Next Article in Journal
Dried Plum’s Polyphenolic Compounds and Carbohydrates Contribute to Its Osteoprotective Effects and Exhibit Prebiotic Activity in Estrogen Deficient C57BL/6 Mice
Previous Article in Journal
The Brown Algae Ishige sinicola Extract Ameliorates Ovariectomy-Induced Bone Loss in Rats and Suppresses Osteoclastogenesis through Downregulation of NFATc1/c-Fos
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 14
Issue 9
10.3390/nu14091684
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Metabolic Syndrome Is Associated with Cataract in a Large Taiwanese Population Study

by
Jung-Hsiu Chang
1,†,
I-Hua Chen
2,†,
Jiun-Hung Geng
3,4,
Pei-Yu Wu
5,6,
Jiun-Chi Huang
5,6,7 and
Szu-Chia Chen
5,6,7,8,*
1
Department of Post Baccalaureate Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
2
Division of Endocrinology and Metabolism, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
3
Department of Urology, Kaohsiung Municipal Siaogang Hospital, Kaohsiung Medical University, Kaohsiung 812, Taiwan
4
Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
5
Department of Internal Medicine, Kaohsiung Municipal Siaogang Hospital, Kaohsiung Medical University, Kaohsiung 812, Taiwan
6
Division of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
7
Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
8
Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Nutrients 2022, 14(9), 1684; https://doi.org/10.3390/nu14091684
Submission received: 23 March 2022 / Revised: 8 April 2022 / Accepted: 18 April 2022 / Published: 19 April 2022
(This article belongs to the Section Nutritional Epidemiology)
Browse Figure
Versions Notes

Abstract

:
Cataract is the leading cause of blindness worldwide, and metabolic syndrome (MetS) is a known risk factor. In this study, we investigated the association between the risk of cataract with MetS and its components in a large-scale study. Data were derived from the Taiwan Biobank, and 121,380 individuals were included. The NCEP-ATP III criteria modified for use in an Asian population were used to define MetS and its components. The occurrence of cataract was identified through a standardized interview and self-reported questionnaire. Multivariable analysis showed that MetS (OR, 1.129; 95% CI, 1.0175–1.184; p < 0.001), low high-density lipoprotein (HDL)-cholesterol (OR, 1.057; 95% CI, 1.005–1.113; p = 0.032), and hyperglycemia (OR, 1.162; 95% CI, 1.108–1.218; p < 0.001) were significantly associated with cataract. Furthermore, a stepwise increase in the prevalence of cataract corresponding to the number of MetS components was found. The presence of three MetS components (vs. 0; OR, 1.103; 95% CI, 1.024–1.188; p = 0.010), four MetS components (vs. 0; OR, 1.137; 95% CI, 1.040–1.242; p = 0.005), and five MetS components (vs. 0; OR, 1.208; 95% CI, 1.059–1.378; p = 0.005) were significantly associated with cataract. In conclusion, significant associations were found between a high incidence of cataract with MetS and its components, including low HDL-cholesterolemia and hyperglycemia. Further, a stepwise increase in the prevalence of cataract corresponding to the number of MetS components was also found. The results of this study indicate that MetS may increase the development of cataract in Taiwan.

Graphical Abstract

1. Introduction

Cataract is defined as opacity of the crystalline lens, and approximately 12% of adults aged >40 years in Taiwan were reported to have cataract in 2013 [1]. Age, smoking [2,3], alcohol use [3], sunlight exposure [4], metabolic syndrome (MetS) [5], diabetes mellitus (DM) [3,5], and systemic corticosteroid use [6] are known risk factors for cataract, as they may induce either damage to the lens or effect the water–electrolyte balance of the aqueous humor [7,8], which will then change the transparency of the crystalline lens. Cataract has been associated with a poor quality of life and psychosocial distress [9,10], and typical symptoms include glare, diplopia, and visual dysfunction. Moreover, cataract is the leading cause of blindness worldwide [11], especially in low- and middle-income regions, where it has been reported to account for one in six cases of visual dysfunction and one in three cases of blindness [12]. Therefore, it is very important to detect the risk factors as early as possible in high-risk populations to allow for both prompt interventions and education on how to prevent cataract. The relationship between exercise and cataracts is another interesting topic due to the need to establish some preventive strategies. Jiang et al. [13] performed a dose-response meta-analysis to evaluate the relation between physical activity and age-related cataract (ARC) risk and revealed in quantity that the risk of ARC decreased by 2% for every 6 metabolic equivalents of task per day increase in activity. In addition, according to a Cox proportional hazard analyses conducted by Williams et al. [14], which assessed the different effects on cataract risk decrease between vigorous (running) and moderate exercise (walking), the results showed both were significantly associated with lower cataract risk and increasing exercise energy expenditure was associated with decrease of cataract risk linearly.
MetS is a cluster of metabolic abnormalities that include hyperlipidemia, hypertension, obesity, and insulin resistance [15,16,17]. However, the definition of MetS varies between guidelines such as the International Diabetes Federation (IDF), World Health Organization, Adult Treatment Panel III (ATP III), and others. Overall MetS prevalence rates of 15.7% and 22% have been reported in Taiwan and the United States, respectively [18,19]. In addition, MetS has been significantly associated with type 2 DM [20] and cardiovascular disease [21], and previous studies and animal experiments have revealed that MetS and its components may be biochemically and genetically related to cataract [22,23,24]. Different mechanisms have been postulated for the pathophysiological pathways between MetS and cataract [25,26], for example, lens protein glycation induced by DM leading to an increase in the hyperosmotic effects of sorbitol [24], and obesity-associated hyperleptinemia and leptin resistance resulting in the accumulation of reactive oxygen species, which has been strongly linked to lens opacification [27,28]. However, few large-scale studies have investigated the association between MetS and its components with cataract [22,29,30].
Four previous studies have reported on associations between cataract and/or cataract extraction with MetS [5,22,31,32]. Lindblad et al. examined the associations among middle-aged and older women and men [5,32], and Ghaem et al. reported that high glucose and obesity were associated with a higher incidence of cortical cataract over a 5 year period [22].
Due to the lack of studies using the ATP III criteria for MetS, we conducted this study using data from the Taiwan Biobank (TWB) on over 120,000 Taiwanese participants to evaluate the relationships among the five components of MetS defined according to the ATP III criteria and cataract. In addition, we also analyzed relationships between the number of MetS components and cataract. The aim of this study was to evaluate whether MetS, its components, and the number of MetS components were associated with a higher risk of cataract.

