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. 2025 Aug 1;14(8):10.
doi: 10.1167/tvst.14.8.10.

Photoreceptor Layer Thinning as Biomarker for Circulatory Premature Mortality: UK Biobank Cohort Study

Affiliations

Photoreceptor Layer Thinning as Biomarker for Circulatory Premature Mortality: UK Biobank Cohort Study

Mayinuer Yusufu et al. Transl Vis Sci Technol. .

Abstract

Purpose: To explore associations between optical coherence tomography (OCT) parameters and mortality risk.

Methods: This study used data from the UK Biobank participants with eligible OCT data. Feature selection was conducted with the least absolute shrinkage and selection operator. Selected parameters were fitted into Cox regression, with the full model adjusting for demographic, socioeconomic, lifestyle, and genetic factors.

Results: During a median follow-up duration of 10.6 years, 3174 were deceased. After matching the deceased and surviving participants (1:3) by age and gender, 12,696 were included. Ten out of 18 parameters showed significant associations with all-cause mortality. Each standard deviation increase in optic disc diameter parameters (hazard ratios [HRs] ranging from 1.042 to 1.052), thinning of ganglion cell-inner plexiform layer (HR = 0.958, 0.920-0.998), thinning of the photoreceptor layer and its sublayers (HRs = 0.937-0.960) were significant biomarkers of all-cause mortality. Cause-specific analyses by mortality age revealed that thinner photoreceptor layer and sublayers were significantly associated with circulatory premature mortality (HRs = 0.856-0.915).

Conclusions: Enlarging disc diameter, thinning of ganglion cell-inner plexiform layer, and thinning of photoreceptor layers are associated with all-cause mortality, with photoreceptor thinning especially linked to premature circulatory mortality.

Translational relevance: These findings suggest that specific OCT parameters could serve as noninvasive biomarkers for mortality risk assessment, potentially enhancing early identification of individuals at higher risk of premature death, particularly from circulatory diseases.

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Conflict of interest statement

Disclosure: M. Yusufu, None; M. Kang, None; A. Dayimu, None; D. Shi, None; L.Z. Zhu, None; R. Chen, None; A.J. Vingrys, None; X. Shang, None; M. He, None; L. Zhang, None

Figures

Figure 1.
Figure 1.
Study design. LASSO, Least Absolute Shrinkage and Selection Operator; RNFL, retinal nerve fiber layer. This figure used images from Elisa Galliano. Retina circuit. DOI: 10.5281/zenodo.4756818 from SciDraw and Chilton, J. (2020). Ependymal cell.Zenodo. https://doi.org/10.5281/zenodo.3926497 from SciDraw.
Figure 2.
Figure 2.
Participants selection process.
Figure 3.
Figure 3.
OCT parameters and their associations with all-cause mortality. This plot shows the hazard ratio associated with each standard deviation change in OCT parameters. Asterisk indicates false discovery rate (FDR) adjusted P < 0.05, VCDR, vertical cup-to-disc ratio. Model 1 was unadjusted, yet the P value was further adjusted with FDR. Model 2 included both OCT measurements and demographic factors, including ethnicity, Townsend index (social deprivation), and education. Model 3 used covariates from Model 2 and added lifestyle and health factors (BMI, smoking, alcohol, and physical activity). Model 4 was adjusted for Model 3 plus the genetic risk score.
Figure 4.
Figure 4.
OCT parameters and their associations with mortality by cause in the multivariate model. This plot shows the hazard ratio associated with each standard deviation change in OCT parameters. VCDR, vertical cup-to-disc ratio. The multivariate model adjusted for demographic factors, including ethnicity, Townsend index (social deprivation), education, lifestyle, and health factors (BMI, smoking, alcohol, and physical activity) plus the genetic risk score.
Figure 5.
Figure 5.
OCT parameters and their associations with circulatory mortality in the multivariate model. Notes: This plot shows the hazard ratio associated with each standard deviation change in OCT parameters. VCDR, vertical cup-to-disc ratio. The multivariate model adjusted for demographic factors, including ethnicity, Townsend index (social deprivation), education, lifestyle, and health factors (BMI, smoking, alcohol, and physical activity) plus the genetic risk score.

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