Precision Subtyping and Prognostic Study of Heart Failure Based on Multi-Omics Integration and Clinical Indicators: A Prospective Single-Center Cohort Study

Overview

This is a prospective single-center cohort study conducted at The First Affiliated Hospital of Xinjiang Medical University, aiming to enroll 400 patients with chronic heart failure (including HFrEF, HFmrEF, HFpEF) and 200 healthy controls.We will collect clinical data (e.g., NYHA class, NT-proBNP), multi-omics samples (genome, proteome, metabolome, gut microbiome), and imaging indicators (e.g., EAT density, myocardial strain) from participants at baseline. For patients treated with SGLT2 inhibitors, we will also track dynamic changes in multi-omics during follow-up.The main purpose is to build a composite risk prediction model (integrating multi-omics and clinical indicators) to predict the 1-year composite endpoint (heart failure rehospitalization or all-cause death). Secondary goals include identifying specific molecular profiles related to heart failure phenotypes, exploring the "gut-heart axis" mechanism, and finding early biomarkers for SGLT2 inhibitor response.All participants will be followed up for at least 12 months, and the study will strictly comply with ethical norms and protect the privacy of participants.

Detailed description

This prospective single-center cohort study focuses on chronic heart failure (CHF) patients (HFrEF, HFmrEF, HFpEF) and healthy controls, with the core objective of establishing a precision risk stratification model for CHF via multi-omics-clinical integration.

Study Design \& Enrollment Participants: 400 CHF patients (meeting 2022 ESC HF guidelines) and 200 age/gender-matched healthy controls (no cardiovascular disease history). Exclusion criteria include acute decompensated HF, end-stage renal disease, active malignancies, and recent antibiotic use (to avoid gut microbiome interference).

Recruitment: Conducted at The First Affiliated Hospital of Xinjiang Medical University over 12 months; eligible participants will provide written informed consent prior to enrollment.

Data \& Sample Collection

Baseline:

Clinical data: NYHA functional class, NT-proBNP, echocardiography (LVEF, LVGLS), cardiac CT (EAT density/volume); Multi-omics samples: Plasma (proteome via Olink, metabolome via LC-MS), blood (genome via microarray), feces (gut microbiome via 16S rRNA sequencing; shotgun metagenomics for 200 patients); Follow-up: 3/6/12-month visits to collect clinical outcomes (rehospitalization, mortality), KCCQ quality-of-life scores, and dynamic multi-omics samples (only for SGLT2 inhibitor-treated patients).

Key Analyses Multi-omics characterization: Identify phenotype-specific molecular signatures (e.g., HFpEF-related metabolic profiles) via differential expression and correlation network analysis; Mechanistic exploration: Link gut microbiome composition to circulating metabolites/inflammatory proteins to clarify the "gut-heart axis" in CHF; Model construction: Integrate multi-omics and clinical/imaging indicators to build 4 prediction models (clinical-only, single-omics, multi-omics, integrated), with validation via Harrell's C-statistic and time-dependent ROC.

Quality Control Biological samples: Labeled with unique IDs, stored at -80°C; Imaging data: Independent review by 2 cardiologists (third-party arbitration for discrepancies); Data management: REDCap platform for electronic data capture; independent Data Monitoring Committee (DMC) reviews progress/safety every 6 months.

Primary outcome measures

• 1-Year Composite Endpoint by HF Subtype (Heart Failure Rehospitalization or All-Cause Death) [Time frame: 12 months]

Secondary outcome measures (1)

• 1-Year Heart Failure-Related Rehospitalization (Independent Endpoint) [Time frame: 12 months]

Eligibility criteria

Inclusion Criteria:(1)For chronic heart failure (CHF) patients:1.Meet the 2022 European Society of Cardiology (ESC) diagnostic criteria for CHF, classified into heart failure with reduced ejection fraction (HFrEF), heart failure with mildly reduced ejection fraction (HFmrEF), and heart failure with preserved ejection fraction (HFpEF) per ESC guidelines;2.Aged 18 to 80 years (inclusive);3.Able to provide written informed consent independently (or via a legal guardian if cognitively impaired, with a Mini-Mental State Examination \[MMSE\] score ≥ 24).

