Age-related cataract - Prevalence, epidemiological pattern and emerging risk factors in a cross-sectional study from Central India

Abstract

Purpose:

To study the epidemiological pattern, prevalence, types, and correlates of age-related cataracts in a tertiary care center in central India.

Methods:

This hospital-based single-center cross-sectional study was conducted on 2,621 patients diagnosed with cataracts for 3 years. Data pertaining to demography, socio-economic profile, cataract grading, cataract types, and associated risk factors were evaluated. Statistical analysis using unadjusted odds ratio (OR) and multivariate logistic regression was performed, with P-value <0.05 considered significant with the power of the study being 95%.

Results:

The commonest age group affected was 60–79 years, closely followed by the 40–59 years age group. The prevalence of nuclear sclerosis (NS), cortical (CC), and posterior subcapsular cataract (PSC) was found to be 65.2% (3,418), 24.6% (1,289), and 43.4% (2,276), respectively. Among mixed cataracts, (NS + PSC) had the highest prevalence of 39.8%. Smokers were found to have 1.17 times higher odds of developing NS than non-smokers. Diabetics had 1.12 times higher odds of developing NS cataracts and 1.04 times higher odds of developing CC. Patients with hypertension showed 1.27 times higher odds of developing NS and 1.32 times higher odds of developing CC.

Conclusion:

The prevalence of cataracts in the pre-senile age group (<60 years) was found to have increased significantly (35.7%). A higher prevalence of PSC (43.4%) was found in studied subjects, as compared to the data of previous studies. Smoking, diabetes, and hypertension were found to have a positive association with a higher prevalence of cataracts.

The human lens is a transparent biconvex structure, which helps to refract and focus light onto the retina. Cataract refers to loss of transparency due to opacification of the lens and is the commonest curable cause of blindness worldwide. Cataract remains the major contributor to blindness in adults, accounting for over 15 million people (45%).[1] According to the national blindness and visual impairment (NPCB and VI) survey 2015–2019, cataract contributes to 66.2% of blindness and 71.2% of visual impairment in the population above 50 years in India.[2] Factors attributing to the rise in cataract-related blindness are increased life expectancy, environmental factors, and barriers to the uptake of surgical facilities.

It is important to evaluate the epidemiology, prevalence, demographic pattern, morphology, and attributable risk factors in different population groups, which would subsequently aid in estimating the adequacy of existing resources, cataract surgical coverage, and accessibility.

Considering the huge burden of cataract-related blindness, diagnosis and management of age-related cataracts (ARC) are important considerations globally to reduce the burden of avoidable blindness. Patterns and distribution of cataracts vary in different population groups most likely due to variations in geographic, genetic, and environmental factors. Reliable estimates of the prevalence, pattern, and epidemiological characteristics of cataracts are essential for providing newer insights relating to cataractogenesis, thereby developing effective prevention strategies and implementation of health programs. Several studies have been conducted in past on the demographic pattern of cataracts;[3-5] however, there is a lack of recent data on the demographic and morphologic distribution of cataracts in central India.

This study was conceptualized to evaluate the present epidemiology and attributable causes of cataracts and compare it with previous knowledge. The primary objective of this study was to identify the epidemiological characteristics, prevalence patterns, and causative associations of ARC in a tertiary eye care center in central India.

Methods

A retrospective hospital-based cross-sectional study included all the cases of ARC, attending the department of ophthalmology from March 2019 to February 2022. This study was the first of its kind to reflect the magnitude of ARC in a large sample of the central Indian population and adequately puts down the epidemiological, sociodemographic pattern, and attributable risk factors of ARC in central India, which have not been studied earlier. A Medline search was initiated with PubMed and Medline Plus for a combination of a cluster of keywords—prevalence, incidence, epidemiology, etiology, socio-demographic, intervention, cataract, age-specific, ocular risk, central India, and outcome. One keyword/phrase from each cluster was used, unless repeated. All reports consisting of ≥ 25 patients published between January 2000 and January 2022 were evaluated.

Study setting

Hospital-based, single-center study.

Study population

Patients diagnosed with ARC during the study period. The study center situated in central India caters to around 30 lakh population of the region and serves as a referral center for the entire state.