2. Materials and Methods

2.1. Taiwan Biobank

The TWB was established by the Ministry of Health and Welfare and approved by the Institutional Review Board on Biomedical Science Research, Academia Sinica, Taiwan, and the Ethics and Governance Council of the TWB. It includes data on community-dwelling people aged between 30 and 70 years with no prior history of cancer including lifestyle, genetic and medical factors [33,34]. All enrollees in the TWB undergo interviews and physical examinations, and blood samples are obtained. In this study, we included 121,390 participants, all of whom provided written informed consent, and the study was approved by the Institutional Review Board of Kaohsiung Medical University Hospital (KMUHIRB-E(I)-20210058) on 8 April 2021. In addition, this study followed the Declaration of Helsinki [35].

2.2. Collection of Study Variables

Hip circumference (HC), body height (BH), waist circumference (WC), body weight (BW), systolic blood pressure (BP) and diastolic BP were recorded during physical examinations. HC, BH, WC, and BW were measured once. BP data were measured by trained personnel by using a digital BP machine. The patients were advised to avoid caffeine, exercise, and smoking for at least 30 min before the first BP measurement. Each BP was measured three times, and separate repeated measurements were done in 1–2 min intervals. We used the average value of three readings of systolic BP and diastolic BP for later analysis. All participants enrolled in the TWB complete a questionnaire during an in-person interview. Data on personal medical history (such as hypertension and DM), lifestyle factors (such as exercise), sex and age were obtained using a questionnaire during the interviews. For the purpose of this study, we defined regular exercise as participating in an activity such as a sport, jogging, swimming, yoga, cycling, hiking, and exercise-based apps, for more than 30 min on three or more occasions in one week. Activities related to employment were not included in this study.
The following variables were also recorded: estimated glomerular filtration rate (eGFR—calculated using the Modification of Diet in Renal Disease study equation [36]), total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, fasting glucose, hemoglobin, and uric acid.

2.3. Definitions of Cataract and MetS

The participants were also asked if they had ever had cataract, and those who answered “Yes” were classified into the cataract group.
MetS was defined as the presence of three or more of the following criteria in accordance with the modified NCEP-ATP III criteria for use in an Asian population [37]: (1) systolic/diastolic BP of ≥130 mmHg or ≥85 mmHg, respectively, participants with a diagnosis of hypertension, and those prescribed hypertensive medications; (2) triglyceride concentration ≥150 mg/dL; (3) HDL-cholesterol concentration in women and men of <50 mg/dL and <40 mg/dL, respectively; (4) abdominal obesity (WC in women and men of ≥80 cm and ≥90 cm, respectively); (5) hyperglycemia (participants with a fasting whole-blood glucose concentration ≥100 mg/dL or a diagnosis of DM).

2.4. Statistical Analysis

Statistical Package for the Social Sciences version 19.0 was used to conduct the analysis (SPSS Inc., Chicago, IL, USA). Continuous variables are given as means ± standard deviations, and compared using the independent t test. Categorical variables are given as frequencies and percentages, and compared using the chi-squared test. Associations among MetS, the number of MetS components, and individual components with cataract were analyzed with multivariable logistic regression analysis, which included significant variables in univariable analysis. A p-value less than 0.05 was considered statistically significant.

3. Results

A total of 121,380 participants (mean age 49.9 ± 11.0 years; 43,616 men; 77,764 women) were enrolled in this study. All participants were classified into cataract (n = 17,320; 9.0%) and non-cataract (n = 110,413; 91.0%) groups.

3.1. Comparisons of the Characteristics between the Cataract and Non-Cataract Groups

Comparisons of the characteristics between the two groups are shown in Table 1. Compared to the non-cataract group, the cataract group were predominantly female, older, had higher prevalence rates of DM and hypertension, and lower prevalence rates of smoking and alcohol history. They also had a higher rate of regular exercise, higher systolic BP, diastolic BP, WC, fasting glucose, triglycerides, total cholesterol, and HDL-C, and lower HC, BH, BW, hemoglobin, and eGFR. Regarding MetS, the participants with cataract had a high prevalence of MetS, higher number of MetS components, and higher prevalence of all five components (central obesity, low HDL-C, hypertension, hypertriglyceridemia, and hyperglycemia).

3.2. Association of MetS and Cataract

Multivariable logistic regression analysis was conducted (adjusted for eGFR, uric acid LDL-C, total cholesterol, hemoglobin, age, sex, smoking habit, alcohol consumption, and regular exercise) to identify the factors associated with cataract, and the results are shown in Table 2. The results showed that MetS (odds ratio [OR], 1.129; 95% confidence interval [CI], 1.0175–1.184; p < 0.001), low uric acid (p < 0.001), low eGFR (p = 0.008), low LDL-C (p < 0.001), low hemoglobin (p < 0.001), smoking history (p < 0.001), female sex (p < 0.001), and older age (p < 0.001) were significantly associated with cataract.

3.3. Associations between the Number of MetS Components and Cataract

Associations between the number of MetS components and cataract were analyzed using multivariable logistic regression analysis adjusted for eGFR, uric acid LDL-C, total cholesterol, hemoglobin, age, sex, smoking habit, alcohol consumption, and regular exercise (Table 3). The results showed that the participants with three MetS components (vs. 0; OR, 1.103; 95% CI, 1.024–1.188; p = 0.010), four MetS components (vs. 0; OR, 1.137; 95% CI, 1.040–1.242; p = 0.005), and five MetS components (vs. 0; OR, 1.208; 95% CI, 1.059–1.378; p = 0.005) were significantly associated with cataract.

