Association of Nutrient Patterns with Metabolic Syndrome and Its Components in Iranian Adults.
Zahra AkbarzadeMohammad Reza AminiAlireza JafariKhadijeh MirzaeiFatemeh MohtashaminiaMaryam MajdiElham BazshahiKourosh DjafarianCain Craig Truman ClarkSakineh ShabbidarPublished in: Clinical nutrition research (2020)
We aimed to examine the association between nutrient patterns and metabolic syndrome (MetS) in Iranian adults. In a cross-sectional study of 850 self-certified healthy women and men aged 20-59 years old, dietary data were assessed using three 24-hour recall. Anthropometric measures were done and blood samples were collected to measure serum fasting serum glucose and lipid profile. The MetS was defined using the International Diabetes Federation. Major nutrient patterns were identified using principle competent analysis. In the first nutrient pattern, the individuals in the fifth quintile had a higher intake of vitamins B1, B2, B3, B5, B6, B12, zinc, iron, saturated fatty acids (SFAs), and protein. In the second nutrient pattern, individuals in the first quintile had lower consumption of zinc, SFAs, vitamin E, α-tocopherol, oleic acid, polyunsaturated fatty acids, β-carotene, linolenic acid, and monounsaturated fatty acids, compared to the fifth quintile. Furthermore, in the third nutrient pattern, the individuals in the fifth quintile had a higher intake of potassium, magnesium, phosphorous, calcium, protein, carbohydrate, vitamin C, and folate compared to other quintiles. We identified the second pattern had an indirect association with systolic and diastolic blood pressure, triglycerides, fasting blood sugar (p < 0.001 for all), and total cholesterol (p = 0.04) when it was controlled for body weight. Our findings showed that nutrient patterns may have an association with MetS components with mediating body weight.
Keyphrases
- body weight
- blood pressure
- metabolic syndrome
- fatty acid
- insulin resistance
- blood glucose
- left ventricular
- type diabetes
- cardiovascular disease
- heart failure
- polycystic ovary syndrome
- protein protein
- heart rate
- machine learning
- risk factors
- adipose tissue
- electronic health record
- small molecule
- deep learning
- data analysis
- pregnancy outcomes