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NCT Number: NCT07791589

Fibrosis and Metabolic Markers in Relation to PREVENT Cardiovascular Risk in MASLD

This retrospective observational study evaluates the relationship between non-invasive liver fibrosis and metabolic markers and estimated cardiovascular risk in adults with metabolic dysfunction-associated steatotic liver disease (MASLD). The primary objective is to assess the association between the Fibrosis-4 index (FIB-4) and 10-year cardiovascular disease risk estimated using the American Heart Association PREVENT equations, with particular attention to the effect of age on this association. Secondary analyses evaluate the Fibrosis-3 index (FIB-3), triglyceride-glucose index (TyG), and liver stiffness measurement (LSM) in relation to PREVENT-estimated risks of cardiovascular disease, atherosclerotic cardiovascular disease, and heart failure.

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Key information

Conditions

Metabolic Dysfunction-Associated Steatotic Liver Disease

Age range

30 year–79 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Göztepe Prof. Dr. Süleyman Yalçın City Hospital

Istanbul, 34722, Turkey (Türkiye)

About this study

This is a retrospective, single-center, observational study of adults with metabolic dysfunction-associated steatotic liver disease (MASLD) evaluated between January 1, 2024 and April 30, 2026. MASLD was defined by hepatic steatosis on ultrasonography together with at least one cardiometabolic risk factor, after exclusion of alternative causes of chronic liver disease.

Clinical and laboratory data were obtained from electronic medical records. Non-invasive indices included the Fibrosis-4 index (FIB-4), Fibrosis-3 index (FIB-3), triglyceride-glucose index (TyG), and liver stiffness measurement (LSM) by transient elastography when available.

Cardiovascular risk was estimated using the American Heart Association Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) base equations. Ten-year estimates were calculated for total cardiovascular disease (PREVENT-CVD), atherosclerotic cardiovascular disease (PREVENT-ASCVD), and heart failure (PREVENT-HF).

The primary analysis examines the association between FIB-4 and PREVENT-CVD risk. FIB-3 and TyG are evaluated as secondary markers, while analyses involving PREVENT-ASCVD, PREVENT-HF, and LSM are supportive. Because age is incorporated into FIB-4 and is also a major determinant of cardiovascular risk, crude associations are compared with age-adjusted partial Spearman correlations. A sensitivity analysis is also performed in participants without diabetes.

Participants with prior cardiovascular disease were excluded from the primary prevention cohort. Analyses are based on complete cases, without imputation of missing data.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Adults aged 30 to 79 years. Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by hepatic steatosis on ultrasonography together with at least one cardiometabolic risk factor.

Clinical data available for calculation of the PREVENT cardiovascular risk estimates within the supported input ranges.

Exclusion criteria

  • Alternative causes of chronic liver disease. One or more required PREVENT input variables outside the supported ranges: systolic blood pressure 90-200 mmHg, total cholesterol 130-320 mg/dL, HDL cholesterol 20-100 mg/dL, eGFR 15-140 mL/min/1.73 m², or BMI 18.5-39.9 kg/m².

Prior cardiovascular disease, defined as a history of stroke, coronary artery disease, heart failure, atrial fibrillation, or peripheral arterial disease.

Treatment and study plan

Primary outcomes

  1. Correlation between FIB-4 score and 10-year PREVENT-CVD risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    FIB-4 score (unitless) was calculated as (age × AST)/(platelet count × √ALT). Ten-year PREVENT-CVD risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

Secondary outcomes

  1. Correlation between FIB-3 score and 10-year PREVENT-CVD risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    FIB-3 score (unitless) was calculated as 5 × ln(AST) - 2 × ln(ALT) - 0.18 × (platelet count/10) - 5. Ten-year PREVENT-CVD risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

  2. Correlation between TyG index and 10-year PREVENT-CVD risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    The triglyceride-glucose (TyG) index (unitless) was calculated as ln[(fasting triglycerides × fasting glucose)/2], with triglycerides and glucose expressed in mg/dL. Ten-year PREVENT-CVD risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

  3. Correlation between liver stiffness measurement and 10-year PREVENT-CVD risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    Liver stiffness measurement (LSM) was expressed in kilopascals (kPa) and measured by transient elastography using a FibroScan 502 Touch system (Echosens, Paris, France). Ten-year PREVENT-CVD risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

  4. Correlation between FIB-4 score and 10-year PREVENT-ASCVD risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    FIB-4 score (unitless) was calculated as (age × AST)/(platelet count × √ALT). Ten-year PREVENT-ASCVD risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

  5. Correlation between FIB-3 score and 10-year PREVENT-ASCVD risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    FIB-3 score (unitless) was calculated as 5 × ln(AST) - 2 × ln(ALT) - 0.18 × (platelet count/10) - 5. Ten-year PREVENT-ASCVD risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

  6. Correlation between TyG index and 10-year PREVENT-ASCVD risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    The triglyceride-glucose (TyG) index (unitless) was calculated as ln[(fasting triglycerides × fasting glucose)/2], with triglycerides and glucose expressed in mg/dL. Ten-year PREVENT-ASCVD risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

  7. Correlation between liver stiffness measurement and 10-year PREVENT-ASCVD risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    Liver stiffness measurement (LSM) was expressed in kilopascals (kPa) and measured by transient elastography using a FibroScan 502 Touch system (Echosens, Paris, France). Ten-year PREVENT-ASCVD risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

  8. Correlation between FIB-4 score and 10-year PREVENT-HF risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    FIB-4 score (unitless) was calculated as (age × AST)/(platelet count × √ALT). Ten-year PREVENT-HF risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

  9. Correlation between FIB-3 score and 10-year PREVENT-HF risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    FIB-3 score (unitless) was calculated as 5 × ln(AST) - 2 × ln(ALT) - 0.18 × (platelet count/10) - 5. Ten-year PREVENT-HF risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

  10. Correlation between TyG index and 10-year PREVENT-HF risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    The triglyceride-glucose (TyG) index (unitless) was calculated as ln[(fasting triglycerides × fasting glucose)/2], with triglycerides and glucose expressed in mg/dL. Ten-year PREVENT-HF risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

  11. Correlation between liver stiffness measurement and 10-year PREVENT-HF risk

    Time frame: At the index clinical evaluation during the retrospective study period (January 1, 2024 to April 30, 2026)

    Liver stiffness measurement (LSM) was expressed in kilopascals (kPa) and measured by transient elastography using a FibroScan 502 Touch system (Echosens, Paris, France). Ten-year PREVENT-HF risk was expressed as a percentage (%) and calculated using the published sex-specific American Heart Association PREVENT base equation. Correlation was quantified using Spearman's rank correlation coefficient (rho; unitless, range -1 to +1). An age-adjusted partial Spearman correlation was also calculated.

Interested in participating?

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Sponsors and collaborators

Lead sponsor

Özgür Bahadır

Other

Registry information

Official study title

Fibrosis Versus Metabolic Risk in MASLD: Age-Adjusted Associations With PREVENT Cardiovascular Risk

Acronym: MASLD-PREVENT

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Aug 28, 2026
Registry last updated
Aug 28, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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