Dietary patterns associated with body mass index (BMI) and lifestyle in Mexican adolescents

Abstract

BackgroundThe objetive in this study is to determine the relationship between dietary patterns, BMI, type 2 diabetes mellitus family history (T2DMFH) and some lifestyle variables such as smoking and skipping breakfast in a Mexican adolescent population.MethodsCross-sectional, observational, analytical study.Subjetcts: 14-16 years old male and female adolescents (n 373). A previously validated food frequency questionnaire (FFQ) was used and dietary patterns were derived using principal component analysis (PCA). Scores for dietary patterns were categorized by tertiles.ResultsThree major dietary patterns that explained 47 % of variance were found: westernized, high in protein/fat and prudent pattern. Subjects at the highest tertile of prudent pattern had lower BMI. And was also associated with less T2DMFH and less smoking habit when compared with the lowest tertile. We found a positive correlation between BMI and high scores for westernized and high in protein/fat patternConclusionsDietary patterns of adolescents are a public health concern because there is a direct association between inadequate diet at this early age and obesity

Association of eating patterns and abdominal adiposity in Brazilian

“…It is similar to patterns found by Gutiérrez-Pliego et al [3], named as "high protein and animal fat", in a study with the Spanish population, and by Rodrigues et al [26] and Ferreira et al [27], in studies with the Brazilian population, who named the patterns as "western and snacks" and "weekend meals", respectively.…”

“…Although some epidemiological investigations have shown a significant increase in the sale of ultraprocessed products, which between 2000 and 2013 increased by 43.7% worldwide [23,24], the traditional pattern was the most representative of our population. It includes food items similar to those studies conducted by Gutiérrez-Pliego et al [3] and Vilela et al [6], from Spain and Brazil, respectively, who named them as prudent, and McEvoy et al [25], who investigated diet patterns in several European countries and named them as healthy.…”

“…The identification of dietary patterns emerged as an alternative method for studying the relationship between diet and chronic non-communicable diseases. Studies based on nutritional epidemiology have traditionally focused on the relationship between disease and specific nutrients and/or foods, however, the analysis of patterns focuses on the study of the combinations of nutrients and/or foods that the population usually consumes [2,3]. Thus, it has the advantage of allowing a more complete view of the diet, and how it affects health and disease [4].…”

Padrões alimentares de adolescentes brasileiros por regiões geográficas: análise do Estudo de Riscos Cardiovasculares em Adolescentes (ERICA)

“…Due to the large volume of food consumption data, we opted to group these foods according to the similarity of macronutrients, creating food groups with similar nutritional composition. This procedure is often used in the specific literature 30,31 and was based on a grouping performed in a previous publication 4 which also used the food consumption data from ERICA. The reported foods were categorized in 19 groups (Box 1), and final consumption was estimated in grams for each of the food groups.…”

Determines Hyperglycaemia Spreads in Generations with Multiple Complications That Imposing Towards Death

“…Several important risk factors for the disease have been identified which include: obesity, poor diet, physical inactivity, increasing age, family history of diabetes, ethnicity, poor nutrition during pregnancy affecting the developing child, just to name a few [5]. Various studies have also explained the associations between several risk factors and the risk of type 2 diabetes such as body mass index (BMI), lipids, hypertension, smoking, low education, dietary patterns, and recently specific genes [6][7][8][9][10][11][12][13][14][15]. Female had complex pervasiveness of diabetes compared to male both in urban and rural (urban female-8.5%, male 7.7% and rural female-2.5%, male-1.9%).…”