Clinical ophthalmology (Auckland, N.Z.) · 2026
Частота воздействия факторов риска на презенциальные катаракты среди пациентов в возрасте 30-50 лет: перекрестное исследование в больнице Шафа, Керман (2024-2025 годы).
Название и аннотация: Машинный перевод · не проверен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Sharifi A, Farajpour B
Аннотация
ЦЕЛЬ: Это исследование было направлено на определение частотности и характеристик ключевых факторов риска для катаракты передозировки у пациентов в возрасте от 30 до 50 лет в Кермане, Иран, регионе, характеризующемся высоким воздействием солнечной энергии и конкретными социально-поведенческими тенденциями. МЕТОДЫ: В 2024 году в отделении офтальмологии больницы Шафа было проведено аналитическое перекрестное исследование. Согласно методу переписи 245 пациентов (468 глаз) нуждались в катаракте. Данные о демографических, профессиональных, системных и поведенческих факторах собирались с использованием структурированного регистрационного бланка. Данные были проанализированы с использованием описательной статистики, ци-квадратических тестов и АНОВА.Результаты: В исследовании приняли участие 127 (51,84 процента) мужчин со средним возрастом 44,42 года (SD 5.78). Наиболее распространенным типом была катаракта Posterior subcapsular (PSC), которая затронула 55,51% пациентов. Значительное число пациентов работали на свежих работах или работах с высоким уровнем загрязнения, при этом 50,61% из них были фермерами или водителями. К числу основных факторов риска относятся диабет (20,82%), опиоидное потребление (34,69%) и кортикостероид. Возраст сильно повлиял на катаракту обоих глаз (правый: P < 0,001; левый: P = 0,01). Значительная связь была также обнаружена между полом и катарактом, но только в левом глазу (P = 0,013).ЗАКЛЮЧЕНИЕ: Презенциальная катаракта в этой популяции является результатом множественных факторов, тесно связанных с изменяемыми экологическими и поведенческими рисками. Основными причинами этого являются воздействие ультрафиолетового излучения на производстве, опиоидная зависимость и такие системные заболевания, как диабет. Заметное различие в гендерных связях свидетельствует о потенциальном влиянии местных внешних факторов. Стратегии в области общественного здравоохранения должны быть направлены на защиту глаз работников, борьбу со злоупотреблением психоактивными веществами и регулирование системных условий, с тем чтобы уменьшить бремя престарелой катаракты.
Оригинал публикации
Frequency of Risk Factors for Presenile Cataract Among Patients Aged 30-50: A Cross-Sectional Study in Shafa Hospital, Kerman (2024-2025).
OBJECTIVE: This study aimed to determine the frequency and profile of key risk factors for presenile cataract among patients aged 30 to 50 years in Kerman, Iran, a region characterized by high solar exposure and specific socio-behavioral patterns.
METHODS: An analytical cross-sectional study was conducted in 2024 at the Ophthalmology Department of Shafa Hospital. A census sampling method enrolled 245 patients (468 eyes) requiring cataract surgery. Data on demographic, occupational, systemic, and behavioral factors were collected using a structured registration form. Data were analyzed using descriptive statistics, chi-square tests, and ANOVA.
RESULTS: The study included 127 (51.84%) male participants with a mean age of 44.42 years (SD 5.78). Posterior subcapsular (PSC) cataract was the most common type, affecting 55.51% of patients. A significant number of patients worked in outdoor or high-exposure occupations, with 50.61% being farmers or drivers. Key risk factors included diabetes (20.82%), opioid use (34.69%), and corticosteroid use. Age significantly influenced cataract type in both eyes (right: P < 0.001; left: P = 0.01). A significant relationship was also found between gender and cataract type, but only in the left eye (P = 0.013).
CONCLUSION: Presenile cataract in this population results from multiple factors, strongly linked to modifiable environmental and behavioral risks. Occupational UV exposure, opioid addiction, and systemic diseases such as diabetes are major contributors. The notable difference in gender association indicates a potential influence from local external factors. Public health strategies should focus on protecting workers' eyes, controlling substance abuse, and managing systemic conditions to reduce the burden of presenile cataract.
Перевод полного текста: Перевод в очереди
Читать найденный полный текст
https://creativecommons.org/licenses/by-nc/4.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v4.0) License ( http://creativecommons.org/licenses/by-nc/4.0/ ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php ).
Introduction
Cataracts remain a major contributing factor to visual impairment and blindness worldwide. 1 Although cataracts have traditionally been considered an age-related problem, the phenomenon of presenile cataracts, occurring in individuals younger than 50–60 years, has generated significant public health interest. Such cases in younger individuals impose a considerable public health burden, with quality of life, economic potential, and mental health significantly affected during their most productive years. 2 Therefore, understanding the factors contributing to presenile cataracts in young individuals is of critical importance.
Globally, cataract distribution patterns vary between regions. In developed countries such as the United States and the United Kingdom, visually disabling cataracts account for approximately half of all blindness cases. 3 In Australian adults over 55 years, the incidence of age-related cataract has been reported at 3.4%, 4 while in Canada, cataracts account for up to 71% of ophthalmic surgeries in the elderly population. 5 Similar trends are observed in Iran’s neighboring countries. In Turkey, 4.5% of urban residents aged 40 years and older had clinically significant cataracts affecting vision. 6 In Pakistan, cataracts remain one of the leading causes of blindness, representing a major preventable cause of visual impairment. 7 In Iran, although comprehensive national data are limited, research in Tehran demonstrated cataracts in 36.2% of individuals over 60 years, 8 and annual incidence data from Isfahan reported 3.5 per 1000 persons per year for age-related cataracts. 9 However, specific data regarding central and southeastern Iran, particularly Kerman province, remain scarce, with limited systematic analysis of risk factors for presenile cataracts.
Several factors necessitate investigation in Kerman province. First, the region experiences high solar intensity throughout the year with elevated ambient ultraviolet levels, an established environmental risk factor for cataracts. 10 Furthermore, the increasing prevalence of systemic diseases such as diabetes may further increase the risk of early-onset cataracts in this region. 11 Therefore, assessing the distribution of such risk factors among patients at Shafa Hospital, a principal referral center in Kerman, is essential.
The current study was designed to assess the frequency of major risk factors—including diabetes, eye trauma, corticosteroid intake, family history, sunlight exposure, and smoking—in patients aged 30–50 years with presenile cataracts at Shafa Hospital in Kerman, Iran, during 2024–2025. By identifying these risk factors in the community, this study aims to establish a foundation for potential future public health programs.
Methods
Study Design
This was an analytical cross-sectional study conducted in 2024 at the Ophthalmology Department of Shafa Hospital in Kerman Province, Iran.
Study Population and Sampling
This cross-sectional study included all patients with presenile cataract who met the following eligibility criteria: age between 30 and 50 years, indication for cataract surgery, and admission to the Ophthalmology Department of Shafa Hospital, Kerman, during the year 2024. Given complete access to the entire target population during the study period, a census sampling method was employed, whereby all eligible patients were consecutively enrolled. Patients who died during the study period or those with incomplete medical records (missing key data) were excluded from the analysis. A total of 245 patients (468 eyes) were ultimately included in the study.
Data Collection Tool
Data were collected using a structured form developed specifically for this study, which covered all key variables including demographic characteristics (age, gender, occupation, economic status classified as low, moderate, or high based on monthly household income relative to the national minimum wage, family history of cataracts, and history of protective eyewear use such as sunglasses or hats during outdoor activities), clinical measurements (intraocular pressure measured using Goldmann applanation tonometry with the HAAG-STREIT AT 900®, Haag-Streit AG, Köniz, Switzerland; cup-to-disc ratio assessed by fundoscopic examination; and body mass index calculated as weight in kilograms divided by height in meters squared), ophthalmological findings (cataract type including nuclear sclerotic, posterior subcapsular, cortical, or mixed, and laterality as right, left, or bilateral, as diagnosed by a trained ophthalmologist using slit-lamp examination), and systemic and lifestyle factors (underlying comorbidities such as diabetes and hypertension, medication history including corticosteroids, high-risk behaviors defined as opioid use, smoking, or alcohol consumption, and occupational exposure to hot environments defined as exposure to temperatures ≥ 40°C for at least 2 hours daily). The form was developed based on a thorough literature review and piloted on 10 patients prior to data collection. Data were extracted from medical records, supplemented by face-to-face interviews, and confirmed through clinical examinations.
