Dietary Patterns with Healthy and Unhealthy Traits Among Overweight/Obese Hispanic Women with or at High Risk for Type 2 Diabetes.
Mayra Arias-GastélumNangel M LindbergMichael C LeoMeg BrueningCorrie M WhisnerCheryl Der AnanianSteven P HookerErin S LeBlancVictor J StevensElizabeth ShusterRichard T MeenanSara GilleKatherine A VaughnAnn TurnerSonia Vega-LόpezPublished in: Journal of racial and ethnic health disparities (2020)
Hispanic women are at high risk for type 2 diabetes (T2D), with obesity and unhealthy eating being important contributing factors. A cross-sectional design was used in this study to identify dietary patterns and their associations with diabetes risk factors. Participants completed a culturally adapted Food Frequency Questionnaire capturing intake over the prior 3 months. Overweight/obese Hispanic women (n = 191) with or at risk for T2D were recruited from a community clinic into a weight loss intervention. Only baseline data was used for this analysis. Dietary patterns and their association with diabetes risk factors (age, body mass index, abdominal obesity, elevated fasting blood glucose [FBG], and hemoglobin A1c). An exploratory factor analysis of dietary data adjusted for energy intake was used to identify eating patterns, and Pearson correlation coefficient (r) to assess the association of the eating patterns with the diabetes risk factors. Six meaningful patterns with healthful and unhealthful traits emerged: (1) sugar and fat-laden, (2) plant foods and fish, (3) soups and starchy dishes, (4) meats and snacks, (5) beans and grains, and (6) eggs and dairy. Scores for the "sugar and fat-laden" and "meats and snacks" patterns were negatively associated with age (r = - 0.230, p = 0.001 and r = - 0.298, p < 0.001, respectively). Scores for "plant foods and fish" were positively associated with FBG (r = 0.152, p = 0.037). Being younger may be an important risk factor for a diet rich in sugar and fat; this highlights the need to assess dietary patterns among younger Hispanic women to identify traits potentially detrimental for their health.
Keyphrases
- weight loss
- glycemic control
- blood glucose
- risk factors
- type diabetes
- bariatric surgery
- weight gain
- roux en y gastric bypass
- polycystic ovary syndrome
- gastric bypass
- adipose tissue
- body mass index
- insulin resistance
- african american
- healthcare
- genome wide
- mental health
- pregnancy outcomes
- cardiovascular disease
- randomized controlled trial
- public health
- electronic health record
- big data
- cervical cancer screening
- metabolic syndrome
- computed tomography
- magnetic resonance imaging
- machine learning
- artificial intelligence
- skeletal muscle
- pregnant women
- risk assessment
- psychometric properties