Abstract
Background
Cataract is a leading cause of blindness worldwide. However, little is known about sex differences in cataracts. Our study aimed to explore potential sex differences in the relationships between key social, lifestyle, and physical health risk factors and the incidence of cataracts.
Methods
A total of 117,972 participants from the UK Biobank were included in this prospective cohort study. Cox proportional hazard models were used to calculate hazard ratios (HRs) and female-to-male ratios of HRs (RHRs) with 95% confidence intervals (CIs) for cataract risk factors. Poisson regression was used to assess the incidence of cataracts (per 10,000 person-years).
Results
A total of 117,972 individuals without preexisting eye diseases were enroled in the analysis. 4172 subjects (54.8% female) were diagnosed with cataracts during follow-up. The crude incidence rates per 10,000 person-years were 35.06 for females and 29.15 for males. The incidence of cataracts increased in both males and females with factors such as Asian or Black ethnicity, smoking status, obesity, diabetes, and myopia. However, males who consumed alcohol or were unemployed suffered a greater risk of cataracts compared to their female counterparts, while high socioeconomic status, elevated blood pressure and metabolic syndrome were associated with a greater risk of cataracts in females than in males.
Conclusion
This study provides a comprehensive overview of sex differences in the associations between cataracts and various risk factors. Our findings highlight that socioeconomic and lifestyle risk factors are more strongly linked to cataract risk in males, whereas females with systemic diseases face a greater risk of developing cataract.
Subject terms: Refractive errors, Risk factors
Introduction
Cataract, characterized by the opacification of the lens, stands as the most prevalent ocular disease worldwide and a primary cause of blindness. As the global population ages and life expectancy increases, it is estimated that by 2025, approximately 40 million individuals will experience visual impairment due to cataracts [1]. This condition not only necessitates surgical intervention but also significantly diminishes quality of life by exacerbating physical and mental health challenges and constraining life opportunities. Given these implications, the prevention and delay of cataract development are crucial in public health efforts.
Cataract development is closely associated with aging and is also significantly influenced by various risk factors. Social elements (ethnicity, socioeconomic status), lifestyle habits (smoking, alcohol use, UV exposure), and health issues (diabetes, metabolic syndrome, hypertension) have been identified as key contributors that exacerbate lens opacity [2–9]. Additionally, ocular disorders like glaucoma and retinal detachment disrupt lens metabolism, further increasing cataract risk. Understanding related risk factors is vital for the formulation of targeted management strategies aimed at delaying the development of cataracts.
Furthermore, sex has been recognized as a significant factor influencing the susceptibility, progression, and response to treatment of various diseases [10]. Physiological and pathological differences in the lens have been noted between females and males, with the lens epithelial cells in females displaying a higher density and benefiting from the protective effects of estrogen [11, 12]. In addition, females demonstrate a higher incidence of cataracts. Nevertheless, the precise impact of sex in the association between cataracts and risk factors remains unknown. Meanwhile, the potential sex differences in specific subpopulations exposed to certain risk factors have not been directly characterized, underscoring the need for large-scale prospective studies focusing on a diverse patient population with cataract.
Aimed at elucidating the impact of sex on cataract development, this study planned to provide a comprehensive assessment of the association between key risk factors for cataract and the risk of incident cataract by utilizing data from females and males in the UK Biobank.
Methods
Study population
The UK Biobank is a large-scale prospective cohort study that recruited over 500,000 participants aged between 40 and 69 years from 2006 to 2010 across the United Kingdom. The data collected during the initial visit included physical measurement, health, lifestyle information, biological samples, genetic data. Additional data have been collected during the long-term follow-up.
The study has received approval from the North West Multi-Centre Research Ethics Committee (Ref: 11/NW/0382, https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics), and has been conducted using the UK Biobank Resource under Application Number 86091. All participants provided electronic informed consent. This study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement reporting guidelines for cohort studies.
In our analysis, we included 131,397 subjects who had no reported eye problems or disorders at baseline. Specifically, we recruited individuals who selected ‘none of the above’ in response to the question on the ACE touchscreen: ‘Has a doctor told you that you have any of the following problems with your eyes?’ (Field ID 6148), to minimize the impact of other eye diseases on our findings. Participants were excluded if they had a baseline diagnosis of cataract (n = 416), were missing data on cataract outcomes (n = 331), or were ineligible for inclusion in the propensity score matching (PSM) analysis (n = 13,423). Ultimately, 117,972 participants were enroled in our study.
