Impact of Metabolic Syndrome on Cataracts: A Systematic Review and Meta-Analysis

Abstract

Cataracts are the leading cause of blindness worldwide. Obesity, a key component of metabolic syndrome (MetS), may represent a modifiable risk factor for cataracts. However, the evidence regarding the association between MetS or its components and cataracts remains controversial. Therefore, our meta-analysis aims to clarify the relationship between MetS or its components and cataracts. A search was conducted until February 2024 via the MEDLINE, EMBASE, and Cochrane databases. The primary endpoint included the association between MetS or its components, including body mass index (BMI), diabetes mellitus (DM), hypertension (HT), and dyslipidemia (DLP), and cataracts. Secondary endpoints included the association between levels of blood pressure, blood sugar, and lipid profiles and cataracts. A total of 27 studies were included with a total of 1,010,014 patients (134,498 with cataracts and 875,516 without cataracts). Of these, there were 2 prospective cohort studies, 6 case–control studies, and 19 cross-sectional studies. The prevalence of cataracts was 13.3%. MetS was significantly associated with cataracts with an odds ratio (OR) of 1.60 (95% confidence interval [CI]: 1.44–1.77, I² = 93%). Among MetS components, high BMI and being diagnosed with DM, HT, and DLP were associated with cataracts with ORs of 1.45 (95% CI: 1.33–1.58, I² = 80%), 1.77 (95% CI: 1.69–1.85, I² = 51%), 1.23 (95% CI: 1.19–1.28, I² = 62%), and 1.38 (95% CI: 1.30–1.46, I² = 0%). Among the metabolic parameters, high total cholesterol, high triglycerides, low high-density lipoprotein cholesterol, and high low-density lipoprotein cholesterol were associated with cataracts with ORs of 1.25 (95% CI: 1.04–1.49, I² = 48%), 1.05 (95% CI: 1.04–1.06, I² = 68%), 1.07 (95% CI: 1.01–1.13, I² = 0%), and 1.30 (95% CI: 1.10–1.53, I² = 71%), respectively. MetS, obesity, DM, HT, and DLP are associated with cataracts. Prevention or treatment of MetS or its components likely represent a modifiable risk factor in cataract development.

Keywords

cataracts metabolic syndrome

Introduction

In the past three decades, the prevalence of blindness has increased by up to 50%.¹ The number of people with moderate-to-severe visual impairment is estimated to reach 474 million by 2050. Of these, 61 million people are expected to be blind.¹ Cataracts are the most common cause of blindness worldwide, and several risk factors contribute to cataract formation.² Among these, nonmodifiable risks include, but are not limited to, older age, female sex, genetics, and concurrent ocular diseases.² However, other factors related to cataracts are modifiable, such as ultraviolet exposure,³ excessive alcohol consumption,⁴ smoking,⁵ and certain metabolic diseases, including diabetes mellitus (DM), hypertension (HT), and obesity.⁶

Metabolic syndrome (MetS) is highly prevalent globally and is expected to increase up to 53% by 2035.⁷ MetS is a constellation of abdominal obesity, DM, HT, and dyslipidemia (DLP).^8,9 Obesity may be one of the modifiable risk factors for cataracts. According to a meta-analysis in 2014,¹⁰ obesity is associated with all types of cataracts, including nuclear, cortical, and posterior subcapsular (PSC) cataracts. However, later Mendelian randomization studies have shown no association between obesity and cataracts.^11,12 Furthermore, evidence gaps exist regarding the association between other components of MetS and cataracts. Therefore, we performed a meta-analysis to determine the association between MetS or its components and cataracts.

Methods

Our meta-analysis was registered with PROSPERO (CRD Number 420251001712). All processes were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.

Search strategy

A systematic search was conducted for studies published up to February 2024 via MEDLINE, EMBASE, and the Cochrane Database of Systematic Reviews, along with manual searching of the references. The search objective was to identify the association between MetS or its components and cataracts. Two authors (P.T. and S.P.) searched independently, and discrepancies were reviewed by the third author (P.P.) until reaching consensus. Search terms included “metabolic syndrome,” “cataracts,” “obesity,” “body mass index,” “waist circumference,” “diabetes mellitus,” “hypertension,” “hyperlipidemia,” and “dyslipidemia,” as shown in Supplementary Table S1.

Selection criteria

We included studies reporting a head-to-head comparison regarding cataracts between patients who had MetS or its components and those without. MetS components included body mass index (BMI), waist circumference (WC), DM, HT, and DLP. We also included studies comparing different levels of blood pressure (BP), both systolic BP (SBP) and diastolic BP (DBP), and metabolic parameters, including total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and fasting blood sugar (FBS). Eligible study designs included randomized controlled trials, cohort, case–control, and cross-sectional studies. There were no limitations regarding language or settings.

