Astigmatism Is A Danger Of High BMI

Astigmatism, a prevalent refractive error characterized by an irregular curvature of the cornea or lens, results in blurred or distorted vision. This condition significantly impacts public health due to its widespread occurrence among children and adults. Astigmatism’s causes vary, including genetic and environmental factors, with distinct patterns observed across different age groups and ethnicities. Understanding the link between body mass index (BMI) and astigmatism, particularly during adolescence, is crucial for early diagnosis and intervention. This study investigates the potential link between BMI and astigmatism in adolescents undergoing medical screening before mandatory military service.

 

THE STUDY BACKGROUND

Astigmatism is a standard refractive error caused by an uneven curvature of the lens or cornea, leading to blurred or distorted vision. This eye condition affects a significant portion of the global population, with a prevalence of 14.9% in children and 40.4% in adults, highlighting its importance in public health discussions [1]. The condition’s aetiology is multifactorial, encompassing genetic factors, structural anomalies of the eye, and environmental influences, with variations observed across different ethnicities and age groups [2,3]. These variations indicate that the prevalence and severity of astigmatism can change with age, transitioning from against-the-rule (ATR) astigmatism, where the steepest meridian is horizontal, to with-the-rule (WTR) astigmatism, where the steepest meridian is vertical, particularly from infancy into early childhood. This pattern shifts again in adulthood, with younger adults predominantly exhibiting WTR astigmatism, while older adults tend to develop ATR astigmatism [2,3].

The impact of astigmatism extends beyond visual impairment, affecting individuals’ quality of life. Poor visual acuity associated with uncorrected astigmatism can lead to difficulties in daily activities, academic challenges, and developmental delays in children [2]. Additionally, uncorrected astigmatism is a risk factor for amblyopia, commonly known as lazy eye, and may also contribute to the development of myopia [5,6]. It underscores the need for timely diagnosis and appropriate corrective measures to prevent long-term visual impairment and associated complications.

Previous research has explored the relationships between various body measurements and ocular conditions, often using body mass index (BMI) to indicate body composition [7]. Evidence suggests a correlation between higher adolescent BMI and certain ocular diseases, such as myopia and keratoconus, and broader associations with vision quality [8-10]. However, studies investigating the link between BMI and astigmatism are relatively rare and have produced inconclusive results [11,12]. Understanding the relationship between BMI and astigmatism could help in developing targeted public health strategies and facilitate early diagnosis for those most affected.

This study aims to explore the relationship between BMI and astigmatism in a nationally representative sample of adolescents undergoing medical screening before mandatory military service. By characterizing the prevalence patterns of astigmatism about BMI, the study seeks to contribute valuable insights into the potential risk factors and inform strategies for early intervention and management of astigmatism.

 

THE STUDY METHOD

The study focused on Israeli adolescents aged 16-20 undergoing mandatory pre-draft medical screening, which included comprehensive assessments of their medical history, sociodemographic background, cognitive and behavioural evaluations, and vision tests. This screening process, conducted between January 1, 2011, and December 31, 2022, aimed to assess military eligibility. Participants were included if ocular data were available for both eyes and weight and height measurements, ensuring a nationally representative sample, except for certain minorities exempt from military service. The Israel Defense Forces Medical Corps Institutional Review Board approved the study, adhering to ethical guidelines and strictly maintaining participants’ anonymity.

Body mass index (BMI) was calculated using measurements of weight and height taken with individuals barefoot and in light clothing. BMI was then categorized based on age- and sex-adjusted percentiles according to the standards set by the United States Centers for Disease Control and Prevention (CDC), which are validated for Israeli adolescents. The categories included underweight, low-normal, high-normal, overweight, mild obesity (class 1 obesity), and severe obesity (class 2 and 3 obesity). These classifications helped in analyzing the relationship between BMI and astigmatism.

Ocular measurements were conducted using non-cycloplegic refraction by qualified optometrists or ophthalmologists. Astigmatism was defined as having a cylinder power of 0.75 diopters (D) or greater and was further classified into low-moderate (0.75 to <3.00 D) or high (≥3.00 D) categories. The study distinguished between with-the-rule (WTR), against-the-rule (ATR), and oblique (OBL) astigmatism based on the orientation of the steepest meridian. Correct eye data were primarily used in the analyses for consistency, given the high correlation between the spherical equivalent measurements of both eyes.

 

THE STUDY ANALYSIS

The researchers analyzed data from males and females separately to account for potential sex-based differences in the BMI-astigmatism relationship. The link between BMI and astigmatism was assessed using the logistic regression models. Also, multinomial regression models were used to explore the relationship between BMI and astigmatism power and axis orientation. Adjusted models accounted for various sociodemographic variables such as socioeconomic status, cognitive performance, education level, and country of birth. Subgroup analyses were also performed, excluding individuals with confounding conditions or specific characteristics that could affect the results. Statistical significance was determined with a two-sided p-value <0.05, with the analyses conducted using SPSS version 29.0 and R software version 4.3.1.

