Identification of the Metabolic Signature of Aging Retina

Abstract

Purpose: This study aims to explore the metabolic signature of aging retina and identify the potential metabolic biomarkers for the diagnosis of retinal aging.

Methods: Retinal samples were collected from both young (two months) and aging (14 months) mice to conduct an unbiased metabolic profiling. Liquid chromatography–tandem mass spectrometry analysis was conducted to screen for the metabolic biomarkers and altered signaling pathways associated with retinal aging.

Results: We identified 166 metabolites differentially expressed between young and aged retinas using a threshold of orthogonal projection to latent structures discriminant analysis variable importance in projection >1 and P < 0.05. These metabolites were significantly enriched in several metabolic pathways, including purine metabolism, citrate cycle, phenylalanine, tyrosine and tryptophan biosynthesis, glycerophospholipid metabolism, and alanine, aspartate and glutamate metabolism. Among these significantly enriched pathways, glycerophospholipid metabolites emerged as promising candidates for retinal aging biomarkers. We assessed the potential of these metabolites as biomarkers through an analysis of their sensitivity and specificity, determined by the area under the receiver-operating characteristic (ROC) curves. Notably, the metabolites like PC (15:0/22:6), PC (17:0/14:1), LPC (P-16:0), PE (16:0/20:4), and PS (17:0/16:1) demonstrated superior performance in sensitivity, specificity, and accuracy in predicting retinal aging.

Conclusions: This study sheds light on the molecular mechanisms underlying retinal aging by identifying distinct metabolic profiles and pathways. These findings provide a valuable foundation for developing future clinical applications in diagnosing, identifying, and treating age-related retinal degeneration.

Translational Relevance: This study sheds light on novel metabolic profiles and biomarkers in aging retinas, potentially paving the way for targeted interventions in preventing, diagnosing, and treating age-related retinal degeneration and other retinal diseases.

Introduction

Aging is recognized as a significant risk factor for various retinal degenerative diseases. As individuals age, the progressive degeneration of retinal structures could lead to vision impairment, affecting visual function and quality of life in older adults.¹ The process of aging can lead to irreversible retinal degeneration, which in turn significantly raises the risk of developing several serious degenerative ocular diseases, such as age-related macular degeneration, diabetic retinopathy, and glaucomatous retinopathy.^2,3 Retinal aging is significantly influenced by a complex interplay of factors, such as genetic variants, environmental factors like light exposure,⁴ dietary habits such as high-sugar or high-fat diets,^5,6 and lifestyle choices such as tobacco smoking.⁷ The mechanisms underlying retinal aging involve genome instability, transcriptional changes, epigenomic alterations, proteostasis decline, inflammation, and metabolic dysregulation.¹ Notably, the retina is one of the most metabolically active tissues in mammals. Metabolic dysregulation has been identified as a prominent signature of retinal aging within the intricate interplay of epigenome-metabolism nexus.⁸ The presence of altered metabolites or disrupted metabolic pathways is strongly linked to the process of retinal aging.¹ Although retinal aging is recognized, the exact metabolic mechanisms are still not well understood. The complexity and interwoven nature of these pathways present significant challenges in elucidating their relationships.⁹ Therefore it is still required to further investigate the metabolic mechanisms underlying retinal aging. 

Metabolomics involves the characterization of small-molecule metabolites within biofluids, cells, and tissues, encompassing both their qualitative and quantitative analysis.¹⁰ Untargeted metabolome profiling enables the detection and quantification of small-molecule metabolites across the metabolome. This approach captures a wide array of compounds, including but not limited to amino acids, polyols, lipids, and organic acids.^10,11 Its high sensitivity permits the identification of subtle shifts in metabolomic pathways, offering critical insights into the mechanisms that drive a spectrum of physiological conditions and diseases.¹¹ As a result, metabolomics has emerged as a promising method for evaluating diagnostic accuracy and facilitating the early detection of therapeutic responses.¹⁰ Metabolomic profiling of both young and aged samples has uncovered significant alterations in a wide array of metabolic pathways that are associated with aging. These include pathways related to steroid metabolism, amino acid metabolism, lipid metabolism, purine metabolism, and energy metabolism.^9,12 These changes are tightly associated with the pathology of various age-related diseases. Thus metabolomics emerges as a promising avenue for identifying biomarkers indicative of retinal aging. 

