Epigenetic Changes in Long COVID Patients (ECLIPSE)

Epigenetic Changes in Long COVID Patients: Unraveling the Gut-Immune Axis and Therapeutic Targets

The goal of this observational study is to improve the understanding of the biological mechanisms underlying long COVID and to identify molecular biomarkers that may support its diagnosis, prognosis, and future precision medicine approaches in adults with long COVID, adults who have fully recovered from COVID-19, and healthy control participants.

The main questions it aims to answer are:

• What molecular, immunological, epigenetic, and microbiome profiles distinguish individuals with long COVID from recovered COVID-19 participants and healthy controls?
• How are viral persistence, immune dysregulation, and alterations in the gut-immune axis associated with the development and clinical manifestations of long COVID?
• Which molecular biomarkers may improve disease diagnosis, patient stratification, and the identification of potential therapeutic targets?

Participants will:

• Undergo clinical evaluation and provide information about their medical history and symptoms.
• Provide biological samples, including blood and, when clinically indicated, intestinal biopsy tissue collected during routine colonoscopy procedures.
• Undergo comprehensive molecular analyses, including immunological, epigenetic, transcriptomic, proteomic, and microbiome profiling.
• Have their clinical and molecular data integrated using advanced computational approaches to identify biological signatures associated with long COVID.

The results of this study may improve the understanding of the biological mechanisms underlying long COVID and support the development of novel biomarkers and future precision medicine approaches for diagnosis, prognosis, patient stratification, and therapeutic target identification.

Study Overview

• Status: Not yet recruiting
• Conditions: Long COVID-19 Syndrome; Long COVID Symptoms

Detailed Description

The study aims to identify the immunological and molecular mechanisms underlying Long COVID, with particular focus on persistent cardiopulmonary manifestations and the role of gut-resident immunity. The project integrates clinical, immunological, transcriptomic, epigenomic, microbiome and computational analyses to identify biomarkers associated with disease phenotypes and potential therapeutic targets. The study is both retrospective and perspective, providing a significant numerosity. The study is non-profit and its execution does not involve interventions outside of the normal clinical pathway established for the patient.

Three complementary objectives will be addressed. Aim 1 will identify immunological and molecular signatures associated with Long COVID. Approximately 800 participants enrolled in the San Raffaele Hospital Long COVID outpatient cohort (OSR COVID-BIOB Clinical study, NCT04318366), together with recovered COVID-19 subjects and pre-pandemic healthy controls, will be analyzed. Clinical data include acute infection characteristics, disease course, cardiopulmonary manifestations and longitudinal follow-up. Peripheral blood samples already collected will undergo transcriptomic, epigenomic and immunological characterization. Bulk RNA sequencing and DNA methylation profiling will identify differentially expressed genes and epigenetic alterations, which will be validated in independent cohorts. Immunophenotyping will assess T-, B- and NK-cell subsets, regulatory T cells, SARS-CoV-2-specific antibodies, interferon responses, complement activation and memory B cells. Standardized sample processing, technical and biological quality controls, correction for batch effects and adjustment for relevant confounders will ensure data reliability.

Aim 2 will investigate gut-resident immune cells and intestinal alterations in Long COVID using left-over intestinal biopsies collected from patients undergoing clinically indicated colonoscopy within the MedMol Biobank. Multi-omic characterization will evaluate transcriptomic and epigenomic profiles of intestinal immune and epithelial cells through bulk and single-cell RNA sequencing, spatial transcriptomics, whole-genome DNA methylation analysis and chromatin accessibility profiling. Mast-cell activation will be assessed using circulating and tissue biomarkers, while intestinal inflammation, permeability and gut microbiome composition will be evaluated through blood, stool and biopsy analyses. Computational cell-cell interaction analyses and complementary in vitro experiments will investigate communication pathways between gut-resident immune cells and systemic immune responses.

Aim 3 will integrate immunological, intestinal and clinical findings using machine-learning approaches to identify molecular signatures associated with Long COVID phenotypes. Multi-omics datasets and clinical variables will be harmonized and analyzed using supervised and unsupervised learning methods to identify patient subgroups, prioritize biomarkers, predict disease evolution and generate models supporting personalized therapeutic strategies. Model performance will be evaluated using independent validation datasets and cross-validation procedures.

Clinical information will be collected from approximately 800 participants, including demographic characteristics, acute COVID-19 presentation, disease progression, cardiopulmonary function, gastrointestinal manifestations and longitudinal follow-up evaluations. Peripheral blood samples will be processed according to standardized operating procedures for isolation of peripheral blood mononuclear cells, plasma and nucleic acids. Intestinal biopsies will be collected exclusively from residual tissue obtained during clinically indicated colonoscopies.