Measurement of social risk factors
Ethnicity, education degree, employment status and socioeconomic status (SES) were identified as social risk factors for cataract in the study. Ethnicity was self-reported and categorized into white and non-white (Asian, Black, Chinese, Mixed, or other ethnic groups). Education degree was classified as ‘college’ or ‘not college’. Employment status was defined as employed (including those in paid employment or self-employed, retired, doing unpaid or voluntary work, or being full or part-time students) or unemployed (including those looking after the home, unable to work, or unemployed). SES was assessed using the Townsend Deprivation Index and classified the population into three levels: high (≤−2.08), middle (−2.08 to 1.04), and low (≥1.40), with a higher Townsend Deprivation Index reflecting greater economic deprivation [13].
Measurement of lifestyle risk factors
We identified smoking status, alcohol intake, exercise intensity and sun exposure as lifestyle risk factors for cataracts. Smoking status was self-reported by participants and categorized as never, former, or current smokers. Similarly, alcohol consumption was categorized into never, former, or current drinkers. Exercise frequency was assessed using the International Physical Activity Questionnaire (IPAQ) and classified into low, moderate, and high categories. Sun exposure was categorized into summer, winter, and average (calculated as the mean of exposure time in summer and winter) after excluding instances of outdoor lighting time exceeding 16 h. A strong correlation was observed between the duration of outdoor lighting exposure reported for summer and winter, indicating that participants with greater exposure in summer also tended to have increased outdoor time in winter [14].
Measurement of physical health risk factors
We confirmed several physical health risk factors for cataracts, including blood pressure, myopia, body mass index (BMI), diabetes, and metabolic syndrome. Baseline seated systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured twice using the OMRON HEM-7015IT digital sphygmomanometer. We categorized blood pressure into four stages: ‘normal’ for SBP < 120 mmHg and DBP < 80 mmHg; ‘elevated’ for SBP 120–129 mmHg and DBP < 80 mmHg; ‘stage 1 hypertension’ for SBP 130–139 mmHg or DBP 80–89 mmHg; and ‘stage 2 hypertension’ for SBP ≥ 140 mmHg or DBP ≥ 90 mmHg. The mean spherical equivalent (MSE) refractive error was calculated using the formula: Spherical Power + (0.5×Cylindrical Power). Subjects were classified as myopic if the MSE in either eye was <−1.00D. BMI was calculated by dividing the weight in kilograms by the square of the height in metres and classified into four categories: ‘underweight’ ( < 18.5 kg/m²), ‘healthy weight’ (18.5–24.9 kg/m²), ‘overweight’ (25.0–29.9 kg/m²), and ‘obese’ (>30 kg/m²). Diabetes was recorded through touchscreen questionnaires, answering the question ‘Has a doctor ever told you that you have diabetes?’. Metabolic syndrome was defined by the presence of at least three of the following criteria: unhealthy waist circumference (≥88 cm for females, ≥102 cm for males), hypertension (SBP ≥ 130 mmHg or DBP ≥ 85 mmHg, or on antihypertensive medication), dyslipidaemia (HDL cholesterol <40 mg/dL for males or <50 mg/dL for females, or on statin medication), hypertriglyceridemia (triglyceride levels ≥150 mg/dL or on triglyceride-lowering medication), and hyperglycaemia (fasting glucose >110 mg/dL or on diabetes medication). All codes for these risk factors in the UK Biobank were performed in Table S1.
Measurement of outcomes
Cataract cases in the UK Biobank Study were defined using hospital inpatient records. The study endpoints were senile cataract, other cataract, and cataract surgery records. The study utilized the International Classification of Diseases, Tenth Revision (ICD-10) codes (H25.0, H25.1, H25.2, H25.8, H25.9, H26.0, H26.1, H26.2, H26.3, H26.4, H26.8 and H26.9) and the Classification of Interventions and Procedures (OPCS-4) codes C71.2 for ‘Phacoemulsification of lens’ or C75.1 for ‘Insertion of prosthetic replacement for lens’ to identify participants with cataracts. The follow-up period commenced from the date of each participant’s inclusion in the UK Biobank and concluded on April 28th, 2021, or upon the participant’s death or the diagnosis of senile cataract, whichever occurred first.
