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圆锥角膜及其进展的非遗传危险因素。

Non-genetic risk factors for keratoconus and its progression.

作者信息

Cheung Isabella M Y, Angelo Lize, Gokul Akilesh, Ziaei Mohammed

机构信息

Department of Ophthalmology, University of Auckland, Auckland, New Zealand.

出版信息

Clin Exp Optom. 2025 Aug;108(6):648-656. doi: 10.1080/08164622.2024.2443454. Epub 2025 Jan 6.

DOI:10.1080/08164622.2024.2443454
PMID:39762118
Abstract

Keratoconus is a multifaceted corneal ectatic disorder characterized by a range of genetic and environmental risk factors. While genetic predisposition significantly influences global disease prevalence rates as well as severity and progression rates, emerging evidence highlights the critical interplay between environmental factors and genetic susceptibility. This article provides a comprehensive overview of environmental risk factors implicated in the onset and progression of keratoconus. Key factors include contact lens wear, eye rubbing, atopy, UV-A exposure, geographical influences, nutritional and hormonal imbalances, and socioeconomic determinants. The objective of this review is to summarise current evidence on these environmental risk factors and, where feasible, offer guidance on managing modifiable risk factors. Early identification and intervention in modifiable behaviours, especially in young patients exhibiting rapidly progressive disease, are expected to yield the most significant therapeutic benefits. Addressing these environmental factors proactively can enhance long-term patient outcomes, reducing visual impairment and improving quality of life.

摘要

圆锥角膜是一种多方面的角膜扩张性疾病,由一系列遗传和环境风险因素所致。虽然遗传易感性显著影响全球疾病患病率以及严重程度和进展速度,但新出现的证据突出了环境因素与遗传易感性之间的关键相互作用。本文全面概述了与圆锥角膜发病和进展相关的环境风险因素。关键因素包括佩戴隐形眼镜、揉眼、特应性、紫外线A暴露、地理影响、营养和激素失衡以及社会经济决定因素。本综述的目的是总结关于这些环境风险因素的现有证据,并在可行的情况下,就管理可改变的风险因素提供指导。对可改变行为进行早期识别和干预,尤其是在表现出快速进展性疾病的年轻患者中,有望产生最显著的治疗效果。积极应对这些环境因素可以改善患者的长期预后,减少视力损害并提高生活质量。

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引用本文的文献

1
Quality of Life Enhancement After Penetrating Keratoplasty in Keratoconus: A Vision-Related Functional Perspective.圆锥角膜穿透性角膜移植术后生活质量的提高:与视力相关的功能视角
J Clin Med. 2025 Jul 28;14(15):5325. doi: 10.3390/jcm14155325.
2
Machine Learning-Driven Transcriptome Analysis of Keratoconus for Predictive Biomarker Identification.机器学习驱动的圆锥角膜转录组分析以识别预测性生物标志物
Biomedicines. 2025 Apr 24;13(5):1032. doi: 10.3390/biomedicines13051032.