Undiagnosed diabetic retinopathy in Northeast China: prevalence and determinants.

Objective: To report the prevalence and contributing factors of undiagnosed diabetic retinopathy (DR) in a population from Northeastern China. Subjects/Methods: A total of 800 subjects from the Fushun Diabetic Retinopathy Cohort Study were enrolled. A questionnaire assessing incentives and barriers to diagnosis of DR was administered. Logistic regression was used to identify clinical and sociodemographic factors associated with undiagnosed DR. In a prespecified subgroup analysis, we divided patients into vision-threatening diabetic retinopathy (VTDR) and non-VTDR (NVTDR) subgroups. Results: Among 800 participants with DR, 712 (89.0%) were undiagnosed. Among 601 with NVTDR, 566 (94.2%) were undiagnosed. Among 199 with VTDR, 146 (73.4%) were undiagnosed. The risk factors affecting the timely diagnosis of NVTDR and VTDR exhibit significant disparities. In multivariate models, factors associated with undiagnosed VTDR were age over 60 years (OR = 2.966; 95% CI = 1.205-7.299; P = 0.018), duration of diabetes over 10 years (OR = 0.299; 95% CI = 0.118-0753; P = 0.010), visual impairment or blindness (OR = 0.310; 95% CI = 0.117-0.820; P = 0.018), receiving a reminder to schedule an eye examination (OR = 0.380; 95% CI = 0.163-0.883; P = 0.025), and the belief that "people with diabetes are unlikely to develop an eye disease" (OR = 4.691; 95% CI = 1.116-19.724; P = 0.035). However, none of the factors were associated with undiagnosed NVTDR (all P ≥ 0.145). Conclusion: Our research has uncovered a disconcerting trend of underdiagnosis in cases of DR within our population. Addressing determinants of undiagnosed DR may facilitate early detection.

Gut Microbiota Composition and Fecal Metabolic Profiling in Patients With Diabetic Retinopathy.

Recent evidence suggests there is a link between metabolic diseases and gut microbiota. To investigate the gut microbiota composition and fecal metabolic phenotype in diabetic retinopathy (DR) patients. DNA was extracted from 50 fecal samples (21 individuals with type 2 diabetes mellitus-associated retinopathy (DR), 14 with type 2 diabetes mellitus but without retinopathy (DM) and 15 sex- and age-matched healthy controls) and then sequenced by high-throughput 16S rDNA analysis. Liquid chromatography mass spectrometry (LC-MS)-based metabolomics was simultaneously performed on the samples. A significant difference in the gut microbiota composition was observed between the DR and healthy groups and between the DR and DM groups. At the genus level, Faecalibacterium, Roseburia, Lachnospira and Romboutsia were enriched in DR patients compared to healthy individuals, while Akkermansia was depleted. Compared to those in the DM patient group, five genera, including Prevotella, were enriched, and Bacillus, Veillonella, and Pantoea were depleted in DR patients. Fecal metabolites in DR patients significantly differed from those in the healthy population and DM patients. The levels of carnosine, succinate, nicotinic acid and niacinamide were significantly lower in DR patients than in healthy controls. Compared to those in DM patients, nine metabolites were enriched, and six were depleted in DR patients. KEGG annotation revealed 17 pathways with differentially abundant metabolites between DR patients and healthy controls, and only two pathways with differentially abundant metabolites were identified between DR and DM patients, namely, the arginine-proline and α-linolenic acid metabolic pathways. In a correlation analysis, armillaramide was found to be negatively associated with Prevotella and Subdoligranulum and positively associated with Bacillus. Traumatic acid was negatively correlated with Bacillus. Our study identified differential gut microbiota compositions and characteristic fecal metabolic phenotypes in DR patients compared with those in the healthy population and DM patients. Additionally, the gut microbiota composition and fecal metabolic phenotype were relevant. We speculated that the gut microbiota in DR patients may cause alterations in fecal metabolites, which may contribute to disease progression, providing a new direction for understanding DR.

Metabolite Changes in the Aqueous Humor of Patients With Retinal Vein Occlusion Macular Edema: A Metabolomics Analysis.

Macular edema (ME) is the main cause of visual impairment in patients with retinal vein occlusion (RVO). The degree of ME affects the prognosis of RVO patients, while it lacks objective laboratory biomarkers. We aimed to compare aqueous humor samples from 28 patients with retinal vein occlusion macular edema (RVO-ME) to 27 age- and sex-matched controls by ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry, so as to identify the key biomarkers and to increase the understanding of the mechanism of RVO-ME at the molecular level. Through univariate and multivariate statistical analyses, we identified 60 metabolites between RVO-ME patients and controls and 40 differential metabolites in mild RVO-ME [300 µm ≤ central retinal thickness (CRT) < 400 µm] patients compared with severe RVO-ME (CRT ≥ 400 µm). Pathway enrichment analysis showed that valine, leucine, and isoleucine biosynthesis; ascorbate and aldarate metabolism; and pantothenate and coenzyme A biosynthesis were significantly altered in RVO-ME in comparison with controls. Compared with mild RVO-ME, degradation and biosynthesis of valine, leucine, and isoleucine; histidine metabolism; beta-alanine metabolism; and pantothenate and coenzyme A biosynthesis were significantly changed in severe RVO-ME. Furthermore, the receiver operating characteristic (ROC) curve analysis revealed that adenosine, threonic acid, pyruvic acid, and pyro-L-glutaminyl-l-glutamine could differentiate RVO-ME from controls with an area under the curve (AUC) of >0.813. Urocanic acid, diethanolamine, 8-butanoylneosolaniol, niacinamide, paraldehyde, phytosphingosine, 4-aminobutyraldehyde, dihydrolipoate, and 1-(beta-D-ribofuranosyl)-1,4-dihydronicotinamide had an AUC of >0.848 for distinguishing mild RVO-ME from severe RVO-ME. Our study expanded the understanding of metabolomic changes in RVO-ME, which could help us to have a good understanding of the pathogenesis of RVO-ME.

rs10490924 surrounding HTRA1/ARMS2 regulates the susceptibility of age-related macular degeneration.

Multiple studies have assessed the contribution of rs10490924 on chromosome 10q26 surrounding HTRA1/ARMS2 gene to age-related macular degeneration (AMD) risk. However, the causal allele at this locus is still inconclusive. In this meta-analysis, we systematically characterized the potential association between rs10490924 polymorphism and AMD risk. Data available from 12 case-control studies, including a total of 5244 cases and 2755 controls in three different ethnic populations, were used to evaluate the correlation between rs10490924 G/T polymorphism (Ala69Ser) and AMD risk. In overall populations, the results indicated the Ala69Ser polymorphism was significantly associated with AMD under allelic (OR = 0.35, 95% CI = 0.30-0.40), homozygous (OR = 0.12, 95%CI = 0.09-0.17), dominant (OR = 0.18, 95%CI = 0.14-0.24), recessive (OR = 0.33, 95%CI = 0.28-0.39), and heterozygous genetic models (OR = 0.26, 95% CI = 0.21-0.33). Similar results were observed in subgroup analysis. This meta-analysis suggests that rs10490924 (Ala69Ser) polymorphism was significantly associated with the susceptibility of AMD in all ethnicities, Ala69 carriers are resistant to AMD risk.