Statistical analysis
To minimize the confounding effect of age on cataract development, we employed a 1:1 propensity score matching (PSM) method, ensuring that baseline characteristics, particularly age, were evenly balanced between females and males. Baseline characteristics were presented as numbers (percentages) for categorical variables and means (standard deviations) for continuous variables and analysed by t-test and chi-square test.
Cox regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for each cataract risk factor. A In addition, Poisson regression models were utilized to calculate incidence rates per 10,000 person-years. To control for potential confounders, all risk factors were incorporated as covariates in these regression models. We also integrated an interaction term for each risk factor with sex to analyse the female-to-male ratio of HRs. To ensure the robustness of our findings, a sensitivity analysis was conducted, excluding participants with a follow-up duration of less than 365 days after baseline assessment, which could suggest the presence of visually significant cataract baseline.
Analyses were performed with R version 4.3.1 (The R Foundation for Statistical Computing, using the package ‘pROC’) and Stata version 17 (StataCorp LLC) statistical software. A p-value below 0.05 was considered statistically significant.
Results
In the main analysis, a total of 117,972 participants were enroled, with a mean baseline age of 56.50 years. Baseline characteristics revealed notable differences between females and males. Males were more likely to be vocationally engaged, attain higher education degrees, and have higher rates of alcohol and cigarette consumption as well as sun exposure. Conversely, female participants exhibited healthier weights and blood pressure levels, along with a lower prevalence of diabetes compared to their male counterparts. Over a median follow-up of 11.02 years, 4172 cataract cases were documented, with females constituting 54.8% of the affected individuals (Table S2). Interestingly, no statistically significant difference in cataract occurrence was observed between sexes among individuals under 55 years of age. However, for those aged over 55, an increased incidence of cataracts was noted in females compared to males (Table 1).
Table 1.
Baseline descriptive characteristics for female and male respondents.
| Variables | Female (N = 58,986) | Male (N = 58,986) | P values |
|---|---|---|---|
| Baseline age, y | 56.50 (8.23) | 56.50 (8.23) | 0.92 |
| Ethnicity, % | <0.001 | ||
| White | 53,676 (91.00) | 53,931 (91.43) | |
| Asian | 1845 (3.13) | 2179 (3.69) | |
| Black | 1824 (3.09) | 1493 (2.53) | |
| Other | 1641 (2.78) | 1383 (2.34) | |
| Educational qualification, % | <0.001 | ||
| College or university degree | 19,395 (32.88) | 21,090 (35.75) | |
| Others | 39,591 (67.12) | 37,896 (64.25) | |
| Employment, % | <0.001 | ||
| Yes | 53,167 (90.13) | 53,867 (91.32) | |
| No | 5583 (9.50) | 4846 (8.21) | |
| Socioeconomic Status, % | <0.001 | ||
| Low | 12,741 (21.60) | 13,203 (22.38) | |
| Middle | 19,430 (32.94) | 18,973 (32.17) | |
| High | 26,815 (45.46) | 26,810 (45.45) | |
| Drinking status, % | <0.001 | ||
| Never | 3873 (6.57) | 1763 (2.99) | |
| Former | 2227 (3.78) | 2133 (3.62) | |
| Current | 52,829 (89.65) | 55,030 (93.39) | |
| Smoking status, % | <0.001 | ||
| Never | 35,077 (59.69) | 29,354 (49.94) | |
| Former | 18,549 (31.56) | 22,075 (37.55) | |
| Current | 5144 (8.75) | 7355 (12.51) | |
| Physical activity, % | <0.001 | ||
| Low | 8232 (17.82) | 9418 (18.84) | |
| Moderate | 19,875 (43.03) | 19,187 (38.38) | |
| High | 18,079 (39.14) | 21,384 (42.78) | |
| Sun exposure, h | <0.001 | ||
| Average | 2.48 (1.63) | 3.13 (2.23) | |
| Summer | 3.44 (2.10) | 4.09 (2.64) | |
| Winter | 1.53 (1.48) | 2.18 (2.13) | |
| Refractive error, % | 0.252 | ||
| Myopia | 11,656 (19.76) | 11,966 (20.29) | |
