scholarly journals Metabolomic markers of healthy dietary patterns in US postmenopausal women

2019 ◽  
Vol 109 (5) ◽  
pp. 1439-1451 ◽  
Author(s):  
Marjorie L McCullough ◽  
Maret L Maliniak ◽  
Victoria L Stevens ◽  
Brian D Carter ◽  
Rebecca A Hodge ◽  
...  
Keyword(s):  
Postmenopausal Women ◽  
Healthy Eating ◽  
Dietary Patterns ◽  
Partial Correlation ◽  
Healthy Eating Index ◽  
Healthy Diet ◽  
Diet Patterns ◽  
Diet Pattern ◽  
Risk Of Cancer ◽  
Diet Score

ABSTRACT Background Healthy diet patterns are associated with lower risk of cancer and other chronic diseases. Metabolomics has the potential to expand dietary biomarker development to include dietary patterns, which may provide a complement or alternative to self-reported diet. Objective This study examined the correlation of serum untargeted metabolomic markers with 4 diet pattern scores—the alternate Mediterranean diet score (aMED), alternate Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, and the Healthy Eating Index (HEI)-2015—and used multivariate methods to identify discriminatory metabolites for each pattern. Methods Among 1367 US postmenopausal women with serum metabolomic data in the Cancer Prevention Study-II Nutrition Cohort, we conducted partial correlation analysis, adjusted for demographic and lifestyle variables, to examine cross-sectional correlations between serum metabolomic markers and healthy diet pattern scores. In a randomly selected “training” set (50%), we conducted orthogonal partial least-squares discriminant analysis to identify metabolites that discriminated the top from bottom diet score quintiles. Combinations of metabolites with a variable importance in projection (VIP) score ≥2.5 were tested for predictability in the “testing” set based on the use of receiver operating characteristic curves. Results Out of 1186 metabolites, 32 unique metabolites were considered discriminatory based on a VIP score ≥2.5 in the training dataset with some overlap across scores (aMED = 16; AHEI = 17; DASH = 13; HEI = 12). Spearman partial correlation analyses, applying a cut-point (|r| ≥ 0.15) and Bonferroni correction (P < 1.05 × 10−5), identified similar key metabolites. The top 5 metabolites for each pattern mostly distinguished high compared with low scores; 4 of the 5 (fish-derived) metabolites were the same for aMED and AHEI, 2 of which were identified for HEI; 4 DASH metabolites were unique. Conclusions Metabolomic methods that used a split-sample approach identified potential biomarkers for 4 healthy diet patterns. Similar metabolites across scores reflect fish consumption in healthy dietary patterns. These findings should be replicated in independent populations.

Dietary Patterns And Risk Of Cancer: Current Evidence And Future Directions

2018 ◽  
Author(s):  
Fang Fang Zhang
Keyword(s):  
Healthy Eating ◽  
Cancer Incidence ◽  
Diet Quality ◽  
Dietary Patterns ◽  
Healthy Eating Index ◽  
Quality Indices ◽  
Alternative Healthy Eating Index ◽  
Incidence And Mortality ◽  
Risk Of Cancer ◽  
Diet Score

Dietary patterns capture the overall diet and its constituent foods and nutrients, representing a powerful approach to identifying the effect of nutrition on health and disease. In this review, we describe the two main approaches being used to characterize dietary patterns: a prior approach that defines dietary patterns using predefined diet quality indices, and a posterior approach that derives dietary patterns using factor or cluster analysis. Methods to define diet quality indices (Healthy Eating Index, Alternative Healthy Eating Index, Alternative Mediterranean Diet Score, Dietary Approaches to Stop Hypertension Score) are presented, and their similarities and differences are discussed among the different approaches. We review the recent evidence on the relationships between dietary patterns and cancer outcomes, including all-cancer incidence and mortality and the incidence of colorectal, breast, prostate, and lung cancers. Despite the different methods that are used to characterize dietary patterns in different studies, results consistently suggest that adherence to existing dietary guidelines is associated with a reduced risk of cancer incidence and mortality. Given the important role of dietary patterns in cancer prevention, clinicians need to consider providing appropriate nutrition counseling  to improve patients’ dietary patterns. Continuous efforts need to be devoted to better characterize the relationships between dietary patterns and cancer risk by studying specific cancer types, different cancer subtypes, and population subgroups, with a better approach that can accurately assess dietary patterns throughout the life cycle. This review contains 3 figures, 6 tables and 91 references Key words: Alternative Healthy Eating Index, breast cancer, cancer incidence, cancer mortality, cluster analysis, colorectal cancer, Dietary Approaches to Stop Hypertension, dietary patterns, diet quality index, factor analysis, Healthy Eating Index, lung cancer, Mediterranean Diet Score, prostate cancer, Recommended Food Score

scholarly journals Dietary Pattern Indices and Incident Peripheral Arterial Disease in Women: A Prospective Cohort Study

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 513-513
Author(s):  
Guochong Chen ◽  
Yasmin Mossavar-Rahmani ◽  
Xiaonan Xue ◽  
Bernhard Haring ◽  
Aladdin Shadyab ◽  
...  
Keyword(s):  
Postmenopausal Women ◽  
Healthy Eating ◽  
Dietary Patterns ◽  
Dietary Pattern ◽  
Lower Risk ◽  
Kidney Diseases ◽  
Healthy Eating Index ◽  
Cox Proportional Hazards ◽  
Processed Meat ◽  
Incident Cases

