Breakfast: Shaping Guidelines for Food and Nutrient Patterns

2019 ◽  
pp. 133-142
Author(s):  
Michael J. Gibney ◽  
Irina Uzhova
Keyword(s):  
Nutrient Patterns

Nutrient pattern of unsaturated fatty acids and vitamin E increase resting metabolic rate of overweight and obese women

2020 ◽  
pp. 1-9
Author(s):  
Habib Yarizadeh ◽  
Leila Setayesh ◽  
Caroline Roberts ◽  
Mir Saeed Yekaninejad ◽  
Khadijeh Mirzaei
Keyword(s):  
Vitamin E ◽  
Metabolic Rate ◽  
Unsaturated Fatty Acids ◽  
Resting Metabolic Rate ◽  
Blood Parameters ◽  
Cross Sectional Study ◽  
Obese Women ◽  
Cross Sectional ◽  
Dietary Nutrients ◽  
Nutrient Patterns

Abstract. Objectives: Obesity plays an important role in the development of chronic diseases including cardiovascular disease and diabetes. A low resting metabolic rate (RMR) for a given body size and composition is a risk factor for obesity, however, there is limited evidence available regarding the association of nutrient patterns and RMR. The aim of this study was to determine the association of nutrient patterns and RMR in overweight and obese women. Study design: This cross-sectional study was conducted on 360 women who were overweight or obese. Method: Dietary intake was assessed using a semi-quantitative standard food frequency questionnaire (FFQ). Nutrient patterns were also extracted by principal components analysis (PCA). All participants were evaluated for their body composition, RMR, and blood parameters. Result: Three nutrient patterns explaining 64% of the variance in dietary nutrients consumption were identified as B-complex-mineral, antioxidant, and unsaturated fatty acid and vitamin E (USFA-vit E) respectively. Participants were categorized into two groups based on the nutrient patterns. High scores of USFA-vit E pattern was significantly associated with the increase of RMR (β = 0.13, 95% CI = 0.79 to 68.16, p = 0.04). No significant associations were found among B-complex-mineral pattern (β = −0.00, 95% CI = −49.67 to 46.03, p = 0.94) and antioxidant pattern (β = 0.03, 95% CI −41.42 to 22.59, p = 0.56) with RMR. Conclusion: Our results suggested that the “USFA-vit E” pattern (such as PUFA, oleic, linoleic, vit.E, α-tocopherol and EPA) was associated with increased RMR.

scholarly journals A Priori and a Posteriori Dietary Patterns among Pregnant Women in Johannesburg, South Africa: The NuPED Study

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 565
Author(s):  
Cornelia Conradie ◽  
Jeannine Baumgartner ◽  
Linda Malan ◽  
Elizabeth A. Symington ◽  
Marike Cockeran ◽  
...  
Keyword(s):  
South Africa ◽  
Pregnant Women ◽  
Dietary Patterns ◽  
Dietary Pattern ◽  
A Priori ◽  
Dietary Quality ◽  
A Posteriori ◽  
Urban South ◽  
Quality Diet ◽  
Nutrient Patterns

Dietary pattern analyses allow assessment of the diet as a whole. Limited studies include both a priori and a posteriori dietary pattern analyses. This study aimed to explore the diet of pregnant women in urban South Africa through both a priori and a posteriori dietary pattern analyses and associated maternal and household factors. Dietary data were collected during early pregnancy using a quantified food frequency questionnaire from 250 pregnant women enrolled in the Nutrition During Pregnancy and Early Development (NuPED) cohort. A priori dietary patterns were determined using the Diet Quality Index-International (DQI-I), and a posteriori nutrient patterns using exploratory factor analysis. Based on the DQI-I, the study population followed a borderline low-quality diet. Three a posteriori nutrient patterns were identified: Pattern 1 “plant protein, iron, thiamine, and folic acid”; pattern 2 “animal protein, copper, vitamin A, and vitamin B12”; pattern 3 “fatty acids and sodium”. Pattern 1 was associated with higher dietary quality (p < 0.001), lower maternal educational level (p = 0.03) and socioeconomic status (p < 0.001). Pattern 3 was significantly associated with lower dietary quality. The low dietary quality among pregnant women residing in urban South Africa should be addressed to ensure optimal maternal and offspring health outcomes.

