INTERPLAY OF TECHNOLOGY AND APPLICATION OF GERONTOLOGIC BIOSTATISTICS AND DATA SCIENCE FOR CAREER IN AGING - Vol. 9 - S2

Sep 29, 2026

Gerontologic biostatistics, updated to include data science, is presented as a set of analytical methods designed to address complex research data in studies of older adults. Advances over the past 15 years are linked to emerging technologies and new data modalities. The work explains three technologies—16S rRNA sequencing, accelerometer-based physical activity measurement, and MRI multimodal neuroimaging—and illustrates how GBDS methods analyze microbiome, activity-related muscle function, and brain connectome differences across major aging cohorts.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages2

Reading Time5 min

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Abstract

Technologies and data modalities

GBDS applications in aging cohorts

Innovation in Aging, 2025, Vol. 9, No. S2 405 Abstract citation ID: igaf122.1380 INTERPLAY OF TECHNOLOGY AND APPLICATION OF GERONTOLOGIC BIOSTATISTICS AND DATA SCIENCE FOR CAREER IN AGING Michelle Shardell1, Joanna Lynn Borgogna2, Fatemeh Adelnia3, Chixiang Chen1, Shuo Chen1, and Jennifer Schrack4, 1. University of Maryland School of Medicine, Baltimore, Maryland, United States, 2. Montana State University, Bozeman, Montana, United States, 3. Vanderbilt University Medical Center, Nashville, Tennessee, United States, 4. Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States The term ‘gerontologic biostatistics’ was introduced in 2010 and recently updated to include data science to describe a 406 Innovation in Aging, 2025, Vol. 9, No. S2 collection of analytical methods that addresses the challenging features of research data in studies of older adults. Many of the advances in gerontologic biosta∼ tistics and data science (GBDS) that have occurred in the past 15 years were motivated by new data modalities generated from recently developed technologies. As a result, the ability of early career scientists to leverage these data for innovative research depends on their understanding of the underlying technologies that produced these data. Herein, we will describe three such technologies, the data they generate, and examples of studies conducted by analyzing these data using GBDS methods. Specifically, we describe 16S rRNA amplicon sequencing technology that characterizes relative abundance of microbial taxa, accelerometers that provide objective measurements of physical activity, and magnetic resonance imaging-based multimodal neuroimaging that measures both brain structure and function. Specific examples include longitudinal cervicovaginal microbiota and reproductive aging among 750 women enrolled in the Human Papillomavirus in Perimenopause study, the role of physical activity in maintaining muscle oxidative capacity among 384 adults enrolled in the Baltimore Longitudinal Study of Aging, and age-related differences in whole-brain connectome networks among 39,675 adults enrolled in UK Biobank and 499 adults enrolled in the Lifespan Human Connectome Project in Aging study. These research studies exemplify advantages for scientists—at any career stage—to understand technological developments, the data they generate, and the application of GBDS for aging research.

FAQs

What does the updated term gerontologic biostatistics include?

It was introduced in 2010 and recently updated to include data science, describing analytical methods for challenging research data in older-adult studies.

Which three technologies are described as generating relevant data?

The text highlights 16S rRNA amplicon sequencing, accelerometers for objective physical activity, and magnetic resonance imaging-based multimodal neuroimaging.

How are the generated data used in aging research studies?

The studies analyze longitudinal cervicovaginal microbiota and reproductive aging, the role of physical activity in muscle oxidative capacity, and age-related connectome network differences across large cohorts.

Sep 29, 2026

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