Quality assessment

We used multiple assessment criteria based on study designs. For cohort and case–control studies, quality was assessed using the Newcastle–Ottawa scale (NOS).¹³ Meanwhile, cross-sectional studies were assessed using the modified NOS.

Statistical analyses

The primary analysis included the odds ratio (OR) between MetS or its components and the presence of any cataract in at least one eye. Secondary analyses were performed by stratifying continuous data into high and low groups, categorized by the cutoff value used in each study. Since there was heterogeneity in the cutoff values, further analysis was conducted to select only studies that used the same cutoff values, including BMI at 25 kg/m², SBP at 140 mm Hg, DBP at 90 mm Hg, TC at 200 mg/dL, and TG at 150 mg/dL.

Weighted treatment estimates and 95% confidence intervals (95% CIs) were computed using arcsine transformation. Cochrane Q test and I² statistics were used to define statistical heterogeneity, using cutoff values of 50% and 75% for considerable and substantial heterogeneity, respectively. Publication bias was assessed by visualizing the symmetry of funnel plots. All analyses were performed using the “meta” package in R software version 4.4.3 (Vienna, Austria).

Results

Study selection and characteristics

A total of 18,130 studies were identified from our search strategy. After removing duplicates, 17,287 studies remained for screening. Another 17,216 studies were excluded after abstract screening, leaving 71 studies that underwent full-text review. Finally, 27 studies were included in the data collection and analysis. The selection process is summarized in the PRISMA flow diagram (Fig. 1).

FIG. 1.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.

Of the final 27 studies,^14–40 2 were prospective cohort studies, 6 were case–control studies, and 19 were cross-sectional studies. A total of 1,010,014 patients were included, comprising 134,498 patients with cataracts and 875,516 patients without cataracts. The prevalence of cataracts was 13.3%. The baseline characteristics and quality assessment of all studies are presented in Table 1.

Table 1.

