Osteoarthritis endotype discovery via clustering of biochemical marker data.

OBJECTIVES: Osteoarthritis (OA) patient stratification is an important challenge to design tailored treatments and drive drug development. Biochemical markers reflecting joint tissue turnover were measured in the IMI-APPROACH cohort at baseline and analysed using a machine learning approach in order to study OA-dominant phenotypes driven by the endotype-related clusters and discover the driving features and their disease-context meaning. METHOD: Data quality assessment was performed to design appropriate data preprocessing techniques. The k-means clustering algorithm was used to find dominant subgroups of patients based on the biochemical markers data. Classification models were trained to predict cluster membership, and Explainable AI techniques were used to interpret these to reveal the driving factors behind each cluster and identify phenotypes. Statistical analysis was performed to compare differences between clusters with respect to other markers in the IMI-APPROACH cohort and the longitudinal disease progression. RESULTS: Three dominant endotypes were found, associated with three phenotypes: C1) low tissue turnover (low repair and articular cartilage/subchondral bone turnover), C2) structural damage (high bone formation/resorption, cartilage degradation) and C3) systemic inflammation (joint tissue degradation, inflammation, cartilage degradation). The method achieved consistent results in the FNIH/OAI cohort. C1 had the highest proportion of non-progressors. C2 was mostly linked to longitudinal structural progression, and C3 was linked to sustained or progressive pain. CONCLUSIONS: This work supports the existence of differential phenotypes in OA. The biomarker approach could potentially drive stratification for OA clinical trials and contribute to precision medicine strategies for OA progression in the future. TRIAL REGISTRATION NUMBER: NCT03883568.

Endotrophin is a risk marker of complications in CANagliflozin cardioVascular Assessment Study (CANVAS): a randomized controlled trial.

BACKGROUND: Enhanced de-novo collagen type VI (COL VI) formation has been associated with kidney and cardiovascular fibrosis. We hypothesized that endotrophin (ETP), a product specifically generated during collagen type VI formation, may be prognostic for heart failure (HF), cardiovascular death (CVD), kidney endpoints, and all-cause mortality in patients with type 2 diabetes. METHODS: We measured ETP in plasma (P-ETP) and urine (U-ETP) samples collected at baseline and follow-up (year 3) from the randomized controlled trial, CANagliflozin cardioVascular Assessment Study (CANVAS), by use of the PRO-C6 ELISA measuring COL VI formation and ETP. At baseline, plasma and urine samples were available for 3531 and 3423 patients, respectively. At year 3, plasma and urine samples were available for 2178 (61.7%) and 2070 (60.5%) patients, respectively Patients were followed for a median of 6.1 years, and endpoints included: incident HF, CVD, three kidney composite endpoints, and all-cause mortality. Backward selection was used to identify variables to be included in the analyses. Robustness of the association with outcome was assessed by bootstrap analyses. RESULTS: In univariable analysis, P-ETP predicted all investigated outcomes (all p < 0.0001), remained independently associated with all outcomes after adjustment for conventional risk factors (all p < 0.004), and increased C-statistics of the models for the outcomes HF, CVD, HFCVD, all-cause mortality, and kidney composite 2 (ΔC ≥ 0.002). In bootstrap analysis, P-ETP was retained with a frequency ranging from 41.0 to 98.4% for all outcomes. Levels of U-ETP were associated with outcomes in univariable analysis, but associations with most outcomes were lost after adjustment for conventional risk factors. The increase in P-ETP over time was greater with increasing albuminuria stage (p < 0.0001) and was independently associated with the kidney endpoints (p < 0.03). In the placebo arm, the increase in P-ETP was prognostic for all-cause mortality (HR [95% CI]; 1.14 [1.05-1.23], p = 0.003). Whereas levels of P-ETP were not impacted by treatment, levels of U-ETP significantly increased with canagliflozin treatment. CONCLUSIONS: P-ETP generated during COL VI formation predicts cardiovascular, kidney and mortality outcomes in patients with type 2 diabetes. As ETP identifies patients at increased risk of experiencing relevant outcomes, it may be used for patient enrichment in future clinical trials. Trial Registry Number (ClinicalTrials.gov Identifier): NCT01032629.

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.

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.

A serological type II collagen neoepitope biomarker reflects cartilage breakdown in patients with osteoarthritis.

Objectives: There is an unmet medical need for biomarkers in OA which can be applied in clinical drug development trials. The present study describes the development of a specific and robust assay measuring type II collagen degradation (T2CM) and discusses its potential as a noninvasive translational biomarker. Methods: A type II collagen specific neoepitope (T2CM) was identified by mass spectrometry and monoclonal antibodies were raised towards the epitope, employed in a chemiluminescence immunoassay. T2CM was assessed in bovine cartilage explants with or without MMP-13 inhibitor, and explant supernatants were analyzed by Western blot. T2CM was measured in plasma samples from one study (n â€‹= â€‹48 patients) where OA patients were referred to total knee replacement (TKR). Additionally, T2CM was quantified in serum from OA patients receiving salmon calcitonin treatment (sCT) (n â€‹= â€‹50) compared to placebo (n â€‹= â€‹57). Results: The T2CM assay was technically robust (13/4 â€‹% inter/intra-variation) and specific for the type II collagen fragment cleaved by MMP-1 and -13. The MMP-13 inhibitor reduced the T2CM release from bovine cartilage explants receiving catabolic treatment. These results were confirmed by Western blot. In human end-stage OA patients (scheduled for TKR), the T2CM levels were elevated compared to moderate OA (p<0.004). The OA patients receiving sCT had lower levels of T2CM compared to placebo group after 1, 6, and 24 months of treatment (p â€‹= â€‹0.0285, p â€‹= â€‹0.0484, p â€‹= â€‹0.0035). Conclusions: To our knowledge, T2CM is the first technically robust serological biomarker assay which has shown biological relevance in ex vivo models and OA cohorts. This suggests that T2CM may have potential as a translational biomarker for cartilage degradation.