Transcriptomic analyses will include bulk RNA sequencing and targeted validation of differentially expressed genes. Epigenomic analyses will include whole-genome bisulfite sequencing and complementary large-scale DNA methylation profiling. Chromatin accessibility and histone modifications will be evaluated in selected samples. Immunological characterization will include multiparametric flow cytometry, cytokine profiling, complement activity, antibody quantification and functional immune assays. Gut analyses will include transcriptomics, epigenomics, spatial transcriptomics, microbiome characterization, intestinal permeability markers and inflammatory biomarkers. Data quality will be ensured through standardized operating procedures covering patient recruitment, sample collection, processing, storage, laboratory analyses, data entry and statistical analyses. Biological samples will be pseudonymized, tracked using barcode-based systems and stored in certified biobanks under controlled conditions. Clinical and laboratory data will undergo predefined quality control procedures, including range and consistency checks, automated validation rules and regular monitoring to identify missing, inconsistent or out-of-range values. Source data verification will compare registry data with clinical records and biobank documentation where appropriate. Standardized data dictionaries, harmonized coding systems and version-controlled analytical pipelines will ensure reproducibility across participating centers.

Data management will comply with the General Data Protection Regulation (GDPR). Secure databases with controlled access, automated backups and version control will be used throughout the study. Laboratory procedures will include technical and biological replicates, instrument calibration, standardized protocols and correction for batch effects to minimize technical variability.

The statistical analysis plan includes descriptive statistics, differential expression and differential methylation analyses, mixed-effects models for longitudinal and cell-specific analyses, and appropriate parametric or non-parametric tests according to data distribution. Multiple-testing correction will be performed using the Benjamini-Hochberg procedure. Potential confounders, including age, sex and time from infection, will be included in multivariable analyses where appropriate. Multi-omics integration will combine transcriptomic, epigenomic, immunological, microbiome and clinical data to identify molecular pathways associated with Long COVID. Supervised machine-learning models will be developed to predict clinical outcomes, while unsupervised approaches will identify novel patient subgroups. Feature selection and biomarker prioritization will be performed using established computational methods, and biological pathway enrichment analyses will support interpretation of identified molecular signatures. Model performance will be assessed using independent validation datasets, cross-validation procedures and standard performance metrics. Power calculations indicate that the planned sample size provides adequate statistical power to detect clinically meaningful transcriptomic, epigenomic and immunological differences between Long COVID subgroups and controls. The study includes discovery and validation cohorts to increase robustness and reproducibility of identified biomarkers.

Missing data will be managed using multiple imputation approaches when appropriate, together with sensitivity analyses to evaluate the impact of incomplete observations. Batch effects and technical variability will be addressed using standardized preprocessing pipelines and computational correction methods. Quality control will be performed throughout all analytical steps.

Potential risks include variability in sample quality, patient heterogeneity, missing data and integration of high-dimensional datasets. These risks will be mitigated through standardized collection and processing procedures, predefined inclusion criteria, comprehensive documentation of clinical covariates, rigorous quality control, randomized laboratory processing, blinded analyses, appropriate technical controls and independent validation of computational models. Alternative analytical strategies are planned if specific methodologies prove unsuitable for individual datasets.

The study is expected to generate a comprehensive molecular characterization of Long COVID by integrating systemic immunity, intestinal immune responses and clinical manifestations. The identification of robust biomarkers and molecular pathways associated with different disease phenotypes may improve patient stratification, support prediction of long-term outcomes and facilitate the development of personalized therapeutic approaches. The analytical framework developed in this project may also be applicable to other post-viral and chronic inflammatory disorders.

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

• Study Contact: Name: Carlo Gaetano, Professor; Phone Number: +390382592262; Email: carlo.gaetano@icsmaugeri.it
• Study Contact Backup: Name: Michela Gottardi Zamperla, PhD; Phone Number: +390382593563; Email: michela.gottardizamperla@icsmaugeri.it

Study Locations

• Italy
  • Milan, Italy, 20132
    • Ospedale San Raffaele S.r.l.
  • Pavia, Italy, 27100
    • Istituti Clinici Scientifici Maugeri
    • Contact: Michela Gottardi Zamperla, PhD; Phone Number: +390382593563; Email: michela.gottardizamperla@icsmaugeri.it
    • Contact: Carlo Gaetano, Professor; Phone Number: +3903822262; Email: carlo.gaetano@icsmaugeri.it
  • Milano
    • Cusano Milanino, Milano, Italy, 20095
      • Istituto Auxologico Italiano
      • Contact: Davide Gentilini, Professor; Phone Number: +3902619113038; Email: d.gentilini@auxologico.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Post COVID-19 patients: Retrospective population of patients that manifested COVID-19 and recovered without sequelae.

