Biology subjects

Glynn, N. W.

Publications and source records attributed to Glynn, N. W..

4 recordsLinked to original sources

bioRxiv2026

MicroRNA regulatory targets of VO2peak in older adult participants of the Study of Muscle Mobility and Aging (SOMMA)

Skeletal muscle aging (sarcopenia) is associated with reduced peak oxygen consumption (VO peak) during exercise, a key determinant of physical function and overall health. However, the molecular mechanisms linking muscle aging to low VO peak remain poorly understood. We aimed to identify miRNA signatures and miRNA-gene regulatory networks associated with VO peak in older adults. Using small RNA and mRNA sequencing, we analyzed skeletal muscle from 72 SOMMA participants (70-79 years old) with low or high VO peak (n = 18/group) and from 36 participants spanning the full VO peak spectrum. Differential expression was assessed using LIMMA, with pathway and network analyses performed using Ingenuity Pathway Analysis (IPA) and Weighted Gene Co-expression Network Analysis (WGCNA). We detected 1,408 miRNAs and 16,210 genes; among these, 14 miRNAs and 2,018 genes were differentially expressed (FDR < 0.05). The 14 miRNAs regulated 142 genes, and expression of 10 miRNAs inversely correlated with 50 genes enriched in mitochondrial, sirtuin-1, and nitric oxide signaling pathways. Regression analyses identified 21 miRNAs and 1,744 genes significantly correlated with VO peak after adjusting for age and sex. WGCNA revealed 10 co-expression modules associated with VO peak, with the cyan module showing the strongest correlation and enrichment for nitric oxide signaling genes. These findings highlight novel miRNA-mediated molecular pathways potentially contributing to low VO peak and skeletal muscle aging in older adults. Future studies will further investigate these miRNA-gene interactions to uncover therapeutic targets for preserving muscle function with age.

molecular biology↗

bioRxiv2025

MicroRNA signatures of VO2peak in older adult participants of the Study of Muscle, Mobility and Aging

BackgroundPeak oxygen consumption during exercise (VO2peak), is a direct measure of cardiorespiratory fitness (CF), a key indicator of physical function and overall health. However, the molecular changes that underpin VO2peak variation are not clear. Our objective is to understand the miRNA signatures that relate to VO2peak variation, which could provide insights to novel mechanisms that contribute to low VO2peak. MethodsWe used small RNA sequencing to analyze serum samples from 72 participants (70-79 yrs old, 53% female) of the Study of Muscle, Mobility and Aging (SOMMA). We analyzed samples from individuals with low or high VO2peak (N=18/group) as well as samples from 36 randomly selected participants spanning the entire spectrum of VO2peak. We used LIMMA analysis package for regression analysis and to identify differentially expressed miRNAs. We used receiver operating characteristic curve analysis to evaluate the Area Under the Curve (AUC) and sensitivity and specificity rates. ResultsWe identified 1,055 miRNAs expressed in all serum samples. Expression of 65 miRNAs differed between participants with low and high VO2peak (p < 0.05). After p-value adjustment, expression of 5 miRNAs (miR-1301-3p, -431-5p, -501-5p, -519a-3p, and -18a-3p) remained significantly different (FDR = 0.05). The five miRNAs had AUC ranging from 0.77 to 0.84. The optimal sensitivity and specificity ranged from 70 to 80% and 80 to 90%, respectively. After adjustment for age and sex covariates, 46 miRNAs significantly correlated with VO2peak (p < 0.05) and miR-519a-3p remained significant based on adjusted of p-values. ConclusionsWe identified a miRNA signature of VO2peak in older individuals that might provide insights to novel mechanisms that drive low VO2peak. Future studies will validate the findings in a larger, longitudinal study cohort.

molecular biology↗

bioRxiv2019

Classification of human physical activity based on the raw accelerometry data via spherical coordinate transformation

Human health is strongly associated with persons lifestyle and levels of physical activity. Therefore, characterization of daily human activity is an important task. Accelerometers have been used to obtain precise measurements of body acceleration. Wearable accelerometers collect data as a three-dimensional time series with frequencies up to 100Hz. Using such accelerometry signal, we are able to classify different types of physical activity.\n\nIn our work, we present a novel procedure for physical activity classification based on the raw accelerometry signal. Our proposal is based on the spherical representation of the data. We classify four activity types: resting, upper body activities (sitting), upper body activities (standing) and lower body activities. The classifier is constructed using decision trees with extracted features consisting of spherical coordinates summary statistics, moving averages of the radius and the angles, radius variance and spherical variance.\n\nThe classification accuracy of our method has been tested on data collected on a sample of 47 elderly individuals who performed a series of activities in laboratory settings. The achieved classification accuracy is over 90% when the subject-specific data are used and 84% when the group data are used. Main contributor to the classification accuracy is the angular part of the collected signal, especially spherical variance. To the best of our knowledge, spherical variance has never been previously used in the analysis of the raw accelerometry data. Its major advantage over other angular measures is its invariance to the accelerometer location shifts.

biophysics↗

bioRxiv2018

Accelerometry data in health research: challenges and opportunities. Review and examples

Wearable accelerometers provide detailed, objective, and continu-ous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popula-rity of wearable technology in health research. An ever increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper we discuss problems related to the collection and analysis of raw acce-lerometry data and provide insights into potential solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability and the effects of sensor location on the body. We also provide a short tutorial for dealing with sampling frequency, device calibration, data labeling and multiple PA monitors synchronization. We illustrate these po-ints using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment.

epidemiology↗