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.

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.

Cancer risk in relation to body fat distribution, evaluated by DXA-scans, in postmenopausal women - the Prospective Epidemiological Risk Factor (PERF) study.

Studies with direct measures of body fat distribution are required to explore the association between central and general obesity to cancer risk in postmenopausal women. This study investigates the association between central obesity and general obesity to overall/site-specific cancer risk in postmenopausal women. The analysis included 4,679 Danish postmenopausal women. Body fat distribution was evaluated by whole-body dual-energy X-ray absorptiometry scanners. Cancer diagnoses were extracted from the Danish Cancer Registry and multivariable Cox regression models explored the association between cancer risk and central obesity after adjusting for BMI. Our results showed that high central obese women had a 50% increased risk of overall cancer relative to low central obese women (Q1vs.Q4: [HR:1.50, CI:1.20-1.88]). For site-specific cancers, central obesity was significantly associated with Respiratory (Q1vs.Q4: [HR:2.01, CI:1.17-3.47]), Gastrointestinal (Q1vs.Q4: [HR:1.55, CI:0.99-2.41]) and Female genital organs (Q1vs.Q4: [HR:1.95, CI:1.00-3.78]) cancer diagnoses. Sub-analyses stratified by smoking-habits found a significant association between central obesity and a cancer diagnosis for current (Q1vs.Q4: [HR:1.93, CI:1.25-2.99]) and former smokers (Q1vs.Q4: [HR:1.90, CI:1.23-2.94]). These analyses suggest that central obesity is associated with some cancers in postmenopausal women independent of BMI.

5-tert-Butyl-2-(4'-[18F]fluoropropynylphenyl)-1,3-dithiane oxides: potential new GABA A receptor radioligands.

As potential new ligands targeting the binding site of gamma-aminobutyric acid (GABA) receptor ionophore, trans-5-tert-butyl-2-(4'-fluoropropynylphenyl)-2-methyl-1,1-dioxo-1,3-dithiane (1) and cis/trans-5-tert-butyl-2-(4'-fluoropropynylphenyl)-2-methyl-1,1,3,3-tetroxo-1,3-dithiane (2) were selected for radiolabeling and initial evaluation as in vivo imaging agents for positron emission tomography (PET). Both compounds exhibited identical high in vitro binding affinities (K(i)=6.5 nM). Appropriate tosylate-substituted ethynyl precursors were prepared by multistep syntheses involving stepwise sulfur oxidation and chromatographic isolation of desired trans isomers. Radiolabeling was accomplished in one step using nucleophilic [(18)F]fluorination. In vivo biodistribution studies with trans-[(18)F]1 and trans-[(18)F]2 showed significant initial uptake into mouse brain and gradual washout, with heterogeneous regional brain distributions and higher retention in the cerebral cortex and cerebellum and lower retention in the striatum and pons-medulla. These regional distributions of the new radioligands correlated with in vitro and ex vivo measures of standard radioligands binding to the ionophore- and benzodiazepine-binding sites of GABA(A) receptor in rodent brain. A comparison of these results with previously prepared radiotracers for other neurochemical targets, including successes and failures as in vivo radioligands, suggests that higher-affinity compounds with increased retention in target brain tissues will likely be needed before a successful radiopharmaceutical for human PET imaging can be identified.

Behavioral depression and positron emission tomography-determined serotonin 1A receptor binding potential in cynomolgus monkeys.

CONTEXT: Current animal models of depression are inadequate to further our understanding of depression. New models that allow for analysis of cognitive function and sex differences are needed. OBJECTIVE: To characterize serotonin 1A (5-HT(1A)) receptor binding potential (BP) and its relationship with specific characteristics of behavioral depression in cynomolgus monkeys. DESIGN: A 23-month case-control study. SETTING: Small social groups in the laboratory. Subjects Seventeen adult female cynomolgus monkeys. MAIN OUTCOME MEASURES: Serotonin 1A receptor BP was examined by positron emission tomography using the radioligand 4,2"-(methoxyphenyl)-1-[2"-(N-2"-pyridinyl)-p-fluorobenzamido]ethylpiperazine in the raphe, amygdala, hippocampus, and anterior cingulate cortex in monkeys characterized by behavioral observation as depressed or not depressed. Aggression, submission, affiliation, pathologic behaviors, and activity levels were determined by behavioral observation. Heart rate and hypothalamic-pituitary-adrenal function were also determined. RESULTS: Throughout the brain areas examined, there was a reduction in 5-HT(1A) BP in depressed monkeys. The 5-HT(1A) BP in the amygdala and hippocampus was associated with aggression and submission. Friendly interaction, grooming, and locomotion were associated with 5-HT(1A) BP in the left cingulate cortex, whereas attention directed toward the environment was associated with 5-HT(1A) BP in the right cingulate cortex. The 5-HT(1A) receptor BP was inversely associated with heart rate in the raphe, left cingulate, and right amygdala. CONCLUSIONS: This is the fourth in a series of studies that suggest that depressive behavior in adult female cynomolgus monkeys is similar to that observed in humans. It has been observed in 2 large groups of monkeys randomly selected from feral populations, suggesting that the capacity for depression is inherent in the species. This animal model holds promise to further our understanding of the basic mechanisms of affective behavior, the neuropathophysiologic characteristics of depression and the cognitive dysfunction that accompanies them, genetic and environmental factors that may affect depression risk, and the role of reproductive function in the excess depression risk in women.