Exploring the Link between Hematological Parameters and Ocular Surface Disease: Insights from Pterygium

Abstract

Background:

Pterygium is a common ocular surface disorder characterized by fibrovascular growth onto the cornea. Its pathogenesis is multifactorial, involving chronic inflammation, oxidative stress, and systemic factors.

Aim:

To evaluate the associations between pterygium severity and systemic indicators including complete blood count (CBC), lipid profile, and random blood sugar (RBS).

Methods:

A retrospective observational study was conducted. Records of patients with clinically diagnosed pterygium were reviewed. Sociodemographic, clinical, and laboratory data were analyzed across grades using Kruskal–Wallis, Chi-square, and Spearman correlation tests.

Results:

Ninety-eight patients were included. The mean age was 40.8 ± 14.2 years. Grade II was the most common (41.8%), with nasal involvement predominating (88.8%). Most CBC indices did not vary significantly; however, eosinophil counts were lower in Grade IV (P = 0.04). Lipid parameters and RBS showed no significant differences. Hematocrit correlated negatively with disease severity (ρ = −0.212, P = 0.03).

Conclusion:

While systemic parameters were largely comparable across grades, advanced pterygium was associated with reduced eosinophil counts and lower hematocrit, suggesting possible systemic inflammatory and oxidative stress mechanisms. Larger prospective studies are needed to validate their role as prognostic markers.

Introduction

Pterygium is a chronic fibrovascular growth of the conjunctiva onto the cornea, typically triangular and most often located in the interpalpebral fissures.[1] It is often linked to ultraviolet (UV) radiation exposure, environmental irritants, and inflammatory pathways.[2]

UV radiation damages limbal stem cells, triggering the activation of growth factors, angiogenesis, fibroblast invasion, and conjunctivalization of the cornea. It may also induce p53 gene mutations, leading to abnormal epithelial changes in pterygium.[3,4]

The prevalence of pterygium varies globally, with the highest burden observed within the “pterygium belt” described by Cameron, located between 37° north and south of the equator. Reported rates range from 0.3% to 29% worldwide.[5] In India, the prevalence is estimated to be between 9.5% and 13%. Studies from other regions report prevalence rates of 23.4% in Black Barbadians, 10.1% in Singapore, 30.8% in Japan, 14.49% in Tibet, and 17.9% in elderly Mongolians.[6,7,8]

According to earlier research, pterygium may be more likely to occur in those who are older, male, and work outside.[5,7,9]

Although it is traditionally considered a localized ocular condition, recent evidence suggests systemic associations, including lipid dysregulation, glucose metabolism abnormalities, and hematological alterations.[10,11] These systemic factors may influence the pathogenesis of pterygium through the mechanisms such as oxidative stress, endothelial dysfunction, and impaired wound healing.[12,13]

High-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and total cholesterol (TC) are essential in systemic health but may also contribute to ocular surface pathology. Studies have shown conflicting results on lipid abnormalities in pterygium.[11] Similarly, hematological indices such as red cell distribution width (RDW), neutrophil-lymphocyte ratio (NLR), and platelet indices have been proposed as the markers of systemic inflammation and oxidative stress, with potential relevance in pterygium.[12] Furthermore, hyperglycemia in diabetes mellitus can impair immune responses and wound healing, possibly exacerbating ocular surface disease.[13]

This study aimed to evaluate the associations between pterygium and systemic health indicators, namely complete blood count (CBC), lipid profile, and random blood sugar (RBS), to better understand the systemic influences on pterygium.

Materials and Methods

This retrospective observational study was conducted in the Department of Ophthalmology at a tertiary care center in North India, from August 2024 to March 2025. Ethical clearance was obtained from the Bioethics Cell, All India Institute of Medical Sciences, Raebareli, Uttar Pradesh (Reference No: F. 8/BIOETHICS/AIIMS-RBL/COMM/OTHER/2024-8/3, dated October 18, 2024). The study population comprised all patients presenting with clinically diagnosed pterygium during the study period. Patients were eligible for inclusion if they had a definitive clinical diagnosis of pterygium, characterized by fleshy conjunctival encroachment onto the cornea, associated with symptoms such as redness, irritation, foreign body sensation, watering, or diminution of vision. Patients with other corneal pathologies, conjunctivalization, corneal scarring, prior ocular surface surgery, or those unwilling to provide informed consent were excluded to avoid confounding factors.

