RESUMO
Insights into the pathogenesis of age-related macular degeneration (AMD), a leading cause of blindness, point towards a complex interplay of genetic and lifestyle factors triggering various systemic pathways. This study aimed to characterize metabolomic profiles for AMD and to evaluate their position in the trias with genetics and lifestyle. This study included 5923 individuals from five European studies. Blood metabolomics were assessed using a nuclear magnetic resonance platform of 146 metabolites. Associations were studied using regression analyses. A genetic risk score (GRS) was calculated using ß-values of 49 AMD variants, a lifestyle risk score (LRS) using smoking and diet data, and a metabolite risk score (MRS) using metabolite values. We identified 61 metabolites associated with early-intermediate AMD, of which 94% were lipid-related, with higher levels of HDL-subparticles and apolipoprotein-A1, and lower levels of VLDL-subparticles, triglycerides, and fatty acids (false discovery rate (FDR) p-value < 1.4 × 10-2). Late AMD was associated with lower levels of the amino acids histidine, leucine, valine, tyrosine, and phenylalanine, and higher levels of the ketone bodies acetoacetate and 3-hydroxybutyrate (FDR p-value < 1.5 × 10-3). A favorable lifestyle characterized by a healthy diet was associated with higher levels of amino acids and lower levels of ketone bodies, while an unfavorable lifestyle, including smoking, showed opposite effects (FDR p-value < 2.7 × 10-2). The MRS mediated 5% of the effect of the GRS and 20% of that of the LRS on late AMD. Our findings show that metabolomic profiles differ between AMD stages and show that blood metabolites mostly reflect lifestyle. The severity-specific profiles spur further interest into the systemic effects related to disease conversion.
RESUMO
Phenotypic heterogeneity is commonly observed in diseased tissue, specifically in tumors. Multimodal imaging technologies can reveal tissue heterogeneity noninvasively in vivo, enabling imaging-based profiling of receptors, metabolism, morphology, or function on a macroscopic scale. In contrast, in vitro multiomics, immunohistochemistry, or histology techniques accurately characterize these heterogeneities in the cellular and subcellular scales in a more comprehensive but ex vivo manner. The complementary in vivo and ex vivo information would provide an enormous potential to better characterize a disease. However, this requires spatially accurate coregistration of these data by image-driven sampling as well as fast sample-preparation methods. Here, a unique image-guided milling machine and workflow for precise extraction of tissue samples from small laboratory animals or excised organs has been developed and evaluated. The samples can be delineated on tomographic images as volumes of interest and can be extracted with a spatial accuracy better than 0.25 mm. The samples remain cooled throughout the procedure to ensure metabolic stability, a precondition for accurate in vitro analysis.