3.4. Associations between MetS Components and Cataract

Associations between the MetS components and cataract were analyzed using multivariable logistic regression adjusted for uric acid, eGFR, LDL-C, HDL-C, total cholesterol, triglycerides, hemoglobin, fasting glucose, diastolic BP, systolic BP, regular exercise, alcohol consumption, smoking history, hypertension, DM, sex, and age (Table 4). The results showed that the participants with low HDL-C (OR, 1.057; 95% CI, 1.005–1.113; p = 0.032) and hyperglycemia (OR, 1.162; 95% CI, 1.108–1.218; p < 0.001) were significantly associated with cataract. However, abdominal obesity (p = 0.806), hypertriglyceridemia (p = 0.094), and hypertension (p = 0.102) were not associated with cataract.

4. Discussion

The results of this large-scale study showed that after adjusting for confounding variables, the participants with MetS had a higher prevalence of cataract than those without MetS. In addition, in analysis of MetS components, low HDL-cholesterolemia and hyperglycemia were associated with a higher risk of cataract. Furthermore, we found a stepwise increase in cataract prevalence corresponding to the number of MetS components, although the trend was not significant.
In a prospective cohort study of 4508 women in Sweden who underwent cataract extraction with 98 months of follow-up, Lindblad et al. [5] investigated associations between the incidence of cataract extraction with MetS and its components. Although they lacked data on HDL-cholesterol and triglycerides, they found a 68% higher risk of cataract extraction in women with the other three components of MetS (hypertension, abdominal adiposity, and hyperglycemia) compared to women without these components. In particular, those under 65 years of age with these components had an approximately 3-fold higher risk of cataract extraction [5]. In another prospective cohort study of 45,049 men, Lindblad et al. [32] found similar results, in that men under 65 years of age or younger with abdominal adiposity, hyperglycemia, and hypertension had a relative risk of 2.43-fold for cataract extraction. In addition, in a prospective cohort study conducted in Australia, Ghaem et al. [22] reported that MetS defined according to the IDF criteria and its components were associated with the incidence of cortical cataract and posterior subcapsular cataract (PSC) over a 5-year period. In addition, they found that hyperglycemia was associated with the 10- and 5-year incidence rates of PSC cataract and cortical cataract, and low HDL-C was associated with the 10-year incidence of cortical cataract. In another cohort study conducted by Tan et al. [31], baseline MetS was associated with higher risks of incident PSC, nuclear, and cortical cataracts. In addition, significant associations were found between high MetS index values and cortical cataracts, and a borderline non-significant association with PSC cataract was also found [31]. In a population-based study conducted in Singapore, Sabanayagam et al. [30] investigated 2794 adults aged 40 to 80 years, and found a significant association between cataract and MetS only when the patients had ≥4 MetS components. The association between cataract and MetS has been proposed to be through oxidative stress and inflammatory processes [38,39]. Several studies have reported associations between cataract with pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and C-reactive protein (CRP) [40,41]. In addition, many studies have shown an association between each component of MetS and increased levels of CRP and cytokines [42,43,44]. In addition, a linear association between an elevated CRP level with a higher number of MetS components has also been reported [45]. Oxidative stress and inflammation may therefore partially explain the association between MetS and cataract.
Another main finding is the significant association between a low HDL-C level and cataract. Ghaem et al. did not find a significant difference in the 10-year cumulative incidence of cataract between participants with or without low HDL-cholesterol; however, they found an association between a low-HDL-C level and a higher 10-year incidence of cortical cataract [22]. In addition, both Tan et al. and Sabanayagam et al. found no significant associations between low HDL-C and the incidence of cataract or cataract surgery [31]. Low HDL-C has been associated with higher CRP levels, especially in women [42,45], and Schaumberg et al. suggested that a high CRP level may be a marker for one of the fundamental biological mechanisms causing cataract [40]. Although some animal studies have reported associations between low HDL-C with oxidative stress and inflammatory processes leading to cataract formation, more accurate pathophysiological mechanisms linking low HDL-cholesterol with age-related cataract remain unclear [46,47].
We also found that the participants with hyperglycemia had a higher incidence of cataract, which is consistent with other cohort studies [5,22,31,32], cross-sectional studies [29,30,48], and a case-control study [49]. Two main mechanisms may contribute to cataract: First, a hyperosmotic effect leading to the accumulation of sorbitol in the lens, and subsequently swelling and rupture of lens fibers [50]. Second, the higher level of nonenzymatic glycation of protein in people with DM leading to an increase in the production of advanced glycation end products (AGEs) in the lens [51]. In addition, AGEs can induce TNF-α, IL-1α, and IL-6, which suggests that inflammation may link DM and cataract [44].
In addition, we did not find a significant association between abdominal obesity, defined by WC, and cataract. Many studies have reported a significant association between obesity and cataract, however most of them used body mass index (BMI) to define obesity, and only one included Asian people [5,22,31,52,53,54,55,56,57,58,59,60,61,62]. Only two prospective cohort studies have used WC to define obesity, both of which reported no significant association between cataract and obesity [32,57]. Using WC as a measure of obesity may be more appropriate in an Asian population because Asians tend to have more body fat per BMI than Caucasians [63], which indicates a greater potential for the development of hypertension, DM, and dyslipidemia at a lower BMI. In addition, due to the clear link between obesity and cataract caused by chronic inflammation and inflammatory markers such as CRP, IL-6 and TNF-α, and urinary isoprostanes, it is apparent that obesity is a risk factor contributing to cataract [42,43,64]. Therefore, despite the inconsistent results between studies using BMI and WC as the definition of obesity, obesity remains a strong risk factor for cataract.
Moreover, we did not find a significant association between hypertriglyceridemia and cataract in this study, which is consistent with many previous studies [22,31]. Ridker et al. investigated relationships between incident cardiovascular events, MetS, and CRP in 14,719 healthy women, and found that each component of MetS contributed to CRP level [45]. However, Tan et al. and Ghaem et al. reported that hypertriglyceridemia was not significantly related with incident cataract [22,31].
Finally, we found that high BP was not significantly associated with cataract. An association between cataract and hypertension has been reported in previous cohort studies [5,31,65], cross-sectional studies [30,48,66,67], and a case-control study [49], however the results were inconsistent. Associations between hypertension with TNF-α, IL-6 [68], and CRP [45] have been reported, suggesting that inflammation may link cataract and hypertension [40,41]. However, it was difficult to assess the degree of hypertension control with medications in each participant, and this may have confounded the analysis of the association between cataract and hypertension.
The main strengths of this study include the large cohort of healthy individuals living in the community, and that we controlled for confounding factors including smoking, lifestyle, and cardiovascular risk factors. However, there are also several limitations. First, data on the use of medications which may have influenced hypertension, the level of fasting glucose, lipid profiles, and the development or prevention of MetS such as anti-hypertensive, anti-diabetic, and lipid-lowering medications were not available in the TWB, and this may have led to an underestimation of the association between MetS and cataract. Second, due to the cross-sectional design of this study, we could not determine how long each participant had cataract. Therefore, we could not elucidate the causal relationship between cataract and MetS. Further longitudinal studies are warranted to investigate the risk of MetS and its components to incident cataract. Furthermore, we can further explore if intervention to improve MetS and its components can reduce the incidence of cataract. Third, the occurrence of cataract was determined through unverified self-reported questionnaires, and thus the type or severity of cataract could not be ascertained. Fourth, the enrolled participants were all ethnically Chinese, and this may limit the generalizability of our findings to other groups.
In conclusion, we found that MetS and its components, including low HDL-cholesterolemia and hyperglycemia, were associated with a high prevalence of cataract. Further, we found a stepwise increase in the prevalence of cataract corresponding to the number of MetS components. Our findings suggest that MetS may play a role in the risk of cataract in Taiwan.

