Background: Liver transplantation is the only curative treatment for patients with end-stage liver disease. Short-term survival has improved due to improved surgical techniques and greater efficacy of immunosuppressive drugs. At present, the 10-year survival after liver transplantation is 60%, but long-term survival has not improved to the same extent the short-term survival. In addition to liver- and transplant-related causes, comorbidities such as cardiovascular, pulmonary, renal, and metabolic diseases have emerged as leading causes of morbidity and mortality in liver transplant recipients. The objective of this study is to assess the burden of comorbidities and identify both liver- and transplant-related risk factors as well as traditional risk factors that contribute to the pathogenesis of comorbidity in liver transplant recipients. Methods/design: The DACOLT study is an observational, longitudinal study. The investigators aim to include all adult liver transplant recipients in Denmark. Participants will be matched by sex and age to controls from the Copenhagen General Population Study (CGPS) and the Copenhagen City Heart Study (CCHS). Physical and biological measures including blood pressure, ancle-brachial index, spirometry, exhaled nitric oxide, electrocardiogram, transthoracic echocardiography, computed tomography (CT) angiography of the heart, unenhanced CT of chest and abdomen and blood samples will be collected using uniform protocols in participants in CGPS, CCHS and DACOLT. Blood samples will be collected and stored in a research biobank. Follow-up examinations at regular intervals up to 10 years of follow-up are planned. Discussion: There is no international consensus standard for optimal clinical care or monitoring of liver transplant recipients. The study will determine prevalence, incidence and risk factors for comorbidity in liver transplant recipients and may be used to provide evidence for guidelines on screening and long-term treatment and thereby contribute to improvement of the long-term survival.
Age range
20 Years – 100 Years
Sex
ALL
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A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Prevalence of coronary artery disease
Timeframe: Baseline cross-sectional data
Change in Coronary artery disease
Timeframe: 10 years follow-up
Cardiac function
Timeframe: Baseline cross-sectional data
Change in Cardiac function
Timeframe: 10 years follow-up
Cardiac structure
Timeframe: Baseline cross-sectional data
Change in cardiac structure
Timeframe: 10 years follow-up
Cardiac structure
Timeframe: Baseline cross-sectional data
Change in Cardiac structure
Timeframe: 10 years follow-up
Cardiac function
Timeframe: Baseline cross-sectional data
Change in Cardiac function
Timeframe: 10 years follow-up
Dynamic lung function indices assessed by spirometry
Timeframe: Baseline cross-sectional data
Change in Dynamic lung function indices assessed by spirometry
Timeframe: 10 years follow-up
Renal function
Timeframe: Baseline cross-sectional data
Change in Renal function
Timeframe: 10 years follow-up
Metabolic diseases
Timeframe: Baseline cross-sectional data
Metabolic diseases
Timeframe: 10 years follow-up
Metabolic diseases
Timeframe: Baseline cross-sectional data
Metabolic diseases
Timeframe: 10 years follow-up