Feline Hypertrophic Cardiomyopathy: A Spontaneous Large Animal Model of Human HCM.

Hypertrophic cardiomyopathy (HCM) is a common disease in pet cats, affecting 10-15% of the pet cat population. The similarity to human HCM, the rapid progression of disease, and the defined and readily determined endpoints of feline HCM make it an excellent natural model that is genotypically and phenotypically similar to human HCM. The Maine Coon and Ragdoll cats are particularly valuable models of HCM because of myosin binding protein-C mutations and even higher disease incidence compared to the overall feline population. The cat overcomes many of the limitations of rodent HCM models, and can provide enhanced translation of information from in vitro and induced small animal models to human clinical trials. Physicians and veterinarians working together in a collaborative and interdisciplinary approach can accelerate the discovery of more effective treatments for this and other cardiovascular diseases affecting human and veterinary patients

Validation and preliminary data from a health-related quality of life questionnaire for owners of dogs with cardiac disease

BACKGROUND: Cardiac disease in dogs impacts the quality of life (QoL) of their owners, but owners\u27 QoL has not been comprehensively assessed in this population. OBJECTIVES: To develop, validate, and provide preliminary data from a health-related QoL (hrQoL) questionnaire for owners of dogs with cardiac disease. SUBJECTS: A total of 141 owners of dogs with cardiac disease were studied. METHODS: An owner hrQoL (O-hrQoL) questionnaire containing 20 items related to areas of a person\u27s life that could be impacted by caring for a dog with cardiac disease was developed and administered to owners of dogs with cardiac disease. The highest possible total score was 100, with higher scores indicating a worse hrQoL. Readability, internal consistency, face and construct validity, and item-total correlations were assessed. RESULTS: Median O-hrQoL score was 35 (range, 0-87). The questionnaire had good internal consistency (Cronbach\u27s alpha = 0.933), construct validity (Spearman\u27s r = 0.38-0.53; Kendall\u27s tau = 0.30-0.43; P \u3c .001), and item-total correlation (Spearman\u27s r = 0.44-0.79; Kendall\u27s tau = 0.34-0.66; all P \u3c .001). Fifty percent of owners indicated a negative effect of dogs\u27 cardiac disease on their own QoL, but all owners responded that caring for their dogs either had strengthened (n = 76; 53.9%) or had no effect on their relationship with their dog (n = 65; 46.1%). CONCLUSIONS AND CLINICAL IMPORTANCE: The O-hrQoL questionnaire had good validity, and results suggest that owners\u27 QoL is significantly impacted by caring for dogs with cardiac disease. Additional research on effective approaches to minimizing the negative effects of a dog\u27s cardiac disease on the owner is warranted

Gut Microbiome-Linked Metabolites in the Pathobiology of Major Depression With or Without Anxiety—A Role for Bile Acids

BackgroundThe gut microbiome may play a role in the pathogenesis of neuropsychiatric diseases including major depressive disorder (MDD). Bile acids (BAs) are steroid acids that are synthesized in the liver from cholesterol and further processed by gut-bacterial enzymes, thus requiring both human and gut microbiome enzymatic processes in their metabolism. BAs participate in a range of important host functions such as lipid transport and metabolism, cellular signaling and regulation of energy homeostasis. BAs have recently been implicated in the pathophysiology of Alzheimer's and several other neuropsychiatric diseases, but the biochemical underpinnings of these gut microbiome-linked metabolites in the pathophysiology of depression and anxiety remains largely unknown.MethodUsing targeted metabolomics, we profiled primary and secondary BAs in the baseline serum samples of 208 untreated outpatients with MDD. We assessed the relationship of BA concentrations and the severity of depressive and anxiety symptoms as defined by the 17-item Hamilton Depression Rating Scale (HRSD17) and the 14-item Hamilton Anxiety Rating Scale (HRSA-Total), respectively. We also evaluated whether the baseline metabolic profile of BA informs about treatment outcomes.ResultsThe concentration of the primary BA chenodeoxycholic acid (CDCA) was significantly lower at baseline in both severely depressed (log2 fold difference (LFD) = −0.48; p = 0.021) and highly anxious (LFD = −0.43; p = 0.021) participants compared to participants with less severe symptoms. The gut bacteria-derived secondary BAs produced from CDCA such as lithocholic acid (LCA) and several of its metabolites, and their ratios to primary BAs, were significantly higher in the more anxious participants (LFD's range = [0.23, 1.36]; p's range = [6.85E-6, 1.86E-2]). The interaction analysis of HRSD17 and HRSA-Total suggested that the BA concentration differences were more strongly correlated to the symptoms of anxiety than depression. Significant differences in baseline CDCA (LFD = −0.87, p = 0.0009), isoLCA (LFD = −1.08, p = 0.016) and several BA ratios (LFD's range [0.46, 1.66], p's range [0.0003, 0.049]) differentiated treatment failures from remitters.ConclusionIn patients with MDD, BA profiles representing changes in gut microbiome compositions are associated with higher levels of anxiety and increased probability of first-line treatment failure. If confirmed, these findings suggest the possibility of developing gut microbiome-directed therapies for MDD characterized by gut dysbiosis

