Bibliographic record
Abstract
Glaucoma is characterized by the progressive loss and degeneration of retinal ganglion cells (RGCs) and the axons of the optic nerve. This results in a decline in the visual field and can lead to blindness. About 80 million people are affected by this neurodegenerative disease with an increasing trend. Glaucoma is a complex disease with many different risk factors, including elevated intraocular pressure (IOP), high blood pressure, and advanced age. By now, the pathomechanisms of glaucoma are not fully understood yet. The treatment options are limited to lowering the IOP of the patients, resulting in a slower progression of the disease, but not curing it.1 Therefore, it is of the highest interest to find new therapeutic approaches for this neurodegenerative disease. Here, mouse models are commonly used to artificially recreate the pathology of glaucomatous degeneration. However, mostly young mice are used in ophthalmologic research due to the availability and handling. Nonetheless, this neglects the fact glaucoma onset is strongly correlated with advanced age.2 In general, aging is described by an irreversible pathophysiological process, such as the decline in tissue and cell functions. Age-related diseases can be accompanied by neurodegenerative aspects, e.g., in glaucoma, or can also be associated with cardiovascular or metabolic diseases. The prevalence of these diseases is strongly increasing with age, and with modern medicine promoting human health and extending life expectancy, more people will be affected.3 Hence, neurodegenerative diseases have become one of the main topics in health science. With aging, the structures in the organism undergo a change in morphology. For the eye, the lamina cribrosa of the optic nerve head becomes stiffer. These changes in the elasticity might increase the susceptibility of the optic nerve to glaucomatous damage.4 Altogether, aged retinas are prone to damage through inflammation and senescence. Cellular senescence is defined by irreversible growth and cell cycle arrest, as well as the resistance to apoptosis. These cells undergo a change in morphology: they have an enlarged, flattened cell body and secrete proinflammatory factors, including cytokines, growth factors, and neurotoxic metabolites. Hereby, they contribute to oxidative stress, tissue dysfunctions, or the accumulation of senescent RGCs and trabecular meshwork cells in the eye. However, mice age differently than humans. Comparing the life expectancy of mice and humans, 3- to 6-month-old mice are equivalent to 20- to 30-year-old humans. To be comparable with 56- to 69-year-old humans (the age group for glaucoma), 18- to 24-month-old mice would be needed.5 For this reason, we used 20-month-old βB1-connective tissue growth factor (CTGF) mice to mimic the inflammatory processes ongoing in aged patients in our study. In this model, the overexpression of CTGF stiffens the actin filament in the trabecular meshwork in the eye leading to an elevated IOP.6 We wanted to determine whether transgenic glaucoma mice age faster or show advanced aging processes compared to age-matched wild-type mice with no ocular hypertension. Additionally, our goal was to evaluate inflammatory processes and retinal pathology in aged high-pressure glaucoma mice. After IOP measurements, the retinas of 20-month-old βB1-CTGF mice were analyzed using immunohistology and quantitative real-time polymerase chain reaction, while age-matched wild-type mice served as controls.7 We noted elevated IOP levels in the aged βB1-CTGF mice, while the healthy wild-type mice had a normal IOP. The elevated pressure appears to remain stable with age in the βB1-CTGF mice, since similar values were measured in 4-month-old ones.8 This might also be the case in persons with untreated glaucoma. Accordingly, significantly fewer RGCs were found in βB1-CTGF mice compared to healthy controls (Figure 1).7 This is a typical manifestation of the disease, most probably caused by the increased IOP. Furthermore, in the retinas of aged transgenic glaucoma mice, a significant increase of glial fibrillary acidic protein (GFAP)+ astroglia and a corresponding upregulation of Gfap mRNA expression was detected compared to age-matched controls (Figure 1).7 Astrocytes are a type of macroglia in the retina. They are commonly found in the ganglion cell layer, where they support RGCs. These glial cells react sensitively to the loss of neurons to limit the corresponding damage. However, if this protective function fails, a negative effect on the surrounding cells occurs, including the development of gliosis, which, in turn, further damage the retina. Besides macroglia, there are also microglia in the retina. Microglia are the immune cells of the central nervous system. In the retina, their activation is a response to pathologic mechanisms. Once