Nutrient patterns and risk of esophageal squamous cell carcinoma: a case-control study

2011 ◽  
Vol 25 (5) ◽  
pp. 442-448 ◽  
Author(s):  
B. Hajizadeh ◽  
M. Jessri ◽  
M. Akhoondan ◽  
S. M. Moasheri ◽  
B. Rashidkhani
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Case Control Study ◽  
Case Control ◽  
Nutrient Patterns ◽  
Control Study

Nutrient patterns and risk of fracture in older subjects: results from the Three-City Study

2012 ◽  
Vol 24 (4) ◽  
pp. 1295-1305 ◽  
Author(s):  
C. Samieri ◽  
V. Ginder Coupez ◽  
S. Lorrain ◽  
L. Letenneur ◽  
B. Allès ◽  
...  
Keyword(s):  
Risk Of Fracture ◽  
Nutrient Patterns ◽  
Older Subjects

Association between nutrient patterns and bone mineral density among ageing adults

2017 ◽  
Vol 22 ◽  
pp. 97-106 ◽  
Author(s):  
Yohannes Adama Melaku ◽  
Tiffany K. Gill ◽  
Anne W. Taylor ◽  
Robert Adams ◽  
Zumin Shi
Keyword(s):  
Bone Mineral Density ◽  
Bone Mineral ◽  
Mineral Density ◽  
Nutrient Patterns

scholarly journals Nutrient Patterns and Their Association with Socio-Demographic, Lifestyle Factors and Obesity Risk in Rural South African Adolescents

Nutrients ◽  
2015 ◽  
Vol 7 (5) ◽  
pp. 3464-3482 ◽  
Author(s):  
Pedro Pisa ◽  
Titilola Pedro ◽  
Kathleen Kahn ◽  
Stephen Tollman ◽  
John Pettifor ◽  
...  
Keyword(s):  
South African ◽  
Lifestyle Factors ◽  
Obesity Risk ◽  
Rural South ◽  
Nutrient Patterns

scholarly journals A treelet transform analysis to relate nutrient patterns to the risk of hormonal receptor-defined breast cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC)

2015 ◽  
Vol 19 (2) ◽  
pp. 242-254 ◽  
Author(s):  
Nada Assi ◽  
Aurelie Moskal ◽  
Nadia Slimani ◽  
Vivian Viallon ◽  
Veronique Chajes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Principal Component Analysis ◽  
Lower Risk ◽  
Menopausal Status ◽  
Principal Component ◽  
Component Analysis ◽  
Cox Proportional Hazards ◽  
Hormonal Receptor ◽  
Nutrient Patterns ◽  
Prospective Investigation

AbstractObjectivePattern analysis has emerged as a tool to depict the role of multiple nutrients/foods in relation to health outcomes. The present study aimed at extracting nutrient patterns with respect to breast cancer (BC) aetiology.DesignNutrient patterns were derived with treelet transform (TT) and related to BC risk. TT was applied to twenty-three log-transformed nutrient densities from dietary questionnaires. Hazard ratios (HR) and 95 % confidence intervals computed using Cox proportional hazards models quantified the association between quintiles of nutrient pattern scores and risk of overall BC, and by hormonal receptor and menopausal status. Principal component analysis was applied for comparison.SettingThe European Prospective Investigation into Cancer and Nutrition (EPIC).SubjectsWomen (n 334 850) from the EPIC study.ResultsThe first TT component (TC1) highlighted a pattern rich in nutrients found in animal foods loading on cholesterol, protein, retinol, vitamins B12 and D, while the second TT component (TC2) reflected a diet rich in β-carotene, riboflavin, thiamin, vitamins C and B6, fibre, Fe, Ca, K, Mg, P and folate. While TC1 was not associated with BC risk, TC2 was inversely associated with BC risk overall (HRQ5 v. Q1=0·89, 95 % CI 0·83, 0·95, Ptrend<0·01) and showed a significantly lower risk in oestrogen receptor-positive (HRQ5 v. Q1=0·89, 95 % CI 0·81, 0·98, Ptrend=0·02) and progesterone receptor-positive tumours (HRQ5 v. Q1=0·87, 95 % CI 0·77, 0·98, Ptrend<0·01).ConclusionsTT produces readily interpretable sparse components explaining similar amounts of variation as principal component analysis. Our results suggest that participants with a nutrient pattern high in micronutrients found in vegetables, fruits and cereals had a lower risk of BC.

Specific nutrient patterns are associated with higher structural brain integrity in dementia-free older adults

NeuroImage ◽  
2019 ◽  
Vol 199 ◽  
pp. 281-288 ◽  
Author(s):  
Federica Prinelli ◽  
Laura Fratiglioni ◽  
Grégoria Kalpouzos ◽  
Massimo Musicco ◽  
Fulvio Adorni ◽  
...  
Keyword(s):  
Older Adults ◽  
Nutrient Patterns ◽  
Structural Brain
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