Drinking orange juice daily may fine-tune genes tied to cardiovascular wellness

By Vijay Kumar MalesuReviewed by Susha Cheriyedath, M.Sc.Nov 9 2025

A daily glass of orange juice may do more than refresh; it can fine-tune thousands of genes tied to blood pressure and metabolism, with the benefits varying depending on your body weight.

Study: A Global Transcriptomic Analysis Reveals Body Weight-Specific Molecular Responses to Chronic Orange Juice Consumption in Healthy Individuals. Image Credit: Sunlight_s / Shutterstock

In a recent study published in the journal Molecular Nutrition & Food Research, a group of researchers investigated how chronic orange juice (OJ) intake affects the transcriptomes of peripheral blood mononuclear cells (PBMCs) in healthy adults, and whether responses vary by body mass index (BMI) status. This was a single-arm pre–post intervention without a control beverage; findings show transcriptomic associations and do not establish causality. Fold-change ranges for individual genes were reported in supplementary data but were not emphasized in the main text.

Nutrigenomic Potential of Citrus Flavanones

What if a breakfast staple could quietly tune the genes that steer blood pressure, lipids, and inflammation? Citrus fruits, especially OJ, supply flavanones such as hesperidin and naringenin that may influence vascular tone, lipid handling, and immune signaling. Yet, most people ask whether a daily glass truly alters biology in ways that matter, and whether body weight affects the response.

Mapping gene activity in circulating immune cells can link a kitchen habit to outcomes families care about, although the mechanistic paper did not newly assess clinical endpoints; prior publications from the same cohort reported reductions in blood pressure and body-fat percentage with 500 mL/day OJ over 60 days.

Participant Profile and Study Design

Healthy adults (n = 20; 10 men, 10 women; 21–36 years) without chronic disease consumed 500 mL/day of pasteurized OJ for 60 days, split into two home doses, after a three-day citrus-free washout; participants also avoided citrus foods during the intervention.

Fasting blood was drawn at baseline (T0) and day 60 (T60). PBMCs were isolated and total ribonucleic acid (RNA) extracted. Global transcriptomes were profiled on Clariom D microarrays; differentially expressed features were defined at false discovery rate-adjusted p < 0.05.

Multi-Omics and Computational Analyses

Pathway enrichment was performed using GeneTrail with the Kyoto Encyclopedia of Genes and Genomes (KEGG), WikiPathways, and BioCarta; protein-protein interaction networks were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING).

Predicted transcription factors were identified with Enrichr. MicroRNA (miRNA) targets were derived via Mienturnet/miRTarBase; long non-coding RNA (lncRNA) targets via LncRRIsearch; small nucleolar RNA (snoRNA) changes were also cataloged. Disease associations employed the Comparative Toxicogenomics Database.

Molecular Docking of Flavanone Metabolites

In silico molecular docking (SwissDock) tested Phase II flavanone metabolites (for example, hesperetin- and naringenin-glucuronides/sulfates) and gut-derived catabolites against candidate transcription factors including nuclear factor kappa B (NF-κB) subunit 1, aryl hydrocarbon receptor (AHR), peroxisome proliferator-activated receptor alpha (PPARA), activating transcription factor 4 (ATF4), plasminogen activator, urokinase (PLAU), proto-oncogene (MYC), nuclear respiratory factor 1 (NRF1), Yin-Yang 1 (YY1), E26 transformation-specific (ETS) transcription factor ELK4 (ELK4), RELA (p65 subunit of NF-κB), retinoid X receptor alpha (RXRA), interferon regulatory factor 9 (IRF9), and tumor protein 53 (TP53). Subgroup analyses were conducted to contrast normal-weight (NW) and overweight (OW) participants by BMI.

Transcriptomic Remodeling After Orange Juice Intake

Chronic OJ intake remodeled the PBMC transcriptome: 3,790 oligonucleotides changed, including 1,705 protein-coding genes (mostly downregulated), 66 miRNAs, 19 lncRNAs, and 67 snoRNAs. Principal components, partial least squares–discriminant analysis (PLS-DA), and clustering analyses successfully separated T60 from T0, indicating a consistent intervention signal.

Enriched pathways mapped to blood pressure control (aldosterone synthesis/secretion, renin secretion, angiotensin-converting enzyme inhibitor-related signaling), lipid metabolism (thermogenesis, adipogenesis, mitochondrial fatty-acid β-oxidation), inflammation (toll-like receptor, tumor necrosis factor, interleukin-17 (IL17)), cell adhesion (focal adhesion, actin cytoskeleton), and major signaling axes (mitogen-activated protein kinase (MAPK), vascular endothelial growth factor receptor 2 (VEGFR2), phosphoinositide 3-kinase-Akt (PI3K-Akt), epidermal growth factor (EGF) receptor, cyclic adenosine monophosphate (cAMP), insulin, and advanced glycation end product–receptor for advanced glycation end products). Additional enrichment included AHR signaling and endoplasmic reticulum (ER) protein processing.