Author Contributions

Conceptualization, J.-H.C., I.-H.C., P.-Y.W., J.-C.H. and S.-C.C.; Data curation, J.-H.G., P.-Y.W., J.-C.H. and S.-C.C.; Formal analysis, J.-H.G. and S.-C.C.; Funding acquisition, S.-C.C.; Writing—original draft, J.-H.C. and I.-H.C.; Writing—review and editing, S.-C.C.; Supervision, S.-C.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported partially by the Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan, from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan and by Kaohsiung Medical University Research Center Grant (KMU-TC110A01Ans and KMUTC111IFSP01).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUHIRB-E(I)-20210058 and 2021/4/8 approval.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data underlying this study are from the Taiwan Biobank. Due to restrictions placed on the data by the Personal Information Protection Act of Taiwan, the minimal data set cannot be made publicly available. Data may be available upon request to interested researchers. Please send data requests to: Szu-Chia Chen, PhD, MD. Division of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

MetSMetabolic Syndrome
DMDiabetes Mellitus
IDFInternational Diabetes Federation
ATP IIIAdult Treatment Panel III
TWBTaiwan Biobank
HCHip Circumference
BHBody Height
WCWaist Circumference
BWBody Weight
BPBlood Pressure
eGFREstimated Glomerular Filtration Rate
LDL-CLow-Density Lipoprotein Cholesterol
HDL-CHigh-Density Lipoprotein Cholesterol
OROdds Ratio
CIConfidence Interval
PSCPosterior Subcapsular Cataract
TNF-αTumor Necrosis Factor-α
IL-6Interleukin-6
CRPC-Reactive Protein
AGEsAdvanced Glycation End Products
BMIBody Mass Index