Data were extracted from the electronic hospital records and patient charts. Sociodemographic details such as age and sex were documented, along with comprehensive ocular examination findings including visual acuity, slit-lamp biomicroscopy, and clinical grading of pterygium. Pterygium was classified into four Grades (I–IV) based on the extent of fibrovascular growth onto the cornea.[14]

• Grade I: Head at the limbus

• Grade II: Head between limbus and midpoint between limbus and pupil

• Grade III: Head between midpoint and pupillary margin

• Grade IV: Head crosses pupillary margin.

Laboratory parameters were simultaneously reviewed and included CBC, RBS, and lipid profile.

Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA). Quantitative variables were summarized as mean ± standard deviation for normally distributed data and median with interquartile range for skewed distributions, whereas categorical variables were expressed as frequencies and percentages. Intergroup comparisons across different pterygium grades were evaluated using the Kruskal–Wallis h-test for nonparametric continuous variables and the Chi-square test for categorical variables. Spearman’s rank correlation was applied to explore the associations between pterygium severity and systemic parameters. P <0.05 was considered statistically significant.

Results

The sociodemographic and clinical characteristics of the study population are summarized in Table 1. The mean age of the presentation was 40.85 ± 14.21 years, with the majority of patients belonging to the 30–45 years’ age group (48.0%). A slight female predominance (54.1%) was observed. In terms of occupation, homemakers (35.7%) and farmers (30.6%) were the most frequently affected groups. Clinically, Grade 2 pterygium was the most common (41.8%), followed by Grade 1 (24.5%) and Grade 3 (26.5%), while Grade 4 was relatively uncommon (7.1%). Laterality assessment revealed that unilateral involvement was more frequent, affecting the left eye in 40.8% and the right eye in 34.7%, whereas bilateral disease was present in 24.9%. The majority of cases showed nasal localization (88.8%), while temporal involvement (10.1%) and combined nasal-temporal involvement (1.1%) were less frequent [Table 1].

Table 1.

Sociodemographic profile and clinical presentation of pterygium patients

Sociodemographic and clinical presentation variables	n (%)
Age group (years)
<30	22 (22.4)
30–45	47 (48.0)
46–60	18 (18.4)
>60	11 (11.2)
Mean±SD, median (IQR)	40.85±14.21 39.00 (31.75–49.00)
Gender
Male	45 (45.9)
Females	53 (54.1)
Occupation
Homemaker	35 (35.7)
Farmer	30 (30.6)
Student	9 (9.2)
Teacher	5 (5.1)
Others	19 (19.4)
Grade of pterygium#
I	24 (24.5)
II	41 (41.8)
III	26 (26.5)
IV	7 (7.1)
Eye involved
Left	40 (40.8)
Right	34 (34.7)
Both sides eyes	24 (24.9)
Presentation
Nasal	87 (88.8)
Temporal	10 (10.1)
Bilateral both sides	1 (1.1)

^#Highest grade selected in case to both eyes involved or bilateral presentation. SD: Standard deviation; IQR: Interquartile range

Table 2 summarizes red blood cell parameters comparisons across pterygium grades. Table 3 summarizes white blood cell parameters across pterygium grades, while Table 4 analyzes Platelet parameters across pterygium grades. No statistically significant differences were found in most CBC parameters, including hemoglobin, total leukocyte count, RBC count, platelet indices, NLR, and platelet-lymphocyte ratio (PLR) (P > 0.05). However, eosinophil counts showed a significant association (P = 0.040), with grade IV pterygium cases exhibiting lower eosinophil levels compared to Grades I–III. In addition, hematocrit values demonstrated a near-significant trend (P = 0.055) across grades. These findings indicate that while hematological parameters remain largely comparable between grades, variations in eosinophil levels may have a potential role in advanced disease.