(2)For healthy controls:1.No history of cardiovascular disease, confirmed by medical history review and baseline echocardiography;2.Aged 18 to 80 years (inclusive), matched 1:2 with CHF patients by age and gender;3.Able to provide written informed consent.

Exclusion Criteria:(1)Acute decompensated heart failure (admitted for acute HF exacerbation within 72 hours prior to enrollment);(2)End-stage renal disease, defined as an estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73m² (confirmed by serum creatinine testing);(3)Active malignancies (receiving systemic treatment within 6 months prior to enrollment) or severe systemic diseases (e.g., severe liver failure, active autoimmune diseases);(4)Antibiotic use within 2 weeks prior to enrollment (may interfere with gut microbiome analysis);(5)Inability to complete 12-month follow-up (e.g., planned long-term overseas residence) or provide required biological samples (e.g., venous blood, fecal samples).

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model

Cohort

Study locations

China · 1 center

• The First Affiliated Hospital of Xinjiang Medical University — Xinjiang

Publications

• Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016 Sep 29;375(13):1216-9. doi: 10.1056/NEJMp1606181. No abstract available. PMID 27682033
• Antonopoulos AS, Antoniades C. The role of epicardial adipose tissue in cardiac biology: classic concepts and emerging roles. J Physiol. 2017 Jun 15;595(12):3907-3917. doi: 10.1113/JP273049. Epub 2017 Mar 13. PMID 28191635
• Medvedofsky D, Kebed K, Laffin L, Stone J, Addetia K, Lang RM, Mor-Avi V. Reproducibility and experience dependence of echocardiographic indices of left ventricular function: Side-by-side comparison of global longitudinal strain and ejection fraction. Echocardiography. 2017 Mar;34(3):365-370. doi: 10.1111/echo.13446. Epub 2017 Feb 9. PMID 28185312
• Tang WH, Wang Z, Levison BS, Koeth RA, Britt EB, Fu X, Wu Y, Hazen SL. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N Engl J Med. 2013 Apr 25;368(17):1575-84. doi: 10.1056/NEJMoa1109400. PMID 23614584
• Cheng ML, Wang CH, Shiao MS, Liu MH, Huang YY, Huang CY, Mao CT, Lin JF, Ho HY, Yang NI. Metabolic disturbances identified in plasma are associated with outcomes in patients with heart failure: diagnostic and prognostic value of metabolomics. J Am Coll Cardiol. 2015 Apr 21;65(15):1509-20. doi: 10.1016/j.jacc.2015.02.018. PMID 25881932
• Zhang X, Schulz BL, Punyadeera C. The current status of heart failure diagnostic biomarkers. Expert Rev Mol Diagn. 2016;16(4):487-500. doi: 10.1586/14737159.2016.1144474. Epub 2016 Feb 17. PMID 26788983
• Shah S, Henry A, Roselli C, Lin H, Sveinbjornsson G, Fatemifar G, Hedman AK, Wilk JB, Morley MP, Chaffin MD, Helgadottir A, Verweij N, Dehghan A, Almgren P, Andersson C, Aragam KG, Arnlov J, Backman JD, Biggs ML, Bloom HL, Brandimarto J, Brown MR, Buckbinder L, Carey DJ, Chasman DI, Chen X, Chen X, Chung J, Chutkow W, Cook JP, Delgado GE, Denaxas S, Doney AS, Dorr M, Dudley SC, Dunn ME, Engstrom G PMID 31919418
• Ibrahim NE, Januzzi JL Jr. Beyond Natriuretic Peptides for Diagnosis and Management of Heart Failure. Clin Chem. 2017 Jan;63(1):211-222. doi: 10.1373/clinchem.2016.259564. Epub 2016 Oct 10. PMID 28062619

Identifiers

NCT: NCT07355088 · XJMU-HF-MultiOmics-2025-001

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