An observational study in Steatosis of Liver, sponsored by Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau. Recruiting at 2 sites in Spain. Open to participants aged 20 Years to 80 Years. Per ClinicalTrials.gov, last updated 2026-06-17.
Sponsored by Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau · Observational
The goal of this observational study is to determine the differences in the prevalence, characteristics and evolution of frailty between men and women diagnosed with MASLD, and to establish its relationship with clinical, functional, hormonal and social parameters.
662 studies on the registry are indexed under Fatty Liver; 184 are open to participants now.
This study's planned enrollment of 140 is close to the median of 150 across 206 observational studies indexed under Fatty Liver.
Browse Fatty Liver studies →Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau is the lead sponsor of 343 studies on the registry; 65 are open to participants now.
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The degree of frailty will be compared between patients with MASLD and a cohort of individuals without liver disease. For this purpose, we have a historical cohort of 135 individuals without liver disease from primary care clinics whose frailty was assessed using the Fried frailty index.
From this cohort, we will select participants by matching sex and age with the MASLD patients recruited for the present study.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
> 30 g/day in men and > 20 g/day in women. AUDIT score: > 7 points in men, > 5 in women.
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
A condition in which the immune system attacks the body’s own tissues, such as rheumatoid arthritis, lupus, psoriasis, type 1 diabetes or inflammatory bowel disease.
Immunotherapy studies often leave out active autoimmune disease, and some name exceptions, such as thyroid disease on replacement hormone. Check the exceptions this study lists.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Autoimmune disease
A condition in which the immune system attacks the body’s own tissues, such as rheumatoid arthritis, lupus, psoriasis, type 1 diabetes or inflammatory bowel disease.
Immunotherapy studies often leave out active autoimmune disease, and some name exceptions, such as thyroid disease on replacement hormone. Check the exceptions this study lists.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Consecutive patients diagnosed with MASLD at any degree of fibrosis between the ages of 20 and 80 will be included. The main variable will be the degree of frailty assessed by the LFI and the Fried Index.
Change in Liver Frailty Index (LFI) score
Change in Liver Frailty Index (LFI) score from baseline to follow-up. Higher values indicate worsening frailty, whereas lower values indicate improvement in frailty.
Time frame: Baseline to Month 12
Fried Frailty Index
The Fried Frailty Index assesses frailty based on five components: unintentional weight loss, exhaustion, weakness, slow walking speed, and low physical activity. Higher scores indicate greater frailty. Positive change scores indicate worsening frailty, whereas negative change scores indicate improvement.
Time frame: Baseline to Month 12
Association Between Frailty and Functional Performance Measures
Association between frailty status (LFI and Fried Frailty Index) and functional performance assessed by gait speed, Timed Up and Go test, handgrip strength, and balance.
Time frame: Baseline to Month 12
Association Between Frailty and Inflammatory, Metabolic, and Oxidative Stress Biomarkers
Relationship between frailty status and circulating inflammatory, metabolic, and oxidative stress biomarkers.
Time frame: Baseline to Month 12.
Association Between Frailty and Hormonal Parameters
Relationship between frailty status and hormonal parameters, including female-specific variables such as menstrual status, menopausal status, and hormone replacement therapy use.
Time frame: Baseline to Month 12
Association Between Frailty and Body Composition
Relationship between frailty status and body composition parameters assessed by bioelectrical impedance analysis, including phase angle.
Time frame: Baseline to Month 12
Association Between Frailty and Gut Microbiota Composition
Relationship between frailty status and fecal microbiota composition assessed by shotgun metagenomic sequencing.
Time frame: Baseline to Month 12
Association Between Frailty and Cognitive Function, Quality of Life, and Self-Esteem
Relationship between frailty status and cognitive performance, quality of life, illness perception, and self-esteem assessed using validated questionnaires.
Time frame: Baseline to Month 12
Association Between Frailty and Social Determinants of Health and Gender Roles
Relationship between frailty status and social determinants of health, health literacy, and gender-related factors.
Time frame: Baseline to Month 12
Development of an Artificial Intelligence-Based Frailty Risk Prediction Model
Development and evaluation of a predictive model integrating clinical, functional, biochemical, hormonal, microbiota, cognitive, psychosocial, and gender-related variables to identify patients at increased risk of frailty progression over one year.
Time frame: Baseline to Month 12
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