Statistical Analysis
Statistical analyses were performed using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA). The normality of continuous variables was assessed using the Shapiro–Wilk test. As the data were not normally distributed, non-parametric tests were employed where appropriate. Continuous variables are presented as mean ± standard deviation (SD), and categorical variables are presented as frequencies and percentages. Since a census sampling approach was used (all eligible patients during the study period were enrolled), no sample size calculation was required. Group comparisons were performed using one-way ANOVA (for normally distributed data) and Fisher’s exact test (for categorical variables). A two-sided P-value < 0.05 was considered statistically significant.
Ethical Considerations
This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of Kerman University of Medical Sciences (Ethical Code: IR.KMU.REC.1402.209). All participants provided written informed consent prior to enrollment. Participants were assured that their data would be kept confidential, that they could withdraw from the study at any time without consequences, and that there would be no additional costs associated with their participation. The results are reported in a grouped and anonymous manner to ensure participant privacy.
Results
A total of 468 eyes from 245 participants were analyzed. Of the participants, 52% were male. A positive family history of cataracts was reported in 11% of cases. Regarding cataract type, posterior subcapsular cataract (PSC) was the most prevalent diagnosis, affecting 55% (135 eyes) of right eyes and 56% (137 eyes) of left eyes. Overall, PSC was the predominant cataract type in 56% of patients (272 individuals). Cortical cataracts were observed in 19.18% of cases, and mixed (NS + PSC) cataracts in 3.27%.
Most participants described their economic status as moderate (78%, 190 people). Half were unemployed, retired, or homemakers (51%, 124 people). Detailed demographic and baseline characteristics are presented in Table 1 . Table 1 Demographic and Clinical Characteristics of the Study Participants Variable Levels n % Sex Male 127 51.84 Female 118 48.16 Family History (FH) of Cataract No 218 88.98 Yes 27 11.02 Right Eye Cataract Type PSC (Posterior Subcapsular) 135 55.10 NS (Nuclear Sclerotic) 55 22.45 CC (Cortical Cataract) 14 5.71 MAT (Mature Cataract) 20 8.16 PP (Posterior Polar) 2 0.82 Mixed (PSC + NS) 8 3.27 Mixed (PSC + CC) 2 0.82 No Cataract 9 3.67 Left Eye Cataract Type PSC (Posterior Subcapsular) 137 55.92 NS (Nuclear Sclerotic) 57 23.27 CC (Cortical Cataract) 18 7.35 MAT (Mature Cataract) 11 4.49 PP (Posterior Polar) 2 0.82 Mixed (PSC + NS) 6 2.45 Mixed (PSC + CC) 1 0.41 No Cataract 13 5.31 Combined Cataract Type (Right + Left) PSC (Posterior Subcapsular) 272 55.51 NS (Nuclear Sclerotic) 112 22.85 CC (Cortical Cataract) 32 6.53 MAT (Mature Cataract) 31 6.33 PP (Posterior Polar) 4 0.81 Mixed (PSC + NS) 14 2.85 Mixed (PSC + CC) 3 0.61 No Cataract 22 4.49 Socioeconomic Status Poor 51 20.82 Middle 190 77.55 Good 4 1.63 Residence Area Urban 113 46.12 Rural 132 53.88 Occupation Unemployed, Retired, Homemaker 124 50.61 Farmer, Animal Herder, Produce Worker 38 15.51 Laborer, Construction Worker, Painter, Tiler 13 5.31 Teacher, Office Worker, Military Personnel 13 5.31 Mechanic, Electrician, Auto Body Repairman 7 2.86 Shopkeeper, Baker, Barber, Pastry Chef 17 6.94 Tailor 8 3.27 Welder 10 4.08 Driver 15 6.12 Works in a Hot Environment No 238 97.14 Yes 7 2.86 Comorbidity None 159 64.90 HLP (Hyperlipidemia) 4 1.63 HTN (Hypertension) 13 5.31 DM (Diabetes Mellitus) 51 20.82 TB (Tuberculosis) 0 0.00 Others 1 0.41 Hormone Disorder 1 0.41 Depression 1 0.41 HLP + DM 2 0.82 HTN + DM 4 1.63 DM + HTN 5 2.04 DM + Others 1 0.41 DM + HLP + HTN 1 0.41 DM + HTN + HLP 1 0.41 DM + HTN + Others 1 0.41 Ocular Disease None 242 98.78 RP (Retinitis Pigmentosa) 2 0.82 Coloboma 1 0.41 Uveitis 0 0.00 Ocular Trauma 0 0.00 DR (Diabetic Retinopathy) 0 0.00 Others 0 0.00 Medication Use None 198 81.48 AHTN (Antihypertensives) 7 2.88 DM (Diabetes Medication) 12 4.94 STAT (Statins) 5 2.06 ASP (Aspirin) 3 1.23 STEROID 7 2.88 HORMON (Hormone Therapy) 1 0.41 Antidepressants/Antipsychotics 2 0.82 AHTN + DM 3 1.23 AHTN + STEROID 1 0.41 DM + STAT 2 0.82 STAT + HORMON 2 0.82 High-Risk Behaviors None 115 46.54 Opium Use 85 34.69 Cigarette Smoking 18 7.35 Alcohol Consumption 3 1.22 Opium + Cigarette 24 9.80
Table 1 Demographic and Clinical Characteristics of the Study Participants Variable Levels n % Sex Male 127 51.84 Female 118 48.16 Family History (FH) of Cataract No 218 88.98 Yes 27 11.02 Right Eye Cataract Type PSC (Posterior Subcapsular) 135 55.10 NS (Nuclear Sclerotic) 55 22.45 CC (Cortical Cataract) 14 5.71 MAT (Mature Cataract) 20 8.16 PP (Posterior Polar) 2 0.82 Mixed (PSC + NS) 8 3.27 Mixed (PSC + CC) 2 0.82 No Cataract 9 3.67 Left Eye Cataract Type PSC (Posterior Subcapsular) 137 55.92 NS (Nuclear Sclerotic) 57 23.27 CC (Cortical Cataract) 18 7.35 MAT (Mature Cataract) 11 4.49 PP (Posterior Polar) 2 0.82 Mixed (PSC + NS) 6 2.45 Mixed (PSC + CC) 1 0.41 No Cataract 13 5.31 Combined Cataract Type (Right + Left) PSC (Posterior Subcapsular) 272 55.51 NS (Nuclear Sclerotic) 112 22.85 CC (Cortical Cataract) 32 6.53 MAT (Mature Cataract) 31 6.33 PP (Posterior Polar) 4 0.81 Mixed (PSC + NS) 14 2.85 Mixed (PSC + CC) 3 0.61 No Cataract 22 4.49 Socioeconomic Status Poor 51 20.82 Middle 190 77.55 Good 4 1.63 Residence Area Urban 113 46.12 Rural 132 53.88 Occupation Unemployed, Retired, Homemaker 124 50.61 Farmer, Animal Herder, Produce Worker 38 15.51 Laborer, Construction Worker, Painter, Tiler 13 5.31 Teacher, Office Worker, Military Personnel 13 5.31 Mechanic, Electrician, Auto Body Repairman 7 2.86 Shopkeeper, Baker, Barber, Pastry Chef 17 6.94 Tailor 8 3.27 Welder 10 4.08 Driver 15 6.12 Works in a Hot Environment No 238 97.14 Yes 7 2.86 Comorbidity None 159 64.90 HLP (Hyperlipidemia) 4 1.63 HTN (Hypertension) 13 5.31 DM (Diabetes Mellitus) 51 20.82 TB (Tuberculosis) 0 0.00 Others 1 0.41 Hormone Disorder 1 0.41 Depression 1 0.41 HLP + DM 2 0.82 HTN + DM 4 1.63 DM + HTN 5 2.04 DM + Others 1 0.41 DM + HLP + HTN 1 0.41 DM + HTN + HLP 1 0.41 DM + HTN + Others 1 0.41 Ocular Disease None 242 98.78 RP (Retinitis Pigmentosa) 2 0.82 Coloboma 1 0.41 Uveitis 0 0.00 Ocular Trauma 0 0.00 DR (Diabetic Retinopathy) 0 0.00 Others 0 0.00 Medication Use None 198 81.48 AHTN (Antihypertensives) 7 2.88 DM (Diabetes Medication) 12 4.94 STAT (Statins) 5 2.06 ASP (Aspirin) 3 1.23 STEROID 7 2.88 HORMON (Hormone Therapy) 1 0.41 Antidepressants/Antipsychotics 2 0.82 AHTN + DM 3 1.23 AHTN + STEROID 1 0.41 DM + STAT 2 0.82 STAT + HORMON 2 0.82 High-Risk Behaviors None 115 46.54 Opium Use 85 34.69 Cigarette Smoking 18 7.35 Alcohol Consumption 3 1.22 Opium + Cigarette 24 9.80
Demographic and Clinical Characteristics of the Study Participants
As shown in Table 1 , only 3% (7 people) worked in hot environments. Diabetes was the most common underlying comorbidity, found in 21% (51 people). Almost all participants (99%, 242 people) had no other ocular diseases, and the majority (81%, 198 people) were not taking any medications. Approximately half (47%, 115 people) reported no high-risk behaviors, while one-third (35%, 85 people) reported opium use.