Study design

Retrospective hospital-based single center study.

Sample size calculation and technique

Convenient sampling was done for the population, with the inclusion of all patients fulfilling the study criteria during the study period.

The sample size was calculated using the Cochrane formula (4pq/d2) based on “Prevalence of cataract in an older population in India” conducted by Vasisht et al.[3] Considering the power of study as 80% and the confidence interval (CI) of 95%, the minimum sample size was calculated as 399 subjects.

(Where P = prevalence from previous studies, Q = 100-P, d = allowable error [5–20% of P]).

Inclusion criteria

1. Patients who were diagnosed to have cataracts during the study duration.

Exclusion criteria

1. Presence of ocular comorbidities such as uveitis, glaucoma, intraocular tumors, retinal vascular diseases, and previous history of retinal surgery.

2. The use of medications is likely to be the cause of cataract—steroids, busulfan, phenothiazine, and chloroquine.

3. Incomplete clinical records.

Study protocol

Data collection was done from outpatient department (OPD) papers and digital records. Patients diagnosed with cataracts and admitted to the ophthalmology ward were sorted. Patient records fulfilling inclusion and exclusion criteria were enrolled for tabulation and analysis. Incomplete patient records were excluded from the study.

A total of 3,128 datasheets were initially identified, after the exclusion of non-relevant datasheets in adherence to inclusion criteria 507 datasheets were excluded. The remaining datasheets were reviewed, and 2,621 datasheets with relevant and complete records were analyzed [Fig. 1].

Figure 1.

Flow diagram showing patient selection and data analysis

Data pertaining to demography, socio-economic profile, occupation, cataract grading, environmental associations, and risk factors, ophthalmic examination records including visual acuity, slit-lamp examination, tonometry, and indirect ophthalmoscopy were noted.

Operational definitions

Age

Age-standardized prevalence estimate (ASPE) of cataracts and its subtypes in total by direct standardization and using the World Health Organization (WHO) population categorization were used to adjust the structural age between different age groups and regions. Looking into the prevalence of cataract in Central India, the prevalence of cataract was found to be more in patients >60 years age group; hence, in statistical compliance, a subgroup analysis of 60–80 years and above 80 years were done. Thus, the study groups comprised 20–40 years, 40–60 years, 60–80 years, and above 80 years.

Clinical examination

Slit lamp-aided clinical evaluation, which included anterior and posterior segment examination was conducted for cataract grading and to rule out any other ocular comorbidity. To provide a common scale for analysis, all data of visual acuities were converted into logMAR units.

Residence

Patients were categorized to belong to urban or rural based on their residence as mentioned in the datasheet. Residential address under a panchayat administration was considered rural, whereas residential addresses coming under the jurisdiction of a Municipality, Municipal Corporation, Nagar Panchayat, or Notified area committees was considered urban.[6]

Socioeconomic status

Based on below poverty line (BPL) categorization. BPL is a benchmark used by the government of India to indicate economic disadvantage and identify individuals and households in need of government assistance and aid.[7] This parameter is reflective of the economic status of the patient.

Smoking

History of addictions including smoking tobacco substances was evaluated. As per the WHO’s smoking and tobacco use policy,[8] a smoker is someone who smokes any tobacco product, either daily or occasionally.

Hypertension

Patients with blood pressure above 140/90 mm of Hg or under treatment with anti-hypertensive medications were considered hypertensive as per the JNC8 criteria.[9]

Diabetes

Patients with fasting blood sugar above 126 mg/dL or postprandial blood sugar above 200 mg/dL or HBA1C above 6.5% are categorized as diabetic as per the American Diabetic Association (ADA).[10]

Cataract grading

Slit lamp-aided cataract grading was conducted as per the lens opacity classification system (LOCS 3) grading.[11] Cataract was categorized into nuclear sclerosis (NS), cortical (CC), posterior subcapsular (PSC), and mature cataract (MC).