| No myopia | 32,381 (54.90) | 32,669 (55.38) | |
| BMI type, % | <0.001 | ||
| Underweight | 466 (0.80) | 147 (0.25) | |
| Healthy weight | 22,960 (39.20) | 14,709 (25.16) | |
| Overweight | 21,405 (36.54) | 28,947 (49.50) | |
| Obese | 13,745 (23.46) | 14,670 (25.09) | |
| Hypertension, % | <0.001 | ||
| Normal | 11,857 (20.10) | 5420 (9.19) | |
| Elevated Blood pressure | 7482 (12.68) | 6920 (11.73) | |
| Stage 1 hypertension | 15,016 (25.46) | 15,962 (27.06) | |
| Stage 2 hypertension | 24,631 (41.76) | 30,684 (52.02) | |
| Diabetes, % | 1893 (3.22) | 3266 (5.56) | <0.001 |
| Metabolic syndrome, % | 16,177 (27.57) | 15,915 (27.13) | 0.096 |
| Incident cataract during follow-up, % | 2285 (3.87) | 1887 (3.20) | <0.001 |
| < 55 years | 0.76 (176/23,023) | 0.80 (184/23,023) | 0.672 |
| 55–65 years | 4.10 (992/24,205) | 3.48 (843/24,205) | <0.001 |
| > 65 years | 9.50 (1117/11,758) | 7.31 (860/11,758) | <0.001 |
BMI body mass index.
Bold values denote statistical significance at P < 0.05 level.
Social risk factors
In our study, we examined several social risk factors in our study, including ethnicity, education degree, employment status and socioeconomic status (SES). We observed a statistically significant sex difference in the effect of employment status on cataract incidence (Fig. 1). Specifically, unemployed males showed a higher risk of developing cataracts compared to their employed counterparts (HR = 1.48, 95% CI = 1.15–1.81), while employment status did not significantly affect cataract incidence in females. Intriguingly, unemployed females demonstrated a 47% lower risk of cataract development compared to unemployed males, with a corresponding women-to-men ratio of HRs of 0.53 (95% CI = 0.38–0.73). Further sensitive adjustment did not materially change this interaction (Fig. S1).
Fig. 1. Adjusted HRs for cataracts incidences associated with risk factors, by sex.
The forest plot displayed the risk of cataract associated with factors between females and males. The red label represented females, the blud label represented males. Horizontal lines indicate the corresponding 95% confidence intervals (CIs) around the hazard ratios (HRs). BMI body mass index, HR hazard ratio, IPAQ International Physical Activity Questionnaire, RHR rate of hazard ratios.
Regarding SES, we did not find a significant association between SES and cataract incidence among females. Nevertheless, high SES was associated with a lower risk of cataract in males compared to those with low and middle SES (HR = 0.71, 95%CI = 0.61–0.83). Notably, males with high SES exhibited a 31% lower risk of cataract incidence compared to females with high SES, and the multiple-adjusted women-to-men ratio of HRs for cataract was 1.31 (95% CI = 1.06–1.62) (Fig. 1).
In terms of ethnicity, both Asians and Blacks performed a greater risk of cataracts compared to Whites regardless of sex. However, there was no significant difference in cataract incidence between females and males across ethnic groups. Similarly, the impact of educational background on cataract incidence was similar in females and males (Fig. 1). Consistent results were observed in the sensitivity analysis (Fig. S1).
Lifestyle risk factors
The established lifestyle risk factors for cataracts include alcohol intake, smoking status, exercise intensity, and sun exposure. In our study, males who consumed alcohol had a 35% lower risk of developing cataract compared to female drinkers (RHR = 0.65, 95% CI = 0.44–0.96). The impact of abstinence on the incidence of cataract did not vary by gender. Furthermore, our analysis revealed no significant difference in the influence of smoking status, exercise intensity, or sun exposure duration on the prevalence of cataract incidence between females and males (Fig. 1). These conclusions remained consistent in the results of the sensitivity analysis (Fig. S1).