References

  1. Shih, Y.H.; Chang, H.Y.; Lu, M.I.; Hurng, B.S. Time trend of prevalence of self-reported cataract and its association with prolonged sitting in Taiwan from 2001 and 2013. BMC Ophthalmol. 2014, 14, 128. [Google Scholar] [CrossRef] [Green Version]
  2. Christen, W.G.; Manson, J.E.; Seddon, J.M.; Glynn, R.J.; Buring, J.E.; Rosner, B.; Hennekens, C.H. A prospective study of cigarette smoking and risk of cataract in men. JAMA 1992, 268, 989–993. [Google Scholar] [CrossRef] [PubMed]
  3. West, S.K.; Valmadrid, C.T. Epidemiology of risk factors for age-related cataract. Surv. Ophthalmol. 1995, 39, 323–334. [Google Scholar] [CrossRef] [PubMed]
  4. West, S.K.; Duncan, D.D.; Muñoz, B.; Rubin, G.S.; Fried, L.P.; Bandeen-Roche, K.; Schein, O.D. Sunlight exposure and risk of lens opacities in a population-based study: The Salisbury Eye Evaluation project. JAMA 1998, 280, 714–718. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Lindblad, B.E.; Håkansson, N.; Philipson, B.; Wolk, A. Metabolic syndrome components in relation to risk of cataract extraction: A prospective cohort study of women. Ophthalmology 2008, 115, 1687–1692. [Google Scholar] [CrossRef] [PubMed]
  6. Huscher, D.; Thiele, K.; Gromnica-Ihle, E.; Hein, G.; Demary, W.; Dreher, R.; Zink, A.; Buttgereit, F. Dose-related patterns of glucocorticoid-induced side effects. Ann. Rheum. Dis. 2009, 68, 1119–1124. [Google Scholar] [CrossRef]
  7. Bansal, A.; Amin, H.; Rekha, R. Correlation of aqueous humor electrolytes with serum electrolytes in cataract patients. Indian J. Ophthalmol. 2021, 69, 2675–2677. [Google Scholar] [CrossRef]
  8. Mirsamadi, M.; Nourmohammadi, I.; Imamian, M. Comparative study of serum Na(+) and K(+) levels in senile cataract patients and normal individuals. Int. J. Med. Sci. 2004, 1, 165–169. [Google Scholar] [CrossRef] [Green Version]
  9. Walker, J.G.; Anstey, K.J.; Lord, S.R. Psychological distress and visual functioning in relation to vision-related disability in older individuals with cataracts. Br. J. Health Psychol. 2006, 11, 303–317. [Google Scholar] [CrossRef]
  10. Li, X.; Lin, J.; Chen, Z.; Jin, G.; Zheng, D. The Impact of Cataract Surgery on Vision-Related Quality of Life and Psychological Distress in Monocular Patients. J. Ophthalmol. 2021, 2021, 4694577. [Google Scholar] [CrossRef]
  11. Asbell, P.A.; Dualan, I.; Mindel, J.; Brocks, D.; Ahmad, M.; Epstein, S. Age-related cataract. Lancet 2005, 365, 599–609. [Google Scholar] [CrossRef]
  12. Khairallah, M.; Kahloun, R.; Bourne, R.; Limburg, H.; Flaxman, S.R.; Jonas, J.B.; Keeffe, J.; Leasher, J.; Naidoo, K.; Pesudovs, K.; et al. Number of People Blind or Visually Impaired by Cataract Worldwide and in World Regions, 1990 to 2010. Investig. Ophthalmol. Vis. Sci. 2015, 56, 6762–6769. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Jiang, H.; Wang, L.N.; Liu, Y.; Li, M.; Wu, M.; Yin, Y.; Ma, L.; Wu, C.R. Physical activity and risk of age-related cataract. Int. J. Ophthalmol. 2020, 13, 643–649. [Google Scholar] [CrossRef] [PubMed]
  14. Williams, P.T. Walking and running are associated with similar reductions in cataract risk. Med. Sci. Sports Exerc. 2013, 45, 1089–1096. [Google Scholar] [CrossRef] [Green Version]
  15. Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z. The metabolic syndrome. Lancet 2005, 365, 1415–1428. [Google Scholar] [CrossRef]
  16. Alberti, K.G.; Zimmet, P.; Shaw, J. The metabolic syndrome—A new worldwide definition. Lancet 2005, 366, 1059–1062. [Google Scholar] [CrossRef]
  17. Gupta, A.; Gupta, V. Metabolic syndrome: What are the risks for humans? Biosci. Trends 2010, 4, 204–212. [Google Scholar]
  18. Ford, E.S.; Giles, W.H.; Dietz, W.H. Prevalence of the metabolic syndrome among US adults: Findings from the third National Health and Nutrition Examination Survey. JAMA 2002, 287, 356–359. [Google Scholar] [CrossRef]
  19. Hwang, L.C.; Bai, C.H.; Chen, C.J. Prevalence of obesity and metabolic syndrome in Taiwan. J. Formos. Med. Assoc. 2006, 105, 626–635. [Google Scholar] [CrossRef] [Green Version]
  20. Hanson, R.L.; Imperatore, G.; Bennett, P.H.; Knowler, W.C. Components of the “metabolic syndrome” and incidence of type 2 diabetes. Diabetes 2002, 51, 3120–3127. [Google Scholar] [CrossRef] [Green Version]
  21. Gami, A.S.; Witt, B.J.; Howard, D.E.; Erwin, P.J.; Gami, L.A.; Somers, V.K.; Montori, V.M. Metabolic syndrome and risk of incident cardiovascular events and death: A systematic review and meta-analysis of longitudinal studies. J. Am. Coll. Cardiol. 2007, 49, 403–414. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Maralani, H.G.; Tai, B.C.; Wong, T.Y.; Tai, E.S.; Li, J.; Wang, J.J.; Mitchell, P. Metabolic syndrome and risk of age-related cataract over time: An analysis of interval-censored data using a random-effects model. Investig. Opthalmol. Vis. Sci. 2013, 54, 641–646. [Google Scholar] [CrossRef] [PubMed]
  23. Mehra, S.; Kapur, S.; Mittal, S.; Sehgal, P.K. Common genetic link between metabolic syndrome components and senile cataract. Free Radic. Res. 2012, 46, 133–140. [Google Scholar] [CrossRef] [PubMed]
  24. Reddy, P.Y.; Giridharan, N.V.; Reddy, G.B. Activation of sorbitol pathway in metabolic syndrome and increased susceptibility to cataract in Wistar-Obese rats. Mol. Vis. 2012, 18, 495–503. [Google Scholar] [PubMed]