Table 2.

Comparison of red blood cell parameters in different grades of pterygium patients

RBC parameters	Range	Mean±SD	Median	IQR	P*
Hemoglobin (g/dL)
Grade I	9.9–16.6	13.14±1.79	12.90	11.62–14.60	0.364
Grade II	6.7–16.3	13.03±2.04	13.20	12.15–14.15
Grade III	9.3–16.4	12.59±1.89	12.05	11.65–14.47
Grade IV	11.9–17.3	13.75±1.95	13.10	12.40–15.60
RBC count
Grade I	4.17–6.83	4.91±0.66	4.94	4.36–5.23	0.058
Grade II	3.58–5.62	4.54±0.54	4.52	4.16–5.00
Grade III	3.24–5.76	4.40±0.66	4.35	3.85–4.97
Grade IV	3.92–5.62	4.75±0.57	4.69	4.38–5.31
RDW
Grade I	11.70–18.50	14.36±1.43	14.10	13.70–14.67	0.706
Grade II	11.80–20.70	14.24±1.70	13.90	13.09–15.15
Grade III	12.00–19.70	14.66±1.65	14.40	13.80–15.12
Grade IV	12.00–15.30	14.00±1.07	14.00	13.50–14.70
Hematocrit (PCV)
Grade I	34.50–66.00	44.90±8.68	42.75	38.42–48.87	0.055
Grade II	24.60–55.00	41.95±6.20	42.30	38.70–46.65
Grade III	30.90–48.10	39.20±4.98	38.40	36.00–43.35
Grade IV	37.10–52.20	42.70±5.14	40.80	40.10–47.10

*Kruskal-Wallis H test was applied. SD: Standard deviation; IQR: Interquartile range; PCV: Packed cell volume; RDW: Red cell distribution width; RBC: Red blood cell

Table 3.

Comparison of white blood cell parameters in different grades of pterygium patients

	Range	Mean±SD	Median	IQR	P
TLC
Grade I	4.98–11.14	7.95±1.64	7.82	6.67–8.77	0.600
Grade II	4.93–11.81	7.52±1.59	7.48	6.20–8.44
Grade III	3.44–10.30	7.78±1.65	7.80	6.65–9.26
Grade IV	0.10–11.56	6.55±3.62	7.10	4.46–8.51
Neutrophil count
Grade I	41.00–72.00	61.13±8.23	61.00	57.25–68.00	0.867
Grade II	45.00–75.00	59.76±7.59	60.00	55.00–66.00
Grade III	25.00–80.00	59.12±11.99	59.90	52.75–67.50
Grade IV	41.00–81.00	60.71±15.45	54.00	47.00–75.00
Lymphocyte count
Grade I	20.00–48.00	27.13±7.27	25.00	21.25–29.75	0.954
Grade II	14.00–338.00	34.17±49.06	26.00	21.50–31.50
Grade III	14.00–60.00	27.62±9.34	26.00	22.50–31.25
Grade IV	9.00–49.00	30.86±14.68	36.00	20,00–44.00
Monocyte count
Grade I	4.00–11.00	6.96±2.25	6.50	5.00–9.00	0.316
Grade II	4.00–10.00	6.98±1.40	7.00	6.00–8.00
Grade III	4.00–12.00	7.73±1.99	8.00	6.00–9.25
Grade IV	4.00–8.00	6.57±1.51	5.00	8.00–7.00
NLR
Grade I	0.92–3.60	2.42±0.72	2.57	2.04–2.86	0.906
Grade II	0.15–4.85	2.39±0.93	2.33	1.74–3.02
Grade III	0.41–5.33	2.46±1.16	2.42	2.42–2.82
Grade IV	0.83–9.00	3.03±3.03	1.50	1.06–3.75
PLR
Grade I	3.37–13.4	8.45±2.98	7.94	6.20–11.58	0.751
Grade II	0.81–16.64	8.59±3.46	8.68	6.16–10.60
Grade III	3.78–15.1	8.08±3.04	7.38	5.45–10.00
Grade IV	2.79–26.77	9.05±8.53	6.26	2.88–10.70
Monocyte/HDL
Grade I	0.04–0.38	0.17±0.07	0.17	0.11–0.19	0.325
Grade II	0.46–0.36	0.15±0.07	0.14	0.10–0.19
Grade III	0.06–0.96	0.22±0.18	0.18	0.14–0.23
Grade IV	0.07–0.23	0.15±0.06	0.14	0.09–0.23
Eosinophils
Grade I	0.00–12.00	4.38±3.18	4.00	2.00–6.75	0.040##
Grade II	1.00–17.00	5.41±3.77	4.00	3.00–8.00
Grade III	0.00–25.00	5.04±5.14	3.50	3.00–8.00
Grade IV	1.00–3.00	1.86±0.90	2.00	2.00–5.75
Basophils
Grade I	0.00–1.00	0.04±0.20	0.00	0.00–0.00	0.741
Grade II	0.00–1.00	0.02±0.16	0.00	0.00–0.00
Grade III	0.00–0.00	0.00±0.00	0.00	0.00–0.00
Grade IV	0.00–0.00	0.00±0.00	0.00	0.00–0.00