Statistical analysis

Data obtained were tabulated in MS Excel™ and analyzed using the IBM SPSS Software™ version 24. The association of different types of cataracts with various risk factors was studied using the Chi-square test, and the strength of association was determined using an unadjusted odds ratio (OR). Multivariable logistic regression was applied to adjust for the confounders and ascertain those risk factors, which were the predictors of different types of cataracts. P-value <0.05 was considered significant considering the power of the study as 95%.

Results

This retrospective cross-sectional study evaluated a total of 2,621 participants from varied strata.

Among the study population, 1,415 (54%) were of the age group of 60–79 years, whereas 35.7%, 6%, 2.9%, and 1.4% were of the age group of 40–59 years, more than 80 years, 20–39 years, and less than 20 years, respectively [Table 1].

Table 1.

Socio-demographic and clinical characteristics of participants (n=2,621)

Variables	No.	%
Age group
<20 years	38	1.4
20-39 years	76	2.9
40-59 years	935	35.7
60-79 years	1415	54.0
≥80 years	157	6.0
Mean age in years	60.67±13.15 years
Gender
Male	1334	50.9
Female	1287	49.1
Residence
Rural	848	32.4
Urban	1773	67.6
Socio economic status
APL	2313	88.2
BPL	308	11.8
Hypertension
Absent	1414	53.9
Present	1207	46.1
Diabetes mellitus
Absent	1962	74.9
Present	659	25.1
Smoking
Absent	1882	71.8
Present	739	28.2

The distribution among gender was equivalent with 1,334 (50.9%) males and 1,287 (49.1%) females [Table 1].

Socio-demographic pattern

In all 1,773 (67.6%) subjects were residing in an urban locale, whereas 848 (34.3%) were residing in a rural locale. Also, 308 (11.2%) were BPL card holders as opposed to 2,318 (88.8%) above the poverty line.

Among the study population, 1,882 (71.8%) were non-smokers, whereas 739 (11.2%) of the study population used tobacco in the form of smoking [Table 1].

Type of Cataract

The most prevalent cataract type in the study population was NS comprising 65.2%, followed by PSC and CC types comprising 43.4% and 24.6%, respectively. MC was found in 11.4% of the population [Table 2].

Table 2.

Prevalence of different types of cataracts among the study participants

Type of cataract	n (%)*
Nuclear sclerosis cataract	3,418 (65.2%)
Cortical cataract	1,289 (24.6%)
Posterior subcapsular cataract	2,276 (43.4%)
Mature cataract	596 (11.4%)
Congenital	36 (0.7%)
Traumatic	44 (0.8%)

*An individual has more than a single type of cataract

Among the mixed variants, (NS + PSC) was most common, comprising 39.8% of the population, followed by (NS + CC) 23.6%, (PSC + CC) 15.3%, and (NS + PSC + CC) 21.3% of the population [Table 3].

Table 3.

Prevalence of mixed cataracts among the study population

Type of mixed cataract	n (%)
NS + CC	1,229 (23.6%)
NS + PSC	2,070 (39.8%)
PSC + CC	801 (15.3%)
NS + PSC + CC	743 (21.3%)

Prevalence pattern in correlation to the type of cataract

Subjects belonging to the higher age group were found to have higher odds of developing NS, and the correlation was found to be significant (P < 0.001). Males had 1.04 times higher odds (P = 0.53) of developing NS than females; in addition, subjects residing in urban locale had 1.77 times higher odds of developing NS (P < 0.001) than the rural population. Lower socio-economic strata had 1.05 times higher odds (P = 0.59) than higher socio-economic strata.

Subjects belonging to the higher age group were found to have higher odds of developing CC, and the correlation was found to be significant (P < 0.001). Females had 1.15 times higher odds (P = 0.02) of developing CC than males, in addition to subjects residing in the urban locale who had 1.27 times higher odds (P = 0.001), lower socio-economic strata, and had 1.76 times higher odds (P < 0.001) of developing CC compared to higher socio-economic strata population.

Subjects belonging to the higher age group were found to have higher odds of developing PSC, and the correlation was found to be significant (P < 0.001). Males had 1.03 times higher odds (P = 0.69) of developing PSC than females, in addition to subjects residing in the urban locale who had 1.21 times higher odds (P < 0.001), higher socio-economic strata, 1.06 times higher odds (P = 0.55) of developing PSC.