Baseline Characteristics of the Studies Included in the Meta-Analysis

Author	Year	Study location	Continent	Center	Study period	Study design	Population	Age	Female (%)	N, total	N, cataract	N, no cataract	Quality
Athanasiov P.A. et al.¹⁹	2008	Myanmar	Asia	Multi	2008	Cross-sectional	Inhabitants aged 40 years or older	56.3	58	2481	960	1521	S4C2O3
Athanasiov P.A. et al.¹⁹	2009	Sri Lanka	Asia	Multi	2006–2007	Cross-sectional	Inhabitants aged 40 years or older	57.0 ± 10.6	60	1302	436	866	S4C2O3
Chang J. et al.³⁰	2022	Taiwan	Asia	Multi	2021–2022	Cross-sectional	Participants aged between 30 and 70 years	49.9 ± 11.0	64	121,380	10,967	110,413	S4C2O3
Foster P.J. et al.²⁶	2002	Singapore	Asia	Multi	1997–1998	Cross-sectional	Chinese men and women aged 40–81 years	NA	55	1005	430	575	S4C2O3
Galeone C. et al.¹⁴	2010	Italy	Europe	Multi	1991–2003	Case–control	Participants younger than 80 years	NA	53	2283	761	1522	S4C2E2
Heydari B. et al.³¹	2011	Iran	Asia	Single	2005–2006	Case–control	Patient attending the hospital during 2005–2006	NA	50	180	90	90	S4C1O3
Kim T. et al.²⁷	2014	Korea	Asia	Multi	2008–2009	Cross-sectional	Subjects from KNHANES age 19 years or older	NA	NA	10,248	2408	7840	S4C2O3
Leske M.C. et al.³⁸	1999	Barbados	North America	Multi	NA	Cross-sectional	Population aged 40–84 years	59 ± 12	57	4231	1800	2431	S4C2O2
Li S. et al.³²	2018	China	Asia	Single	2016–2017	Case–control	Subjects 45 years and older from Shanghai	NA	57	447	219	228	S4C2E2
Lim L.S. et al.²¹	2009	Singapore	Asia	Multi	2004–2006	Cross-sectional	Adults aged 40–80 years	NA	55	2779	1325	1454	S4C2O3
Lu Z. et al.²²	2012	China	Asia	Multi	2009–2011	Case–control	Participants aged 45–85 years	NA	0	724	362	362	S4C2E2
Park S. et al.³⁶	2013	Korea	Asia	Multi	NA	Cross-sectional	Subjects 50 years and older who did not have previous cataract operation	M 63.4 ± 8.9	56	3248	2040	1208	S4C2O3
Park S. et al.³⁷	2014	Korea	Asia	Multi	2014	Cross-sectional	Subjects from KNHANES 2008	52.9 ± 13.9	58	2852	1070	1782	S4C2O3
Park Y. et al.³⁷	2014	Korea	Asia	Multi	2008–2010	Cross-sectional	Subjects from KNHANES IV age 40 years or older	NA	57	11,076	4264	6812	S4C2O3
Paunksnis A. et al.¹⁵	2007	Lithuania	Europe	Multi	2001–2002	Cross-sectional	Population aged 35–64 years	NA	NA	1282	236	1046	S4C1O3
Phaswana N. et al.³⁹	2017	South Africa	Africa	Multi	2007–2008	Cross-sectional	Participants aged ≥50	NA	56	3649	167	3482	S4C2O3
Raman R. et al.²³	2010	India	Asia	Multi	NA	Cross-sectional	Participants aged ≥40 with T2DM	NA	NA	1283	614	669	S4C2O3
Rim T. et al.¹⁶	2014	Korea	Asia	Multi	2008–2010	Cross-sectional	Participants aged 40 years or older	58.4 ± 0.1	57	11,591	4648	6943	S4C2O3
Rim T.H. et al.²⁸	2015	Korea	Asia	Multi	2008–2012	Cross-sectional	Adults aged 40 years or older	NA	54	715,554	88,464	627,090	S4C2O3
Sabanayagam C. et al.¹⁸	2010	Singapore	Asia	Multi	NA	Cross-sectional	Adults aged 40–80 years	NA	52	2794	1268	1526	S4C2O3
Shah S.P. et al.²⁴	2009	Pakistan	Asia	Multi	NA	Cross-sectional	Adults aged 30 years or older	NA	53	20,498	4096	16,402	S4C2O3
Tang Y. et al.³⁵	2018	China	Asia	Multi	2012	Cross-sectional	Subjects aged 45 years or older	NA	59	10,233	4260	5973	S4C2O3
Tavani A. et al.²⁵	1995	Italy	Europe	Multi	1983–1992	Case–control	Women aged 75 years or less	NA	100	1512	287	1225	S4C2E2
Tomić M. et al.³³	2021	Croatia	Europe	Single	2021	Cross-sectional	Subjects with T2DM ≥5 years, age 55 years or older	65.71 ± 7.82	38	90	74	16	S4C2O3
Tung T. et al.³⁴	2005	Taiwan	Asia	Multi	1991–1999	Prospective cohort	Residents in Kinmen aged 30 years or older with T2DM	NA	60	578	179	399	S4C2O2
Ughade S.N. et al.²⁹	1998	India	Asia	Single	1998	Case–control	NA	NA	48	524	262	262	S4C1E2
Yoshida M. et al.⁴⁰	2010	Japan	Asia	Multi	1990–2003	Prospective cohort	Subjects aged 45–74 years	NA	54	76,190	2811	73,379	S4C2O3

KNHANES, Korea National Health and Nutrition Examination Survey; NA, not available; T2DM, type 2 diabetes mellitus.

MetS and cataracts

A total of five studies^14–18 evaluated data on MetS and cataracts. MetS is significantly associated with cataracts, with an OR of 1.60 (95% CI: 1.44–1.77, I² = 93%). Among the components of MetS, pooled results from nine studies^18–26 showed that high BMI was associated with cataracts, with an OR of 1.45 (95% CI: 1.33–1.58, I² = 80%). Pooled results from two studies^36,40 reported that BMI >25 kg/m² was associated with cataracts, with an OR of 1.23 (95% CI: 1.10–1.37, I² = 0%). Pooled results from eight studies^{14,16,18,25–29} showed that DM was associated with cataracts, with an OR of 1.77 (95% CI: 1.69–1.85, I² = 51%). Pooled results from 10 studies^{14,16,18,23–29} showed that HT was associated with cataracts, with an OR of 1.23 (95% CI: 1.19–1.28, I² = 62%). Pooled results from three studies^14,25,28 showed that DLP was associated with cataracts, with an OR of 1.38 (95% CI: 1.30–1.46, I² = 0%). However, pooled results from three studies^14,30,39 showed no association between WC and cataracts with an OR of 1.03 (95% CI: 0.98–1.09, I² = 75%). All analyses on MetS or its components and cataracts are shown in Table 2.

Table 2.