  25. Yousefi, R. Crystallins as Important Pathogenic Targets for Accumulation of Structural Damages Resulting in Protein Aggregation and Cataract Development: Introduction to This Special Issue of Biochemistry (Moscow). Biochemistry 2022, 87, 87–90. [Google Scholar] [CrossRef]
  26. Zhang, R.; Fang, Y.; Bai, S.W.; Gao, H.W.; Chen, H.B.; Zhang, Z.G.; Du, J.; Shen, B.; Wang, Y. Effect of oxLDL on transcriptional expression of human lens epithelial cells. Int. J. Clin. Exp. Pathol. 2020, 13, 655–663. [Google Scholar]
  27. Bouloumie, A.; Marumo, T.; Lafontan, M.; Busse, R. Leptin induces oxidative stress in human endothelial cells. FASEB J. 1999, 13, 1231–1238. [Google Scholar] [CrossRef]
  28. Spector, A. Oxidative stress-induced cataract: Mechanism of action. FASEB J. 1995, 9, 1173–1182. [Google Scholar] [CrossRef] [Green Version]
  29. Park, Y.H.; Shin, J.A.; Han, K.; Yim, H.W.; Lee, W.C.; Park, Y.M. 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, e85068. [Google Scholar] [CrossRef] [Green Version]
  30. Sabanayagam, C.; Wang, J.J.; Mitchell, P.; Tan, A.G.; Tai, E.S.; Aung, T.; Saw, S.M.; Wong, T.Y. Metabolic syndrome components and age-related cataract: The Singapore Malay eye study. Investig. Opthalmol. Vis. Sci. 2011, 52, 2397–2404. [Google Scholar] [CrossRef] [Green Version]
  31. Tan, J.S.; Wang, J.J.; Mitchell, P. Influence of diabetes and cardiovascular disease on the long-term incidence of cataract: The Blue Mountains eye study. Ophthalmic Epidemiol. 2008, 15, 317–327. [Google Scholar] [CrossRef] [PubMed]
  32. Lindblad, B.E.; Håkansson, N.; Wolk, A. Metabolic syndrome and some of its components in relation to risk of cataract extraction. A prospective cohort study of men. Acta Ophthalmol. 2019, 97, 409–414. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Chen, C.H.; Yang, J.H.; Chiang, C.W.K.; Hsiung, C.N.; Wu, P.E.; Chang, L.C.; Chu, H.W.; Chang, J.; Song, I.W.; Yang, S.L.; et al. Population structure of Han Chinese in the modern Taiwanese population based on 10,000 participants in the Taiwan Biobank project. Hum. Mol. Genet. 2016, 25, 5321–5331. [Google Scholar] [CrossRef] [Green Version]
  34. Fan, C.T.; Hung, T.H.; Yeh, C.K. Taiwan Regulation of Biobanks. J. Law Med. Ethics J. Am. Soc. Law Med. Ethics 2015, 43, 816–826. [Google Scholar] [CrossRef] [PubMed]
  35. World Medical Association. World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA 2013, 310, 2191–2194. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Levey, A.S.; Bosch, J.P.; Lewis, J.B.; Greene, T.; Rogers, N.; Roth, D. A more accurate method to estimate glomerular filtration rate from serum creatinine: A new prediction equation. Modification of Diet in Renal Disease Study Group. Ann. Intern. Med. 1999, 130, 461–470. [Google Scholar] [CrossRef]
  37. Isomaa, B.; Henricsson, M.; Almgren, P.; Tuomi, T.; Taskinen, M.R.; Groop, L. The metabolic syndrome influences the risk of chronic complications in patients with type II diabetes. Diabetologia 2001, 44, 1148–1154. [Google Scholar] [CrossRef] [Green Version]
  38. Vinson, J.A. Oxidative stress in cataracts. Pathophysiology 2006, 13, 151–162. [Google Scholar] [CrossRef]
  39. Ando, K.; Fujita, T. Metabolic syndrome and oxidative stress. Free Radic. Biol. Med. 2009, 47, 213–218. [Google Scholar] [CrossRef]
  40. Schaumberg, D.A.; Ridker, P.M.; Glynn, R.J.; Christen, W.G.; Dana, M.R.; Hennekens, C.H. High levels of plasma C-reactive protein and future risk of age-related cataract. Ann. Epidemiol. 1999, 9, 166–171. [Google Scholar] [CrossRef]
  41. Klein, B.E.; Klein, R.; Lee, K.E.; Knudtson, M.D.; Tsai, M.Y. Markers of inflammation, vascular endothelial dysfunction, and age-related cataract. Am. J. Ophthalmol. 2006, 141, 116–122. [Google Scholar] [CrossRef] [PubMed]
  42. Santos, A.C.; Lopes, C.; Guimaraes, J.T.; Barros, H. Central obesity as a major determinant of increased high-sensitivity C-reactive protein in metabolic syndrome. Int. J. Obes. 2005, 29, 1452–1456. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Pou, K.M.; Massaro, J.M.; Hoffmann, U.; Vasan, R.S.; Maurovich-Horvat, P.; Larson, M.G.; Keaney, J.F., Jr.; Meigs, J.B.; Lipinska, I.; Kathiresan, S.; et al. Visceral and subcutaneous adipose tissue volumes are cross-sectionally related to markers of inflammation and oxidative stress: The Framingham Heart Study. Circulation 2007, 116, 1234–1241. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Wang, X.; Bao, W.; Liu, J.; Ouyang, Y.Y.; Wang, D.; Rong, S.; Xiao, X.; Shan, Z.L.; Zhang, Y.; Yao, P.; et al. Inflammatory markers and risk of type 2 diabetes: A systematic review and meta-analysis. Diabetes Care 2013, 36, 166–175. [Google Scholar] [CrossRef] [Green Version]
  45. Ridker, P.M.; Buring, J.E.; Cook, N.R.; Rifai, N. C-reactive protein, the metabolic syndrome, and risk of incident cardiovascular events: An 8-year follow-up of 14 719 initially healthy American women. Circulation 2003, 107, 391–397. [Google Scholar] [CrossRef]
  46. Tsutsumi, K.; Inoue, Y.; Yoshida, C. Acceleration of development of diabetic cataract by hyperlipidemia and low high-density lipoprotein in rats. Biol. Pharm. Bull. 1999, 22, 37–41. [Google Scholar] [CrossRef] [Green Version]
