METHODS
word of mouth
BLOOD, SECONDARY
A single‐blinded randomized controlled trial design was used to investigate the effects of HIIT on the following dependent variables: 10‐item Perceived Stress Scale (PSS‐10) (Lee, Trial participants were recruited from local communities surrounding the Loma Linda University campus via flyers, approved social media, and word of mouth. The Loma Linda University Institutional Review Board approved the study on 11/18/2021 (IRB# 5210437, Study participants were instructed to avoid modifying their usual physical activity level or diet for the duration of the four‐week study protocols, except for the additional HIIT assigned to exercise group. All participants maintained a compliance log, comprised of two questions weekly. For the control group: Have you performed more than your usual amount of physical activity this week? Second, have you made any significant changes in your diet this week? For the exercise group: Excluding the exercise assigned to you in this study, have you performed more than your usual amount of physical activity this week? Second, have you made any significant changes in your diet this week?All participants arrived at the laboratory between the hours of 8 am and 11 am, and baseline measures were obtained. Body composition measurements were obtained using the InBody 770 body composition and body water analyzer (InBody USA), surveys were completed in a private room, and a single vial of blood was collected by a certified phlebotomist from the antecubital vein into a PAXgene® Blood RNA Tube, PLH 16X100 2.5 PLBLCE CLR (Becton Dickinson).Following the completion of Day‐1 data collection, exercise group participants returned the next day to begin the HIIT protocol that took place at the Loma Linda University Physical Fitness Laboratory. The authors chose a routinely studied 10X1 HIIT protocol that has been determined as safe and effective in various groups, including sedentary individuals (Ito, Participants rotated between three exercise machines (randomly assigned rotation order at outset): A Concept2 rowing ergometer, Concept2 bicycle ergometer, and a Noraxon PhysTread Pressure treadmill. Participants used a different machine each day so that they used each of the three exercise machines once per week.Each participant wore a Polar Verity Sense Optical HR monitor (Polar Electro, FI) during all exercise sessions, and their real‐time HR was monitored by a lab assistant continuously. The lab assistant would cue the participant to modify effort if HR approached our prespecified target boundaries of 77%–93% predicted HR maximum. This ensured that all participants experienced the same relative workload throughout the exercise intervention.Following the conclusion of the four‐week control and exercise protocols, all participants returned for result collection. Exercise group data were collected approximately 48 hours after their last HIIT session. All blood collection procedures were standardized, and all collection took place within the same three‐hour window for pre‐ and postblood draws. Samples were stored at −79 degrees Celsius until RNA extraction (Qiagen RNeasy), quality assurance assays, mRNA sequencing, and related statistical analyses of differential gene expression and interpretive bioinformatics were performed by the UCLA Social Genomics Core Laboratory. Transcriptional profiling utilized a high‐efficiency mRNA targeted reverse transcription and cDNA library synthesis system (QuantSeq 3' FWD; Lexogen Inc.) with cDNA libraries sequenced on in Illumina NovaSeq system by Lexogen Services GmbH. Assays targeted 5 million sequencing reads per sample (achieved median = 7.1 million), each of which was mapped to the GRCh38 reference human transcriptome using the STAR aligner (median 99.7% mapping rate) and quantified as gene transcripts per million total mapped reads, with values floored at 1 transcript per million (TPM) to suppress spurious low‐range variability, and log2‐transformed to stabilize variance. One follow‐up sample yielded insufficient sequencing reads for valid analysis (<1 million reads), and that sample and its paired pre‐intervention baseline sample were excluded from all subsequent analyses. These data served as input into the RNA AGE Calc Shiny App for computation of the TRAP RNA age score. RNA AGE Calc Shiny App inputs were as follows: Tissue type: Blood, type of gene expression data: Count, samples used when building the calculator: All samples, gene ID type: Ensembl ID, signature: Peters.A secondary analysis of differentially expressed genes (DEGs) was performed using two sets of cutoff criteria. First, genes that displayed a group × time interaction expression fold change greater than 1.5 or less than 0.5 were selected for analysis. Also, an exploratory/hypothesis‐generation analysis was performed using more liberal fold change values greater than 1.2 or less than 0.8. Genes were screened into pathway analyses based on differential expression effect size because effect‐size—screened gene lists have been found to be more replicable than those based on 