Association Between Metabolic Syndrome or Its Components and Cataracts

Category	Comparison	k	OR	95% CI	P value	I²
MetS	MetS vs. no MetS	5	1.597	1.444–1.766	<0.001	93%
BMI	>25 kg/m² vs. 23–24.9 kg/m²	2	1.226	1.098–1.369	<0.001	0%
BMI	High BMI vs. low BMI	9	1.450	1.329–1.583	<0.001	80%
WC	High WC vs. low WC	3	1.030	0.976–1.086	0.281	75%
DM	DM vs. no DM	8	1.769	1.691–1.852	<0.001	51%
HT	HT vs. no HT	10	1.234	1.187–1.283	<0.001	62%
DLP	DLP vs. no DLP	3	1.378	1.298–1.464	<0.001	0%

BMI, body mass index; CI, confidence interval; DLP, dyslipidemia; DM, diabetes mellitus; HT, hypertension; k, number of studies included in each analysis; MetS, metabolic syndrome; OR, odds ratio; WC, waist circumference.

BP and cataracts

A total of four studies^15,18,30,37 evaluated data on BP level and cataracts. High BP was associated with cataracts with an OR of 1.06 (95% CI: 1.00–1.12, I² = 72%). Among BP components, pooled results from four studies^18,34,35,38 showed that high SBP was associated with cataracts with an OR of 1.35 (95% CI: 1.23–1.49, I² = 59%), of which two studies^34,35 showed that SBP >140 mm Hg was associated with cataracts with an OR of 1.34 (95% CI: 1.20–1.49, I² = 0%). Pooled results from five studies^{18,33–35,38} showed that high DBP was associated with cataracts with an OR of 1.08 (95% CI: 1.01–1.14, I² = 52%), while pooled results from two studies^34,35 showed that DBP >90 mm Hg was not associated with cataracts with an OR of 1.19 (95% CI: 0.96–1.49, I² = 84%). All analyses on BP and cataracts are presented in Table 3.

Table 3.

Association Between Blood Pressure and Cataracts

Category	Comparison	k	OR	95% CI	P value	I ²
BP	High BP vs. low BP	4	1.06	1.002–1.12	0.042	72%
SBP	>140 mm Hg vs. <140 mm Hg	2	1.338	1.204–1.488	<0.001	0%
SBP	High SBP vs. low SBP	4	1.351	1.229–1.485	<0.001	59%
DBP	>90 mm Hg vs. <90 mm Hg	2	1.193	0.955–1.491	0.120	84%
DBP	High DBP vs. low DBP	5	1.075	1.013–1.14	0.017	52%

BP, blood pressure; CI, confidence interval; DBP, diastolic blood pressure; k, number of studies included in each analysis; OR, odds ratio; SBP, systolic blood pressure.

Metabolic parameters and cataracts

A total of 10 studies^{15,16,18,23,30–35} evaluated data on metabolic parameters and cataracts. Among lipid profiles, pooled results from four studies^16,31,33,34 showed that high TC was associated with cataracts with an OR of 1.25 (95% CI: 1.04–1.49, I² = 48%), of which two studies^31,34 showed no association between TC >200 mg/dL and cataracts with an OR of 1.36 (95% CI: 0.88–2.08, I² = 80%). Pooled results from eight studies^{15,18,23,30–34} showed that high TG was associated with cataracts with an OR of 1.05 (95% CI: 1.04–1.06, I² = 68%), of which five studies^{15,18,30–32} showed no association between TG >150 mg/dL and cataracts with an OR of 1.05 (95% CI: 1.04–1.06, I² = 78%). Pooled results from five studies^{15,18,30,31,35} showed that low HDL-C was associated with cataracts with an OR of 1.07 (95% CI: 1.01–1.13, I² = 0%). Pooled results from five studies^{23,31–33,35} showed that high LDL-C was associated with cataracts with an OR of 1.30 (95% CI: 1.10–1.53, I² = 71%). Among glycemic profiles, pooled results from four studies^15,30,31,34 showed that high FBS was associated with cataracts with an OR of 1.16 (95% CI: 1.10–1.23, I² = 77%). All analyses on metabolic parameters and cataracts are shown in Table 4.

Table 4.

Association Between Metabolic Parameters and Cataracts

Category	Comparison	k	OR	95% CI	P value	I²
TC	>200 mg/dL vs. <200 mg/dL	2	1.358	0.884–2.084	0.162	80%
TC	High TC vs. low TC	4	1.245	1.039–1.492	0.017	48%
TG	>150 mg/dL vs. <150 mg/dL	5	1.05	1.038–1.063	<0.001	78%
TG	High TG vs. low TG	8	1.05	1.038–1.063	<0.001	68%
HDL-C	Low HDL-C vs. high HDL-C	5	1.065	1.008–1.126	0.026	0%
LDL-C	High LDL-C vs. low LDL-C	5	1.297	1.102–1.527	0.002	71%
FBS	High FBS vs. low FBS	4	1.161	1.097–1.229	<0.001	77%

CI, confidence interval; FBS, fasting blood sugar; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; k, number of studies included in each analysis; OR, odds ratio; TC, total cholesterol; TG, triglyceride.