Protein-protein interaction hubs included serine/threonine kinase AKT1, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), catenin beta-1 (CTNNB1), heat-shock protein 90 alpha (HSP90AA1), and eukaryotic elongation factor 2 (EEF2).

Gene-Level and Non-Coding RNA Modulation

Cardiometabolic relevance emerged at the gene level. Blood-pressure-linked genes nicotinamide phosphoribosyltransferase (NAMPT) and NLR family pyrin domain containing 3 (NLRP3) were downregulated, alongside nuclear receptor subfamily 4 group A member 2 (NR4A2), period circadian regulator 1 (PER1), salt-inducible kinase 1 (SIK1), G protein-coupled receptor 183 (GPR183), and serum/glucocorticoid regulated kinase 1 (SGK1), aligning with mechanisms that favor lower blood pressure.

Inflammatory mediators decreased: IL1B, IL6, prostaglandin-endoperoxide synthase 2 (PTGS2/COX-2), and regulator of G-protein signaling 1 (RGS1), consistent with dampened NF-κB activity and reduced cytokine tone.

Lipid/adipocyte programs also shifted: genes such as Kruppel-like factor 4 (KLF4), receptor-interacting serine/threonine-protein kinase 1 (RIPK1), perilipin-2 (PLIN2), and C-X-C motif chemokine ligand 8 (CXCL8) moved toward a profile linked to better metabolic control.

Non-coding layers mirrored these trends. Among 66 altered miRNAs, weight-loss-associated species (for example, miR-548 family, miR-1185-1) rose, while inflammation-associated miR-640 and miR-1248 declined; miR-1305 increased, a change reported with anti-inflammatory effects.

19 lncRNAs changed, including downregulation of small nucleolar RNA host gene 16 (SNHG16) and upregulation of apoptosis-associated transcript in bladder cancer (AATBC), a human adipocyte plasticity regulator. 67 snoRNAs shifted, 61 of which were downregulated, including reduced RPL13A cluster members (SNORD U32/U33/U34/U35), a pattern tied to lower oxidative stress and inflammation.

Fold-change magnitudes for these RNA classes varied across transcripts, typically within a −1.5 to −8.0 range for downregulated features and +1.5 to +5.0 for upregulated ones, according to supplemental data. Disease-mapping linked the signature to heart and vascular disease, hypertension, diabetes, obesity, and glucose-metabolism disorders, underscoring clinical relevance.

BMI-Specific Transcriptomic Differences

Overweight participants exhibited a unique modulation of lipid metabolism and adipogenesis pathways, characterized by distinct regulation of glycogen synthase kinase 3 beta (GSK3B), G protein-coupled receptor kinase 6 (GRK6), and miRNAs, including miR-548i and miR-1292-3p. Normal-weight participants exhibited unique modulation of inflammatory pathways, characterized by changes in signal transducer and activator of transcription 3 (STAT3), solute carrier family 16 member 6 (SLC16A6), B-cell lymphoma 2 (BCL2), MAPK1, and miR-1185-2-5p. Thus, two people drinking the same OJ may experience different molecular benefits depending on BMI.

Mechanistic Plausibility of Flavanone–Gene Interactions

Molecular docking supported direct interactions between Phase II flavanone metabolites (for example, hesperetin-3-glucuronide, hesperetin-7-glucuronide, hesperetin-3-sulfate; naringenin-4-glucuronide, naringenin-7-glucuronide) and transcription factors, including NFKB1, AHR, PPARA, ATF4, PLAU, NRF1, IRF9, MYC, YY1, ELK4, RELA, RXRA, and TP53, with free-energy range −6.29 to −9.63 kcal/mol; interactions <-6 kcal/mol were considered significant, offering a plausible route from juice metabolites to gene-regulatory effects.

Clinical Interpretation and Research Outlook

Daily OJ, a familiar food, reprogrammed immune-cell gene networks tied to blood pressure, lipids, and inflammation, with layered changes across protein-coding genes, miRNA, lncRNA, and snoRNA. Predicted interactions between flavanone metabolites and transcription factors, including NFKB1, AHR, and PPARA, provide mechanistic plausibility.

Importantly, BMI-stratified effects revealed that lipid pathways dominated in overweight adults, while inflammation pathways shifted in normal-weight adults. However, the results are limited by the small sample size (n=20), the absence of a control beverage, the use of a microarray platform, and the exploratory nature of in-silico docking, which remains hypothesis-generating. 

Future studies should integrate fold-change magnitude data with targeted functional assays to validate these transcriptomic signatures. For individuals and clinicians, this supports tailoring “simple” dietary advice to body weight, to turn an everyday drink into a more precise cardiometabolic lever.

Personalized nutrition requires both molecular evidence and practical application; these findings offer early molecular insights that can inform such individualized dietary guidance. Further research is needed to confirm and translate these transcriptomic effects into clinical outcomes.