Long COVID-19 patients: Retrospective and prospective populations of patients that manifested COVID-19 and developed sequelae. The retrospective cohort includes subjects with long COVID-19 with or without cardio-pulmonary symptoms. The prospective cohort includes patients with long COVID-19 and gastrointestinal symptoms.

Control Group: Retrospective population of subjects who never had SARS-CoV-2 infection.

Description

Inclusion Criteria (Post COVID-19 patients):

• Age ≥ 18 years.
• Previous SARS-CoV-2 infection documented by molecular or serological testing.
• Absence of persistent symptoms 2 months after acute infection.
• Willingness to provide written informed consent.

Inclusion Criteria (Long COVID-19 patients):

• Age ≥ 18 years.
• Previous SARS-CoV-2 infection documented by molecular or serological testing.
• Persistent symptoms at least 2 months after acute infection, according to the WHO definition of long COVID [https://www.who.int/europe/news-room/fact-sheets/item/post-covid-19-condition].
• Willingness to provide written informed consent.

Inclusion Criteria (Control Group):

• Age ≥ 18 years.
• No previous SARS-CoV-2 infection (documented by serology).
• Blood sample collected according to the COVID-BioVac protocol (NCT05276388) before the first administration of the SARS-CoV-2 vaccine.
• Willingness to provide written informed consent.

Exclusion Criteria:

• Inability to provide informed consent.
• Presence of severe systemic autoimmune diseases or congenital/acquired immunodeficiencies that may confound the interpretation of immunological data.
• Current systemic immunosuppressive therapy or treatment within the last 6 months prior to enrollment.
• Active malignancies or those treated within the last 12 months (except basal or squamous cell carcinomas in situ).
• Pregnancy or breastfeeding at the time of enrollment.
• Any other clinical condition that, in the investigator's opinion, could compromise the reliability of the data collected.

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort
Post COVID-19 patients; Retrospective cohort of subjects that manifested COVID-19 and recovered without sequelae. Inclusion criteria: Age ≥ 18 years.; Previous SARS-CoV-2 infection documented by molecular or serological testing.; Absence of persistent symptoms 2 months after acute infection.; Willingness to provide written informed consent.
Long COVID-19 patients; Retrospective and prospective cohorts of patients that manifested COVID-19 and developed sequelae. The retrospective cohort includes subjects with long COVID-19 with or without cardiac symptoms. The prospective cohort includes patients with long COVID-19 and gastrointestinal symptoms. Inclusion criteria: Age ≥ 18 years.; Previous SARS-CoV-2 infection documented by molecular or serological testing.; Persistent symptoms at least 2 months after acute infection, according to the WHO definition of long COVID [https://www.who.int/europe/news-room/fact-sheets/item/post-covid-19-condition].; Willingness to provide written informed consent.
Control Group; Retrospective cohort of subjects who never had SARS-CoV-2 infection. Inclusion criteria: Age ≥ 18 years.; No previous SARS-CoV-2 infection (documented by serology).; Blood sample collected according to the COVID-BioVac protocol (NCT05276388) before the first administration of the SARS-CoV-2 vaccine.; Willingness to provide written informed consent.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Gene expression profile of peripheral blood cells	Transcriptomic profiling will be performed in peripheral blood leukocytes using RNA sequencing. Gene expression will be expressed as normalized gene expression counts. Differential transcript abundance will be compared across participants with Long COVID with cardiopulmonary manifestations, Long COVID without cardiopulmonary manifestations, COVID-19 participants without persistent sequelae, and pre-pandemic healthy controls.	Baseline
DNA methylation profile of peripheral blood cells	Genome-wide DNA methylation will be measured using the EPIC-v2 array and/or whole-genome bisulfite sequencing. Results will be expressed as DNA methylation β-values or methylation percentages (%). Methylation profiles will be compared across study groups.	Baseline
Frequency of peripheral blood immune cell populations	Frequency of circulating immune cell subsets will be measured by multiparameter flow cytometry. Results will be expressed as the percentage (%) of the parent cell population. The analyses will include investigation of CD4+ T cells, CD8+ T cells, NK cells, B cells and regulatory T cells. Immune profiles will be compared among study groups.	Baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Chromatin accessibility in peripheral blood leukocytes	Genome-wide chromatin accessibility will be measured through ATAC-seq in peripheral blood leukocytes and expressed as normalized chromatin accessibility signal.	Baseline
Gut microbiome composition	Gut microbiome composition will be assessed by 16S rRNA sequencing in residual intestinal biopsy samples obtained during clinically indicated endoscopy. Results will be expressed as the relative abundance (%) of bacterial taxa.	Baseline