Subjects belonging to the higher age group were found to have higher odds of developing MC (P < 0.001). Females had 1.24 times higher odds (P < 0.001) of developing MC than males, in addition to subjects residing in the rural locale who demonstrated 2.86 times higher odds (P < 0.001) of developing MC than subjects residing in urban areas. Subjects belonging to higher socio-economic strata had 1.14 times higher odds of developing MC though the correlation was not significant (P = 0.37) [Table 4].

Table 4.

Univariate and multivariate logistic regression to assess the risk factors for various types of cataracts following adjustment of confounders

Variables A)	Types of cataract		Total	Unadjusted odds ratio (95% CI)	P	Adjusted odds ratio (95% CI)	P
	Nuclear sclerosis
	Absent	Present
Age
<20 years	56	0	56
20-39 years	100	48 (32.4%)	148	0.16 (0.11-0.23)	<0.001
40-59 years	813	1037 (56.1%)	1850	0.55 (0.39-0.50)	<0.001	0.46 (0.41-0.53)	<0.001
≥60 years	811 (25.8%)	2333 (74.2%)	3144	2.56 (2.28-2.89)	<0.001	2.40 (2.12-2.72)	<0.001
Gender
Male (ref)	888 (33.8%)	1736 (66.2%)		-	0.53
Female	892 (34.7%)	1682 (65.3%)		0.96 (0.86-1.08)
Residence
Urban	1074 (30.6%)	2432 (69.4%)		1.62 (1.43-1.82)	<0.001	1.77 (1.56-2.01)	<0.001
Rural (ref)	706 (41.7%)	986 (58.3%)
Socioeconomic status
APL (ref)	1575 (34.4%)	3007 (65.6%)			0.59
BPL	205 (33.3%)	411 (66.7%)		1.05 (0.87-1.25)
Smoking
No (ref)	1316 (35.3%)	2416 (64.7%)			0.01		0.83
Yes	464 (31.7%)	1002 (68.3%)		1.27 (1.13-1.33)		1.17 (0.86-1.22)
Diabetes
Absent (ref)	1400 (36.1%)	2480 (63.9%)			<0.001		0.11
Present	380 (28.8%)	938 (71.2%)		1.39 (1.21-1.59)		1.12 (0.97-1.1.29)
Hypertension
Absent (ref)	1132 (40.7%)	1652 (59.3%)			<0.001		<0.001
Present	648 (26.8%)	1766 (73.2%)		1.86 (1.66-2.10)		1.27 (1.12-1.45)
B)	Cortical cataract		Total	Unadjusted odds ratio	P	Adjusted odds ratio (95% CI)	P
	Absent	Present
Age
<20 years	56 (100.0%)	0	56
20-39 years	136 (91.9%)	12 (8.1%)	148	0.22 (0.12-0.40)	<0.001	0.21 (0.11-0.49)	<0.001
40-59 years	1465 (79.2%)	385 (20.8%)	1850	0.66 (0.56-0.76)	<0.001	0.68 (0.59-0.78)	<0.001
≥60 years	2252 (71.6%)	892 (28.4%)	3144	1.65 (1.44-1.89)	<0.001	1.59 (1.38-1.84)	<0.001
Gender
Male (ref)	2008 (76.5%)	616 (23.5%)	2624		0.02		0.51
Female	1901 (73.9%)	673 (26.1%)	2574	1.15 (1.01-1.30)		1.05 (0.90-1.22)
Residence
Urban	2651 (74.8%)	895 (25.2%)	3506	1.11 (0.97-1.27)	0.08	1.27 (1.10-1.47)	0.001
Rural (ref)	1302 (76.8%)	394 (23.2%)	1692
Socioeconomic status
APL (ref)	3490 (76.2%)	1092 (23.8%)	4582		<0.001		<0.001
BPL	419 (68.0%)	197 (32.0%)	616	1.50 (1.25-1.80)		1.76 (1.45-2.14)
Smoking
No (ref)	2782 (74.5%)	950 (25.5%)	3732				<0.01