  47. von Eckardstein, A.; Hersberger, M.; Rohrer, L. Current understanding of the metabolism and biological actions of HDL. Curr. Opin. Clin. Nutr. Metab. Care 2005, 8, 147–152. [Google Scholar] [CrossRef]
  48. Leske, M.C.; Wu, S.Y.; Hennis, A.; Connell, A.M.; Hyman, L.; Schachat, A. Diabetes, hypertension, and central obesity as cataract risk factors in a black population. The Barbados Eye Study. Ophthalmology 1999, 106, 35–41. [Google Scholar] [CrossRef]
  49. Galeone, C.; Petracci, E.; Pelucchi, C.; Zucchetto, A.; La Vecchia, C.; Tavani, A. Metabolic syndrome, its components and risk of age-related cataract extraction: A case-control study in Italy. Ann. Epidemiol. 2010, 20, 380–384. [Google Scholar] [CrossRef]
  50. Kinoshita, J.H.; Fukushi, S.; Kador, P.; Merola, L.O. Aldose reductase in diabetic complications of the eye. Metabolism 1979, 28, 462–469. [Google Scholar] [CrossRef]
  51. Singh, V.P.; Bali, A.; Singh, N.; Jaggi, A.S. Advanced glycation end products and diabetic complications. Korean J. Physiol. Pharmacol. 2014, 18, 1–14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Tan, A.G.; Tham, Y.C.; Chee, M.L.; Mitchell, P.; Cumming, R.G.; Sabanayagam, C.; Wong, T.Y.; Wang, J.J.; Cheng, C.Y. Incidence, progression and risk factors of age-related cataract in Malays: The Singapore Malay Eye Study. Clin. Exp. Ophthalmol. 2020, 48, 580–592. [Google Scholar] [CrossRef] [PubMed]
  53. Glynn, R.J.; Rosner, B.; Christen, W.G. Evaluation of risk factors for cataract types in a competing risks framework. Ophthalmic Epidemiol. 2009, 16, 98–106. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Floud, S.; Kuper, H.; Reeves, G.K.; Beral, V.; Green, J. Risk Factors for Cataracts Treated Surgically in Postmenopausal Women. Ophthalmology 2016, 123, 1704–1710. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Glynn, R.J.; Christen, W.G.; Manson, J.E.; Bernheimer, J.; Hennekens, C.H. Body mass index. An independent predictor of cataract. Arch. Ophthalmol. 1995, 113, 1131–1137. [Google Scholar] [CrossRef]
  56. Hiller, R.; Podgor, M.J.; Sperduto, R.D.; Nowroozi, L.; Wilson, P.W.; D’Agostino, R.B.; Colton, T. A longitudinal study of body mass index and lens opacities. The Framingham Studies. Ophthalmology 1998, 105, 1244–1250. [Google Scholar] [CrossRef]
  57. Howard, K.P.; Klein, B.E.; Lee, K.E.; Klein, R. Measures of body shape and adiposity as related to incidence of age-related eye diseases: Observations from the Beaver Dam Eye Study. Investig. Opthalmol. Vis. Sci. 2014, 55, 2592–2598. [Google Scholar] [CrossRef] [Green Version]
  58. Schaumberg, D.A.; Glynn, R.J.; Christen, W.G.; Hankinson, S.E.; Hennekens, C.H. Relations of body fat distribution and height with cataract in men. Am. J. Clin. Nutr. 2000, 72, 1495–1502. [Google Scholar] [CrossRef]
  59. Weintraub, J.M.; Willett, W.C.; Rosner, B.; Colditz, G.A.; Seddon, J.M.; Hankinson, S.E. A prospective study of the relationship between body mass index and cataract extraction among US women and men. Int. J. Obes. Relat. Metab. Disord. 2002, 26, 1588–1595. [Google Scholar] [CrossRef] [Green Version]
  60. Younan, C.; Mitchell, P.; Cumming, R.; Rochtchina, E.; Panchapakesan, J.; Tumuluri, K. Cardiovascular disease, vascular risk factors and the incidence of cataract and cataract surgery: The Blue Mountains Eye Study. Ophthalmic Epidemiol. 2003, 10, 227–240. [Google Scholar] [CrossRef]
  61. Klein, B.E.; Klein, R.; Lee, K.E. Diabetes, cardiovascular disease, selected cardiovascular disease risk factors, and the 5-year incidence of age-related cataract and progression of lens opacities: The Beaver Dam Eye Study. Am. J. Ophthalmol. 1998, 126, 782–790. [Google Scholar] [CrossRef]
  62. Leske, M.C.; Wu, S.Y.; Nemesure, B.; Hennis, A. Risk factors for incident nuclear opacities. Ophthalmology 2002, 109, 1303–1308. [Google Scholar] [CrossRef]
  63. Deurenberg, P.; Deurenberg-Yap, M.; Guricci, S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes. Rev. 2002, 3, 141–146. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Selin, J.Z.; Lindblad, B.E.; Rautiainen, S.; Michaëlsson, K.; Morgenstern, R.; Bottai, M.; Basu, S.; Wolk, A. Are increased levels of systemic oxidative stress and inflammation associated with age-related cataract? Antioxid. Redox Signal. 2014, 21, 700–704. [Google Scholar] [CrossRef]
  65. Schaumberg, D.A.; Glynn, R.J.; Christen, W.G.; Ajani, U.A.; Stürmer, T.; Hennekens, C.H. A prospective study of blood pressure and risk of cataract in men. Ann. Epidemiol. 2001, 11, 104–110. [Google Scholar] [CrossRef]
  66. Paunksnis, A.; Bojarskiene, F.; Cimbalas, A.; Cerniauskiene, L.R.; Luksiene, D.I.; Tamosiunas, A. Relation between cataract and metabolic syndrome and its components. Eur. J. Ophthalmol. 2007, 17, 605–614. [Google Scholar] [CrossRef]
  67. Richter, G.M.; Torres, M.; Choudhury, F.; Azen, S.P.; Varma, R. Risk factors for cortical, nuclear, posterior subcapsular, and mixed lens opacities: The Los Angeles Latino Eye Study. Ophthalmology 2012, 119, 547–554. [Google Scholar] [CrossRef] [Green Version]
