# Data-driven approaches in defining knee osteoarthritis phenotypes and factors  associated with fast progression

> **NIH NIH K01** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $130,950

## Abstract

Osteoarthritis (OA) affects 14 million individuals in the US and over 300 million adults worldwide. The
disease is characterized by joint pain and functional limitations and is associated with poor health-
related quality of life and increased healthcare utilization. OA of the hips and knees ranks as the 11th
highest contributor to global disability. Despite the clinical and economic impact of knee OA, no
disease-modifying agents are currently available; current treatments are limited to symptom control and
are only modestly efficacious. While several promising treatments are in the pipeline, developing and
testing treatments for OA is complicated by disease heterogeneity. We urgently need to identify the
right patient for the right treatment to ensure that new therapies are being tested on the appropriate
population. This proposal aims to use machine learning methods to address gaps in our understanding
of disease heterogeneity in knee OA. We will use publicly available data from the FNIH OA Biomarkers
Consortium project. This study of 600 subjects with knee OA includes over 200 parameters that
describe the joint structure and disease severity, including measures of cartilage, bone, ligaments,
menisci, and inflammation. An unsupervised learning approach using model-based clustering will be
used to distinguish disease phenotypes. To implement phenotyping in practice a minimal set of
biomarkers must be identified that meets the challenges of both predictive accuracy and feasibility.
Thus the second aim will investigate variable selection methods in model-based clustering in order to
identify important variables and develop a prediction model to determine phenotype. Finally, a
supervised machine learning approach via super learning will investigate algorithms to predict disease
progression. The applicant, Dr. Jamie Collins, is a biostatistician at the Orthopedic and Arthritis Center
for Outcomes Research at Brigham and Women’s Hospital. Dr. Collins is a committed investigator in
rheumatology research with eight first author publications in the field. She holds a career development
award from the Rheumatology Research Foundation and pilot funding from the Brigham Research
Institute. This proposal will provide protected time and rigorous training so that the applicant can
expand her current biostatistical skill set to encompass the burgeoning fields of data science and
machine learning. She will take coursework at the Harvard TH Chan School of Public Health and will
have access to courses, seminars, and training provided by the Brigham Research Institute and the
Harvard Catalyst Program. The applicant will be supported by mentorship from Drs. Elena Losina and
Tuhina Neogi, and input from the advisory committee of Drs. Tianxi Cai, Jeffrey Duryea, Ali Guermazi,
Tina Kapur, Virginia Kraus, Katherine Liao, and Kurt Spindler. The research and training proposed in
this award will address critical research gaps in our understanding of OA heterogeneity and
p...

## Key facts

- **NIH application ID:** 9976687
- **Project number:** 1K01AR075879-01A1
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Jamie E. Collins
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $130,950
- **Award type:** 1
- **Project period:** 2020-07-02 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9976687

## Citation

> US National Institutes of Health, RePORTER application 9976687, Data-driven approaches in defining knee osteoarthritis phenotypes and factors  associated with fast progression (1K01AR075879-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9976687. Licensed CC0.

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