Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Confirmatory factor analysis and validation of the Hip Disability and Osteoarthritis Outcome Score for Joint Replacement in Italian patients: a tool for functional assessment in rehabilitation.

Disability and rehabilitation·2026

Same author

Comparison of cervical sagittal alignment in normal subjects and adolescents and young adults with idiopathic scoliosis.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2026

Same author

Reliability, Minimum Detectable Change and Construct Validity of the Functional Rating Index in Italian Patients with Chronic Non-Specific Low Back Pain.

Medicina (Kaunas, Lithuania)·2026

Same author

Obstetric and urogynecological history are associated with lower limb strength and physical performance in middle-age and older women: a community-based cross-sectional study.

BMC women's health·2026

Same author

Indirect estimates of cellular hydration and relative water content and their associations with muscle strength and physical function in older adults: a path analysis from the Pro-Eva study.

European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity·2026

Same author

The Association of Pain With Physical Performance Among Community-Dwelling Older Adults in the PRO-EVA Study.

Journal of the American Medical Directors Association·2026

Search research articles

Related Experiment Video

Updated: Sep 4, 2025

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint

Published on: July 22, 2021

Predicting osteoarthritis in adults using statistical data mining and machine learning.

Carlo M Bertoncelli¹, Paola Altamura², Sikha Bagui³

¹Department of Computer Science, Hal Marcus College of Science and Engineering, University of West Florida, Pensacola, FL 32514, USA.

Therapeutic Advances in Musculoskeletal Disease

|July 21, 2022

Summary

This summary is machine-generated.

Osteoarthritis risk factors in adults aged 20-50 include being female, older, a smoker, higher BMI, high blood pressure, and physical/mental limitations. Deep neural networks best predicted these early-onset OA risks.

Keywords:

arthritis machine learning osteoarthritis statistical data mining

More Related Videos

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Published on: May 6, 2020

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

Published on: March 7, 2025

Related Experiment Videos

Last Updated: Sep 4, 2025

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint

Published on: July 22, 2021

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Published on: May 6, 2020

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

Published on: March 7, 2025

Area of Science:

Rheumatology
Epidemiology
Data Science

Background:

Osteoarthritis (OA) is typically seen in older adults but can affect younger individuals.
Risk factors for OA in adults aged 20-50 require further investigation.

Purpose of the Study:

To develop a predictive model for OA risk factors in adults aged 20-50.
To compare the performance of various machine learning models for OA risk prediction.

Main Methods:

Utilized data from 19,133 participants (aged 20-50) from the National Health and Nutrition Examination Survey.
Employed supervised machine learning models including logistic regression, deep neural networks (DNN), and support vector machines.
Identified demographic and personal characteristics associated with OA.

Main Results:

Key risk factors identified: female sex, older age, smoking, higher body mass index, high blood pressure, race/ethnicity, and physical/mental limitations.
The model achieved a 75% area under the receiver operating characteristic curve for predictive performance.
Deep neural network (DNN) models demonstrated the best predictive performance.

Conclusions:

Female sex, older age, smoking, elevated BMI, hypertension, specific race/ethnicities, and functional limitations are significant risk factors for OA in younger adults.
Machine learning, particularly DNNs, can effectively identify individuals at risk for early-onset OA.