Publications by authors named "A G Mariam"
Article Synopsis
- - Longitudinal electronic health records (EHR) are being analyzed using advanced unsupervised machine learning algorithms to uncover patterns in disease development and progression, particularly in real-world clinical settings.
- - A study tested 30 state-of-the-art algorithms on simulated clinical data, finding that dynamic time warping techniques performed best in accurately classifying clinical datasets, particularly those with varying blood pressure measurements.
- - The most effective algorithm was applied to cluster a large dataset of pediatric metabolic syndrome cases, revealing distinct patterns of childhood BMI, among which a consistently high BMI cluster showed the highest risk for metabolic syndrome, aligning with existing research findings.
Article Synopsis
- The study examines how reducing glycated hemoglobin (HbA1c) and body weight affects health outcomes in patients with type 2 diabetes (T2D) who are using different antidiabetic medications, including GLP-1 receptor agonists.
- Researchers analyzed health records from a large patient sample at the Cleveland Clinic over a 20-year period to assess the impact of weight and HbA1c changes on conditions like heart failure, hypertension, and kidney disease.
- Findings indicate that even small reductions in weight and HbA1c levels can significantly lower the risk of various health complications in T2D patients, highlighting the importance of these factors in diabetes management.*
The incidence of early-onset colorectal cancer (eoCRC) is rising, and its pathogenesis is not completely understood. We hypothesized that machine learning utilizing paired tissue microbiome and plasma metabolome features could uncover distinct host-microbiome associations between eoCRC and average-onset CRC (aoCRC). Individuals with stages I-IV CRC (n = 64) were categorized as eoCRC (age ≤ 50, n = 20) or aoCRC (age ≥ 60, n = 44).
View Article and Find Full Text PDFIntroduction: We previously identified a genetic subtype (C4) of type 2 diabetes (T2D), benefitting from intensive glycemia treatment in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Here, we characterized the population of patients that met the C4 criteria in the UKBiobank cohort.
Research Design And Methods: Using our polygenic score (PS), we identified C4 individuals in the UKBiobank and tested C4 status with risk of developing T2D, cardiovascular disease (CVD) outcomes, and differences in T2D medications.
Objectives: Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications.
Materials And Methods: No clinically available tools are currently in widespread use that can predict the onset of metabolic diseases in pediatric patients.