Publication bias

Since all funnel plots were symmetrical, no publication bias was detected for any outcome. The forest and funnel plots are presented in Supplementary Figs. S1 and Figs. S2, respectively.

Discussion

Our meta-analysis highlights MetS as a significant risk factor for cataract development. Among its individual components, HT, elevated TG levels, low HDL levels, and high FBS levels were all associated with cataract risk. However, increased WC did not show a statistically significant association. A long-standing question in research has been whether MetS contributes to cataract risk beyond the sum of its individual components.⁴¹ While this notion has gained increasing acceptance, our study further supports the idea that MetS itself is an independent risk factor for cataract.

Although obesity and higher BMI are not formally included in the diagnostic criteria for MetS, they have been linked to various ophthalmological conditions, including age-related macular degeneration, diabetic retinopathy, and cataract.⁴² A previous study demonstrated a dose–response relationship between BMI and cataract risk.⁴³ Our findings, which show that BMI >25 kg/m² is associated with an increased risk of cataracts, further reinforce this relationship. However, conflicting evidence exists, as some studies suggest that while high BMI is a risk factor for cataract onset, it may not necessarily contribute to cataract progression.⁴⁴

While DM is a well-established risk factor for cataracts, emerging evidence suggests that insulin resistance itself may contribute to various ophthalmological conditions, even in nondiabetic individuals.⁴⁴ Insulin resistance is a hallmark of MetS, which can alter lens metabolism by activating the sorbitol pathway, leading to the accumulation of advanced glycation end products.^45,46 Our findings, which show an association between elevated FBS levels and cataract risk, further support the role of glucose dysregulation and insulin resistance in lens opacification. While the presence of diabetes increases cataract risk, these findings suggest that even prediabetic states associated with MetS may elevate cataract risk.

Dyslipidemia has been linked to cataract development, with low HDL and high LDL levels contributing to oxidative stress and lipid peroxidation in the lens. While previous studies suggested a role for high TC, low HDL, and high LDL in various ocular diseases, including cataracts,^33,47 our findings indicate that low HDL and high LDL, rather than TC, are more strongly associated with cataract risk. Given that low HDL and high TGs are key components of MetS, our results further support a connection between lipid imbalance and cataract formation.

We also observed an association between high SBP and cataract risk. While routine preoperative testing is often unnecessary in low-risk surgeries such as cataract extraction,⁴⁸ the presence of HT may warrant closer evaluation due to its association with increased surgical risk.⁴⁹ In addition, unnecessary preoperative investigations can contribute to higher health care costs without a clear clinical benefit.⁵⁰ Further studies are needed to assess the impact of HT on cataract surgery outcomes.

Most cataracts develop as a result of aging, primarily due to oxidative stress and metabolic dysfunction, which compromise the structural integrity of the lens. MetS is characterized by adipocyte tissue dysfunction and insulin resistance, conditions closely linked to chronic inflammation and oxidative stress. These processes involve the NLRP3 inflammasome, which plays a key role in inflammatory responses within adipose tissue.^51,52 Elevated levels of inflammatory markers such as C-reactive protein, interleukin-1, interleukin-6, and plasminogen activator inhibitor-1 have been reported in individuals with MetS,⁵³ further supporting the role of systemic inflammation in cataract development. While the precise mechanisms linking MetS to cataract formation remain unclear, chronic low-grade inflammation and oxidative stress likely contribute to the pathophysiology.

Our study has a few limitations. Most of the included studies are cross-sectional or case–control, with only a limited number of prospective cohort studies, which may impact the ability to establish causality. In addition, most studies did not indicate whether cataract in each eye was counted separately for each patient, which may affect the comparability across studies and the reliability of our findings. Furthermore, our choice of cutoff for obesity with a BMI of 25 or greater may limit generalizability to more developed countries. The choice of BMI cutoff was limited partly due to the inconsistent BMI cutoffs and stratification used across the included studies. Similarly, when assessing the risk of cataract in diabetes, diabetes severity, duration, and presence of diabetes-related complications were not considered in our analysis, as few studies reported these parameters. In addition, high heterogeneity was observed across studies. This variability likely arises from differences in study designs, patient populations, methodologies, and diagnostic criteria, which could influence the consistency and generalizability of our findings. To mitigate this, we performed meta-regression analyses, allowing us to explore potential sources of heterogeneity and improve the robustness of our findings. However, this variability is expected given the large number of studies included, making this one of the largest and most current meta-analyses to date. Future well-designed, large-scale prospective studies are needed to further elucidate the underlying mechanisms and associations.