In this study, untargeted metabolic analysis revealed 166 differential metabolites in aging retinas. Notably, alterations in glycerophospholipid metabolism emerged as a significant factor in the pathogenesis of retinal aging. These findings offer new insights into the metabolic mechanism of retinal aging and suggest potential metabolomic biomarkers for its diagnosis. 

Material and Methods

Animals

The study used male C57BL/6J mice, with the young group consisting of two-month-old individuals and the old group comprising 14-month-old counterparts, both sourced from the Experimental Animal Center at Nanjing Medical University (Nanjing, Jiangsu, China). The mice had unrestricted access to water and food and were accommodated at the Experimental Animal Center (affiliated with the Eye Hospital of Nanjing Medical University) under a controlled environment, including a 12-hour light/dark cycle, ambient temperature maintained at 25°C ± 2°C, and relative humidity kept at 50% ± 10%. All experiments were conducted accordance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and received approval from the Animal Care and Use Committee of the authors’ institute. 

Retina Sample Collection

After a one-week acclimatization, all animals underwent anesthesia via intraperitoneal injection of a ketamine (80 mg/kg) and xylazine (10 mg/kg) mixture. The retinas were carefully extracted from both eyes of each mouse, with the paired retinas combined into a single sample. Immediately after extraction, the samples were flash-frozen in liquid nitrogen for 15 minutes and stored at −80°C for metabolomics analysis. 

Senescence-Associated β-Galactosidase (SA-β-Gal) Staining

The presence of senescent cells was determined using the Senescence β-Galactosidase Staining Kit (Beyotime Biotechnology, Jiangsu, China; cat. no. C0602) on cryopreserved retinal sections.¹³ The sections were first thawed at room temperature and then rinsed with phosphate-buffered saline solution for five minutes. After this, the tissue was fixed with β-galactosidase staining fixative for at least 15 minutes at room temperature. After another set of three five-minute phosphate-buffered saline solution washes, the sections were incubated with freshly prepared senescence-associated β-galactosidase (SA β-Gal) staining solution overnight at 37°C, covered to prevent evaporation. The stained sections were visualized using a Carl Zeiss Anxioplan 2 fluorescence microscope (Carl Zeiss, Inc., White Plains, NY, USA), and bright-field images were captured. 

Metabolomics Sample Preparation and LC−MS/MS Analysis

Sample preparation for metabolomic analysis was performed as described previously with some modifications.¹⁴ In brief, retinal samples from both young and aged C57BL/6J mice (n = 6) were combined with 1 mL of cold methanol/water solution (4:1, v/v) containing 1.5 µg/mL 5-¹³C-glutamine as an internal standard. The extracted samples were then vortexed, centrifuged, concentrated and re-dissolved. 20 µL aliquot of the samples were injected into an ACQUITY UPLC I-Class Plus system (Waters, Milford, USA) using an Acquity UPLC HSS T3 column (1.8 µm, 2.1 mm × 100 mm; Waters, Milford, MA, USA) at a flow rate of 0.4 mL/min. The mobile phase consisted of solvent A (containing 0.1% [v/v] formic acid in water) and solvent B (containing 0.1% [v/v] formic acid in acetonitrile). The separation was achieved using the flowing elution gradient: linear gradient from 5% B-20% B (up to three minutes), ramped to 95% B within six minutes (at three to nine minutes), held for five minutes (at nine to 13 minutes) at 95% B, then back to 5% B (at 13–13.1 minutes), and finally with the stop at 16 minutes. 

The eluent was analyzed using a QE high-resolution mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) with electrospray ionization in both negative and positive ion modes. The operating conditions were optimized as follows: the source temperature was set to 500°C, with ion-spray voltages at −4,000 V for negative mode and 5000 V for positive mode. Collision energy was adjusted from 20 to 60 V, and the mass-to-charge (m/z) scan range was set from 50 to 1000 for analysis. Data acquisition was managed using Analyst TF 1.6.1 software (AB SCIEX). For accurate mass measurement, a calibration delivery system was used, with automatic recalibration occurring after every six samples. 