Yes	1127 (76.9%)	339 (23.1%)	1466	0.88 (0.76-1.01))	0.08	0.78 (0.66-.92)
Diabetes
Absent (ref)	2952 (76.1%)	928 (23.9%)	3880		0.01		0.57
Present	957 (72.6%)	361 (27.4%)	1318	1.20 (1.04-1.38)		1.04 (0.90--1.21)
Hypertension
Absent (ref)	2199 (79.0%)	585 (21.0%)	2784		<0.001		<0.001
Present	1710 (70.8%)	704 (29.2%)	2414	1.54 (1.36-1.75)		1.32 (1.15-1.51)
C)	Posterior subcapsular cataract		Total	Unadjusted odds ratio	P	Adjusted odds ratio (95% CI)	P
	Absent	Present
Age
<20 years	56 (100.0%)	0	56
20-39 years	101 (68.2%)	47 (31.8%)	148	0.52 (0.37-0.75)	<0.001	0.49 (0.34-0.71)	<0.001
40-59 years	1095 (59.2%)	755 (40.8%)	1850	0.78 (0.69-0.87)	<0.001	0.75 (0.66-0.84)	<0.001
≥60 years	1670 (53.1%)	1474 (46.9%)	3144	1.37 (1.23-1.54)	<0.001	1.17 (1.04-1.32)	0.01
Gender
Male (ref)	1468 (55.9%)	1156 (44.1%)	2624		0.69
Female	1454 (56.5%)	1120 (43.5%)	2574	0.97 (0.87-1.09)
Residence
Urban	1927 (55.0%)	1579 (45.0%)	3506	1.16 (1.04-1.31)	<0.01	1.21 (1.07-1.37)	<0.01
Rural (ref)	995 (58.8%)	697 (41.2%)	1692
Socioeconomic status
APL (ref)	2555 (55.8%)	2027 (44.2%)	4582		0.07		0.55
BPL	367 (59.6%)	249 (40.4%)	616	0.85 (0.72-1.01)		0.94 (0.79-1.13)
Smoking
No (ref)	2030 (54.4%)	1702 (45.6%)	3732		<0.001		<0.001
Yes	892 (60.8%)	574 (39.2%)	1466	0.76 (0.67-0.86)		0.71 (0.63-0.81)
Diabetes
Absent (ref)	2195 (56.6%)	1685 (43.4%)	3880		0.38
Present	727 (55.2%)	591 (44.8%)	1318	1.05 (0.93-1.20)
Hypertension
Absent (ref)	1560 (56.1%)	1224 (43.9%)	2784		0.77
Present	1362 (56.4%)	1052 (43.6%)	2414	0.98 (0.88-1.09)
D)	Mature cataract		Total	Unadjusted odds ratio	P	Adjusted odds ratio (95% CI)	P
	Absent	Present
Age
<20 years	56 (100.0%)	0	56
20-39 years	124 (83.8%)	24 (16.2%)	148	2.21 (1.40-3.48)	<0.001	3.46 (2.15-5.59)	<0.001
40-59 years	1531 (82.8%)	319 (17.2%)	1850	2.38 (1.99-2.84)	<0.001	2.40 (1.98-3.96)	<0.001
≥60 years	2891 (92.0%)	253 (8.0%)	3144	0.43 (0.36-0.51)	<0.001	0.35 (0.29-0.42)	<0.001
Gender
Male (ref)	2352 (89.6%)	272 (10.4%)	2624		0.01		<0.001
Female	2250 (87.4%)	324 (12.6%)	2574	1.24 (1.04-1.47)		1.46 (1.18-1.80)
Residence
Urban	3226 (92.0%)	280 (8.0%)	3506	0.37 (0.31-0.44)	<0.001	0.35 (0.29-0.41)	<0.001
Rural (ref)	1376 (81.3%)	316 (18.7%)	1692
Socioeconomic status
APL (ref)	4050 (88.4%)	532 (11.6%)	4582		0.37
BPL	552 (89.6%)	64 (10.4%)	616	0.88 (0.67-1.16)
Smoking
No (ref)	3301 (88.5%)	431 (11.5%)	3732		0.76
Yes	1301 (88.7%)	165 (11.3%)	1466	0.97 (0.80-1.17)
Diabetes
Absent (ref)	3447 (88.8%)	433 (11.2%)	3880
Present	1155 (87.6%)	163 (12.4%)	1318	1.92 (0.92-1.36)	0.23
Hypertension
Absent (ref)	2419 (86.9%)	365 (13.1%)	2784		<0.001		0.67
Present	2183 (90.4%)	231 (9.6%)	2414	0.70 (0.58-0.83)		0.95 (0.79-1.16)

