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Comorbidity and its association with age are of great interest in geroscience. However, there are few model organisms that are well-suited to study comorbidities that will have high relevance to humans. In this light, we turn our attention to the companion dog. The companion dog shares many morbidities with humans. Thus, a better understanding of canine comorbidity relationships could benefit both humans and dogs. We present an analysis of canine comorbidity networks from the Dog Aging Project, a large epidemiological cohort study of companion dogs in the United States. We included owner-reported health conditions that occurred in at least 60 dogs (n=166) and included only dogs that had at least one of those health conditions (n=26,523). We constructed an undirected comorbidity network using a Poisson binomial test, adjusting for age, sex, sterilization status, breed background (i.e., purebred vs. mixed-breed), and weight. The comorbidity network reveals well-documented comorbidities, such as diabetes with blindness and hypertension with chronic kidney disease. In addition, this network also supports less well-studied comorbidity relationships, such as proteinuria with anemia. A directed comorbidity network accounting for time of reported condition onset suggests that diabetes occurs before cataracts, which is consistent with the canine literature. Analysis of age-stratified networks reveals that global centrality measures increase with age and are the highest in the Senior group compared to the Young Adult and Mature Adult groups. Our results suggest that comorbidity network analysis is a promising method to enhance clinical knowledge and canine healthcare management.
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http://dx.doi.org/10.1101/2024.12.18.629088 | DOI Listing |
Drugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
View Article and Find Full Text PDFFront Hum Neurosci
August 2025
Department of Neurosurgery, Affiliated Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Background: Slapping automatism is a type of automatism observed during epileptic seizures, but its underlying electrophysiological mechanisms remain poorly understood. Stereo-electroencephalography (SEEG) provides a unique opportunity to investigate the associated cortical areas with epileptiform discharges during the slapping automatism.
Case Report: We report five cases of drug-resistant epilepsy in which SEEG recordings captured slapping automatism.
Front Oncol
August 2025
Department of Pathology, Institute of Clinical Pathology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Despite the generally favorable prognosis of differentiated thyroid carcinoma (DTC) following surgery and radioactive iodine (RAI) therapy, approximately 10% of cases eventually develop resistance to RAI. This condition, known as radioiodine-refractory differentiated thyroid carcinoma (RAIR-DTC), is associated with a poor prognosis, with a 10-year survival rate of only 10% from the time of metastasis detection. The limited availability of safe and effective alternative treatments poses a significant challenge to clinical management.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
September 2025
Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Objective: This study aimed to investigate comorbidity patterns and potential pathogenic mechanisms in patients with Hashimoto's thyroiditis (HT).
Methods: Patients with HT who visited the outpatient clinic of the Thyroid Department at Dongzhimen Hospital, Beijing University of Chinese Medicine, between June 2021 and December 2024 were included. Association rule analysis and logistic regression analysis were performed using SPSS 25.
J Popul Ther Clin Pharmacol
September 2024
Department of Biology, Howard University, Washington DC 20059, USA.
Developmental Dyslexia (DD) and Attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental disorders that often coexist and share complex genetic underpinnings. Our case study integrates psychological assessments and whole exome sequencing to explore the genetic basis of DD and ADHD co-occurrence in a single proband (a nine-year-old female born to healthy) from a consanguineous Pakistani family. We present a proband with symptoms of impulsivity, inattention, and severe hyperactive behavior, along with speech impairment and moderate learning disabilities.
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