Physical health problems
We confirmed hypertension, myopia, BMI, diabetes, and metabolic syndrome as risk factors for physical health problems. In terms of hypertension, a sex difference in cataract incidence was observed among participants in the elevated blood pressure group. Females exhibited a 63% higher risk of cataracts than males in this group (RHR = 1.63, 95% CI = 1.15–2.31). However, similar results were not observed in the I stage and II stage groups.
Both female and male individuals with myopia showed a higher likelihood of cataract development in comparison to their non-myopia counterparts. The multi-adjusted HRs were 1.80 (95% CI = 1.60–2.02) for females and 1.79 (95% CI = 1.58–2.03) for males. Notably, there was no sex difference in the effect of myopia on cataract incidence, with the corresponding RHR 0.91 (95% CI = 0.77–1.07).
With regard to BMI, underweight (HR = 1.73, 95% CI = 1.01–2.95) and obese (HR = 1.28, 95% CI = 1.09–1.51) females showed higher risks of cataract incidence compared to females with a healthy weight. Conversely, males with different BMI categories demonstrated similar cataract incidence. Notably, BMI did not significantly influence cataract risk between sexes.
Among participants with endocrine disorders, both females and males with diabetes showed a higher risk of suffering from cataract compared to those without diabetes. However, the influence of diabetes on cataract risk did not significantly differ between females and males. Additionally, females diagnosed with metabolic syndrome demonstrated a 36% elevated risk of developing cataracts compared to their male counterparts with metabolic syndrome. The female-to-male ratio for metabolic syndrome was 1.36 (95% CI = 1.11–1.66) (Fig. 1). The results remained consistent in the sensitivity analysis (Fig. S1).
Sex comparison of cataract rates
The incidence rate of cataract per 10,000 person-years was 35.06 (95% CI = 33.62–36.50) in females and 29.15 (95% CI = 27.83–30.46) in males. Among individuals with normal blood pressure, the incidence rate was lower in females than in males, with rates of 16.71 per 10,000 person-years (95% CI = 13.57–19.86) and 17.24 per 10,000 person-years (95% CI = 13.06–21.42) respectively. Notably, unemployed status was associated with a higher cataract rate in males compared to females, with the corresponding rates per 10,000 person-year were 31.51 (95% CI = 29.4–33.63) in females and 23.51 (95% CI = 21.92–25.1) in males respectively. The rates among individuals with diabetes were 42.85 per 10,000 person-years (95% CI = 31.1–54.6) for women and 52.79 per 10,000 person-years (95% CI = 42.91–62.66) for men. Furthermore, higher incidence rates of cataract per 10,000 person-years were observed among females than males for other risk factors (Table 2). These findings were consistent in the sensitivity analyses (Table S3).
Table 2.
Sex-specific multiple-adjusted rates of cataract and female-to-male difference of rate differences for each risk factor.