Data Processing and Statistical Analysis

The raw data from the mass spectrometer (MS) underwent a conversion process to a standardized format (mzXML) using ProteoWizard software. Following this, a custom software program written in R was used to analyze the data. This program incorporates advanced algorithms to identify and align various peaks within the data, allowing for accurate correction of measurement times and identification of specific metabolites. The identification process relies on comparing high-precision mass measurements (accuracy within 10 ppm) and fragmentation patterns (MS/MS data, tolerance < 0.02 Da) with established databases like HMDB, Lipidmaps, and Metlin, along with our own proprietary databases. Finally, to ensure data quality, only features with measurements present in over half the samples from at least one group were retained for further analysis. 

Data analysis was performed using R (version 4.0.3) and various R packages. Data were preprocessed by centering the values using Pareto scaling. Principal component analysis (PCA) provided an overview of the data structure, whereas orthogonal projection to latent structures discriminant analysis (OPLS-DA) focused on identifying metabolic differences between young and aging retinas. Robustness of the models was ensured by evaluating for overfitting using permutation tests. Model performance was assessed through metrics like R² and Q², reflecting the model's ability to explain the data and its predictive power, respectively. MetaboAnalyst 6.0 platform was used for comprehensive analysis of the metabolomics data. Hierarchical clustering analysis (HCA), heatmap analysis, and volcano plot analysis provided different visualization techniques to explore differential metabolic profiles between groups. Pathway analysis further revealed potentially affected metabolic pathways in aging retinas. To identify potential biomarkers of retinal aging, we first used the variable importance in projection (VIP) scores obtained from the OPLS-DA model. VIP scores indicate a metabolite's contribution to differentiating between the young and aging retina groups. We calculated P values from two-tailed Student's t-tests to assess the statistical significance of each metabolite's difference between the groups. For studies involving multiple comparison groups, one-way analysis of variance was used. Metabolites with a VIP score greater than 1.0 and P < 0.05 were considered to be putatively important for retinal aging. The fold change of these candidate biomarkers was calculated as the logarithm of the average mass response ratio between the young and aging retina groups. 

Results

Collection of Aging Retinal Samples

The study used retinas from two-month-old male C57BL/6J mice as the control group, representing the young retina cohort. In contrast, the aging retina group was comprised of retinas from 14-month-old male C57BL/6J mice. SA β-Gal activity, recognized for its reliability in identifying senescent cells, was used as a key marker.¹³ SA β-Gal staining assays were conducted to evaluate the aging status of both young and aging retinas. The aging retina group displayed distinct blue-stained regions, indicative of senescent cells (Fig. 1A). In contrast, the young retina group showed no blue-stained areas, suggesting an absence of senescent cells (Fig. 1A). For comprehensive metabolic profiling, 12 retinal samples were collected, evenly divided between the aging and control groups. The workflow for the metabolic profiling is depicted in Figure 1B. 

Figure 1.

 

Collection of aging retinal samples. (A) SA-β-gal staining of frozen retinal sections obtained from the young retina (Control) group and the aging retina group. The blue-stained areas of frozen retinal sections were observed by a microscope. A representative image of β-gal staining was shown. Scale bar: 100 µm. GCL, ganglion cell layer; INL, inner nuclear layer; ONL, outer nuclear layer. (B) Procedure flowchart of metabolic profiles. For untargeted metabolic analysis, a comprehensive procedure involving six biological replicates for each group (six aging retinas and six young retinas) was implemented. The flowchart illustrates the systematic steps for metabolic profiling, emphasizing the methodology used to collect and analyze the metabolic data.