Causative factors in correlation to the type of cataract

Univariate and multivariate analyses showed that tobacco smokers had 1.17 (P = 0.83) times higher odds of developing NS cataracts than non-smokers, whereas CC, PSC, and MC showed no such correlation with smoking [Table 4].

Diabetics showed 1.12 times higher odds (P = 0.11) of developing NS cataract, 1.04 times higher odds (P = 0.57) of developing CC, 1.05 times higher odds (P = 0.38) of developing PSC, and 1.92 times higher odds (P = 0.23) of developing MC than non-diabetics [Table 4].

Patients with hypertension show 1.27 times higher odds (P < 0.001) of developing NS, and 1.32 times higher odds (P < 0.001) of developing CC than non-hypertensives, whereas it shows no such correlation with PSC or MC [Table 4].

Discussion

A hospital-based, retrospective, cross-sectional study was conducted to review the prevalence of ARC, its epidemiological traits, morphology, associated comorbidities, and etiology. Among the 2,621 studied subjects, 1,572 (60%) were above the age of 60 years. It is important to note that the prevalence of ARC is seen to increase significantly with increasing age, 35.7% of the subjects were aged 40–59 years and 54% were aged 60–79 years,[12] similar findings were noted by Vashist et al.[3] who reported cataracts in people aged above 60 years as 58%. Comparing this with NPCB and VI data,[13] the maximum prevalence of blindness was seen in the age group above 80 years (11.6%), followed by the 70–79 age group (4.1%), 60–69 age group (1.6%), and 50–59 age group (0.5%). The prevalence of cataracts is known to increase with age; however, in recent times, the age-specific prevalence of cataracts is found to have decreased, reflecting an increased prevalence being noted in subjects of 60 years or less. The increase in the prevalence of cataracts in the pre-senile age group can be explained by sun exposure, lifestyle changes, tobacco use, smoke exposure, axial myopia, and hormonal factors, as hypothesized by Das et al.[14] Considering the increase in the aging population and increased prevalence of cataracts in pre-senile age groups, it is expected that the burden of cataracts will increase in the future; hence, the eye care delivery infrastructure needs to be optimized because of changing demographic and epidemiological patterns, to cope with this increasing burden of cataract blindness.

This ascending trend of age-related cataracts was seen for most types of cataracts, including NS, CC, and PSC as well as mixed types. Hence, age is directly correlated with cataracts and their types, which is consistent with findings observed in other studies.[2,3] However, some studies consider the relationship between age and cataracts as a cumulative effect of risk factors such as ultraviolet radiation and oxidative damage.

Although evaluating presenting visual acuity, it was observed that more than 30% of patients presented with best-corrected visual acuity (BCVA) of less than 6/60, indicative of time lapsed before the patients seek medical attention, thus necessitating the need of strengthening the healthcare outreach.

We did not find any significant difference in cataract prevalence between men and women, contrary to the study by Singh et al.[15] The socioeconomic growth of women, positive gender ratios, and greater female literacy rates in the studied region, particularly in the urban group can be attributed to this variation. However, Mahajan et al.[16] reported that though women were commonly affected, cataract surgery was 1.6 times more common among males, which was attributed to low literacy rates and poor socioeconomic status of the region.

In our study, 1,773 subjects (67.6%) were residing in an urban locale, whereas 848 (32.4%) hailed from a rural locale. The prevalence of monotype cataracts was higher when compared to mixed types in both the rural and urban populations. In the monotype group, the most common type was nuclear sclerosis, 22% in the rural and 20% in the urban population, similar findings were shown in the INDEYE feasibility study.[5] However, the prevalence of monotype cataracts was lower in the current study compared to the INDEYE study,[5] this decrease is likely a reflection of the efficacy of the NPCB and VI.

