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A Systematic Review of Phenotypic Features Associated With Cardiac Troponin I Mutations in Hereditary Cardiomyopathies

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CANADIAN JOURNAL OF CARDIOLOGY
卷 31, 期 11, 页码 1377-1385

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ELSEVIER SCIENCE INC
DOI: 10.1016/j.cjca.2015.06.015

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  1. Faculty of Health Sciences, University of Southern Denmark
  2. Research Foundation of Region Southern Denmark

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Background: Genetic investigations have established that mutations in proteins of the contractile unit of the myocardium, known as the sarcomere, may be associated with hypertrophic cardiomyopathy (HCM), restrictive cardiomyopathy (RCM), and dilated cardiomyopathy (DCM). It has become clinical practice to offer genetic testing in affected individuals to identify causative mutations, which provides the basis for presymptomatic testing of relatives who are at risk of disease development. This ensures adequate clinical follow-up of mutation carriers, whereas noncarriers can be discharged. However, before genetic testing can be used for individual risk assessment and prediction of prognosis, it is important to investigate if there is a relation between the clinical disease expression (phenotype) of the condition and mutations in specific disease genes (genotype). Methods: We reviewed the literature in relation to phenotypic features reported to be associated with mutations in cardiac troponin I (cTnI; TNNI3), which is a recognized sarcomeric disease gene in all 3 cardiomyopathies. Results: The results of this review did not identify specific genotype-phenotype relations in HCM or DCM, and cTnI appeared to be the most frequent disease gene in RCM. Conclusions: To further explore if there is a genotype-phenotype relation, long-term follow-up studies are needed. It is essential to investigate the natural history of the condition among affected individuals and to provide clinical follow-up on disease development among healthy mutation carriers. Such information is required to provide evidence-based counselling for affected families and to elucidate if knowledge about specific genotypes can be used in future risk prediction models.

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