Predictors of Long-Term Evolution in Long COVID; 4-Year Follow-Up. (BioICOPER Follow-up Study)

Instituto de Investigación Biomédica de Salamanca

Status: Not yet enrolling

Conditions: Persistent COVID Condition

Treatments: Other: BioICOPER Long COVID Cohort

Study type: Observational

Funder types: Other

Identifiers

NCT07295483

BioICOPER Follow-up Study

Details and patient eligibility

About

Long COVID (persistent COVID) represents a major global health challenge due to its high prevalence (approximately 7%), significant impact on quality of life, and socioeconomic burden. Despite extensive research, diagnostic tools to objectively identify or predict long COVID evolution are still lacking.

The BioICOPER Follow-up Study aims to analyze the influence of biomarker evolution on clinical symptomatology (particularly chronic fatigue) and vascular health after four years of follow-up among 400 participants previously included in the original BioICOPER cohort.

Advanced proteomic analysis, vascular function assessment, and machine-learning-based predictive modeling will be used to identify biomarkers associated with disease progression, stratified by sex. This project will contribute to personalized clinical management of long COVID and improved diagnostic and therapeutic strategies in primary care.

Full description

The study includes a citizen participation component through the IBSAL Citizen Committee for review and dissemination of results. A prospective observational cohort study following 400 adults with a confirmed diagnosis of long COVID, previously enrolled in the BioICOPER baseline study.

Participants will undergo reevaluation four years after their initial inclusion, assessing:

• Clinical symptoms (fatigue, dyspnea, sleep, cognition, nutrition, frailty).
• Lifestyle factors (physical activity, diet, alcohol and tobacco use).
• Vascular structure and function using carotid ultrasound, pulse wave velocity (SphygmoCor®, Vasera®), and retinal imaging.
• Proteomic profiling and quantification of SARS-CoV-2 N and S proteins using ELISA and mass spectrometry.
• Predictive modeling using artificial intelligence (AI) and bioinformatics methods (ESALAB group).

The study will identify biological, vascular, and behavioral determinants of long COVID progression, aiming to build predictive models to support personalized medicine.

Enrollment: 400 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Adults ≥18 years old.
• Confirmed previous SARS-CoV-2 infection.
• Diagnosis of long COVID according to WHO criteria.
• Participation in the baseline BioICOPER study.
• Signed informed consent for re-evaluation.

Exclusion criteria

• Acute illness preventing participation.
• Cognitive or physical impairment limiting data collection.
• Withdrawal of informed consent.
• Age < 18 years old

Trial design

400 participants in 1 patient group

BioICOPER Long COVID Cohort

Description:

Adults previously enrolled in the BioICOPER study with a clinical diagnosis of long COVID.

Treatment:

Other: BioICOPER Long COVID Cohort