Citrullinated and MMP-degraded vimentin is associated with chronic pulmonary diseases and genetic variants in PADI3/PADI4 and CFH in postmenopausal women.

Citrullinated vimentin has been linked to several chronic and autoimmune diseases, but how citrullinated vimentin is associated with disease prevalence and genetic variants in a clinical setting remains unknown. The aim of this study was to obtain a better understanding of the genetic variants and pathologies associated with citrullinated and MMP-degraded vimentin. Patient Registry data, serum samples and genotypes were collected for a total of 4369 Danish post-menopausal women enrolled in the Prospective Epidemiologic and Risk Factor study (PERF). Circulating citrullinated and MMP-degraded vimentin (VICM) was measured. Genome-wide association studies (GWAS) and phenome wide association studies (PheWAS) with levels of VICM were performed. High levels of VICM were significantly associated with the prevalence of chronic pulmonary diseases and death from respiratory and cardiovascular diseases (CVD). GWAS identified 33 single nucleotide polymorphisms (SNPs) with a significant association with VICM. These variants were in the peptidylarginine deiminase 3/4 (PADI3/PADI4) and Complement Factor H (CFH)/KCNT2 gene loci on chromosome 1. Serum levels of VICM, a marker of citrullinated and MMP-degraded vimentin, were associated with chronic pulmonary diseases and genetic variance in PADI3/PADI4 and CFH/ KCNT2. This points to the potential for VICM to be used as an activity marker of both citrullination and inflammation, identifying responders to targeted treatment and patients likely to experience disease progression.

Development of LuxIA, a Cloud-Based AI Diabetic Retinopathy Screening Tool Using a Single Color Fundus Image.

Purpose: Diabetic retinopathy (DR) is the leading cause of vision impairment in working-age adults. Automated screening can increase DR detection at early stages at relatively low costs. We developed and evaluated a cloud-based screening tool that uses artificial intelligence (AI), the LuxIA algorithm, to detect DR from a single fundus image. Methods: Color fundus images that were previously graded by expert readers were collected from the Canarian Health Service (Retisalud) and used to train LuxIA, a deep-learning-based algorithm for the detection of more than mild DR. The algorithm was deployed in the Discovery cloud platform to evaluate each test set. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were computed using a bootstrapping method to evaluate the algorithm performance and compared through different publicly available datasets. A usability test was performed to assess the integration into a clinical tool. Results: Three separate datasets, Messidor-2, APTOS, and a holdout set from Retisalud were evaluated. Mean sensitivity and specificity with 95% confidence intervals (CIs) reached for these three datasets were 0.901 (0.901-0.902) and 0.955 (0.955-0.956), 0.995 (0.995-0.995) and 0.821 (0.821-0.823), and 0.911 (0.907-0.912) and 0.880 (0.879-0.880), respectively. The usability test confirmed the successful integration of LuxIA into Discovery. Conclusions: Clinical data were used to train the deep-learning-based algorithm LuxIA to an expert-level performance. The whole process (image uploading and analysis) was integrated into the cloud-based platform Discovery, allowing more patients to have access to expert-level screening tools. Translational Relevance: Using the cloud-based LuxIA tool as part of a screening program may give diabetic patients greater access to specialist-level decisions, without the need for consultation.

Matrix metalloproteinase-degraded type I collagen is associated with APOE/TOMM40 variants and preclinical dementia.

OBJECTIVE: Dysregulation of type I collagen metabolism has a great impact on human health. We have previously seen that matrix metalloproteinase-degraded type I collagen (C1M) is associated with early death and age-related pathologies. To dissect the biological impact of type I collagen dysregulation, we have performed a genome-wide screening of the genetic factors related to type I collagen turnover. METHODS: Patient registry data and genotypes have been collected for a total of 4,981 Danish postmenopausal women. Genome-wide association with serum levels of C1M was assessed and phenotype-genotype association analysis performed. RESULTS: Twenty-two genome-wide significant variants associated with C1M were identified in the APOE-C1/TOMM40 gene cluster. The APOE-C1/TOMM40 gene cluster is associated with hyperlipidemia and cognitive disorders, and we further found that C1M levels correlated with tau degradation markers and were decreased in women with preclinical cognitive impairment. CONCLUSIONS: Our study provides elements for better understanding the role of the collagen metabolism in the onset of cognitive impairment.

Atrial fibrillation is associated with lean body mass in postmenopausal women.

This study investigated the association between body composition and risk of atrial fibrillation (AF) in postmenopausal women. In a retrospective analysis we assessed data from 5704 postmenopausal women (age 70.7 ± 6.5 yrs.) who in 1999-2001 participated in The Prospective Epidemiological Risk Factor study with body composition assessed by dual-energy X-ray absorptiometry. Outcomes were obtained from Danish Health Registries and body composition association to risk of AF was evaluated by univariable and multivariable Cox Hazard regression. 850 women developed AF after baseline. High lean body mass was associated with increased risk of AF in multivariable analyses, adjusting for body mass index (BMI), height or weight (adjusted for: BMI, hazard ratio (HR) 1.49, 95% Confidence Interval (1.22-1.80); height, HR 1.27 (1.03-1.56); weight, 1.33 (1.06-1.65)). Height and weight were associated with increased risk of AF in multivariable analyses adjusting for body composition measures. When adjusting for total lean mass, only height remained statistically significant (HR 1.34 (1.09-1.64)). In a cohort of elderly Caucasian women, high lean body mass, height and weight were associated with increased risk of AF and the variables remained significant after adjusting for age and other known risk factors of AF.