| Variables | Female (N = 58,986) | Male (N = 58,986) | Difference of rate differences (95% CI) |
|---|---|---|---|
| Ethnicity | |||
| White | 29.83 (27.77–31.89) | 23.55 (21.94–25.16) | – |
| Asian | 60.79 (44.75–76.84) | 42.78 (32.09 –53.46) | 11.74 (−7.67 to 31.16) |
| Black | 25.57 (16.03–35.10) | 26.77 (16.81–36.72) | −7.47 (−21.52 to 6.57) |
| Other | 36.18 (23.53–48.84) | 23.27 (13.24–33.3) | 6.63 (−9.71 to 22.98) |
| Educational Qualification | |||
| College or university degree | 27.49 (24.31–30.67) | 20.29 (18.06–22.53) | – |
| Others | 33 (30.5–35.49) | 26.76 (24.6–28.92) | −0.97 (−6.01 to 4.08) |
| Employment | |||
| Yes | 32.16 (30.02–34.3) | 24.2 (22.58–25.81) | – |
| No | 16.67 (11.95–21.4) | 25.9 (20.02–31.78) | −17.19 (−25.15 to −9.23) |
| Socioeconomic Status | |||
| Low | 28.44 (24.37–32.51) | 25.63 (22.2–29.06) | – |
| Middle | 31.82 (28.52–35.12) | 25.05 (22.38–27.72) | 3.96 (−2.74 to 10.67) |
| High | 31.49 (28.47–34.51) | 23.15 (20.91–25.39) | 5.53 (−1.00 to 12.06) |
| Drinking status | |||
| Never | 42.37 (32.94–51.8) | 24.62 (15.88–33.37) | – |
| Former | 31.08 (20.73–41.43) | 22.99 (15.41–30.58) | −9.66 (−27.67 to 8.35) |
| Current | 30.38 (28.27–32.48) | 24.37 (22.75–25.99) | −11.74 (−24.87 to 1.39) |
| Smoking status | |||
| Never | 27.77 (25.39–30.16) | 21.02 (19.02–23.02) | – |
| Former | 37.15 (33.34–40.96) | 29.66 (26.83–32.5) | 0.73 (−4.88 to 6.35) |
| Current | 26.72 (20.21–33.22) | 23.87 (19.42–28.32) | −3.91 (−12.37 to 4.54) |
| Physical activity | |||
| Low | 34.61 (29.44–39.77) | 28.14 (24.21–32.08) | – |
| Moderate | 33.26 (30.13–36.39) | 23.06 (20.68–25.44) | 3.74 (−3.63 to 11.10) |
| High | 27.28 (24.43–30.13) | 24 (21.63–26.37) | −3.19 (−10.69 to 4.31) |
| Sun exposure | |||
| Short | 27.78 (25.47–30.09) | 21.78 (19.75–23.81) | – |
| Long | 34.41 (31.04–37.77) | 28.01 (25.5–30.52) | 0.40 (−4.82 to 5.62) |
| Refractive error | |||
| No myopia | 27.87 (25.70–30.04) | 21.26 (19.57–22.94) | – |
| Myopia | 39.86 (35.45–44.27) | 33.28 (29.73–36.84) | −0.04 (−6.21 to 6.13) |
| BMI type | |||
| Underweight | 67.64 (30.56–104.72) | 22.5 (−8.74–53.73) | 41.49 (−7.07 to 90.06) |
| Healthy weight | 28.75 (25.42–32.08) | 25.1 (21.79–28.41) | – |
| Overweight | 31.68 (28.53–34.83) | 24.62 (22.42–26.81) | 3.41 (−2.54 to 9.36) |
| Obese | 31.59 (27.44–35.75) | 22.99 (19.86–26.11) | 4.96 (−2.45 to 12.36) |
| Hypertension | |||
| Normal | 17.13 (13.94–20.33) | 18.01 (13.73–22.29) | – |
| Elevated blood pressure | 36.74 (30.92–42.56) | 23.66 (19.36–27.96) | 13.95 (5.12 to 22.79) |
| Stage 1 hypertension | 26.89 (23.5–30.27) | 21.45 (18.75–24.14) | 6.32 (−0.51 to 13.15) |
| Stage 2 hypertension | 35.87 (32.61–39.13) | 28.35 (25.95–30.76) | 8.39 (1.63 to 15.15) |
| Diabetes | |||
| No | 30.16 (28.15–32.16) | 22.81 (21.25–24.37) | – |
| Yes | 44.66 (32.72–56.6) | 54.87 (44.8–64.94) | −17.55 (−33.35 to −1.76) |
| Metabolic syndrome | |||
| No | 27.9 (25,45–30.37) | 24.58 (22.67–26.48) | – |
| Yes | 38.16 (33.83–42.49) | 23.77 (20.57–26.97) | 11.05 (4.49 to 17.62) |
BMI body mass index.
Discussion
This study demonstrated a comprehensive overview of sex differences between key factors and the risk of incident cataracts among 117,972 subjects in the UK Biobank. On average, females performed healthier lifestyle habits and physical conditions at baseline. However, certain risk factors demonstrated notable sex-specific effects on cataract incidence. For instance, unemployed status was associated with a 47% greater risk of cataracts in males than in females. Additionally, current alcohol consumption was linked to a 35% elevated risk of cataracts in males compared to females. Furthermore, among participants with systemic diseases, females in the elevated blood pressure group experienced a 63% higher risk of cataracts than males, while metabolic syndrome was related to a 36% higher risk of cataracts in females than in males.
