Sex Differences, Genetic and Environmental Influences on Dilated Cardiomyopathy.
Angita JainNadine NortonKatelyn A BrunoLeslie T CooperPaldeep S AtwalDeLisa FairweatherPublished in: Journal of clinical medicine (2021)
Dilated cardiomyopathy (DCM) is characterized by dilatation of the left ventricle and impaired systolic function and is the second most common cause of heart failure after coronary heart disease. The etiology of DCM is diverse including genetic pathogenic variants, infection, inflammation, autoimmune diseases, exposure to chemicals/toxins as well as endocrine and neuromuscular causes. DCM is inherited in 20-50% of cases where more than 30 genes have been implicated in the development of DCM with pathogenic variants in TTN (Titin) most frequently associated with disease. Even though male sex is a risk factor for heart failure, few studies have examined sex differences in the pathogenesis of DCM. We searched the literature for studies examining idiopathic or familial/genetic DCM that reported data by sex in order to determine the sex ratio of disease. We found 31 studies that reported data by sex for non-genetic DCM with an average overall sex ratio of 2.5:1 male to female and 7 studies for familial/genetic DCM with an overall average sex ratio of 1.7:1 male to female. No manuscripts that we found had more females than males in their studies. We describe basic and clinical research findings that may explain the increase in DCM in males over females based on sex differences in basic physiology and the immune and fibrotic response to damage caused by mutations, infections, chemotherapy agents and autoimmune responses.
Keyphrases
- heart failure
- genome wide
- copy number
- case control
- oxidative stress
- left ventricular
- systematic review
- blood pressure
- dna methylation
- multiple sclerosis
- squamous cell carcinoma
- early onset
- big data
- radiation therapy
- climate change
- gene expression
- cardiac resynchronization therapy
- pulmonary artery
- congenital heart disease
- locally advanced
- data analysis