Temporal Multi-Step Predictive Modeling of Remission in Major Depressive Disorder Using Early Stage Treatment Data; Star*d Based Machine Learning Approach

BACKGROUND: Artificial intelligence is currently being used to facilitate early disease detection, better understand disease progression, optimize medication/treatment dosages, and uncover promising novel treatments and potential outcomes. METHODS: Utilizing the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, we built a machine learning model to predict depression remission rates using same clinical data as features for each of the first three antidepressant treatment steps in STAR*D. We only used early treatment data (baseline and first follow up) in each STAR*D step to temporally analyze predictive features of remission at the end of the step. RESULTS: Our model showed significant prediction performance across the three treatment steps, At step 1, Model accuracy was 66 %; sensitivity-65 %, specificity-67 %, positive predictive value (PPV)-65.5 %, and negative predictive value (NPV)-66.6 %. At step 2, model accuracy was 71.3 %, sensitivity-74.3 %, specificity-69 %, PPV-64.5 %, and NPV-77.9 %. At step 3, accuracy reached 84.6 %; sensitivity-69 %, specificity-88.8 %, PPV-67 %, and NPV-91.1 %. Across all three steps, the early Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR) scores were key elements in predicting the final treatment outcome. The model also identified key sociodemographic factors that predicted treatment remission at different steps. LIMITATIONS: The retrospective design, lack of replication in an independent dataset, and the use of a complete case analysis model in our analysis. CONCLUSIONS: This proof-of-concept study showed that using early treatment data, multi-step temporal prediction of depressive symptom remission results in clinically useful accuracy rates. Whether these predictive models are generalizable deserves further study

Temporal Multi-Step Predictive Modeling of Remission in Major Depressive Disorder Using Early Stage Treatment Data; Star*D Based Machine Learning Approach

BACKGROUND: Artificial intelligence is currently being used to facilitate early disease detection, better understand disease progression, optimize medication/treatment dosages, and uncover promising novel treatments and potential outcomes. METHODS: Utilizing the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, we built a machine learning model to predict depression remission rates using same clinical data as features for each of the first three antidepressant treatment steps in STAR*D. We only used early treatment data (baseline and first follow up) in each STAR*D step to temporally analyze predictive features of remission at the end of the step. RESULTS: Our model showed significant prediction performance across the three treatment steps, At step 1, Model accuracy was 66 %; sensitivity-65 %, specificity-67 %, positive predictive value (PPV)-65.5 %, and negative predictive value (NPV)-66.6 %. At step 2, model accuracy was 71.3 %, sensitivity-74.3 %, specificity-69 %, PPV-64.5 %, and NPV-77.9 %. At step 3, accuracy reached 84.6 %; sensitivity-69 %, specificity-88.8 %, PPV-67 %, and NPV-91.1 %. Across all three steps, the early Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR) scores were key elements in predicting the final treatment outcome. The model also identified key sociodemographic factors that predicted treatment remission at different steps. LIMITATIONS: The retrospective design, lack of replication in an independent dataset, and the use of a complete case analysis model in our analysis. CONCLUSIONS: This proof-of-concept study showed that using early treatment data, multi-step temporal prediction of depressive symptom remission results in clinically useful accuracy rates. Whether these predictive models are generalizable deserves further study