Preprocessing of the Untargeted Metabolic Data

The first step was to rigorously assess the quality of the untargeted metabolic data to ensure the reliability of our findings. PCA was utilized to discern patterns, revealing both similarities and differences within our metabolic datasets. The PCA score plot visually delineates the distinction between the young retina group and the aging retina group (Fig. 2A). The distinct separation observed between the young and aging retina group in the PCA plot indicates discernible differences in metabolic profiles, reflecting the physiological metabolic shifts that accompany retinal aging. Furthermore, the tight clustering of quality control samples at the PCA score plot's center confirms the high reliability and reproducibility of our metabolic analysis (Fig. 2A). To elucidate the metabolic differences, HCA was conducted to graphically represent the metabolite levels in both young and aging retinas. HCA distinctly segregated the metabolic profiles, with aging retinas and young retinas forming separate branches (Fig. 2B). This clear clustering not only highlights the significant metabolic effects of aging on the retina but also suggests potential alterations in pivotal pathways. 

Figure 2.

 

Preprocessing of the untargeted metabolic data. (A) Principal components analysis (PCA) score plot of the young retina group and the aging retina group. (B) Hierarchical clustering analysis (HCA) of all metabolites in the young retina group and the aging retina group.

Identification of Altered Metabolites in Aging Retinas by Untargeted Metabolomics Analysis

To explore the metabolite alterations in aging retinas, we conducted an untargeted metabolomics analysis on samples from both control and aging groups, identifying 1019 metabolites. An OPLS-DA score plot was then constructed, which clearly differentiated young retinas from aging retinas (Fig. 3A). This distinction highlights significant metabolic variations between the groups, suggesting metabolic shifts linked to the aging process. The quality of the OPLS-DA model was rigorously assessed through a cross-validation test (Fig. 3B). The values of R² and Q² were 0.90583 and 0.84652, respectively, indicating a well-fitted model with high predictive accuracy (Table). Additionally, the validity and stability of the OPLS-DA model were further confirmed by a 200-permutation analysis, with an R²Y value of 0.99 and a Q² value of 0.805 (Fig. 3C). Collectively, these analyses validate the statistical significance of the observed metabolic changes, likely representing an altered metabolic signature in response to aging in the retina. 

Figure 3.

 

Identification of altered metabolites in aging retinas by untargeted metabolic analysis. (A) Orthogonal partial least squares discriminant analysis (OPLS-DA) score plot of the young retina group and the aging retina group. (B) Cross-validation of five components between the young retina group and the aging retina group. An asterisk (*) indicates the maximum value of Q². (C) 200 permutation analysis plot of OPLS-DA model. (D) Volcano plot of untargeted metabolomics according to the criteria (OPLS-DA VIP >1, P < 0.05). (E) The heatmap generated from HCA displaying the top 50 differential metabolites.

Table.

 

Cross-Validation for OPLS-DA Model

 

To comprehensively investigate the altered metabolites in the aging retina, we identified differential metabolites using a two-tailed Student's t-test with criteria of P < 0.05, log²|Fold change (FC)| > 1 or < −1, resulting in a total of 194 altered metabolites in the aging retina group compared to the control group. After compound identification, 166 differential metabolites were selected based on stringent criteria of OPLS-DA VIP > 1 and P < 0.05. The stringent selection process has pinpointed metabolites with the most significant association to aging. Among these, 90 metabolites were found to be up-regulated, whereas 76 were down-regulated in the aging retina group (Fig. 3D). These changes suggest substantial metabolic shifts that are crucial for elucidating the biological mechanisms of aging. 

To visually represent these differential metabolites, a heatmap derived from HCA was used, showing the top 50 differential metabolites (Fig. 3E). This graphical depiction not only aids in discerning patterns of metabolic deregulation but also highlights potential metabolite targets. These targets are instrumental for further investigation into the metabolic pathways that may be impacted by the aging process in the retina. 

