# Image Analysis and Machine Learning Methods for Biomarkers of Age-related and Metabolic Diseases

> **NIH NIH SC3** · DELAWARE STATE UNIVERSITY · 2022 · $109,488

## Abstract

Program Director/Principal Investigator (Last, First, Middle): Makrogiannis, Sokratis, Ph.D.
Abstract
Age-related and metabolic diseases such as type-2 diabetes, cardiovascular diseases, and sarcopenia have
become a worldwide epidemic that affects the quality of life of millions. To give a global perspective, roughly
343.8 million people in the world have type-2 diabetes today, and 175 million do not know they have diabetes at
all. Metabolic diseases, such as diabetes and osteoporosis, are strongly linked to longitudinal changes in body
composition, morphology and function.
 Modern medical imaging technologies offer the opportunity to study the composition and morphometry of
human body in ways that were previously impossible. Contemporary imaging studies that are performed in vivo
on a large number of participants have enabled cross-sectional and longitudinal studies of age-related and
metabolic diseases, and effects of pharmacological interventions. The emergence of advanced imaging
technologies has also created the need for automated image analysis techniques for identification and
quantification of morphological patterns of anatomies and tissues and their changes with increasing age.
 This project will contribute novel and non-invasive medical image analysis techniques for studying the human
body composition to achieve timely prognosis of these pathologies. Our research interests will concentrate on
identification of morphological patterns in the mid-thigh, abdomen and lower leg that will eventually lead to
development of imaging biomarkers. The accumulation of adipose tissue in the human body and changes of its
regional distribution are associated with type-2 diabetes, cardiovascular diseases and the metabolic syndrome.
Age-related changes in skeletal muscle composition are strongly linked to loss in muscle strength and mass,
frequently termed as sarcopenia, leading to decreased mobility and function. Also, trabecular bone structural
changes are associated with osteoporosis. We will use imaging and clinical data collected by the Baltimore
Longitudinal Study of Aging (BLSA) that is the longest ongoing epidemiology study in the US.
 This work will address a technical and a clinical hypothesis. The technical hypothesis is that quantitative
image analysis can accurately and robustly segment, register and fuse body composition data acquired by
modern MRI and CT imaging scanners. The clinical hypothesis is that qualitative body composition phenotypes
on clinical imaging can be used as biomarkers for prognosis and diagnosis of the metabolic syndrome
manifestations. We will build on recent advances in medical image analysis to contribute novel and non-invasive
techniques for studying the human body composition and its longitudinal changes with main applications in tissue
identification and quantification at the mid-thigh, lower leg and the abdomen (aim 1). Then we will develop
statistical machine learning methods to achieve timely diagnosis and progn...

## Key facts

- **NIH application ID:** 10465018
- **Project number:** 5SC3GM113754-06
- **Recipient organization:** DELAWARE STATE UNIVERSITY
- **Principal Investigator:** Sokratis Makrogiannis
- **Activity code:** SC3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $109,488
- **Award type:** 5
- **Project period:** 2015-04-02 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10465018

## Citation

> US National Institutes of Health, RePORTER application 10465018, Image Analysis and Machine Learning Methods for Biomarkers of Age-related and Metabolic Diseases (5SC3GM113754-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10465018. Licensed CC0.

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