Descriptive statistics for continuous variables are summarized in Table 2 . Participants had a mean age of 44.0 years (SD = 5.8). The mean intraocular pressure was 13.1 mmHg (average of both eyes combined). The mean cup-to-disc ratio was 0.35 in the right eye and 0.34 in the left eye. The mean body mass index was 24.6 kg/m 2 . Table 2 Descriptive Statistics of Quantitative Clinical Variables Variable Mean SD Minimum Maximum Median First Quartile (Q1) Third Quartile (Q3) Age (Years) 44.45 5.60 24.00 52.00 46.00 42.00 49.00 Right Eye Intraocular Pressure (mmHg) 13.14 1.95 0.10 18.00 13.50 12.00 14.00 Left Eye Intraocular Pressure (mmHg) 13.10 2.39 0.00 18.00 13.50 12.00 14.00 Right Eye Cup-to-Disc Ratio 0.35 0.08 0.10 0.70 0.40 0.30 0.40 Left Eye Cup-to-Disc Ratio 0.34 0.26 0.00 4.00 0.40 0.30 0.40 Body Mass Index (kg/m 2 ) 24.56 7.02 15.80 119.60 24.10 21.50 26.50
Table 2 Descriptive Statistics of Quantitative Clinical Variables Variable Mean SD Minimum Maximum Median First Quartile (Q1) Third Quartile (Q3) Age (Years) 44.45 5.60 24.00 52.00 46.00 42.00 49.00 Right Eye Intraocular Pressure (mmHg) 13.14 1.95 0.10 18.00 13.50 12.00 14.00 Left Eye Intraocular Pressure (mmHg) 13.10 2.39 0.00 18.00 13.50 12.00 14.00 Right Eye Cup-to-Disc Ratio 0.35 0.08 0.10 0.70 0.40 0.30 0.40 Left Eye Cup-to-Disc Ratio 0.34 0.26 0.00 4.00 0.40 0.30 0.40 Body Mass Index (kg/m 2 ) 24.56 7.02 15.80 119.60 24.10 21.50 26.50
Descriptive Statistics of Quantitative Clinical Variables
Factors associated with cataract type in the right eye are presented in Table 3 . Age was significantly associated with cataract type in the right eye (P < 0.001). Similarly, for the left eye ( Table 4 ), age showed a significant association (P = 0.01), and gender was also significantly associated with cataract type in the left eye (P = 0.013). Table 3 Investigating Key Determinants of Right Eye Cataract Type Right Eye Cataract Type p Variable Levels PSC NS CC MAT PP PSC + NS PSC + CC No Cataract Age, Mean (SD) – 43.67 a (5.67) 47.30 b (3.83) 43.50 a (6.80) 44.15 a,f (4.33) 45.50 c (4.94) 44.87 c,f (6.10) 50 d (0) 40.66 e (8.07) <0.001* High-Risk Behaviors, n (column %) None 57 (42.2) 27 (49.1) 10 (71.4) 8 (40) 1 (50) 4 (50) 1 (50) 7 (77.8) 0.194** Opium Use 52 (38.5) 19 (34.5) 1 (7.1) 6 (30) 1 (50) 4 (50) 1 (50) 1 (11.1) Cigarette Smoking 11 (8.1) 4 (7.3) 2 (14.3) 1 (5) 0 (0) 0 (0) 0 (0) 0 (0) Alcohol Consumption 2 (1.5) 1 (1.8) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 Opium + Cigarette 13 (9.6) 4 (7.3) 1 (7.1) 5 (25) 0 (0) 0 (0) 0 (0) 1 (11.1) Family History of Cataract, n (column %) No 118 (87.4) 51 (92.7) 13 (92.9) 16 (80) 2 (100) 7 (87.5) 2 (100) 9 (100) 0.704** Yes 17 (12.6) 4 (7.3) 1 (7.1) 4 (20) 0 (0) 1 (12.5) 0 (0) 0 (0) Sex, n (column %) Male 73 (54.1) 27 (49.1) 6 (49.2) 14 (70) 0 (0) 3 (37.5) 0 (0) 4 (44.4) 0.275** Female 62 (45.9) 28 (50.9) 8 (57.1) 6 (30) 2 (100) 5 (62.5) 2 (100) (55.6) Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Table 4 Investigating Key Determinants of Left Eye Cataract Type Left Eye Cataract Type p Variable Levels PSC NS CC MAT PP PSC + NS PSC + CC No Cataract Age, Mean (SD) – 43.46 a (5.88) 47.12 d (4.08) 43.50 a (6.92) 44.82 b (5.02) 45.50 c (4.95) 43.50 a (6.53) 43 a (0) 44.42 b (3.40) 0.01* High-Risk Behaviors, n (column %) None 63 (46) 29 (50.9) 11 (61.1) 3 (27.3) 1 (50) 5 (83.3) 0 (0) 2 (16.7) 0.843** Opium Use 51 (37.2) 17 (29.8) 4 (22.2) 3 (27.3) 1 (50) 1 (16.7) 1 (100) 7 (58.3) Cigarette Smoking 9 (6.6) 5 (8.8) 3 (16.7) 0 (0) 0 (0) 0 (0) 0 (0) 1 (8.3) Alcohol Consumption 2 (1.5) 1 (1.8) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) Opium + Cigarette 12 (8.8) 5 (8.8) 0 (0) 5 (45.5) 0 (0) 0 (0) 0 (0) 2 (16.7) Family History of Cataract, n (column %) No 120 (87.6) 52 (91.2) 18 (100) 8 (72.7) 2 (100) 5 (83.3) 1 (100) 11 (91.7) 0.495** Yes 17 (12.4) 5 (8.8) 0 (0) 3 (27.3) 0 (0) 1 (16.7) 0 (0) 1 (8.3) Sex, n (column %) Male 69 (50.4) 28 (49.1) 9 (50) 11 (100) 0 (0) 1 (16.7) 0 (0) 8 (66.7) 0.013** Female 68 (49.6) 29 (50.9) 9 (50) 0 (0) 2 (100) 5 (83.3) 1 (100) 4 (33.3) Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Table 3 Investigating Key Determinants of Right Eye Cataract Type Right Eye Cataract Type p Variable Levels PSC NS CC MAT PP PSC + NS PSC + CC No Cataract Age, Mean (SD) – 43.67 a (5.67) 47.30 b (3.83) 43.50 a (6.80) 44.15 a,f (4.33) 45.50 c (4.94) 44.87 c,f (6.10) 50 d (0) 40.66 e (8.07) <0.001* High-Risk Behaviors, n (column %) None 57 (42.2) 27 (49.1) 10 (71.4) 8 (40) 1 (50) 4 (50) 1 (50) 7 (77.8) 0.194** Opium Use 52 (38.5) 19 (34.5) 1 (7.1) 6 (30) 1 (50) 4 (50) 1 (50) 1 (11.1) Cigarette Smoking 11 (8.1) 4 (7.3) 2 (14.3) 1 (5) 0 (0) 0 (0) 0 (0) 0 (0) Alcohol Consumption 2 (1.5) 1 (1.8) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 Opium + Cigarette 13 (9.6) 4 (7.3) 1 (7.1) 5 (25) 0 (0) 0 (0) 0 (0) 1 (11.1) Family History of Cataract, n (column %) No 118 (87.4) 51 (92.7) 13 (92.9) 16 (80) 2 (100) 7 (87.5) 2 (100) 9 (100) 0.704** Yes 17 (12.6) 4 (7.3) 1 (7.1) 4 (20) 0 (0) 1 (12.5) 0 (0) 0 (0) Sex, n (column %) Male 73 (54.1) 27 (49.1) 6 (49.2) 14 (70) 0 (0) 3 (37.5) 0 (0) 4 (44.4) 0.275** Female 62 (45.9) 28 (50.9) 8 (57.1) 6 (30) 2 (100) 5 (62.5) 2 (100) (55.6) Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Investigating Key Determinants of Right Eye Cataract Type
Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Table 4 Investigating Key Determinants of Left Eye Cataract Type Left Eye Cataract Type p Variable Levels PSC NS CC MAT PP PSC + NS PSC + CC No Cataract Age, Mean (SD) – 43.46 a (5.88) 47.12 d (4.08) 43.50 a (6.92) 44.82 b (5.02) 45.50 c (4.95) 43.50 a (6.53) 43 a (0) 44.42 b (3.40) 0.01* High-Risk Behaviors, n (column %) None 63 (46) 29 (50.9) 11 (61.1) 3 (27.3) 1 (50) 5 (83.3) 0 (0) 2 (16.7) 0.843** Opium Use 51 (37.2) 17 (29.8) 4 (22.2) 3 (27.3) 1 (50) 1 (16.7) 1 (100) 7 (58.3) Cigarette Smoking 9 (6.6) 5 (8.8) 3 (16.7) 0 (0) 0 (0) 0 (0) 0 (0) 1 (8.3) Alcohol Consumption 2 (1.5) 1 (1.8) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) Opium + Cigarette 12 (8.8) 5 (8.8) 0 (0) 5 (45.5) 0 (0) 0 (0) 0 (0) 2 (16.7) Family History of Cataract, n (column %) No 120 (87.6) 52 (91.2) 18 (100) 8 (72.7) 2 (100) 5 (83.3) 1 (100) 11 (91.7) 0.495** Yes 17 (12.4) 5 (8.8) 0 (0) 3 (27.3) 0 (0) 1 (16.7) 0 (0) 1 (8.3) Sex, n (column %) Male 69 (50.4) 28 (49.1) 9 (50) 11 (100) 0 (0) 1 (16.7) 0 (0) 8 (66.7) 0.013** Female 68 (49.6) 29 (50.9) 9 (50) 0 (0) 2 (100) 5 (83.3) 1 (100) 4 (33.3) Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Investigating Key Determinants of Left Eye Cataract Type
Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Discussion
The present study investigated demographic, clinical, and lifestyle-related factors associated with presenile cataracts among patients aged 30–50 years in Kerman, Iran. The findings highlight the predominance of posterior subcapsular cataracts, the significant role of age and gender in cataract type, and the high prevalence of opioid use and unemployment in this population.
Gender and Cataract Type
The findings revealed a significant association between gender and cataract type in the left eye (P = 0.013), while no such association was observed in the right eye (P = 0.204). Previous studies have reported inconsistent findings regarding gender as a risk factor for presenile cataracts. Das et al, 12 Nam et al, 13 and Dhanya et al 14 found no significant gender-related differences in cataract prevalence. Similarly, other research has suggested that environmental factors, lifestyle, and comorbidities such as diabetes and hypertension play more substantial roles than gender alone. 15 , 16 However, some studies have indicated that hormonal changes, particularly in postmenopausal women, may influence cataract development, although this association has not reached statistical significance and warrants further investigation. 17 The differential significance observed between the left and right eyes in this study raises the possibility that unilateral exposure to environmental or behavioral factors—such as sunlight exposure during driving or sleeping habits—may contribute to laterality differences, an area that merits further exploration.
Cataract Types and Clinical Implications
The most common cataract type in this study population was posterior subcapsular (PSC), affecting 55% of right eyes and 56% of left eyes, followed by nuclear sclerotic (NS) cataracts (22.45%) and cortical cataracts (19.18%). This predominance of PSC in younger patients is consistent with previous reports indicating that PSC is more frequently observed in diabetic patients, steroid users, and younger age groups. 18 , 19 Concurrent NS and PSC were observed in 3.27% of patients, suggesting advanced lenticular involvement and more complex clinical presentations requiring prompt therapeutic intervention. 20
Occupational and Environmental Factors
More than half of the participants (50.61%) were farmers, livestock breeders, or produce market personnel, while 6.12% were drivers. These occupations often involve prolonged outdoor work with significant exposure to ultraviolet radiation, dust, and high temperatures—all established risk factors for cataract development. 21 Previous studies have identified welding (4.08% of patients), furnace work, and exposure to ionizing radiation as high-risk occupations due to infrared radiation, high heat, and chemical exposure. 22 The low proportion of patients with hot environment exposure (2.86%) in this study may reflect either underreporting or the predominance of other risk factors such as age, diabetes, and sunlight exposure. 23 , 24 These occupational findings underscore the need for preventive measures including protective eyewear, intermittent work schedules, and regular eye examinations in high-risk groups.
Comorbidities and Medication Use
Underlying diseases were present in 35.1% of patients, with diabetes being the most common (21%). Hyperglycemia can induce lenticular changes through osmotic and metabolic disturbances, increasing cataract risk. 11 , 25 Other conditions such as hypoparathyroidism and Wilson’s disease, although rare, can also affect lens health. 26 , 27 Given the significant contribution of systemic diseases to cataract pathogenesis, comprehensive screening and management of chronic conditions are essential in high-risk patients. Regarding medication use, 18.5% of patients were on chronic medications, with corticosteroids being particularly associated with lens opacification. 25 , 28–31 Self-medication and prolonged use of certain drugs without ophthalmologic follow-up remain important concerns. 32
Opioid Use and Lifestyle Factors
Opioid use was reported in 33.47% of participants, a notably high prevalence consistent with previous studies linking substance abuse to increased cataract risk. 33 , 34 Opioid use may contribute to cataract formation through oxidative stress, metabolic disturbances, and secondary lifestyle factors such as poor nutrition and reduced healthcare-seeking behavior. Smoking and hookah use similarly increase oxidative stress in the lens, accelerating opacification. 33 Public health interventions addressing substance abuse and promoting eye health awareness could substantially reduce cataract burden in this population.