Of mixed cataracts, the most common cataract was (NS + PSC), 33.6% in the urban and 30.4% in the rural population. It is interesting to note that subjects residing in urban locale had 1.21 times higher odds (P < 0.01) of developing PSC as compared to the rural population.

A nuclear cataract is the most common subtype of cataract, according to various studies from the Indian subcontinent. Of the total subjects, 596 (11.4%) had MC. Earlier studies have reported varied prevalence of MC ranging from 1.4% to 7.1%.[17,18] This variation in the prevalence of MC in different regions can be attributed to the level of awareness, socio-economic status, and prevailing healthcare outreach in the region. In the present study, the rural population had a higher prevalence of MC of 19.1% compared to 12% in the urban population, this can be attributed to a lack of awareness and lesser accessibility of healthcare in rural areas. These patients, seeking treatment in an urban tertiary care center, warrant consideration to increase and enhance healthcare outreach in rural areas.

With respect to the type of cataract, PSC was seen to be present in 43.4% of the study subjects. The higher prevalence of PSC cataracts in this population group of central India can be attributed to increased sunlight received in this region (tropic of cancer). Vasisht et al.[3] reported the prevalence of PSC as an average of 21% in India. Further, an association between UV rays and cataracts has also been reported.[19]

Diabetes mellitus is one of the important risk factors for the development of cataracts. In our study, 25.1% of cataract patients had diabetes. Multivariate analysis showed the association of diabetes to be greatest with NS cataracts. A study conducted by Nirmalan et al.[20] has also shown a positive association of diabetes with cataractogenesis.

Interestingly, hypertensives were found to have 32% and 27% higher incidences of CC and NS, respectively. Considering cataractogenesis to be associated with systemic inflammation, hypertension can be a causative agent owing to the inflammatory mechanism. It has also been hypothesized that hypertension could induce conformational structure alteration of proteins in the lens, thereby exacerbating cataract formation.[21] Although several plausible mechanisms have been proposed based on in vitro studies, the conclusions from epidemiologic studies have remained inconsistent.

Tobacco users had 1.17 times higher odds (P = 0.83) of developing NS; however, smoking and tobacco use were not found to have any significant association with the development of CC or PSC. Although the pathophysiology of smoking on ARC is not fully understood, several possible biologic mechanisms for the association of smoking with cataractogenesis have been proposed. Smoking causes an additional oxidative challenge by invoking free radical activity and promoting oxidation and lipid peroxidation. The by-products of tobacco contain heavy metals, such as cadmium, lead, and copper, which accumulate in the lens and cause direct toxicity. Additionally, cyanide and aldehyde levels rise in the blood of smokers with further derangements of aldehydes and isocyanate, which modify the lens proteins, causing lens opacification in vitro. These changes are similar to those seen in human cataracts.[22] Multivariate regression analysis showed a positive causal association of cataractogenesis with smoking, diabetes, and hypertension. The weaker association with CC is due to different pathophysiologic processes in the development of these cataract subtypes.

The major strengths of this study include its large sample, hospital-based, single-center design, and standard documentation of cataracts by LOCS III at a tertiary eye care center. These data would prove to be extremely useful in developing long-term strategies to combat curable blindness. It is encouraging to see a declining prevalence of cataracts and a decrease in gender inequality as compared to previous studies, indicative of an efficient eye care delivery system.

The main limitation of the study is the retrospective nature, and deficient validation of the causal relationship between the risk factors such as sunlight exposure, nutritional history, and presence of cataracts.

Conclusion

The prevalence of cataracts in the pre-senile age group (<60 years) age group was found to have increased significantly (35.7%). A higher prevalence of PSC (43.4%) was found in the study subjects, which warrants further studies to understand the pathophysiology of such occurrence. The study predicts the burden of cataracts to increase in the near future due to an increase in the aging population and pre-senile cataracts. A positive causal association was noted between cataractogenesis and tobacco smoking in addition to hypertension and diabetes, necessitating a directed approach toward health education and awareness activities.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.