 

RESULTS

1. Study Population:

•  A total of 935,989 adolescents were evaluated during the study.
• Of these, 48,664 (5.2%) were excluded from the data analysis due to incomplete ocular, weight, or height data or concurrent ophthalmological diagnoses associated with changes in corneal topography.
•  The final study sample comprised 887,325 adolescents, with 511,465 males (57.6%) and 375,860 females (42.4%).
•  The mean age of the adolescents was 17.2 years.

2. Body Mass Index (BMI) Distribution:

•  The adolescents were categorized into different BMI groups: 49,332 (5.6%) were underweight, 329,764 (37.2%) had low-normal BMI, 314,746 (35.5%) had high-normal BMI, 113,715 (12.8%) were overweight, 62,667 (7.1%) had mild obesity, and 17,101 (1.9%) had severe obesity.

3. Prevalence of Astigmatism:

•  Astigmatism affected 71,400 males (14.0%) and 52,275 females (13.9%).
•  Of these, 67,360 males (13.2%) and 49,721 females (13.2%) had low-moderate astigmatism, while 4,040 males (0.8%) and 2,554 females (0.7%) had high astigmatism.

4. Trends in Astigmatism:

•  Over the study years, the prevalence of total astigmatism gradually increased in both males and females.
•  The prevalence of with-the-rule (WTR) astigmatism increased, while against-the-rule (ATR) astigmatism decreased.
•  The prevalence of oblique (OBL) astigmatism remained relatively unchanged.
•  The prevalence of overweight and obesity also rose over time, with a more pronounced increase in males in recent years.

5. Astigmatism and BMI Relationship:

•  There was a clear link between higher BMI and increased odds of astigmatism.
•  The adjusted odds ratio (OR) for astigmatism in males with severe obesity was 1.65, while for females with severe obesity, it was 1.74.
•  The OR was notably higher in males with high astigmatism, reaching 3.51 and 3.45 in females with severe obesity.
•  Spline models demonstrated a steady rise in the odds ratios for low-moderate astigmatism starting from the normal BMI range in both males and females.
•  Underweight adolescents also exhibited higher odds ratios for low-moderate astigmatism.

 

6. Astigmatism Axis Orientation:

•  Among males, 40,761 (8.0%) had WTR astigmatism, 22,096 (4.3%) had ATR astigmatism, and 8,543 (1.7%) had OBL astigmatism.
•  Among females, 32,244 (8.6%) had WTR astigmatism, 14,016 (3.7%) had ATR astigmatism, and 6,015 (1.6%) had OBL astigmatism.
•  The odds ratios for WTR astigmatism increased with higher BMI, peaking at 2.26 in males and 2.04 in females with severe obesity.
•  Males with underweight also had increased odds ratios for WTR astigmatism (1.17), which was not observed in females.
•  The odds ratios for ATR and OBL astigmatism demonstrated higher variability across different BMI groups in both sexes.
•  Spline models indicated a gradual rise in the odds ratios for WTR astigmatism starting from the reference BMI in both males and females. The odds ratios for ATR astigmatism in males decreased with rising BMI from the reference point, whereas they increased in females starting from a BMI of 26. The odds ratios for OBL astigmatism in males increased from a BMI of 28 to a BMI of 24 in females.

7. Subgroup Analyses:

•  The results of the regression models remained consistent when the study sample was limited to teengers with unimpaired health.
• The results stayed the same when teenagers with best-corrected visual acuity (BCVA) were equal to or better than 6/9.
•  Point estimates were slightly attenuated but remained statistically significant upon excluding adolescents with myopia.
•  The results also persisted when adolescents examined outside of 2013 were excluded, suggesting robustness in the study’s findings.

 

DISCUSSION

This study explored the relationship between body mass index (BMI) and astigmatism in a large, nationally representative sample of adolescents over a decade. The findings indicate a significant association where the odds of developing astigmatism increase with rising BMI in a dose-dependent manner. Specifically, adolescents with severe obesity had odds ratios of 1.65 for males and 1.74 for females, compared to those with low-normal BMI. This relationship was most notable for with-the-rule (WTR) astigmatism, while against-the-rule (ATR) and oblique (OBL) astigmatism displayed more variability across different BMI categories [1].

The study contrasts with previous research, such as a Taiwanese study that found BMI correlated with astigmatism severity but not presence [2] and an Israeli study showing ATR astigmatism’s prevalence up to the 70th BMI percentile before reversing trends [3]. The current study used adolescents without astigmatism as a reference group and consistently found WTR astigmatism to be the most prevalent across all BMI groups. This divergence may be due to the study’s use of age- and sex-adjusted BMI classifications, which might reveal a more refined relationship between BMI and astigmatism [1].

The research also suggests that various mechanisms could explain the BMI-astigmatism association. Obesity-related systemic inflammation and oxidative stress might damage corneal cells, contributing to astigmatism. Additionally, reduced Growth Hormone (GH) secretion in obesity could affect corneal biomechanics, while comorbidities like obstructive sleep apnea might exacerbate astigmatism through intermittent hypoxic events and increased matrix metalloproteinase levels [4][5][6]. The study notes that while these mechanisms may account for changes in corneal structure, they do not fully explain the different astigmatism axis orientations observed [1].