Age and Gender as Determinants
The analysis demonstrated a significant association between age and cataract type in both the right eye (P < 0.001) and left eye (P = 0.01), confirming the well-established role of age-related oxidative stress and lens protein denaturation in cataractogenesis. 1 , 35 The additional significant association between gender and cataract type in the left eye (P = 0.013) suggests that sex-related hormonal, behavioral, or environmental factors may contribute to subtype-specific pathogenesis, as previously proposed. 17 These findings highlight the multifactorial nature of presenile cataracts and support the inclusion of both age and gender stratification in clinical prognostication and future mechanistic studies.
Strengths and Limitations
This study addresses an important gap in the literature concerning presenile cataracts in Iranian adults aged 30–50 years. Its strengths include a comprehensive assessment of demographic, occupational, clinical, and behavioral factors, including opioid use—a significant public health concern in this region.
Several limitations should be acknowledged. The cross-sectional, hospital-based design limits the generalizability of the findings and precludes causal inferences. Reliance on self-reported data for medical history, medication use, and substance abuse may introduce recall and social desirability biases. Additionally, the distribution of responses was skewed toward negative/neutral categories, which may have reduced the statistical power to detect associations for certain variables. Future prospective studies with larger, population-based samples are warranted to confirm these findings and establish causality.
Conclusion
This cross-sectional study at Shafa Hospital in Kerman demonstrates that presenile cataract is influenced by multiple environmental and behavioral risk factors, with occupational UV exposure, opioid addiction, and systemic diseases such as diabetes being major contributors. The significant association between gender and cataract type, observed only in the left eye, suggests a potential influence from local environmental or behavioral factors rather than biological sex alone. Diabetes and corticosteroid use also play a clear role, indicating that broader health issues contribute to this problem. Public health approaches should therefore focus on workplace eye protection, substance abuse control, and chronic disease management to prevent presenile cataract in this population. Future research should investigate the mechanisms underlying laterality differences and the specific pathways through which substance abuse leads to cataract formation.
Cataracts remain a major contributing factor to visual impairment and blindness worldwide. 1 Although cataracts have traditionally been considered an age-related problem, the phenomenon of presenile cataracts, occurring in individuals younger than 50–60 years, has generated significant public health interest. Such cases in younger individuals impose a considerable public health burden, with quality of life, economic potential, and mental health significantly affected during their most productive years. 2 Therefore, understanding the factors contributing to presenile cataracts in young individuals is of critical importance.
Globally, cataract distribution patterns vary between regions. In developed countries such as the United States and the United Kingdom, visually disabling cataracts account for approximately half of all blindness cases. 3 In Australian adults over 55 years, the incidence of age-related cataract has been reported at 3.4%, 4 while in Canada, cataracts account for up to 71% of ophthalmic surgeries in the elderly population. 5 Similar trends are observed in Iran’s neighboring countries. In Turkey, 4.5% of urban residents aged 40 years and older had clinically significant cataracts affecting vision. 6 In Pakistan, cataracts remain one of the leading causes of blindness, representing a major preventable cause of visual impairment. 7 In Iran, although comprehensive national data are limited, research in Tehran demonstrated cataracts in 36.2% of individuals over 60 years, 8 and annual incidence data from Isfahan reported 3.5 per 1000 persons per year for age-related cataracts. 9 However, specific data regarding central and southeastern Iran, particularly Kerman province, remain scarce, with limited systematic analysis of risk factors for presenile cataracts.
Several factors necessitate investigation in Kerman province. First, the region experiences high solar intensity throughout the year with elevated ambient ultraviolet levels, an established environmental risk factor for cataracts. 10 Furthermore, the increasing prevalence of systemic diseases such as diabetes may further increase the risk of early-onset cataracts in this region. 11 Therefore, assessing the distribution of such risk factors among patients at Shafa Hospital, a principal referral center in Kerman, is essential.
The current study was designed to assess the frequency of major risk factors—including diabetes, eye trauma, corticosteroid intake, family history, sunlight exposure, and smoking—in patients aged 30–50 years with presenile cataracts at Shafa Hospital in Kerman, Iran, during 2024–2025. By identifying these risk factors in the community, this study aims to establish a foundation for potential future public health programs.
Methods
Study Design
This was an analytical cross-sectional study conducted in 2024 at the Ophthalmology Department of Shafa Hospital in Kerman Province, Iran.
Study Population and Sampling
This cross-sectional study included all patients with presenile cataract who met the following eligibility criteria: age between 30 and 50 years, indication for cataract surgery, and admission to the Ophthalmology Department of Shafa Hospital, Kerman, during the year 2024. Given complete access to the entire target population during the study period, a census sampling method was employed, whereby all eligible patients were consecutively enrolled. Patients who died during the study period or those with incomplete medical records (missing key data) were excluded from the analysis. A total of 245 patients (468 eyes) were ultimately included in the study.
Data Collection Tool
Data were collected using a structured form developed specifically for this study, which covered all key variables including demographic characteristics (age, gender, occupation, economic status classified as low, moderate, or high based on monthly household income relative to the national minimum wage, family history of cataracts, and history of protective eyewear use such as sunglasses or hats during outdoor activities), clinical measurements (intraocular pressure measured using Goldmann applanation tonometry with the HAAG-STREIT AT 900®, Haag-Streit AG, Köniz, Switzerland; cup-to-disc ratio assessed by fundoscopic examination; and body mass index calculated as weight in kilograms divided by height in meters squared), ophthalmological findings (cataract type including nuclear sclerotic, posterior subcapsular, cortical, or mixed, and laterality as right, left, or bilateral, as diagnosed by a trained ophthalmologist using slit-lamp examination), and systemic and lifestyle factors (underlying comorbidities such as diabetes and hypertension, medication history including corticosteroids, high-risk behaviors defined as opioid use, smoking, or alcohol consumption, and occupational exposure to hot environments defined as exposure to temperatures ≥ 40°C for at least 2 hours daily). The form was developed based on a thorough literature review and piloted on 10 patients prior to data collection. Data were extracted from medical records, supplemented by face-to-face interviews, and confirmed through clinical examinations.
Statistical Analysis
Statistical analyses were performed using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA). The normality of continuous variables was assessed using the Shapiro–Wilk test. As the data were not normally distributed, non-parametric tests were employed where appropriate. Continuous variables are presented as mean ± standard deviation (SD), and categorical variables are presented as frequencies and percentages. Since a census sampling approach was used (all eligible patients during the study period were enrolled), no sample size calculation was required. Group comparisons were performed using one-way ANOVA (for normally distributed data) and Fisher’s exact test (for categorical variables). A two-sided P-value < 0.05 was considered statistically significant.
Ethical Considerations
This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of Kerman University of Medical Sciences (Ethical Code: IR.KMU.REC.1402.209). All participants provided written informed consent prior to enrollment. Participants were assured that their data would be kept confidential, that they could withdraw from the study at any time without consequences, and that there would be no additional costs associated with their participation. The results are reported in a grouped and anonymous manner to ensure participant privacy.
Results
A total of 468 eyes from 245 participants were analyzed. Of the participants, 52% were male. A positive family history of cataracts was reported in 11% of cases. Regarding cataract type, posterior subcapsular cataract (PSC) was the most prevalent diagnosis, affecting 55% (135 eyes) of right eyes and 56% (137 eyes) of left eyes. Overall, PSC was the predominant cataract type in 56% of patients (272 individuals). Cortical cataracts were observed in 19.18% of cases, and mixed (NS + PSC) cataracts in 3.27%.
