Epigenetic and Microbiome Responses to Greens Supplementation in Obese Older Adults: Results from a Randomized Crossover- Controlled Trial

This study evaluated the effects of a 30-day greens-based supplement on epigenetic markers of aging and metabolic health in adults aged 50-65 years with body mass index (BMI) >30 kg/m2, using a 60-day randomized crossover design. Participants were randomized to immediate or delayed supplementation between visits 2 and 3 or visits 3 and 4. During the 30-day intervention period, participants consumed a daily greens supplement. Primary outcomes included peripheral blood mononuclear cell DNA methylation and epigenetic age (Horvath, PCGrimAge, AdaptAge, and DamAge). Secondary measures included clinical metabolic biomarkers, microbiome diversity, breath hydrogen and methane, body composition, actigraphy, dietary intake, and quality of life questionnaires (RAND 12 item short form questionnaire [SF-12], and 21-item Depression, Anxiety, and Stress Scale [DASS-21]). These data are from stool samples that were collected 24-48 h prior to Visits 2, 3, and 4 using OMNIgeneGUT OMR-200 tubes (DNA Genotek, Ottawa, Canada) and returned at study visits. Participants recorded stool consistency using the Bristol Stool Chart and noted date and time of collection. Upon receipt, samples were stored at -80 degrees C until processing. Microbial DNA was extracted with Zymo Fecal/Soil DNA Miniprep kits (Irvine, CA, USA) according to manufacturer instructions. The V4 region of the 16S rRNA gene was amplified and sequenced on an Illumina MiSeq platform. Raw reads were quality-checked with FASTQC and processed in QIIME2, where DADA2 was applied for filtering, denoising, chimera removal, and amplicon sequence variant (ASV) generation. Taxonomic classification was performed against the SILVA reference database. Alpha diversity was calculated using observed species richness, Shannon index, and Faith's phylogenetic diversity, while beta diversity was assessed using Bray-Curtis dissimilarity and UniFrac clustering. Differential abundance testing was performed using ANCOM and DESeq2 to identify microbial taxa significantly associated with the intervention.