of individual MetS components on glaucoma risk, as this could help refine prevention strategies for specific patient subgroups. It is also essential to explore the association between MetS and PACG, as this subtype was not evaluated in the current analysis. Finally, randomized controlled trials evaluating the effects of intervention targeting MetS (such as diet) on the incidence and progression of glaucoma would provide valuable insights into the potential benefits of metabolic interventions for glaucoma patients [46].

Conclusions

In conclusion, this meta-analysis demonstrates that MetS is associated with a significantly increased risk of glaucoma, highlighting the importance of addressing metabolic health in the context of glaucoma prevention and management. Our findings suggest that individuals with MetS may be at heightened risk of developing both POAG and NTG, likely due to the combined effects of metabolic disturbances on IOP regulation, vascular function, and optic nerve health. While the results are consistent across multiple subgroups, further research is needed to better understand the mechanisms underlying this association and to evaluate the potential benefits of metabolic interventions in glaucoma care. Nonetheless, these findings provide a strong rationale for incorporating metabolic risk assessments into routine glaucoma screening and management practices, particularly in populations at high risk of both conditions.