Most participants described their economic status as moderate (78%, 190 people). Half were unemployed, retired, or homemakers (51%, 124 people). Detailed demographic and baseline characteristics are presented in Table 1 . Table 1 Demographic and Clinical Characteristics of the Study Participants Variable Levels n % Sex Male 127 51.84 Female 118 48.16 Family History (FH) of Cataract No 218 88.98 Yes 27 11.02 Right Eye Cataract Type PSC (Posterior Subcapsular) 135 55.10 NS (Nuclear Sclerotic) 55 22.45 CC (Cortical Cataract) 14 5.71 MAT (Mature Cataract) 20 8.16 PP (Posterior Polar) 2 0.82 Mixed (PSC + NS) 8 3.27 Mixed (PSC + CC) 2 0.82 No Cataract 9 3.67 Left Eye Cataract Type PSC (Posterior Subcapsular) 137 55.92 NS (Nuclear Sclerotic) 57 23.27 CC (Cortical Cataract) 18 7.35 MAT (Mature Cataract) 11 4.49 PP (Posterior Polar) 2 0.82 Mixed (PSC + NS) 6 2.45 Mixed (PSC + CC) 1 0.41 No Cataract 13 5.31 Combined Cataract Type (Right + Left) PSC (Posterior Subcapsular) 272 55.51 NS (Nuclear Sclerotic) 112 22.85 CC (Cortical Cataract) 32 6.53 MAT (Mature Cataract) 31 6.33 PP (Posterior Polar) 4 0.81 Mixed (PSC + NS) 14 2.85 Mixed (PSC + CC) 3 0.61 No Cataract 22 4.49 Socioeconomic Status Poor 51 20.82 Middle 190 77.55 Good 4 1.63 Residence Area Urban 113 46.12 Rural 132 53.88 Occupation Unemployed, Retired, Homemaker 124 50.61 Farmer, Animal Herder, Produce Worker 38 15.51 Laborer, Construction Worker, Painter, Tiler 13 5.31 Teacher, Office Worker, Military Personnel 13 5.31 Mechanic, Electrician, Auto Body Repairman 7 2.86 Shopkeeper, Baker, Barber, Pastry Chef 17 6.94 Tailor 8 3.27 Welder 10 4.08 Driver 15 6.12 Works in a Hot Environment No 238 97.14 Yes 7 2.86 Comorbidity None 159 64.90 HLP (Hyperlipidemia) 4 1.63 HTN (Hypertension) 13 5.31 DM (Diabetes Mellitus) 51 20.82 TB (Tuberculosis) 0 0.00 Others 1 0.41 Hormone Disorder 1 0.41 Depression 1 0.41 HLP + DM 2 0.82 HTN + DM 4 1.63 DM + HTN 5 2.04 DM + Others 1 0.41 DM + HLP + HTN 1 0.41 DM + HTN + HLP 1 0.41 DM + HTN + Others 1 0.41 Ocular Disease None 242 98.78 RP (Retinitis Pigmentosa) 2 0.82 Coloboma 1 0.41 Uveitis 0 0.00 Ocular Trauma 0 0.00 DR (Diabetic Retinopathy) 0 0.00 Others 0 0.00 Medication Use None 198 81.48 AHTN (Antihypertensives) 7 2.88 DM (Diabetes Medication) 12 4.94 STAT (Statins) 5 2.06 ASP (Aspirin) 3 1.23 STEROID 7 2.88 HORMON (Hormone Therapy) 1 0.41 Antidepressants/Antipsychotics 2 0.82 AHTN + DM 3 1.23 AHTN + STEROID 1 0.41 DM + STAT 2 0.82 STAT + HORMON 2 0.82 High-Risk Behaviors None 115 46.54 Opium Use 85 34.69 Cigarette Smoking 18 7.35 Alcohol Consumption 3 1.22 Opium + Cigarette 24 9.80
Table 1 Demographic and Clinical Characteristics of the Study Participants Variable Levels n % Sex Male 127 51.84 Female 118 48.16 Family History (FH) of Cataract No 218 88.98 Yes 27 11.02 Right Eye Cataract Type PSC (Posterior Subcapsular) 135 55.10 NS (Nuclear Sclerotic) 55 22.45 CC (Cortical Cataract) 14 5.71 MAT (Mature Cataract) 20 8.16 PP (Posterior Polar) 2 0.82 Mixed (PSC + NS) 8 3.27 Mixed (PSC + CC) 2 0.82 No Cataract 9 3.67 Left Eye Cataract Type PSC (Posterior Subcapsular) 137 55.92 NS (Nuclear Sclerotic) 57 23.27 CC (Cortical Cataract) 18 7.35 MAT (Mature Cataract) 11 4.49 PP (Posterior Polar) 2 0.82 Mixed (PSC + NS) 6 2.45 Mixed (PSC + CC) 1 0.41 No Cataract 13 5.31 Combined Cataract Type (Right + Left) PSC (Posterior Subcapsular) 272 55.51 NS (Nuclear Sclerotic) 112 22.85 CC (Cortical Cataract) 32 6.53 MAT (Mature Cataract) 31 6.33 PP (Posterior Polar) 4 0.81 Mixed (PSC + NS) 14 2.85 Mixed (PSC + CC) 3 0.61 No Cataract 22 4.49 Socioeconomic Status Poor 51 20.82 Middle 190 77.55 Good 4 1.63 Residence Area Urban 113 46.12 Rural 132 53.88 Occupation Unemployed, Retired, Homemaker 124 50.61 Farmer, Animal Herder, Produce Worker 38 15.51 Laborer, Construction Worker, Painter, Tiler 13 5.31 Teacher, Office Worker, Military Personnel 13 5.31 Mechanic, Electrician, Auto Body Repairman 7 2.86 Shopkeeper, Baker, Barber, Pastry Chef 17 6.94 Tailor 8 3.27 Welder 10 4.08 Driver 15 6.12 Works in a Hot Environment No 238 97.14 Yes 7 2.86 Comorbidity None 159 64.90 HLP (Hyperlipidemia) 4 1.63 HTN (Hypertension) 13 5.31 DM (Diabetes Mellitus) 51 20.82 TB (Tuberculosis) 0 0.00 Others 1 0.41 Hormone Disorder 1 0.41 Depression 1 0.41 HLP + DM 2 0.82 HTN + DM 4 1.63 DM + HTN 5 2.04 DM + Others 1 0.41 DM + HLP + HTN 1 0.41 DM + HTN + HLP 1 0.41 DM + HTN + Others 1 0.41 Ocular Disease None 242 98.78 RP (Retinitis Pigmentosa) 2 0.82 Coloboma 1 0.41 Uveitis 0 0.00 Ocular Trauma 0 0.00 DR (Diabetic Retinopathy) 0 0.00 Others 0 0.00 Medication Use None 198 81.48 AHTN (Antihypertensives) 7 2.88 DM (Diabetes Medication) 12 4.94 STAT (Statins) 5 2.06 ASP (Aspirin) 3 1.23 STEROID 7 2.88 HORMON (Hormone Therapy) 1 0.41 Antidepressants/Antipsychotics 2 0.82 AHTN + DM 3 1.23 AHTN + STEROID 1 0.41 DM + STAT 2 0.82 STAT + HORMON 2 0.82 High-Risk Behaviors None 115 46.54 Opium Use 85 34.69 Cigarette Smoking 18 7.35 Alcohol Consumption 3 1.22 Opium + Cigarette 24 9.80
Demographic and Clinical Characteristics of the Study Participants
As shown in Table 1 , only 3% (7 people) worked in hot environments. Diabetes was the most common underlying comorbidity, found in 21% (51 people). Almost all participants (99%, 242 people) had no other ocular diseases, and the majority (81%, 198 people) were not taking any medications. Approximately half (47%, 115 people) reported no high-risk behaviors, while one-third (35%, 85 people) reported opium use.