Our study explored the role of social factors like employment, SES, education, and ethnicity on cataract incidence and uncovered noteworthy insights. Employment status notably impacted male cataract incidence. Specifically, employment status significantly reduced the risk of developing cataracts among males, whereas unemployed males exhibited an increased risk compared to their unemployed female counterparts. This observation marks our study as the first to explore the link between employment status and cataract incidence, although the mechanisms behind this relationship remain unclear and warrant further exploration. SES was also identified as a key factor, with previous studies demonstrating its inverse correlation with cataract burden [15, 16]. Consistent with the literature, our findings indicated that males with high SES had a reduced risk of cataracts, while SES appeared not to significantly influence cataract risk among females. Intriguingly, females with high SES faced a 31% increased risk of developing cataracts compared to males. The inaccessibility of relative medical services and knowledge in females may contribute to the finding. Additionally, higher education levels consistently correlated with a decreased risk of cataracts in both sexes, and ethnicity was found to affect cataract risk equally among males and females [2, 17]. These findings reveal the intricate link between social factors and cataract risk, emphasizing the need for more research and the development of targeted prevention strategies.
Lifestyle factors are recognized as modifiable contributors to cataract prevention. In our study, we collected data on alcohol intake, smoking, activity intensity, and sun exposure time [18, 19]. While previous research has extensively explored the relationship between alcohol consumption and cataract incidence, few studies investigated sex differences in this association [4, 20, 21]. Our study addressed this gap by revealing a significant difference in cataract risk between females and males related to alcohol consumption. Factors such as the type of alcohol, drinking frequency and duration, and the presence of antioxidants in alcoholic beverages might explain this difference [7, 22–24]. Conversely, smoking’s impact on cataract risk was found to be consistent across sexes, indicating a uniformly negative effect. Additionally, our analysis showed no sex differences in the risk associated with ultraviolet radiation from sun exposure, emphasizing its universal threat. Similarly, the effect of exercise on cataract risk was consistent across sexes. Our study reveals similarities and differences in the impact of lifestyle on the incidence of cataract in different sexes, and further calls for people to cultivate good lifestyle habits to reduce the risk of cataracts.
Our study presents significant insights into the effects of hypertension, BMI, and metabolic syndrome on cataract incidence, with distinct differences observed between sexes. We noted a tendency for hypertension to more significantly affect females, possibly due to harsher side effects from antihypertensive medications and lower adherence to treatment, as well as the notable decline in oestrogen levels post-menopause unlike the stable levels in males [25]. The ‘withdrawal effect’ may contribute to an elevated cataract risk among hypertensive females [26]. Furthermore, while existing research has associated obesity with a higher incidence of cataracts, our findings reveal a unique U-shaped relationship between BMI and cataract risk in females, not seen in males [27]. Despite this novel finding, our analysis did not uncover a notable sex difference in the overall influence of BMI on cataract prevalence. Remarkably, our research is the first study to explore the link between being underweight and cataract risk. This novel insight prompts a call for further investigation to validate our hypothesis and to extend the exploration to the relationship between underweight status and other eye diseases. Additionally, our data show that females with metabolic syndrome are more prone to developing cataracts than their male counterparts. Given the increasing prevalence of metabolic syndrome globally and its profound health consequences, our findings emphasize the critical need for vigilant monitoring and proactive management of metabolic syndrome in women to mitigate their higher risk of cataracts [28].
Strengths and limitations of the study
Our study conducted a systematic evaluation of sex differences across various risk factors for cataract development within a large general population. Leveraging a prospective cohort design and a substantial sample size, we were able to objectively observe sex differences in both relative and absolute terms. This approach provides more reliable evidence than previous studies, notably by excluding individuals with existing eye disorders or problems at baseline, thus enhancing the study’s credibility and the confidence in our findings. In addition to corroborating several risk factors previously identified in other research, our study pinpointed employment status as a novel risk factor specifically for males. Conversely, we found that females with physical health issues tend to have a higher risk of developing cataracts. These new insights significantly advance our understanding of cataract risk factors, highlighting the necessity for targeted support and tailored care for distinct demographic groups.