Given the significant impact of cataracts on quality of life and health care costs, our findings suggest that effective management and prevention of MetS may help reduce cataract burden at a population level. Patients with MetS may require more extensive preoperative evaluations, potentially increasing costs and affecting surgical outcomes. This underscores the need for proactive strategies to address MetS as part of comprehensive eye health and cataract management.

Authors’ Contributions

All the authors had access to the data and a role in writing the article.

Footnotes

Author Disclosure Statement

The authors do not have any potential financial or nonfinancial conflicts of interest.

Funding Information

No funding was received for this article.

Acknowledgement

We gratefully acknowledge TERMAURA HEALTH AND WELLNESS CO., LTD., a healthcare provider company from Thailand, for their extensive and expert support during the development and implementation of this research, which substantially strengthened the rigor, progress, and successful completion of the study.

Supplemental Material

References

Bourne

, Steinmetz

, Flaxman

, et al. Trends in prevalence of blindness and distance and near vision impairment over 30 years: An analysis for the Global Burden of Disease Study. Lancet Glob Health, 2021; 9(2):e130–e143; doi: 10.1016/S2214-109X(20)30425-3

Cicinelli

, Buchan

, Nicholson

, et al. Cataracts. Lancet, 2023; 401(10374):377–389; doi: 10.1016/S0140-6736(22)01839-6

Neale

, Purdie

, Hirst

, et al. Sun exposure as a risk factor for nuclear cataract. Epidemiology, 2003; 14(6):707–712; doi: 10.1097/01.ede.0000086881.84657.98

Wang

, Zhang

. Alcohol intake and the risk of age-related cataracts: A meta-analysis of prospective cohort studies. PLoS One, 2014; 9(9):e107820; doi: 10.1371/journal.pone.0107820

, He

, Wang

, et al. Smoking and risk of age-related cataract: A meta-analysis. Invest Ophthalmol Vis Sci, 2012; 53(7):3885–3895; doi: 10.1167/iovs.12-9820

Yuan

, Wolk

, Larsson

. Metabolic and lifestyle factors in relation to senile cataract: A Mendelian randomization study. Sci Rep, 2022; 12(1):409; doi: 10.1038/s41598-021-04515-x

Pitliya

, Kakarlapudi

, Vasudevan

, et al. The global prevalence of metabolic syndrome in connection with sleep duration: A systematic review and meta-analysis. Metab Syndr Relat Disord, 2024; 22(6):411–421; doi: 10.1089/met.2024.0004

Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the 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). JAMA, 2001; 285(19):2486–2497; doi: 10.1001/jama.285.19.2486

Grundy

, Brewer

Jr , Cleeman

, et al.; National Heart, Lung, and Blood Institute. Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation, 2004; 109(3):433–438; doi: 10.1161/01.CIR.0000111245.75752.C6

10.

Pan

C-W

, Lin

. Overweight, obesity, and age-related cataract: A meta-analysis. Optom Vis Sci, 2014; 91(5):478–483; doi: 10.1097/OPX.0000000000000243

11.

Tan

, Kifley

, Flood

, et al. Evaluating the associations between obesity and age-related cataract: A Mendelian randomization study. Am J Clin Nutr, 2019; 110(4):969–976; doi: 10.1093/ajcn/nqz167

12.

Jiang

, Melles

, Sangani

, et al. Association of behavioral and clinical risk factors with cataract: A two-sample Mendelian Randomization Study. Invest Ophthalmol Vis Sci, 2023; 64(10):19; doi: 10.1167/iovs.64.10.19

13.

Stang

. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol, 2010; 25(9):603–605; doi: 10.1007/s10654-010-9491-z

14.

Galeone

, Petracci

, Pelucchi

, et al. Metabolic syndrome, its components and risk of age-related cataract extraction: A case-control study in Italy. Ann Epidemiol, 2010; 20(5):380–384; doi: 10.1016/j.annepidem.2010.01.009

15.

Paunksnis

, Bojarskiene

, Cimbalas

, et al. Relation between cataract and metabolic syndrome and its components. Eur J Ophthalmol, 2007; 17(4):605–614; doi: 10.1177/112067210701700420

16.

Rim

, Kim

, et al. Cataract subtype risk factors identified from the Korea National Health and Nutrition Examination survey 2008–2010. BMC Ophthalmol, 2014; 14:4; doi: 10.1186/1471-2415-14-4

17.