Descriptive statistics for continuous variables are summarized in Table 2 . Participants had a mean age of 44.0 years (SD = 5.8). The mean intraocular pressure was 13.1 mmHg (average of both eyes combined). The mean cup-to-disc ratio was 0.35 in the right eye and 0.34 in the left eye. The mean body mass index was 24.6 kg/m 2 . Table 2 Descriptive Statistics of Quantitative Clinical Variables Variable Mean SD Minimum Maximum Median First Quartile (Q1) Third Quartile (Q3) Age (Years) 44.45 5.60 24.00 52.00 46.00 42.00 49.00 Right Eye Intraocular Pressure (mmHg) 13.14 1.95 0.10 18.00 13.50 12.00 14.00 Left Eye Intraocular Pressure (mmHg) 13.10 2.39 0.00 18.00 13.50 12.00 14.00 Right Eye Cup-to-Disc Ratio 0.35 0.08 0.10 0.70 0.40 0.30 0.40 Left Eye Cup-to-Disc Ratio 0.34 0.26 0.00 4.00 0.40 0.30 0.40 Body Mass Index (kg/m 2 ) 24.56 7.02 15.80 119.60 24.10 21.50 26.50
Table 2 Descriptive Statistics of Quantitative Clinical Variables Variable Mean SD Minimum Maximum Median First Quartile (Q1) Third Quartile (Q3) Age (Years) 44.45 5.60 24.00 52.00 46.00 42.00 49.00 Right Eye Intraocular Pressure (mmHg) 13.14 1.95 0.10 18.00 13.50 12.00 14.00 Left Eye Intraocular Pressure (mmHg) 13.10 2.39 0.00 18.00 13.50 12.00 14.00 Right Eye Cup-to-Disc Ratio 0.35 0.08 0.10 0.70 0.40 0.30 0.40 Left Eye Cup-to-Disc Ratio 0.34 0.26 0.00 4.00 0.40 0.30 0.40 Body Mass Index (kg/m 2 ) 24.56 7.02 15.80 119.60 24.10 21.50 26.50
Descriptive Statistics of Quantitative Clinical Variables
Factors associated with cataract type in the right eye are presented in Table 3 . Age was significantly associated with cataract type in the right eye (P < 0.001). Similarly, for the left eye ( Table 4 ), age showed a significant association (P = 0.01), and gender was also significantly associated with cataract type in the left eye (P = 0.013). Table 3 Investigating Key Determinants of Right Eye Cataract Type Right Eye Cataract Type p Variable Levels PSC NS CC MAT PP PSC + NS PSC + CC No Cataract Age, Mean (SD) – 43.67 a (5.67) 47.30 b (3.83) 43.50 a (6.80) 44.15 a,f (4.33) 45.50 c (4.94) 44.87 c,f (6.10) 50 d (0) 40.66 e (8.07) <0.001* High-Risk Behaviors, n (column %) None 57 (42.2) 27 (49.1) 10 (71.4) 8 (40) 1 (50) 4 (50) 1 (50) 7 (77.8) 0.194** Opium Use 52 (38.5) 19 (34.5) 1 (7.1) 6 (30) 1 (50) 4 (50) 1 (50) 1 (11.1) Cigarette Smoking 11 (8.1) 4 (7.3) 2 (14.3) 1 (5) 0 (0) 0 (0) 0 (0) 0 (0) Alcohol Consumption 2 (1.5) 1 (1.8) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 Opium + Cigarette 13 (9.6) 4 (7.3) 1 (7.1) 5 (25) 0 (0) 0 (0) 0 (0) 1 (11.1) Family History of Cataract, n (column %) No 118 (87.4) 51 (92.7) 13 (92.9) 16 (80) 2 (100) 7 (87.5) 2 (100) 9 (100) 0.704** Yes 17 (12.6) 4 (7.3) 1 (7.1) 4 (20) 0 (0) 1 (12.5) 0 (0) 0 (0) Sex, n (column %) Male 73 (54.1) 27 (49.1) 6 (49.2) 14 (70) 0 (0) 3 (37.5) 0 (0) 4 (44.4) 0.275** Female 62 (45.9) 28 (50.9) 8 (57.1) 6 (30) 2 (100) 5 (62.5) 2 (100) (55.6) Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Table 4 Investigating Key Determinants of Left Eye Cataract Type Left Eye Cataract Type p Variable Levels PSC NS CC MAT PP PSC + NS PSC + CC No Cataract Age, Mean (SD) – 43.46 a (5.88) 47.12 d (4.08) 43.50 a (6.92) 44.82 b (5.02) 45.50 c (4.95) 43.50 a (6.53) 43 a (0) 44.42 b (3.40) 0.01* High-Risk Behaviors, n (column %) None 63 (46) 29 (50.9) 11 (61.1) 3 (27.3) 1 (50) 5 (83.3) 0 (0) 2 (16.7) 0.843** Opium Use 51 (37.2) 17 (29.8) 4 (22.2) 3 (27.3) 1 (50) 1 (16.7) 1 (100) 7 (58.3) Cigarette Smoking 9 (6.6) 5 (8.8) 3 (16.7) 0 (0) 0 (0) 0 (0) 0 (0) 1 (8.3) Alcohol Consumption 2 (1.5) 1 (1.8) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) Opium + Cigarette 12 (8.8) 5 (8.8) 0 (0) 5 (45.5) 0 (0) 0 (0) 0 (0) 2 (16.7) Family History of Cataract, n (column %) No 120 (87.6) 52 (91.2) 18 (100) 8 (72.7) 2 (100) 5 (83.3) 1 (100) 11 (91.7) 0.495** Yes 17 (12.4) 5 (8.8) 0 (0) 3 (27.3) 0 (0) 1 (16.7) 0 (0) 1 (8.3) Sex, n (column %) Male 69 (50.4) 28 (49.1) 9 (50) 11 (100) 0 (0) 1 (16.7) 0 (0) 8 (66.7) 0.013** Female 68 (49.6) 29 (50.9) 9 (50) 0 (0) 2 (100) 5 (83.3) 1 (100) 4 (33.3) Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Table 3 Investigating Key Determinants of Right Eye Cataract Type Right Eye Cataract Type p Variable Levels PSC NS CC MAT PP PSC + NS PSC + CC No Cataract Age, Mean (SD) – 43.67 a (5.67) 47.30 b (3.83) 43.50 a (6.80) 44.15 a,f (4.33) 45.50 c (4.94) 44.87 c,f (6.10) 50 d (0) 40.66 e (8.07) <0.001* High-Risk Behaviors, n (column %) None 57 (42.2) 27 (49.1) 10 (71.4) 8 (40) 1 (50) 4 (50) 1 (50) 7 (77.8) 0.194** Opium Use 52 (38.5) 19 (34.5) 1 (7.1) 6 (30) 1 (50) 4 (50) 1 (50) 1 (11.1) Cigarette Smoking 11 (8.1) 4 (7.3) 2 (14.3) 1 (5) 0 (0) 0 (0) 0 (0) 0 (0) Alcohol Consumption 2 (1.5) 1 (1.8) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 Opium + Cigarette 13 (9.6) 4 (7.3) 1 (7.1) 5 (25) 0 (0) 0 (0) 0 (0) 1 (11.1) Family History of Cataract, n (column %) No 118 (87.4) 51 (92.7) 13 (92.9) 16 (80) 2 (100) 7 (87.5) 2 (100) 9 (100) 0.704** Yes 17 (12.6) 4 (7.3) 1 (7.1) 4 (20) 0 (0) 1 (12.5) 0 (0) 0 (0) Sex, n (column %) Male 73 (54.1) 27 (49.1) 6 (49.2) 14 (70) 0 (0) 3 (37.5) 0 (0) 4 (44.4) 0.275** Female 62 (45.9) 28 (50.9) 8 (57.1) 6 (30) 2 (100) 5 (62.5) 2 (100) (55.6) Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Investigating Key Determinants of Right Eye Cataract Type
Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Table 4 Investigating Key Determinants of Left Eye Cataract Type Left Eye Cataract Type p Variable Levels PSC NS CC MAT PP PSC + NS PSC + CC No Cataract Age, Mean (SD) – 43.46 a (5.88) 47.12 d (4.08) 43.50 a (6.92) 44.82 b (5.02) 45.50 c (4.95) 43.50 a (6.53) 43 a (0) 44.42 b (3.40) 0.01* High-Risk Behaviors, n (column %) None 63 (46) 29 (50.9) 11 (61.1) 3 (27.3) 1 (50) 5 (83.3) 0 (0) 2 (16.7) 0.843** Opium Use 51 (37.2) 17 (29.8) 4 (22.2) 3 (27.3) 1 (50) 1 (16.7) 1 (100) 7 (58.3) Cigarette Smoking 9 (6.6) 5 (8.8) 3 (16.7) 0 (0) 0 (0) 0 (0) 0 (0) 1 (8.3) Alcohol Consumption 2 (1.5) 1 (1.8) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) Opium + Cigarette 12 (8.8) 5 (8.8) 0 (0) 5 (45.5) 0 (0) 0 (0) 0 (0) 2 (16.7) Family History of Cataract, n (column %) No 120 (87.6) 52 (91.2) 18 (100) 8 (72.7) 2 (100) 5 (83.3) 1 (100) 11 (91.7) 0.495** Yes 17 (12.4) 5 (8.8) 0 (0) 3 (27.3) 0 (0) 1 (16.7) 0 (0) 1 (8.3) Sex, n (column %) Male 69 (50.4) 28 (49.1) 9 (50) 11 (100) 0 (0) 1 (16.7) 0 (0) 8 (66.7) 0.013** Female 68 (49.6) 29 (50.9) 9 (50) 0 (0) 2 (100) 5 (83.3) 1 (100) 4 (33.3) Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Investigating Key Determinants of Left Eye Cataract Type
Notes : *One way ANOVA. Data are presented as mean (standard deviation). Different superscript letters (a, b, c, d, e, f) within the same row indicate statistically significant differences between groups based on post-hoc pairwise comparisons (Tukey’s HSD test; P < 0.05). Groups sharing the same superscript letter are not significantly different. Bold values indicate statistically significant differences (P < 0.05). ** Fisher’s exact test.