It is undeniable that some limitations also require to be discussed. First, individuals in the UK Biobank are healthier and more affluent than individuals in the normal cohorts, and the number of cataract participants may limit us from producing accurate sex-specific estimates. Most of the participants were of European ancestry, preventing our findings from being applied to people of other races. In addition, self-reported information such as smoking status, and diabetes may be subject to reporting bias, which affects the generalizability of the results. Finally, we removed a small number of individuals in the sensitive analysis, and further efforts, including the implementation of diverse matching methods and the performance of subgroup analyses, are encouraged to enhance the robustness of our findings.
In conclusion, several risk factors in socioeconomic status and lifestyle are more strongly correlated to the risk of cataract in males, whereas females with physical health problems have an increased risk of cataracts. Despite this, the incidence of cataract in risk factors except for employment status generally remains higher among females than males.
Summary
What was known before
Cataract leads to a decline in quality of life and exacerbates physical and mental health issues.
Addressing risk factors like social determinants, lifestyle choices, and health problems has become crucial for public health.
Recently, studies have reported higher rate of cataract in females than in males, but whether sex difference existed in the association between cataract and risk factors remains unknown.
What this study adds
Exposure to different risk factors for cataracts varies in females and males.
Males who consumed alcohol or were unemployed suffered a greater risk of cataracts compared to their female counterpart.
High socioeconomic status, elevated blood pressure and metabolic syndrome were associated with higher risk of cataracts in females than in males after multi-adjusted.
Supplementary information
Acknowledgements
The study has been conducted using the UK Biobank Resource under Application #86091. We thank the participants of UK Biobank.
Author contributions
Study concept and design: Yuling Xu, Xiayin Zhang, Honghua Yu; Acquisition, analysis, or interpretation: Yuling Xu, Anyi Liang, Xiayin Zhang, Wei Sun; Drafting of the manuscript: Yuling Xu, Qinyi Li, Ting Su, Zijing Du; Critical revision of the manuscript for important intellectual content: Anyi Liang, Xianqi Zheng, Xiayin Zhang, Ziyi Huang, Qiaowei Wu. Statistical analysis: Yuling Xu, Anyi Liang; Obtained funding: Honghua Yu, Xiayin Zhang, Anyi Liang. Administrative, technical, or material support: Ying Fang, Wei Sun, Yijun Hu; Study supervision: Honghua Yu, Xiayin Zhang, Wei Sun.
Funding
The present work was funded by the National Natural Science Foundation of China (82171075, 82301260, 82271125, 82301205), the Medical Scientific Research Foundation of Guangdong Province, China (A2021378), the Science and Technology Program of Guangzhou, China (20220610092, 202103000045), the Outstanding Young Talent Trainee Program of Guangdong Provincial People's Hospital (KJ012019087), the launch fund of Guangdong Provincial People's Hospital for NSFC (8217040546, 8220040257, 8217040449, 8227040339), Personalized Medical Incubator Project, The fund for Precison Medicine Research and Industry Development in SIMQ (2023-31), Guangdong Basic and Applied Basic Research Foundation (2023B1515120028). The funders had no role in the study design, data collection, data analysis, data interpretation, or report writing.
Data availability
All data from the UK Biobank dataset are available at https://biobank.ndph.ox.ac.uk/ by application. The variables used here are detailed in Supplementary Table 1.
Competing interests
The authors declare no competing interests.
Consent for publication
Written informed consent for publication was obtained from all participants.
Ethics approval
UK Biobank was approved by the North West Multi-Centre Research Ethics Committee (Ref: 11/NW/0382, https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics).
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
These authors contributed equally: Yuling Xu, Anyi Liang.
These authors jointly supervised this work: Yijun Hu.
Contributor Information
Wei Sun, Email: [email protected].
Honghua Yu, Email: [email protected].
Xiayin Zhang, Email: [email protected].
Supplementary information
The online version contains supplementary material available at 10.1038/s41433-024-03193-z.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data from the UK Biobank dataset are available at https://biobank.ndph.ox.ac.uk/ by application. The variables used here are detailed in Supplementary Table 1.