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Predictive performance of integrated multi-omic models	Integrated predictive models based on clinical, transcriptomic, epigenomic and immunological data will be tested for Long COVID phenotypic stratification and prediction of symptom persistence. Model performance will be expressed as the area under the receiver operating characteristic curve (AUC).	Baseline through 12 months

Collaborators and Investigators

• Sponsor: Istituti Clinici Scientifici Maugeri SpA
• Collaborators: Istituto Auxologico Italiano; IRCCS Ospedale San Raffaele

Publications and helpful links

General Publications

• Dennis A, Wamil M, Alberts J, Oben J, Cuthbertson DJ, Wootton D, Crooks M, Gabbay M, Brady M, Hishmeh L, Attree E, Heightman M, Banerjee R, Banerjee A; COVERSCAN study investigators. Multiorgan impairment in low-risk individuals with post-COVID-19 syndrome: a prospective, community-based study. BMJ Open. 2021 Mar 30;11(3):e048391. doi: 10.1136/bmjopen-2020-048391.
• Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS, Cook JR, Nordvig AS, Shalev D, Sehrawat TS, Ahluwalia N, Bikdeli B, Dietz D, Der-Nigoghossian C, Liyanage-Don N, Rosner GF, Bernstein EJ, Mohan S, Beckley AA, Seres DS, Choueiri TK, Uriel N, Ausiello JC, Accili D, Freedberg DE, Baldwin M, Schwartz A, Brodie D, Garcia CK, Elkind MSV, Connors JM, Bilezikian JP, Landry DW, Wan EY. Post-acute COVID-19 syndrome. Nat Med. 2021 Apr;27(4):601-615. doi: 10.1038/s41591-021-01283-z. Epub 2021 Mar 22.
• Su Y, Yuan D, Chen DG, Ng RH, Wang K, Choi J, Li S, Hong S, Zhang R, Xie J, Kornilov SA, Scherler K, Pavlovitch-Bedzyk AJ, Dong S, Lausted C, Lee I, Fallen S, Dai CL, Baloni P, Smith B, Duvvuri VR, Anderson KG, Li J, Yang F, Duncombe CJ, McCulloch DJ, Rostomily C, Troisch P, Zhou J, Mackay S, DeGottardi Q, May DH, Taniguchi R, Gittelman RM, Klinger M, Snyder TM, Roper R, Wojciechowska G, Murray K, Edmark R, Evans S, Jones L, Zhou Y, Rowen L, Liu R, Chour W, Algren HA, Berrington WR, Wallick JA, Cochran RA, Micikas ME; ISB-Swedish COVID-19 Biobanking Unit; Wrin T, Petropoulos CJ, Cole HR, Fischer TD, Wei W, Hoon DSB, Price ND, Subramanian N, Hill JA, Hadlock J, Magis AT, Ribas A, Lanier LL, Boyd SD, Bluestone JA, Chu H, Hood L, Gottardo R, Greenberg PD, Davis MM, Goldman JD, Heath JR. Multiple early factors anticipate post-acute COVID-19 sequelae. Cell. 2022 Mar 3;185(5):881-895.e20. doi: 10.1016/j.cell.2022.01.014. Epub 2022 Jan 25.
• Chen C, Haupert SR, Zimmermann L, Shi X, Fritsche LG, Mukherjee B. Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review. J Infect Dis. 2022 Nov 1;226(9):1593-1607. doi: 10.1093/infdis/jiac136.
• Xu E, Xie Y, Al-Aly Z. Long-term gastrointestinal outcomes of COVID-19. Nat Commun. 2023 Mar 7;14(1):983. doi: 10.1038/s41467-023-36223-7.
• Chicco D, Sichenze A, Jurman G. A simple guide to the use of Student's t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics. BioData Min. 2025 Aug 20;18(1):56. doi: 10.1186/s13040-025-00465-6.
• Green GH, Diggle PJ. On the operational characteristics of the Benjamini and Hochberg False Discovery Rate procedure. Stat Appl Genet Mol Biol. 2007;6:Article27. doi: 10.2202/1544-6115.1302. Epub 2007 Oct 11.
• Andrade C. Survival Analysis, Kaplan-Meier Curves, and Cox Regression: Basic Concepts. Indian J Psychol Med. 2023 Jul;45(4):434-435. doi: 10.1177/02537176231176986. Epub 2023 Jun 11.
• May WL, Johnson WD. The validity and power of tests for equality of two correlated proportions. Stat Med. 1997 May 30;16(10):1081-96. doi: 10.1002/(sici)1097-0258(19970530)16:103.0.co;2-x.
• Chatzi A, Doody O. The one-way ANOVA test explained. Nurse Res. 2023 Sep 7;31(3):8-14. doi: 10.7748/nr.2023.e1885. Epub 2023 Jun 15.
• Rosner B, Glynn RJ, Lee ML. The Wilcoxon signed rank test for paired comparisons of clustered data. Biometrics. 2006 Mar;62(1):185-92. doi: 10.1111/j.1541-0420.2005.00389.x.
• Mongelli A, Barbi V, Gottardi Zamperla M, Atlante S, Forleo L, Nesta M, Massetti M, Pontecorvi A, Nanni S, Farsetti A, Catalano O, Bussotti M, Dalla Vecchia LA, Bachetti T, Martelli F, La Rovere MT, Gaetano C. Evidence for Biological Age Acceleration and Telomere Shortening in COVID-19 Survivors. Int J Mol Sci. 2021 Jun 7;22(11):6151. doi: 10.3390/ijms22116151.
• Phetsouphanh C, Darley DR, Wilson DB, Howe A, Munier CML, Patel SK, Juno JA, Burrell LM, Kent SJ, Dore GJ, Kelleher AD, Matthews GV. Immunological dysfunction persists for 8 months following initial mild-to-moderate SARS-CoV-2 infection. Nat Immunol. 2022 Feb;23(2):210-216. doi: 10.1038/s41590-021-01113-x. Epub 2022 Jan 13.
• Schapowal A. [Long COVID, Post-COVID-Syndrom: Langzeitfolgen von SARS-CoV-2-Infektionen und Nutzen von standardisierten Ginkgo-biloba Extrakten]. Complement Med Res. 2025;32(5):432-437. doi: 10.1159/000548075. Epub 2025 Aug 19. German.
• Pettemeridou E, Loizidou M, Trajkovic J, Constantinou M, De Smet S, Baeken C, Sack AT, Williams SCR, Constantinidou F. Cognitive and Psychological Symptoms in Post-COVID-19 Condition: A Systematic Review of Structural and Functional Neuroimaging, Neurophysiology, and Intervention Studies. Arch Rehabil Res Clin Transl. 2025 May 9;7(3):100461. doi: 10.1016/j.arrct.2025.100461. eCollection 2025 Sep.
• National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Global Health; Board on Health Sciences Policy; Committee on Examining the Working Definition for Long COVID; Goldowitz I, Worku T, Brown L, Fineberg HV, editors. A Long COVID Definition: A Chronic, Systemic Disease State with Profound Consequences. Washington (DC): National Academies Press (US); 2024 Jul 9. Available from http://www.ncbi.nlm.nih.gov/books/NBK605676/