  68. Bautista, L.E.; Vera, L.M.; Arenas, I.A.; Gamarra, G. Independent association between inflammatory markers (C-reactive protein, interleukin-6, and TNF-alpha) and essential hypertension. J. Hum. Hypertens. 2005, 19, 149–154. [Google Scholar] [CrossRef]
Table
Table 1. Comparison of clinical characteristics among study participants according to cataract.
Table 1. Comparison of clinical characteristics among study participants according to cataract.
CharacteristicsCataract (−)
(n = 110,413)		Cataract (+)
(n = 10,967)		p
Age (year)48.7 ± 10.661.3 ± 6.5<0.001
Male gender (%)36.232.8<0.001
DM (%)4.214.3<0.001
Hypertension (%)11.024.5<0.001
Smoking history (%)27.624.0<0.001
Alcohol history (%)8.67.90.021
Regular exercise habits (%)38.759.2<0.001
Systolic BP (mmHg)119.8 ± 18.5127.2 ± 19.1<0.001
Diastolic BP (mmHg)73.8 ± 11.474.3 ± 10.8<0.001
Body height (cm)162.2 ± 8.3159.3 ± 7.9<0.001
Body weight (kg)64.0 ± 12.861.5 ± 11.3<0.001
Waist circumference (cm)83.2 ± 10.384.4 ± 9.9<0.001
Hip circumference (cm)96.1 ± 7.295.0 ± 6.7<0.001
Laboratory parameters
Fasting glucose (mg/dL)95.3 ± 20.0101.7 ± 26.3<0.001
Hemoglobin (g/dL)13.8 ± 1.613.7 ± 1.40.010
Triglyceride (mg/dL)115.2 ± 94.6119.8 ± 88.6<0.001
Total cholesterol (mg/dL)195.3 ± 35.8198.7 ± 36.4<0.001
HDL-C (mg/dL)54.5 ± 13.455.0 ± 13.80.001
LDL-C (mg/dL)120.9 ± 31.8121.4 ± 31.80.120
eGFR (mL/min/1.73 m2)104.0 ± 23.796.1 ± 24.0<0.001
Uric acid (mg/dL)5.4 ± 1.45.4 ± 1.40.058
MetS (%)21.632.0<0.001
MetS numbers1.5 ± 1.31.9 ± 1.4<0.001
MetS component
Abdominal obesity (%)45.852.9<0.001
Hypertriglyceridemia (%)20.822.6<0.001
Low HDL-cholesterol (%)25.426.90.001
Hyperglycemia (%)19.533.4<0.001
High blood pressure (%)33.452.5<0.001
Abbreviations. DM, diabetes mellitus; BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; MetS, metabolic syndrome.
Table
Table 2. Association of MetS and cataract using multivariable logistic regression analysis.
Table 2. Association of MetS and cataract using multivariable logistic regression analysis.
VariablesMultivariable (Cataract)
Odds Ratio (95% CI)p
Age (per 1 year)1.173 (1.169–1.177)<0.001
Male vs. female0.766 (0.715–0.821)<0.001
Smoking history 1.120 (1.051–1.193)<0.001
Alcohol history0.971 (0.892–1.055)0.485
Regular exercise habits1.043 (0.998–1.090)0.061
Hemoglobin (per 1 g/dL)0.957 (0.940–0.975)<0.001
Total cholesterol (per 10 mg/dL)1.009 (0.996–1.021)0.176
LDL-cholesterol (per 1 mg/dL)0.997 (0.996–0.998)<0.001
eGFR (per 1 mL/min/1.73 m2)0.999 (0.998–1.000)0.008
Uric acid (per 1 mg/dL)0.958 (0.940–0.977)<0.001
MetS1.129 (1.075–1.184)<0.001
Values are expressed as odds ratio and 95% confidence interval (CI). Abbreviations. LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; MetS, metabolic syndrome.
Table
Table 3. Association of MetS numbers and cataract using multivariable logistic regression analysis.
Table 3. Association of MetS numbers and cataract using multivariable logistic regression analysis.
Number of MetS ComponentsMultivariable (Cataract)
Odds Ratio (95% CI)p
0Reference
10.998 (0.935–1.066)0.957
20.989 (0.924–1.059)0.751
31.103 (1.024–1.188)0.010
41.137 (1.040–1.242)0.005
51.208 (1.059–1.378)0.005
Values are expressed as odds ratio and 95% confidence interval (CI). Adjusted for eGFR, uric acid LDL-C, total cholesterol, hemoglobin, age, sex, smoking habit, alcohol consumption, and regular exercise.
Table
Table 4. Association of MetS components and cataract using multivariable logistic regression analysis.
Table 4. Association of MetS components and cataract using multivariable logistic regression analysis.
MetS ComponentsMultivariable (Cataract)
Odds Ratio (95% CI)p
Abdominal obesity 1.006 (0.961–1.052)0.806
Hypertriglyceridemia 1.047 (0.992–1.106)0.094
Low HDL-cholesterol1.057 (1.005–1.113)0.032
Hyperglycemia 1.162 (1.108–1.218)<0.001
High blood pressure 1.038 (0.993–1.086)0.102
Values are expressed as odds ratio and 95% confidence interval (CI). Abbreviations. DM, diabetes mellitus; BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; MetS, metabolic syndrome. Adjusted for uric acid, eGFR, LDL-C, HDL-C, total cholesterol, triglycerides, hemoglobin, fasting glucose, diastolic BP, systolic BP, regular exercise, alcohol consumption, smoking history, hypertension, DM, sex, and age.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Chang, J.-H.; Chen, I.-H.; Geng, J.-H.; Wu, P.-Y.; Huang, J.-C.; Chen, S.-C. Metabolic Syndrome Is Associated with Cataract in a Large Taiwanese Population Study. Nutrients 2022, 14, 1684. https://doi.org/10.3390/nu14091684

AMA Style

Chang J-H, Chen I-H, Geng J-H, Wu P-Y, Huang J-C, Chen S-C. Metabolic Syndrome Is Associated with Cataract in a Large Taiwanese Population Study. Nutrients. 2022; 14(9):1684. https://doi.org/10.3390/nu14091684

Chicago/Turabian Style

Chang, Jung-Hsiu, I-Hua Chen, Jiun-Hung Geng, Pei-Yu Wu, Jiun-Chi Huang, and Szu-Chia Chen. 2022. "Metabolic Syndrome Is Associated with Cataract in a Large Taiwanese Population Study" Nutrients 14, no. 9: 1684. https://doi.org/10.3390/nu14091684

APA Style

Chang, J. -H., Chen, I. -H., Geng, J. -H., Wu, P. -Y., Huang, J. -C., & Chen, S. -C. (2022). Metabolic Syndrome Is Associated with Cataract in a Large Taiwanese Population Study. Nutrients, 14(9), 1684. https://doi.org/10.3390/nu14091684

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop