Purpose Diabetic retinopathy (DR) is often recognized as a marker of systemic microvascular disease, but the metabolic links to other complications, such as diabetic nephropathy (DN), remain unclear. We aimed to identify systemic metabolic signatures shared by DR and DN and investigate their potential causal mechanisms. Methods Multi-tissue metabolomic profiling of the retina, plasma, and kidney was performed in streptozotocin-induced diabetic mice. Clinical relevance was supported by public human DR and DN transcriptomic datasets. Causal relationships were assessed by two-sample Mendelian randomization (MR) using eQTLGen and genome-wide association study (GWAS) summary statistics. Single-cell in silico perturbation analysis was performed to predict organ-specific functional consequences. Results Cross-organ metabolomic profiling identified a conserved systemic lipotoxic signature, yielding a predictive plasma panel comprised of free carnitine and two long-chain acylcarnitines. Clinical transcriptomics and MR analyses pinpointed the synchronous downregulation of the SLC22A5 and CPT2 axis as a causal genetic signature of this lipid imbalance. Furthermore, in silico single-cell analyses revealed that this shared metabolic disturbance induced distinct transcriptional responses across tissues, suggesting tissue-specific molecular responses that may contribute to organ-specific microvascular dysfunction. Conclusions Both DR and DN are associated with systemic disruption of acylcarnitine metabolism. A circulating carnitine/acylcarnitine signature may serve as a non-invasive indicator of microvascular risk, and the SLC22A5–CPT2 axis represents a potential therapeutic target.
Qian Liu, Yan Liu, Xiongyi Yang et al.· Investigative Ophthalmology...· 0 citations
Background Diabetic retinopathy (DR) and Alzheimer disease (AD) share metabolic and vascular dysfunctions, but the extent to which they reflect overlapping genetic susceptibility and neurovascular-metabolic regulatory pathways remains unclear. We combined multi-omics analyses with population-based data to examine the genetic convergence, cellular pathways, and longitudinal association between DR and AD. Methods We performed a two-sample Mendelian randomisation (MR) to estimate the association between genetically predicted DR liability and AD risk. We used Bayesian colocalisation analysis to identify shared genomic loci, and summary-data-based MR (SMR) to detect expression-mediated genes jointly associated with DR and AD. We analysed single-cell RNA sequencing data to characterise shared cellular features and related biological pathways. We also conducted an MR-based mediation analysis to explore whether lipid-related, metabolic, or inflammatory traits mediated the observed DR-AD association, and a longitudinal analysis of the UK Biobank cohort to assess the association between DR and incident AD. Results With the MR analysis, we found that genetically predicted liability to DR was associated with a modest increase in AD risk. Colocalisation analysis supported a shared genetic signal. We identified three genes with shared expression-mediated associations across DR and AD through SMR. Functional enrichment analyses revealed partially overlapping neurovascular and metabolic pathways. Using MR-based mediation analysis, we found no significant intermediary traits linking DR and AD. Findings from the UK Biobank cohort were directionally consistent with the genetic analyses. Conclusions Genetic liability to DR is associated with an increased risk of AD and is accompanied by shared expression-mediated effects and convergent neurovascular-metabolic pathways. These findings support the possibility that DR may serve as a clinically accessible indicator of increased neurodegenerative vulnerability.
Jing Li, Qian Liu, Qian Ma et al.· Journal of Global Health· 0 citations
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