Helpful Links

• Post COVID-19 condition (Long COVID): brief description by the WHO of Long COVID

Study record dates

Study Major Dates

• Study Start (Estimated): September 1, 2026
• Primary Completion (Estimated): September 1, 2027
• Study Completion (Estimated): December 1, 2027

Study Registration Dates

• First Submitted: July 30, 2026
• First Submitted That Met QC Criteria: August 5, 2026
• First Posted (Actual): August 11, 2026

Study Record Updates

• Last Update Posted (Actual): August 11, 2026
• Last Update Submitted That Met QC Criteria: August 5, 2026
• Last Verified: August 1, 2026

More Information

Terms related to this study

• Keywords: machine learning; Long COVID-19; biomarker; COVID-19; genomics; artificial intelligence; cytokines; gut; epigenetics; Chronic inflammation; omics; methylation; transcriptomics; epigenomics; ai; Immune aging; Multi-omics approaches; spatial omics
• Additional Relevant MeSH Terms: Post-Infectious Disorders; Pathologic Processes; Chronic Disease; Disease Attributes; Respiratory Tract Infections; Infections; RNA Virus Infections; Virus Diseases; Respiratory Tract Diseases; Lung Diseases; Pneumonia, Viral; Pneumonia; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; Pathological Conditions, Signs and Symptoms; COVID-19; Post-Acute COVID-19 Syndrome
• Other Study ID Numbers: ECLIPSE - CTSPV86-25

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: The study consortium has not yet made a final decision regarding the sharing of individual participant data.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No