^##P <0.05 between Grade IV with Grade I (0.03), Grade II (0.031) and Grade III (0.004). NLR: Neutrophil-lymphocyte ratio; PLR: Platelet lymphocyte ratio; SD: Standard deviation; IQR: Interquartile range; TLC: Total leukocyte count; HDL: High-density lipoprotein

Table 4.

Comparison of platelet parameters in different grades of pterygium patients

	Range	Mean±SD	Median	IQR	P
Platelet count
Grade I	81.00–381.00	221.33±72.04	235.50	171.25–276.50	0.441
Grade II	113.00–387.00	221.80±65.18	215.00	177.50–260
Grade III	106.00–390.00	209.23±68.103	182.50	167–245.50
Grade IV	104.00–241.00	185.00±55.38	212.00	123–238.00
MPV
Grade I	9.30–14.40	12.16±1.36	12.00	11.07–13.42	0.363
Grade II	9.40–14.54	11.94±1.13	12.00	11.15–12.80
Grade III	8.90–14.20	12.27±1.29	12.25	11.37–13.42
Grade IV	11.00–15.00	12.91±1.53	13.00	11.50–14.50
PDW
Grade I	9.50–23.60	16.85±4.33	15.05	13.40–21.50	0.471
Grade II	10.10–23.60	16.15±3.13	16.20	13.80–18.35
Grade III	9.20–25.00	17.48±4.16	16.85	13.80–21.22
Grade IV	13.90–24.82	18.71±5.21	15.50	13.90–24.60
PLR
Grade I	3.37–13.4	8.45±2.98	7.94	6.20–11.58	0.751
Grade II	0.81–16.64	8.59±3.46	8.68	6.16–10.60
Grade III	3.78–15.1	8.08±3.04	7.38	5.45–10.00
Grade IV	2.79–26.77	9.05±8.53	6.26	2.88–10.70

MPV: Mean platelet volume; PLR: Platelet lymphocyte ratio; PDW: Platelet distribution width; SD: Standard deviation; IQR: Interquartile range

The analysis of lipid profile and RBS levels across pterygium grades is summarized in Table 5. No statistically significant differences were observed in TC, LDL, HDL, TG, Very LDL (VLDL), or RBS values (P > 0.05). However, patients with Grade IV pterygium exhibited relatively higher mean values of TC (211.57 mg/dL), TG (220.18 mg/dL), and VLDL (43.95 mg/dL) compared to lower grades, along with elevated HDL levels (62.35 mg/dL). Although not statistically significant, these trends may suggest possible metabolic alterations in advanced pterygium that warrant further investigation.

Table 5.