Identification of Altered Metabolic Pathways in Aging Retinas

To uncover the metabolic pathways associated with retinal aging, we conducted a Pathway Analysis using MetaboAnalyst 6.0. This analysis focused on the 166 metabolites that showed significant alterations in aging retinas. The pathway analysis identified the top five enriched metabolic pathways, providing insights into the core biochemical processes affected by aging: (1) purine metabolism; (2) citrate cycle (TCA cycle); (3) phenylalanine, tyrosine, and tryptophan biosynthesis; (4) glycerophospholipid metabolism; (5) alanine, aspartate, and glutamate metabolism (Fig. 4A, P<0.05). These findings suggest that retinal aging may be linked to disruptions in key metabolic pathways, including those of nucleotides, lipids, carbohydrates, and amino acids. These metabolic shifts are likely to play pivotal roles in molecular aging of the retina, potentially contributing to the observed structural and functional deterioration with age. Consistent with the previous study, dysregulated glycerophospholipid metabolism has been reported to be closely associated with the aging process, further emphasizing the intricate relationship between metabolic changes and the aging retina.¹⁵ This pathway plays a crucial role in cellular membrane composition and signaling processes, and was disrupted in aging retinas (Fig. 4B). The disruption in glycerophospholipid metabolism likely leads to impaired cellular functions in aging retinas. Collectively, these findings emphasize the physiological significance of glycerophospholipid metabolism in retinal aging. 

Figure 4.

 

Identification of altered metabolic pathways in aging retinas. (A) Pathway analysis identified the enriched pathways of 166 altered metabolites in the aging retinas in the back-ground of the Mouse KEGG pathway database. (B) The metabolic pathway diagram illustrates the changes within the glycerophospholipid metabolism pathway in aging retinas. Metabolites highlighted in red and green represent those that are significantly upregulated and downregulated, respectively, in the aging retina group when compared to the control group.

Identification of Candidate Metabolic Biomarkers for Retinal Aging

To elucidate the impact of glycerophospholipid metabolism on retinal aging, we concentrated on the metabolites within this pathway that exhibited alterations in aging retinas. We identified significant changes in metabolite levels, as evidenced by VIP scores exceeding 1.5 (Fig. 5A). Specifically, in the aging retina group, we observed a downregulation of PC (15:0/22:6), PC (17:0/14:1), and PS (17:0/16:1) levels, with P values of 4.1978 × 10⁻⁴, 3.7209 × 10⁻⁵, and 2.3757 × 10⁻³, respectively (Figs. 5B, 5C, 5F). Although the levels of LPC (P-16:0) and PE (16:0/20:4) were upregulated, with P values of 9.5302 × 10⁻⁵ and 8.7162 × 10⁻⁶, respectively (Figs. 5D, 5E). Figure 5A offers a comprehensive depiction of the metabolic flux alterations within the glycerophospholipid metabolism pathway in aging retinas. As integral components of eukaryotic cell membranes, glycerophospholipids, a subclass of phospholipids, play a vital role in cellular structure and function. Within this subclass, phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylserine (PS) are the three highest-content types in mammalian cell membranes. PC is the most abundant glycerophospholipid in cell membranes, which are synthesized through CDP-choline (Kennedy) pathway and degraded into lysophosphatidylcholine (LPC) via hydrolysis by phospholipase A2 (PLA2).^16,17 LPC can be converted back to PC by intracellular enzymes lysophosphatidylcholine acyltransferase (LPCAT) in the presence of acyl-CoA. The actions of these two distinct enzymes form a cycle of PC degradation and regeneration.¹⁸ 

Figure 5.

 

Altered glycerophospholipid metabolism in aging retinas. (A) The changes in metabolic flux of glycerophospholipid metabolism in aging retinas. (B–F) Untargeted metabolic analysis revealed reduced levels of PC (15:0/22:6), PC (17:0/14:1), and PS (17:0/16:1), along with elevated levels of LPC (P-16:0) and PE (16:0/20:4) in aging retinas (n = 6, *P < 0.05, two-tailed Student's t-test).

Subsequently, we further investigated the altered metabolites that were enriched in the glycerophospholipid pathway to identify the candidate metabolite biomarkers for retinal aging. Area under the curve-receiver operating characteristic analysis was conducted to assess the diagnostic utility of these altered glycerophospholipid metabolites. Five metabolites, namely PC (17:0/14:1), PC (15:0/22:6), LPC (P-16:0), PE (16:0/20:4), and PS (17:0/16:1), exhibited a higher AUC values exceeding 0.8, indicating the superior sensitivity and specificity for diagnosing retinal aging (Figs. 6A–E). The multivariate receiver operating characteristic (ROC) analysis provided a robust validation of the collective discriminatory power of altered glycerophospholipid metabolites for detecting aging. This analysis demonstrated the ability to accurately distinguish aging retinas with high precision (Fig. 6F). Collectively, our findings indicate that metabolites associated with glycerophospholipid metabolism may offer promising biomarkers for future diagnostics and therapeutic strategies for alleviating the effects of retinal aging. 

