This review focuses on the added value provided by a research strategy applying metabolomics analyses to assess phenotypic flexibility in response to different nutritional challenge tests in the framework of metabolic clinical studies. We discuss findings related to the Oral Glucose Tolerance Test (OGTT) and to mixed meals with varying fat contents and food matrix complexities. Overall, the use of challenge tests combined with metabolomics revealed subtle metabolic dysregulations exacerbated during the postprandial period when comparing healthy and at cardiometabolic risk subjects. In healthy subjects, consistent postprandial metabolic shifts driven by insulin action were reported (e.g., a switch from lipid to glucose oxidation for energy fueling) with similarities between OGTT and mixed meals, especially during the first hours following meal ingestion while differences appeared in a wider timeframe. In populations with expected reduced phenotypic flexibility, often associated with increased cardiometabolic risk, a blunted response on most key postprandial pathways was reported. We also discuss the most suitable statistical tools to analyze the dynamic alterations of the postprandial metabolome while accounting for complexity in study designs and data structure. Overall, the in-depth characterization of the postprandial metabolism and associated phenotypic flexibility appears highly promising for a better understanding of the onset of cardiometabolic diseases.
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http://dx.doi.org/10.3390/nu14030472 | DOI Listing |
J Am Med Inform Assoc
January 2025
Coordinating Center, Observational Health Data Science and Informatics, New York City, NY 10032, United States.
Objective: Propose a framework to empirically evaluate and report validity of findings from observational studies using pre-specified objective diagnostics, increasing trust in real-world evidence (RWE).
Materials And Methods: The framework employs objective diagnostic measures to assess the appropriateness of study designs, analytic assumptions, and threats to validity in generating reliable evidence addressing causal questions. Diagnostic evaluations should be interpreted before the unblinding of study results or, alternatively, only unblind results from analyses that pass pre-specified thresholds.
Arch Public Health
January 2025
Community Medicine, ESIC Medical College & Hospital, K.K. Nagar, Chennai, 600078, India.
Background: In India, approximately 3.5 million children are affected by Developmental Delay (DD), often stemming from preterm births. These delays contribute to neurological and motor development delays, placing a significant financial burden on families.
View Article and Find Full Text PDFBMC Public Health
January 2025
Biomedical Science Programme, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Kuala Lumpur, Malaysia.
Background: After two years of the COVID-19 pandemic, Malaysia began the transition to the endemic phase. students at higher education institutes are among those who were affected by the COVID-19 outbreak and deserve further attention. Hence, this study aimed to assess the knowledge, attitude, and practice (KAP) associated with COVID-19 among public university undergraduate students in Malaysia during the endemic phase.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Infectious Diseases, Nanning Center for Disease Control and Prevention, Nanning, 530023, China.
Introduction: COVID-19 has caused tremendous hardships and challenges around the globe. Due to the prevalence of asymptomatic and pre-symptomatic carriers, relying solely on disease testing to screen for infections is not entirely reliable, which may affect the accuracy of predictions about the pandemic trends. This study is dedicated to developing a predictive model aimed at estimating of the dynamics of COVID-19 at an early stage based on wastewater data, to assist in establishing an effective early warning system for disease control.
View Article and Find Full Text PDFJ Headache Pain
January 2025
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting.
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