Comparison of lipid profile and random blood sugar levels parameters in different grades of pterygium patients

Lipid parameters	Range	Mean±SD	Median	IQR	P
TC
Grade I	116.00–245.00	185.08±33.88	187.00	160.00–209.50	0.539
Grade II	123.00–277.00	184.49±35.81	176.00	159.00–210.50
Grade III	115.00–283.00	181.23±44.68	179.00	141.25–219.25
Grade IV	149.00–309.00	211.57±53.15	194.00	170.00–244.00
LDL
Grade I	81.00–160.10	118.79±24.04	122.20	101.17–139.42	0.763
Grade II	57.46–195.40	113.96±27.19	110.00	97.60–128.70
Grade III	68.10–185.10	116.56±29.71	115.55	87.02–139.80
Grade IV	84.60–180.00	126.02±34.34	119.70	93.50–160.80
HDL
Grade I	35.00–71.6	51.06±10.45	50.75	42.80–58.92	0.467
Grade II	28.30–82.10	50.78±13.49	46.90	40.50–60.00
Grade III	23.60–82.00	50.12±14.27	50.75	39.37–61.75
Grade IV	40.40–93.10	62.35±19.68	56.50	40.40–45.00
TG
Grade I	55.90–284.70	155.63±55.10	144.00	111.32–197.50	0.240
Grade II	57.10–473.00	182.35±78.60	186.00	120.45–211.35
Grade III	46.50–301.00	159.38±73.18	136.50	101.37–209.97
Grade IV	34.70–395.40	220.18±124.01	211.90	110.70–315.00
VLDL
Grade I	11.00–66.40	32.47±14.16	28.00	22.34–42.73	0.465
Grade II	5.32–95.00	36.61±17.14	35.99	23.60–45.12
Grade III	9.00–60.24	32.39±14.84	27.40	20.27–45.01
Grade IV	6.94–79.00	43.95±24.80	42.00	22.00–63.00
RBS
Grade I	80.00–191.10	111.42±25.00	104.60	95.05–122.62	0.758
Grade II	85.70–378.90	119.01±48.66	109.50	95.65–119.45
Grade III	83.70–176.80	108.57±23.38	101.25	93.60–112.15
Grade IV	94.40–198.20	114.14±37.24	101.50	96.30–105.30

SD: Standard deviation; IQR: Interquartile range; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; VLDL: Very LDL; RBS: Random blood sugar; TC: Total cholesterol; TG: Triglycerides

The correlation between pterygium grading and hematological as well as biochemical parameters is depicted in Table 6. Most variables demonstrated weak, nonsignificant correlations (P > 0.05). Notably, hematocrit (Packed cell volume) showed a statistically significant negative correlation with pterygium grade (ρ = −0.212, P = 0.03), indicating that higher grades of pterygium may be associated with reduced hematocrit levels. Other variables, including hemoglobin, RBC count, platelet indices, differential counts, lipid profile, and RBS, did not exhibit significant associations.

Table 6.

Correlation between grading of pterygium with other hematological and lipid profile parameters

Hematological and lipid profile parameters	Spearman correlation coefficient (ρ)	P
Hemoglobin (g/dL)	−0.078	0.44
TLC	−0.040	0.69
Neutrophil count	−0.060	0.55
Platelet count	−0.145	0.15
Lymphocyte count	−0.045	0.65
RBC count	−0.188	0.06
Hematocrit (PCV)	−0.212	0.03**
RDW	0.056	0.58
MPV	0.099	0.33
Monocyte count	−0.111	0.27
PDW	0.124	0.22
NLR	−0.071	0.48
PLR	−0.087	0.39
Monocyte/HDL	−0.056	0.58
Eosinophils	−0.123	0.22
Basophils	−0.112	0.27
TC	−0.020	0.84
LDL	−0.009	0.93
HDL	0.056	0.58
TG	0.055	0.59
VLDL	0.052	0.61
RBS	−0.068	0.50

**P<0.05, Significant correlation. HDL: High-density lipoprotein; LDL: Low-density lipoprotein; VLDL: Very LDL; RBS: Random blood sugar; TC: Total cholesterol; PLR: Platelet lymphocyte ratio; NLR: Neutrophil-lymphocyte ratio; PDW: Platelet distribution width; MPV: Mean platelet volume; TLC: Total leukocyte count; PCV: Packed cell volume; TG: Triglycerides; RBC: Red blood cell; RDW: Red cell distribution width

Discussion

Our study found two notable associations; lower eosinophil counts in advanced pterygium grades and a significant negative correlation between hematocrit and disease severity. These hematological changes may indicate subtle systemic influences on the progression of this localized ocular disorder.