Mechanical factors, such as eyelid pressure and muscle tension, are also discussed. Increased pressure from conditions like floppy eyelid syndrome may contribute to WTR astigmatism, whereas lower eyelid pressure might be linked to ATR astigmatism in underweight individuals. The study highlights a need for further investigation into these mechanical influences and how sex hormones might affect the BMI-astigmatism relationship [7][8]. Given the increasing prevalence of pediatric obesity, integrating astigmatism screening into routine assessments for children with elevated BMI could be beneficial, potentially reducing associated healthcare costs and improving early intervention [1][9].

 

STUDY LIMITATIONS

1. Cross-Sectional Design

•  Precludes inferences about the temporal sequence or causal directionality between BMI and astigmatism.

2. Incomplete Risk Factor Analysis

•  The study did not include potential risk factors for astigmatism, such as time spent outdoors, patterns of physical and near-work activities, and genetic or hereditary predispositions.

3. Body Composition Measurement

•  BMI used as a proxy for body composition does not capture metabolic health aspects; other indices like body fat percentage or waist-to-hip ratio were not included.

4. Lack of Astigmatism Component Differentiation

• The analysis did not differentiate between corneal and lenticular astigmatism components, though corneal astigmatism is the primary contributor in young populations.

5. Unavailable Tissue Volume Measurement

•  Eyelid and periorbital tissue volume measurements were not available due to the screening-based nature of the ocular examination.

Implications

These limitations highlight the need for longitudinal studies to understand better the relationship between body composition, systemic health, and astigmatism, addressing both metabolic and mechanical aspects.

 

CONCLUSION

In conclusion, this study revealed a dose-response relationship between increasing BMI and the likelihood of astigmatism in a nationwide sample of adolescents. This association was particularly significant in adolescents of both sexes with with-the-rule (WTR) astigmatism, suggesting potential underlying biomechanical factors that warrant further investigation. These findings have important implications for enhancing early detection and intervention strategies for astigmatism, especially among populations with obesity. Targeted efforts can reduce the broader impact of astigmatism on affected individuals and their families.

 

References

1. Hashemi H, Fotouhi A, Yekta A, Pakzad R, Ostadimoghaddam H, Khabazkhoob M. Global and regional estimates of the prevalence of refractive errors: systematic review and meta-analysis. J Curr Ophthalmol. 2018;30(1):3-22. (https://doi.org/10.1016/j.joco.2017.08.009)

 

1. Read SA, Vincent SJ, Collins MJ. The visual and functional impacts of astigmatism and its clinical management. Ophthalmic Physiol Opt. 2014;34(3):267-294. doi:10.1111/opo.12128. (https://doi.org/10.1111/opo.12128)

 

1. Namba H, Sugano A, Murakami T, et al. Age-related changes in astigmatism and potential causes. Cornea. 2020;39:S34-S38. (https://doi.org/10.1097/ICO.00000000000025074)

 

1. Wolffsohn JS, Bhogal G, Shah S. Effect of uncorrected astigmatism on vision. J Cataract Refract Surg. 2011;37(3):454-460. (https://doi.org/10.1016/j.jcrs.2010.09.022)

 

1. Pascual M, Huang J, Maguire MG, et al. Risk factors for amblyopia in the vision in preschoolers study. Ophthalmology. 2014;121(3):622-629. (https://doi.org/10.1016/j.ophtha.2013.08.040)

 

1. Gwiazda J, Grice K, Held R, McLellan J, Thorn F. Astigmatism and myopia development in children. Vision Res. 2000;40(8):1019-1026. (https://doi.org/10.1016/S0042-6989(99)00237-0)

 

1. Cheung N, Wong TY. Obesity and eye diseases. Surv Ophthalmol. 2007;52(2):180-195. (https://doi.org/10.1016/j.survophthal.2006.12.003)

 

1. Peled A, Nitzan I, Megreli J, et al. Myopia and BMI: a nationwide study of 1.3 million adolescents. Obesity. 2022;30(8):1691-1698. (https://doi.org/10.1002/oby.23482)

 

1. Eliasi E, Bez M, Megreli J, et al. The association between keratoconus and body mass index: a population-based cross-sectional study among half a million adolescents. Am J Ophthalmol. 2021;224:200-206. (https://doi.org/10.1016/j.ajo.2020.11.021)

 

1. Nitzan I, Shakarchy N, Megreli J, et al. Body mass index and visual impairment in Israeli adolescents: a nationwide study. Pediatr Obes. 2024;19(1):e13083. (https://doi.org/10.1111/IJPO.13083)

 

1. Lai YH, Hsu HT, Wang HZ, Chang CH, Chang SJ. Astigmatism in preschool children in Taiwan. J AAPOS. 2010;14(2):150-154. (https://doi.org/10.1016/j.jaapos.2009.12.168)

Mandel Y, Stone RA, Zadok D. Parameters associated with the different astigmatism axis orientations. Investig Ophthalmol Vis Sci. 2010;51(2):723-730. (https://doi.org/10.1167/iovs.09-4356)

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