Dark adaptation (DA) refers to the slow recovery of visual sensitivity in darkness following exposure to intense or prolonged illumination, which bleaches a significant amount of the rhodopsin. This natural process also offers an opportunity to understand cellular function in the outer retina and evaluate for presence of disease. How our eyes adapt to darkness can be a key indicator of retinal health, which can be altered in the presence of certain diseases, such as age-related macular degeneration (AMD). A specific focus on clinical aspects of DA measurement and its significance to furthering our understanding of AMD has revealed essential findings underlying the pathobiology of the disease. The process of dark adaptation involves phototransduction taking place mainly between the photoreceptor outer segments and the retinal pigment epithelial (RPE) layer. DA occurs over a large range of luminance and is modulated by both cone and rod photoreceptors. In the photopic ranges, rods are saturated and cone cells adapt to the high luminance levels. However, under scotopic ranges, cones are unable to respond to the dim luminance and rods modulate the responses to lower levels of light as they can respond to even a single photon. Since the cone visual cycle is also based on the Muller cells, measuring the impairment in rod-based dark adaptation is thought to be particularly relevant to diseases such as AMD, which involves both photoreceptors and RPE. Dark adaptation parameters are metrics derived from curve-fitting dark adaptation sensitivities over time and can represent specific cellular function. Parameters such as the cone-rod break (CRB) and rod intercept time (RIT) are particularly sensitive to changes in the outer retina. There is some structural and functional continuum between normal aging and the AMD pathology. Many studies have shown an increase of the rod intercept time (RIT), i.e., delays in rod-mediated DA in AMD patients with increasing disease severity determined by increased drusen grade, pigment changes and the presence of subretinal drusenoid deposits (SDD) and association with certain morphological features in the peripheral retina. Specifications of spatial testing location, repeatability of the testing, ease and availability of the testing device in clinical settings, and test duration in elderly population are also important. We provide a detailed overview in light of all these factors.

Purpose: Large-scale genome-wide association studies (GWAS) have reported important single nucleotide polymorphisms (SNPs) with significant associations with age-related macular degeneration (AMD). However, their role in disease development remains elusive. This study aimed to assess SNP–metabolite associations (i.e., metabolite quantitative trait loci [met-QTL]) and to provide insights into the biological mechanisms of AMD risk SNPs. Design: Cross-sectional multicenter study (Boston, Massachusetts, and Coimbra, Portugal). Participants: Patients with AMD (n = 388) and control participants (n = 98) without any vitreoretinal disease (> 50 years). Methods: Age-related macular degeneration grading was performed using color fundus photographs according to the Age-Related Eye Disease Study classification scheme. Fasting blood samples were collected and evaluated with mass spectrometry for metabolomic profiling and Illumina OmniExpress for SNPs profiling. Analyses of met-QTL of endogenous metabolites were conducted using linear regression models adjusted for age, gender, smoking, 10 metabolite principal components (PCs), and 10 SNP PCs. Additionally, we analyzed the cumulative effect of AMD risk SNPs on plasma metabolites by generating genetic risk scores and assessing their associations with metabolites using linear regression models, accounting for the same covariates. Modeling was performed first for each cohort, and then combined by meta-analysis. Multiple comparisons were accounted for using the false discovery rate (FDR). Main Outcome Measures: Plasma metabolite levels associated with AMD risk SNPs. Results: After quality control, data for 544 plasma metabolites were included. Meta-analysis of data from all individuals (AMD patients and control participants) identified 28 significant met-QTL (β = 0.016−0.083; FDR q-value < 1.14 × 10 −2 ), which corresponded to 5 metabolites and 2 genes: ASPM and LIPC . Polymorphisms in the LIPC gene were associated with phosphatidylethanolamine metabolites, which are glycerophospholipids, and polymorphisms in the ASPM gene with branched-chain amino acids. Similar results were observed when considering only patients with AMD. Genetic risk score–metabolite associations further supported a global impact of AMD risk SNPs on the plasma metabolome. Conclusions: This study demonstrated that genomic–metabolomic associations can provide insights into the biological relevance of AMD risk SNPs. In particular, our results support that the LIPC gene and the glycerophospholipid metabolic pathway may play an important role in AMD, thus offering new potential therapeutic targets for this disease.

The purpose of this study was to analyze the association between plasma metabolite levels and dark adaptation (DA) in age-related macular degeneration (AMD). This was a cross-sectional study including patients with AMD (early, intermediate, and late) and control subjects older than 50 years without any vitreoretinal disease. Fasting blood samples were collected and used for metabolomic profiling with ultra-performance liquid chromatography–mass spectrometry (LC-MS). Patients were also tested with the AdaptDx (MacuLogix, Middletown, PA, USA) DA extended protocol (20 min). Two measures of dark adaptation were calculated and used: rod-intercept time (RIT) and area under the dark adaptation curve (AUDAC). Associations between dark adaption and metabolite levels were tested using multilevel mixed-effects linear modelling, adjusting for age, gender, body mass index (BMI), smoking, race, AMD stage, and Age-Related Eye Disease Study (AREDS) formulation supplementation. We included a total of 71 subjects: 53 with AMD (13 early AMD, 31 intermediate AMD, and 9 late AMD) and 18 controls. Our results revealed that fatty acid-related lipids and amino acids related to glutamate and leucine, isoleucine and valine metabolism were associated with RIT (p < 0.01). Similar results were found when AUDAC was used as the outcome. Fatty acid-related lipids and amino acids are associated with DA, thus suggesting that oxidative stress and mitochondrial dysfunction likely play a role in AMD and visual impairment in this condition.