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Related Concept Videos

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Related Experiment Video

Updated: Aug 4, 2025

Author Spotlight: Implementation of BIVA for Analyzing Disease Risk Factors in Patients with Low Body Cell Mass
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Subtyping Patients With Chronic Disease Using Longitudinal BMI Patterns.

Md Mozaharul Mottalib, Jessica C Jones-Smith, Bethany Sheridan

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    Summary
    This summary is machine-generated.

    This study reveals distinct patient subgroups based on body mass index (BMI) trajectories, enhancing chronic disease risk prediction. Machine learning identified novel BMI patterns linked to diseases like diabetes and Alzheimer's.

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    Area of Science:

    • Biomedical Informatics
    • Public Health
    • Machine Learning in Healthcare

    Background:

    • Obesity is a significant risk factor for major chronic diseases, including diabetes, cancer, and stroke.
    • While cross-sectional Body Mass Index (BMI) is studied, BMI trajectories' role in disease risk remains underexplored.

    Purpose of the Study:

    • To utilize machine learning to subtype chronic disease risk based on BMI trajectories.
    • To identify distinct patient subgroups using BMI trajectory data from a large electronic health record (EHR) dataset.

    Main Methods:

    • Employed a machine learning approach on EHR data from approximately two million individuals over six years.
    • Defined nine novel variables from BMI trajectories for k-means clustering.
    • Analyzed cluster characteristics (demographic, socioeconomic, physiological) to define patient subgroups.

    Main Results:

    • Re-established the link between obesity and chronic conditions such as diabetes, hypertension, Alzheimer's, and dementia.
    • Identified distinct patient clusters with specific characteristics associated with various chronic diseases.
    • Findings align with and complement existing knowledge on obesity and chronic disease relationships.

    Conclusions:

    • BMI trajectories offer a nuanced approach to understanding obesity's role in chronic disease risk.
    • Machine learning-driven subtyping can refine personalized risk prediction for conditions like diabetes and neurodegenerative diseases.
    • This study highlights the value of longitudinal BMI data in public health research and clinical practice.