Park

, Lee

. Association between metabolic syndrome and age-related cataract. Int J Ophthalmol, 2015; 8(4):804–811; doi: 10.3980/j.issn.2222-3959.2015.04.29

18.

Sabanayagam

, Wang

, Mitchell

, et al. Metabolic syndrome components and age-related cataract: The Singapore Malay eye study. Invest Ophthalmol Vis Sci, 2011; 52(5):2397–2404; doi: 10.1167/iovs.10-6373

19.

Athanasiov

, Casson

, Sullivan

, et al. Cataract in rural Myanmar: Prevalence and risk factors from the meiktila eye study. Br J Ophthalmol, 2008; 92(9):1169–1174; doi: 10.1136/bjo.2008.139725

20.

Athanasiov

, Edussuriya

, Senaratne

, et al. Cataract in central Sri Lanka: Prevalence and risk factors from the Kandy Eye Study. Ophthalmic Epidemiol, 2010; 17(1):34–40; doi: 10.3109/09286580903324900

21.

Lim

, Tai

, Aung

, et al. Relation of age-related cataract with obesity and obesity genes in an Asian population. Am J Epidemiol, 2009; 169(10):1267–1274; doi: 10.1093/aje/kwp045

22.

, Sun

, Yan

, et al. Cigarette smoking, body mass index associated with the risks of age-related cataract in male patients in northeast China. Int J Ophthalmol, 2012; 5(3):317–322; doi: 10.3980/j.issn.2222-3959.2012.03.13

23.

Raman

, Pal

, Adams

, et al. Prevalence and risk factors for cataract in diabetes: Sankara Nethralaya Diabetic Retinopathy Epidemiology and Molecular Genetics Study, report no. 17. Invest Ophthalmol Vis Sci, 2010; 51(12):6253–6261; doi: 10.1167/iovs.10-5414

24.

Shah

, Dineen

, Jadoon

, et al. Lens opacities in adults in Pakistan: Prevalence and risk factors. Ophthalmic Epidemiol, 2007; 14(6):381–389; doi: 10.1080/09286580701375179

25.

Tavani

, Negri

, La Vecchia

. Selected diseases and risk of cataract in women. A case-control study from Northern Italy. Ann Epidemiol, 1995; 5(3):234–238; doi: 10.1016/1047-2797(94)00111-6

26.

Foster

, Wong

, Machin

, et al. Risk factors for nuclear, cortical and posterior subcapsular cataracts in the Chinese population of Singapore: The Tanjong Pagar Survey. Br J Ophthalmol, 2003; 87(9):1112–1120; doi: 10.1136/bjo.87.9.1112

27.

Kim

, Lee

, et al. Prevalence of and factors associated with lens opacities in a Korean adult population with and without diabetes: The 2008-2009 Korea National Health and Nutrition Examination Survey. PLoS One, 2014; 9(4):e94189; doi: 10.1371/journal.pone.0094189

28.

Rim

, Kim

, et al. Factors associated with cataract in Korea: A Community Health Survey 2008-2012. Yonsei Med J, 2015; 56(6):1663–1670; doi: 10.3349/ymj.2015.56.6.1663

29.

Ughade

, Zodpey

, Khanolkar

. Risk factors for cataract: A case control study. Indian J Ophthalmol, 1998; 46(4):221–227.

30.

Chang

, Chen

, Geng

, et al. Metabolic syndrome is associated with cataract in a large taiwanese population study. Nutrients, 2022; 14(9):1684; doi: 10.3390/nu14091684

31.

Heydari

, Kazemi

, Zarban

, et al. Correlation of cataract with serum lipids, glucose and antioxidant activities: A case-control study. West Indian Med J, 2012; 61(3):230–234; doi: 10.7727/wimj.2011.103

32.

, Li

, Zhang

, et al. Association between serum lipids concentration and patients with age-related cataract in China: A cross-sectional, case-control study. BMJ Open, 2018; 8(4):e021496; doi: 10.1136/bmjopen-2018-021496

33.

Tomić

, Vrabec

, Raštegorac

, et al. Hypertension and hypercholesterolemia are associated with cataract development in patients with type 2 diabetes. High Blood Press Cardiovasc Prev, 2021; 28(5):475–481; doi: 10.1007/s40292-021-00472-8

34.

Tung

, Liu

, Lee

, et al. Community-based study of cataracts among type 2 diabetics in Kinmen. Eur J Epidemiol, 2005; 20(5):435–441; doi: 10.1007/s10654-004-7537-9

35.

Tang

, Wang

, et al. Risk factors of age-related cataract in a Chinese adult population: The Taizhou Eye Study. Clin Exp Ophthalmol, 2018; 46(4):371–379; doi: 10.1111/ceo.13040

36.

