Are Total and Individual Dietary Lignans Related to Cardiovascular Disease and Its Risk Factors in Postmenopausal Women? A Nationwide Study.
Anna Maria WitkowskaAnna WaśkiewiczMałgorzata Elżbieta ZujkoDanuta SzcześniewskaUrszula StepaniakAndrzej PająkWojciech DrygasPublished in: Nutrients (2018)
The study objectives were to examine total and individual lignan intakes and their dietary sources in postmenopausal Polish women and to investigate the relationship between lignan intake and the prevalence of cardiovascular disease (CVD), hypertension, hypercholesterolemia and central obesity. A total of 2599 postmenopausal women, participants of the Multi-centre National Population Health Examination Surveys (WOBASZ and WOBASZ II) were selected. Of them, 916 had a history of CVD. Nutritional data were collected using a single 24-h dietary recall. Data on lignan content in food, i.e., lariciresinol (LARI), matairesinol (MAT), pinoresinol (PINO) and secoisolariciresinol (SECO), were collected from the available lignan databases. In postmenopausal women, total and individual lignan intakes (SECO, PINO, MAT) were not associated with the prevalence of CVD and its risk factors. The intake of LARI was linked by 30% to the reduced odds for hypercholestrolemia. This study reinforces the existing concept that dietary total lignans are not associated with the prevalence of CVD, and provides further evidence that they are not linked to CVD risk factors such as hypertension, hypercholesterolemia and central obesity. However, the intake of LARI should be taken into consideration in further studies with regard to its potentially beneficial effect in hypercholesterolemia.
Keyphrases
- postmenopausal women
- risk factors
- bone mineral density
- cardiovascular disease
- weight gain
- type diabetes
- blood pressure
- cardiovascular events
- weight loss
- metabolic syndrome
- insulin resistance
- electronic health record
- body composition
- polycystic ovary syndrome
- machine learning
- adipose tissue
- climate change
- pregnant women
- cross sectional
- physical activity
- low density lipoprotein
- risk assessment
- body mass index
- deep learning
- drug induced