Our demographic profile was consistent with established epidemiological patterns, with most patients belonging to the 30–45 years age group, a slight female predominance, and a higher prevalence among homemakers and farmers. Most cases were unilateral nasal pterygia, with Grade ΙΙ being the most common presentation.

Hematological analysis showed that most CBC indices, including hemoglobin, total leukocyte count, RBC count, NLR, and PLR did not differ significantly between grades (P > 0.05). However, eosinophil counts were significantly reduced in Grade IV pterygium (P = 0.040) and hematocrit correlated negatively with disease severity (ρ = −0.212, P = 0.03). These findings may reflect immune-inflammatory exhaustion in advanced disease and systemic oxidative stress-related changes. Previous studies have linked RDW and related indices with systemic inflammation in pterygium,[12] supporting the possibility that hematological shifts accompany severe stages.

Biochemical parameters, including lipid profile and RBS, showed no statistically significant variation across grades. Nonetheless, grade IV cases demonstrated higher mean cholesterol, TG, VLDL, and HDL levels. Although not significant, these trends align with Mendelian randomization studies suggesting genetically elevated LDL-C may increase pterygium risk (10). Similarly, population-based evidence from the Dongguan Eye Study indicates associations between elevated HDL-C, LDL-C, and pterygium in older individuals.[11] Our results differ from earlier reports that found stronger links between dyslipidemia and pterygium,[11] which may reflect population-specific factors, environmental exposures, or limitations in sample size. RBS did not vary significantly across grades, likely because only patients with values within the normal range were included. This may have masked potential associations, as earlier evidence indicates that diabetes mellitus can exacerbate ocular surface inflammation and contribute to pterygium development.[13]

The biological implications of these findings highlight eosinophil depletion and reduced hematocrit as possible systemic correlates of advanced pterygium. Eosinophils, key mediators of inflammation, may decline in later stages due to chronic immune exhaustion, while a lower hematocrit may indicate systemic hypoxia or oxidative stress, potentially promoting fibrovascular proliferation. Although our lipid findings were inconclusive, dyslipidemia remains a plausible factor through mechanisms such as endothelial dysfunction and lipid-mediated inflammatory signaling.

This study has several strengths, including the detailed stratification of hematological and biochemical parameters across pterygium grades, which allowed identification of subtle systemic associations. However, limitations include its retrospective, single-center design, relatively small sample size, and the absence of healthy controls, all of which limit causal inference and generalizability.

Future studies should employ prospective, multicenter cohort designs with larger sample sizes, inclusion of healthy controls, and longitudinal follow-up to capture dynamic systemic changes. Advanced profiling of cytokines, oxidative stress markers, and lipid subfractions may yield mechanistic insights, while genetic and Mendelian randomization approaches could help clarify causality between systemic factors and pterygium. Serial monitoring of eosinophil and hematocrit levels could further establish their potential as prognostic markers for disease progression or recurrence.

Conclusion

In this study, most systemic parameters, including CBC indices, lipid profile, and RBS, showed no significant variation across pterygium grades. However, two noteworthy associations emerged, reduced eosinophil counts in Grade IV disease and a negative correlation between hematocrit and severity, potentially reflecting systemic inflammatory and oxidative mechanisms. While lipid and glucose trends were nonsignificant, their elevation in advanced cases raises the possibility of underlying metabolic contributions. These findings highlight the need for larger, prospective studies to validate systemic biomarkers as potential predictors for disease progression and to explore their role in the comprehensive management of pterygium.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.