Figure 6.

 

Identification of candidate metabolic biomarkers for aging retinas. (A–E) Receiver operating characteristic (ROC) curves for the comparison of five metabolites in glycerophospholipid metabolism between the control group and the aging retina group. (F) ROC curve of glycerophospholipid metabolic pathway in the control group and the aging retina group.

Discussion

Understanding the mechanisms and identifying biomarkers of retinal aging are crucial for accurately quantifying the process of retinal aging. This understanding can contribute to precise diagnoses, risk stratification, and early intervention for age-related retinal diseases. The retina, characterized by high metabolism and robust blood flow, exhibits metabolic dysfunction as a prominent hallmark of aging.¹ Prior metabolomics analyses have identified alterations in metabolites across diverse tissue and organ samples, potentially contributing to a better understanding of the metabolic pathways regulated by aging.¹⁹ Despite the importance of understanding the mechanisms and identifying biomarkers of retinal aging, research on metabolic changes in this context remains limited. Our study leveraged LC−MS/MS analysis on retinal samples from young and aged C57BL/6J mice to uncover candidate biomarkers and metabolic pathways associated with aging. We identified 166 differential metabolites across key pathways, including purine metabolism, TCA cycle, and the biosynthesis of phenylalanine, tyrosine, and tryptophan, as well as glycerophospholipid, alanine, aspartate, and glutamate metabolism. Notably, six metabolites within the glycerophospholipid pathway—PC (17:0/14:1), PC (15:0/22:6), LPC (P-16:0), PE (16:0/20:4), and PS (17:0/16:1)—emerged as potential biomarkers for retinal aging, with AUC values exceeding 0.8, demonstrating strong discriminatory potential to differentiate aging retinas from those of younger counterparts. We identified that differential metabolites associated with retinal aging were significantly enriched in pathways such as purine metabolism, TCA cycle, amino acid metabolism, and glycerophospholipid metabolism. Our findings are consistent with previous research that has reported disturbances in the purine metabolism pathway as part of the aging process.¹² Additionally, recent studies have corroborated significant alterations in glucose and amino acid metabolism within aging retina.²⁰ In line with these findings, our study detected significant disruptions in glucose metabolism, specifically within TCA cycle, and in amino acid metabolism in the aging retina. This includes alterations in the biosynthesis pathways of phenylalanine, tyrosine, and tryptophan, which are known precursors to neurotransmitters, as well as changes in the metabolism of alanine, aspartate, and glutamate, all of which play crucial roles in neurotransmitter systems. These metabolic disruptions underscore the intricate relationship between aging and neurotransmission, highlighting potential avenues for intervention in age-related retinal degeneration.²¹ Thus disturbances in the biosynthesis pathways of phenylalanine and tyrosine in the aging retina could contribute to various visual impairments associated with age-related vision diseases. 

Previous study has established a close link between dysregulated glycerophospholipid metabolism and aging.¹⁵ Glycerophospholipids are vital for the structure and function of biological membranes and serum lipoproteins.²² Maintaining proper glycerophospholipid metabolism is crucial for preserving membrane fluidity, integrity, and efficient operation of signaling pathways. Disruptions in this metabolism can lead to cellular aging and heightened vulnerability to oxidative stress. Moreover, prior studies have shown that dysregulated glycerophospholipid metabolism and its associated metabolites are associated with a spectrum of age-related diseases, including age-related macular degeneration,^23,24 Alzheimer's disease,^25–27 Parkinson's disease,²⁸ coronary artery disease,²⁹ type 2 diabetes mellitus,³⁰ and various cancers.³¹ Consequently, metabolic alterations observed in glycerophospholipid metabolism pathways may play a significant role in the aging process of the retina. 