activated, they secrete proinflammatory cytokines and neurotoxic molecules.9 The eyes of the aged βB1-CTGF glaucoma mice displayed a significantly increased number of microglia/macrophages compared to controls. In general, senescent cells are still metabolically active and can induce changes in their environment through pro-inflammatory proteins (senescence-associated secretory phenotype).2 These pro-inflammatory cytokines include interleukins (IL)-1β and IL-6, as well as tumor necrosis factor-α and transforming growth factor-β2. All four cytokines were found to be highly elevated in the retinas of aged glaucoma mice in both immunohistochemical and mRNA analyses, which is an indicator of senescent cells and inflammation (Figure 1).7 We found fewer RGCs accompanied by inflammation in βB1-CTGF mice. The phenotype of so-called “inflammaging” is paired with oxidative stress, which has an impact on the activation of inflammatory pathways and is closely related to the pathological processes of glaucoma. Oxidative damage is known to trigger trabecular meshwork degeneration. Additionally, in human glaucomatous retinas, the release of IL-1β, IL-6, and tumor necrosis factor-α was also observed, contributing to inflammation and leading to the death of RGCs.2,10 The identification of these inflammatory subtypes could make them potential targets for neuroprotective strategies for future glaucoma treatment approaches.11 Besides the strong inflammation in the βB1-CTGF mice, we also observed that the retinas of these mice showed clear signs of cell aging compared to controls. For example, there was a stronger staining of SA-β-galactosidase in our study, which marks senescent cells (Figure 1).7 As already mentioned, these cells are defined by cell cycle arrest and are known to metabolize β-galactose. In addition, we also detected downregulation of Lmnb1 (laminin B1) mRNA expression in the retinas of old glaucoma mice (Figure 1). A loss of laminin B1 indicates that the cell cycle is arrested, and that the cells are senescent.12Figure 1: Summary of study results in aged glaucoma mice.In the study by Reinehr et al.,7 20-month-old mice were analyzed, which corresponds to an age of 70 years in humans. Using quantitative real-time PCR and immunohistochemistry, two groups were compared: older control mice and older mice with glaucoma. A significant loss of retinal ganglion cells in the glaucoma animals was observed. Furthermore, there was an increase in astrocytes and an enhanced number of microglia/macrophages. In addition, more senescent cells were detected in the older glaucoma mice compared to the control animals of the same age. Moreover, a significant increase in various inflammatory factors was observed in the retinas of the older glaucoma mice. Created with Corel Draw (Version 20; Corel Corporation, Ottawa, Canada). IL: Interleukin; TGF-β2: transforming growth factor-β2; TNF-α: tumor necrosis factor-α.This perspective article has some limitations since it is not a systematic review. This article should give a short overview of a manuscript which deals with inflammatory processes in aged glaucoma mice. There might be a risk of bias, which we cannot exclude, since the search for literature can be, to some extent, subjective and this article cannot be very specific for all cited articles. The methodological approaches used in the perspective article might introduce some errors or inaccuracies. Also, the quality of the included literature we used might vary. We conclude that glaucoma is a multifactorial neurodegenerative disease. It is known that other factors besides an elevated IOP are also involved in the complex pathogenesis of glaucoma. Currently, reducing the IOP remains the primary treatment strategy for glaucoma. However, this method does not effectively address the persistent inflammatory processes that contribute to cellular degeneration. In our study using aged βB1-CTGF glaucoma mice, we observed that elevated IOP and RGC loss were closely linked with pronounced inflammation and increased retinal senescence. These findings highlight that, in our glaucoma model, tissue damage is strongly associated with heightened inflammatory activity. This insight could support the development of novel therapeutic approaches that go beyond IOP reduction. Therefore, long-term follow-up studies should be performed to better understand dynamic changes in the progression of glaucoma disease. Moreover, future research should integrate insights from different disciplines, including neuroscience, immunology, and genetics. Such interdisciplinary synergies are critical for translating fundamental discoveries into effective and personalized interventions for glaucoma patients. This work was supported by the Deutsche Forschungsgemeinschaft, No. RE5453/1-1 (to SR).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".