Park

, Kim

, Cho

, et al. Association between cataract and the degree of obesity. Optom Vis Sci, 2013; 90(9):1019–1027; doi: 10.1097/OPX.0b013e31829cae62

37.

Park

, Shin

, Han

, et al. Gender difference in the association of metabolic syndrome and its components with age-related cataract: The Korea National Health and Nutrition Examination Survey 2008-2010. PLoS One, 2014; 9(1):e85068; doi: 10.1371/journal.pone.0085068

38.

Leske

, Wu

, Hennis

, et al. Diabetes, hypertension, and central obesity as cataract risk factors in a black population. The Barbados Eye Study. Ophthalmology, 1999; 106(1):35–41; doi: 10.1016/s0161-6420(99)90003-9

39.

Phaswana-Mafuya

, Peltzer

, Crampin

, et al. Prevalence of self-reported diagnosed cataract and associated risk factors among elderly South Africans. Int J Environ Res Public Health, 2017; 14(12):1523; doi: 10.3390/ijerph14121523

40.

Yoshida

, Inoue

, Iwasaki

, et al.; JPHC Study Group. Association of body mass index with risk of age-related cataracts in a middle-aged Japanese population: The JPHC Study. Environ Health Prev Med, 2010; 15(6):367–373; doi: 10.1007/s12199-010-0153-2

41.

Kahn

, Buse

, Ferrannini

, et al.; European Association for the Study of Diabetes. The metabolic syndrome: Time for a critical appraisal: Joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care, 2005; 28(9):2289–2304; doi: 10.2337/diacare.28.9.2289

42.

Ng Yin Ling

, Lim

, Jonas

, et al. Obesity and risk of age-related eye diseases: A systematic review of prospective population-based studies. Int J Obes (Lond), 2021; 45(9):1863–1885; doi: 10.1038/s41366-021-00829-y

43.

Niazi

, Moshirfar

, Dastjerdi

, et al. Association between obesity and age-related cataract: An updated systematic review and dose-response meta-analysis of prospective cohort studies. Front Nutr, 2023; 10:1215212; doi: 10.3389/fnut.2023.1215212

44.

Tan

, Tham

, Chee

, et al. Incidence, progression and risk factors of age-related cataract in Malays: The Singapore Malay Eye Study. Clin Exp Ophthalmol, 2020; 48(5):580–592; doi: 10.1111/ceo.13757

45.

Reddy

, Giridharan

, Reddy

. Activation of sorbitol pathway in metabolic syndrome and increased susceptibility to cataract in Wistar-Obese rats. Mol Vis, 2012; 18:495–503; doi: 10.4081/ejcm.2012.v3.e80

46.

Stevens

, Rouzer

, Monnier

, et al. Diabetic cataract formation: Potential role of glycosylation of lens crystallins. Proc Natl Acad Sci U S A, 1978; 75(6):2918–2922; doi: 10.1073/pnas.75.6.2918

47.

Betzler

, Rim

, Sabanayagam

, et al. High-density lipoprotein cholesterol in age-related ocular diseases. Biomolecules, 2020; 10(4):645; doi: 10.3390/biom10040645

48.

Thompson

, Fleischmann

, Smilowitz

, et al. 2024 AHA/ACC/ACS/ASNC/HRS/SCA/SCCT/SCMR/SVM guideline for perioperative cardiovascular management for noncardiac surgery: A Report of the American College of. Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol, 2024; 84(19):1869–1969.

49.

Kang

, Lee

, Chomsky

, et al. Risk factors for complications in resident-performed cataract surgery: A systematic review. Surv Ophthalmol, 2024; 69(4):638–645; doi: 10.1016/j.survophthal.2024.04.002

50.

Chen

, Lin

, Bardach

, et al. Preoperative medical testing in Medicare patients undergoing cataract surgery. N Engl J Med, 2015; 372(16):1530–1538; doi: 10.1056/NEJMsa1410846

51.

Neeland

, Lim

, Tchernof

, et al. Metabolic syndrome. Nat Rev Dis Primers, 2024; 10(1):77; doi: 10.1038/s41572-024-00563-5

52.

Vandanmagsar

, Youm

, Ravussin

, et al. The NLRP3 inflammasome instigates obesity-induced inflammation and insulin resistance. Nat Med, 2011; 17(2):179–188; doi: 10.1038/nm.2279

53.

Koh

, Han

, Quon

. Inflammatory markers and the metabolic syndrome: Insights from therapeutic interventions. J Am Coll Cardiol, 2005; 46(11):1978–1985; doi: 10.1016/j.jacc.2005.06.082

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

4.64 MB