Discussion
The present study investigated demographic, clinical, and lifestyle-related factors associated with presenile cataracts among patients aged 30–50 years in Kerman, Iran. The findings highlight the predominance of posterior subcapsular cataracts, the significant role of age and gender in cataract type, and the high prevalence of opioid use and unemployment in this population.
Gender and Cataract Type
The findings revealed a significant association between gender and cataract type in the left eye (P = 0.013), while no such association was observed in the right eye (P = 0.204). Previous studies have reported inconsistent findings regarding gender as a risk factor for presenile cataracts. Das et al, 12 Nam et al, 13 and Dhanya et al 14 found no significant gender-related differences in cataract prevalence. Similarly, other research has suggested that environmental factors, lifestyle, and comorbidities such as diabetes and hypertension play more substantial roles than gender alone. 15 , 16 However, some studies have indicated that hormonal changes, particularly in postmenopausal women, may influence cataract development, although this association has not reached statistical significance and warrants further investigation. 17 The differential significance observed between the left and right eyes in this study raises the possibility that unilateral exposure to environmental or behavioral factors—such as sunlight exposure during driving or sleeping habits—may contribute to laterality differences, an area that merits further exploration.
Cataract Types and Clinical Implications
The most common cataract type in this study population was posterior subcapsular (PSC), affecting 55% of right eyes and 56% of left eyes, followed by nuclear sclerotic (NS) cataracts (22.45%) and cortical cataracts (19.18%). This predominance of PSC in younger patients is consistent with previous reports indicating that PSC is more frequently observed in diabetic patients, steroid users, and younger age groups. 18 , 19 Concurrent NS and PSC were observed in 3.27% of patients, suggesting advanced lenticular involvement and more complex clinical presentations requiring prompt therapeutic intervention. 20
Occupational and Environmental Factors
More than half of the participants (50.61%) were farmers, livestock breeders, or produce market personnel, while 6.12% were drivers. These occupations often involve prolonged outdoor work with significant exposure to ultraviolet radiation, dust, and high temperatures—all established risk factors for cataract development. 21 Previous studies have identified welding (4.08% of patients), furnace work, and exposure to ionizing radiation as high-risk occupations due to infrared radiation, high heat, and chemical exposure. 22 The low proportion of patients with hot environment exposure (2.86%) in this study may reflect either underreporting or the predominance of other risk factors such as age, diabetes, and sunlight exposure. 23 , 24 These occupational findings underscore the need for preventive measures including protective eyewear, intermittent work schedules, and regular eye examinations in high-risk groups.
Comorbidities and Medication Use
Underlying diseases were present in 35.1% of patients, with diabetes being the most common (21%). Hyperglycemia can induce lenticular changes through osmotic and metabolic disturbances, increasing cataract risk. 11 , 25 Other conditions such as hypoparathyroidism and Wilson’s disease, although rare, can also affect lens health. 26 , 27 Given the significant contribution of systemic diseases to cataract pathogenesis, comprehensive screening and management of chronic conditions are essential in high-risk patients. Regarding medication use, 18.5% of patients were on chronic medications, with corticosteroids being particularly associated with lens opacification. 25 , 28–31 Self-medication and prolonged use of certain drugs without ophthalmologic follow-up remain important concerns. 32
Opioid Use and Lifestyle Factors
Opioid use was reported in 33.47% of participants, a notably high prevalence consistent with previous studies linking substance abuse to increased cataract risk. 33 , 34 Opioid use may contribute to cataract formation through oxidative stress, metabolic disturbances, and secondary lifestyle factors such as poor nutrition and reduced healthcare-seeking behavior. Smoking and hookah use similarly increase oxidative stress in the lens, accelerating opacification. 33 Public health interventions addressing substance abuse and promoting eye health awareness could substantially reduce cataract burden in this population.
Age and Gender as Determinants
The analysis demonstrated a significant association between age and cataract type in both the right eye (P < 0.001) and left eye (P = 0.01), confirming the well-established role of age-related oxidative stress and lens protein denaturation in cataractogenesis. 1 , 35 The additional significant association between gender and cataract type in the left eye (P = 0.013) suggests that sex-related hormonal, behavioral, or environmental factors may contribute to subtype-specific pathogenesis, as previously proposed. 17 These findings highlight the multifactorial nature of presenile cataracts and support the inclusion of both age and gender stratification in clinical prognostication and future mechanistic studies.
Strengths and Limitations
This study addresses an important gap in the literature concerning presenile cataracts in Iranian adults aged 30–50 years. Its strengths include a comprehensive assessment of demographic, occupational, clinical, and behavioral factors, including opioid use—a significant public health concern in this region.
Several limitations should be acknowledged. The cross-sectional, hospital-based design limits the generalizability of the findings and precludes causal inferences. Reliance on self-reported data for medical history, medication use, and substance abuse may introduce recall and social desirability biases. Additionally, the distribution of responses was skewed toward negative/neutral categories, which may have reduced the statistical power to detect associations for certain variables. Future prospective studies with larger, population-based samples are warranted to confirm these findings and establish causality.
Conclusion
This cross-sectional study at Shafa Hospital in Kerman demonstrates that presenile cataract is influenced by multiple environmental and behavioral risk factors, with occupational UV exposure, opioid addiction, and systemic diseases such as diabetes being major contributors. The significant association between gender and cataract type, observed only in the left eye, suggests a potential influence from local environmental or behavioral factors rather than biological sex alone. Diabetes and corticosteroid use also play a clear role, indicating that broader health issues contribute to this problem. Public health approaches should therefore focus on workplace eye protection, substance abuse control, and chronic disease management to prevent presenile cataract in this population. Future research should investigate the mechanisms underlying laterality differences and the specific pathways through which substance abuse leads to cataract formation.