Associations between Cardiometabolic Risk Factors and Increased Consumption of Diverse Legumes: A South African Food and Nutrition Security Programme Case Study.
Xolile MkhizeWilna H Oldewage-TheronCarin NapierKevin Jan DuffyPublished in: Nutrients (2024)
The programme aimed to improve selected cardiometabolic risk (CMR) variables using a nutritional intervention among farmers who reported hypertensive disorders as hindrances during agricultural activities. The intervention had two case controls ( n = 103) [experimental group-EG ( n = 53) and control group-CG ( n = 50)] which were tracked and whose blood pressure measurements, dietary intake, blood indices for cholesterol concentration and glucose levels from pre- and post-intervention surveys after the baseline survey ( n = 112) were analysed. The interval for data collection was 12 weeks (±120 days) after five legume varieties were consumed between 3 and 5 times a day, and servings were not <125 g per at least three times per week. Sixty-five per cent of farmers were above 60 years old, with mean age ranges of 63.3 (SD ± 6.3) years for women and 67.2 (SD ± 6.7) for men. The post-intervention survey revealed that EG blood results indicated nutrient improvement with p <= 0.05 for blood glucose ( p = 0.003) and cholesterol ( p = 0.001) as opposed to the CG. A trend analysis revealed that cholesterol ( p = 0.033) and systolic blood pressure (SBP); ( p = 0.013) were statistically significant when comparing genders for all study phases. Interventions focusing on legumes can improve hypertension and cardiovascular disease and fast-track the achievement of SGDs 3 and 12 through community-based programmes.
Keyphrases
- blood pressure
- blood glucose
- randomized controlled trial
- cardiovascular disease
- hypertensive patients
- heart rate
- risk factors
- physical activity
- study protocol
- cross sectional
- low density lipoprotein
- type diabetes
- risk assessment
- glycemic control
- single cell
- heart failure
- climate change
- heavy metals
- clinical trial
- human health
- pregnant women
- middle aged
- deep learning
- machine learning
- coronary artery disease
- cardiovascular risk factors