Furthermore, our study has identified potential metabolic biomarkers for retinal aging. ROC analysis was conducted on the metabolites altered in the top five pathways, assessing their potential for diagnosing retinal aging. Notably, metabolites within the glycerophospholipid metabolism pathway, specifically PC (17:0/14:1), PC (15:0/22:6), LPC (P-16:0), PE (16:0/20:4), and PS (17:0/16:1), demonstrated strong diagnostic potential, suggesting their value as candidate biomarkers for retinal aging. Specifically, our study observed a decrease in the levels of PC (17:0/14:1), PC (15:0/22:6), and PS (17:0/16:1), and an increase in LPC (P-16:0) and PE (16:0/20:4). These fluctuations underscore the fundamental dysregulation of glycerophospholipid metabolism that occurs with retinal aging. 

PC is the predominant glycerophospholipid in cell membranes, serving a vital function in preserving the structural integrity and ensuring the proper functioning of the cell membrane. Beyond its structural role, PC also modulates key cellular processes, including cell proliferation and differentiation.³² Cellular senescence, a hallmark of aging, is characterized by a stable cessation of cell division.³³ Consequently, we hypothesize that decreased PC levels may contribute to the progression of cellular senescence in aging retinas. LPC is a derivative of PC that forms when a fatty acid chain is removed. Functioning as a signaling molecule, LPC is involved in numerous cellular signaling pathways. However, it can exert deleterious effects on cells, including the enhancement of inflammatory responses and the disruption of mitochondrial integrity.¹⁷ Previous studies have revealed that mitochondrial dysfunction and increased inflammation were observed in the aging retina.^3,34 We speculate that elevated levels of LPCs may contribute to the progression of retinal aging through the induction of mitochondrial dysfunction and inflammation. PE, the second most abundant phospholipid in cell membranes, is crucial for sculpting membrane architecture and facilitating the fusion of cellular and organelle membranes. It is also essential for vital cellular processes such as oxidative phosphorylation, mitochondrial biogenesis, and autophagy.³⁵ The abundance of PE positively regulates autophagy. Autophagy is a cellular recycling program that retards aging by eliminating harmful organelles and intracellular protein aggregates.³⁶ An increase in PE within aging retinas may serve a compensatory function, potentially inhibiting the aging process by promoting autophagy. Exogenous supplementation of PS within the range of 300–800 mg/day can significantly mitigate aging-related biochemical alterations and structural decline in neuronal cells of human brain.³⁰ Retinal aging is characterized by alterations in neuronal structure and neuronal function decline. This decline is attributed to changes in the processing and transmission of visual information from the retina to the brain. The reduction in PS level may correlate with compromised structure and function of neurons in the aging retina. 

Nevertheless, several limitations should be acknowledged in this study. Retinal samples were collected at specific time points chosen for metabolic analysis. It is important to consider the potential influence of these selected time points on retinal metabolic profile. To enhance our understanding, future studies should incorporate sampling at multiple time points to capture a broader temporal perspective. The current knowledge regarding glycerophospholipid metabolism in aging retina is limited. Further investigations are warranted to elucidate the precise role and underlying mechanism of these candidate metabolic biomarkers in the progression of retinal aging. In addition, to establish reliable biomarkers for diagnosing retinal aging in a clinical setting, future studies should use the targeted metabolomics focused on glycerophospholipid metabolites using a larger and more diverse set of clinical samples. 

Conclusions

This study investigates the metabolic profiles between young and aging retinas, revealing novel insights into the underlying metabolic mechanisms of retinal aging. Notably, altered metabolites in glycerophospholipid metabolism hold promise as potential metabolic biomarkers for diagnosing retinal aging. Further research involving human retinal samples and longitudinal clinical analysis is essential to decipher the complex pathogenesis of retinal aging. By achieving breakthroughs, this research could unveil new targets for the prevention, diagnosis, and treatment of retinal aging, and even age-related retinal diseases. 

Acknowledgments

Funded by the grants from National Natural Science Foundation of China (Grant No. no. 82171074 and 82225013 to Yan; no. 81570859 and 82070983 to Jiang). 

Disclosure: W. Mu, None; X. Han, None; M. Tong, None; S. Ben, None; M. Yao, None; Y. Zhao, None; J. Xia, None; L. Ren, None; C. Huang, None; D. Li, None; X. Li, None; Q. Jiang, None; B. Yan, None