Subretinal Drusenoid Deposits and Lower Serum High-Density Lipoprotein Cholesterol Levels Possess Latent Relation to Cardiovascular Disease and Can Be a Feasible Predictor

Abstract

Objective

To ascertain whether lipid-related subretinal drusenoid deposits (SDD) are correlated with coexisting cardiovascular disease (CVD), as well as to reveal latent serum markers of CVD.

Methods

Patients older than 50 years and plagued by age-related macular degeneration (AMD) were included. Subjects with other retinal degenerations and vascular diseases, any recent treatment at other medical care institutions, and any previous oculopathy or ophthalmic surgery were excluded. All subjects were examined to ascertain whether they possess SDD, to analyze serum cholesterols, including low-density lipoprotein cholesterol (LDL), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and total cholesterol (TC). Subjects were divided into SDD and non-SDD groups and further divided into subgroups by assessment of pump defect, valve defect, and carotid defect. Finally, logistic model trees and random forest algorithm analysis were performed.

Results

A total of 85 AMD patients including 43 with and 42 without SDD were involved. The 42 AMD (97.67%, 42/43) patients with SDD showed CVD, including 3 subjects presenting valve defect, 3 subjects presenting carotid defect, 8 subjects presenting pump defect, 14 subjects presenting both pump and valve defects, and 14 subjects presenting pump, valve, and carotid defects. By contrast, 5 AMD (11.90%, 5/42) patients without SDD showed CVD. Cholesterol level of SDD subjects presented significant higher TC (5.66 ± 1.01 vs. 5.58 ± 0.72, p = 0.032, Wilcoxon test) and lower HDL cholesterol (61 ± 17 vs. 70 ± 21, p = 0.031, Wilcoxon test) than that of non-SDD. The cases with HDL < 62 mg/dL were significantly related to CVD ( p = 0.013, Wilcoxon test), and the cases with HDL < 40 mg/dL were not ( p = 0.659, Wilcoxon test). Through machine learning based on the image from color fundus photography, the accuracy of predicting CVD was 95%.

Conclusions

The presence of SDD of AMD and lower serum HDL cholesterol level can predict certain CVD for AMD patients. The machine learning based on the SDD image and serum HDL cholesterol may open new avenue for the detection of CVD as a noninvasive approach.