Abstract Objectives We aimed to evaluate diet quality as reflected by multiple a priori dietary pattern indices in relation to incident PAD. Methods We included 138,506 US postmenopausal women aged 50–79 years without known PAD at baseline (1993–1998) of the Women's Health Initiative. Score of 4 dietary pattern indices, including the alternate Mediterranean diet (aMED) index, the alternate Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet index, and the Healthy Eating Index (HEI)-2015, were derived using data collected by a validated food frequency questionnaire. Incident cases of symptomatic PAD in the lower extremities were ascertained and adjudicated through March 2019 by reviewing medical record. Hazard ratios (HR) and 95% confidence interval (CI) were estimated using Cox proportional hazards models, with adjustment for multiple potential confounders including known risk factors for PAD (i.e., smoking, high blood pressure, dyslipidemia, and diabetes). Results During a median 18.6 years of follow-up, 1036 incident cases of PAD were identified. All 4 dietary pattern indices were inversely associated with risk of PAD. The multivariable-adjusted HRs (95% CI) comparing the highest with the lowest score quartiles were 0.74 (0.61–0.91) for aMED index (P-trend across quartile = 0.010), 0.79 (0.65–0.95) for AHEI-2010 (P-trend &lt; 0.001), 0.66 (0.55–0.80) for DASH index (P-trend &lt; 0.001), and 0.68 (0.56–0.82) for HEI-2015 (P-trend &lt; 0.001). Among major foods/nutrients contributing to these dietary patterns, intakes of whole grains (top vs. bottom quartile, HR = 0.81; P-trend = 0.01), legumes (HR = 0.77; P-trend = 0.004), dietary fiber (HR = 0.78; P-trend = 0.01), and vegetable protein (HR = 0.76; P-trend = 0.006) were associated with lower risk of PAD, whereas intakes of red meat (HR = 1.38; P-trend = 0.003), processed meat (HR = 1.36; P-trend = 0.004), and regular soft drinks (HR = 1.26; P-trend = 0.01) were associated with higher risk. Conclusions Adherence to various recommended dietary patterns is associated with lower risk of PAD in a nationwide cohort of US postmenopausal women. Our findings may extend the range of cardiovascular diseases that are potentially preventable by adopting a healthy dietary pattern. Funding Sources National Heart, Lung, and Blood Institute; and National Institute of Diabetes and Digestive and Kidney Diseases.

Does the Healthy Eating Index and Mediterranean Diet Score Identify the Nutritional Adequacy of Dietary Patterns in Chronic Pancreatitis?

2019 ◽  
Vol 64 (8) ◽  
pp. 2318-2326 ◽  
Author(s):  
Kristen M. Roberts ◽  
Paige Golian ◽  
Marcia Nahikian-Nelms ◽  
Alice Hinton ◽  
Peter Madril ◽  
...  
Keyword(s):  
Chronic Pancreatitis ◽  
Mediterranean Diet ◽  
Healthy Eating ◽  
Dietary Patterns ◽  
Healthy Eating Index ◽  
Nutritional Adequacy ◽  
Mediterranean Diet Score ◽  
Diet Score

027: Dietary Patterns, the Alternate Healthy Eating Index and Plasma Levels of Sex Hormones in Postmenopausal Women

2005 ◽  
Vol 161 (Supplement_1) ◽  
pp. S7-S7
Author(s):  
T Fung ◽  
M McCullough ◽  
M Holmes ◽  
F Hu ◽  
S Hankinson
Keyword(s):  
Postmenopausal Women ◽  
Sex Hormones ◽  
Healthy Eating ◽  
Dietary Patterns ◽  
Plasma Levels ◽  
Healthy Eating Index

scholarly journals Diet quality assessment indexes

2014 ◽  
Vol 27 (5) ◽  
pp. 605-617 ◽  
Author(s):  
Kênia Mara Baiocchi de Carvalho ◽  
Eliane Said Dutra ◽  
Nathalia Pizato ◽  
Nádia Dias Gruezo ◽  
Marina Kiyomi Ito
Keyword(s):  
General Population ◽  
Quality Assessment ◽  
Healthy Eating ◽  
Diet Quality ◽  
Dietary Patterns ◽  
Nutritional Quality ◽  
Quality Index ◽  
Healthy Eating Index ◽  
Risk Of Disease ◽  
Diet Score

Various indices and scores based on admittedly healthy dietary patterns or food guides for the general population, or aiming at the prevention of diet-related diseases have been developed to assess diet quality. The four indices preferred by most studies are: the Diet Quality Index; the Healthy Eating Index; the Mediterranean Diet Score; and the Overall Nutritional Quality Index. Other instruments based on these indices have been developed and the words 'adapted', 'revised', or 'new version I, II or III' added to their names. Even validated indices usually find only modest associations between diet and risk of disease or death, raising questions about their limitations and the complexity associated with measuring the causal relationship between diet and health parameters. The objective of this review is to describe the main instruments used for assessing diet quality, and the applications and limitations related to their use and interpretation.

scholarly journals Dietary patterns, the Alternate Healthy Eating Index and plasma sex hormone concentrations in postmenopausal women

2007 ◽  
Vol 121 (4) ◽  
pp. 803-809 ◽  
Author(s):  
Teresa T. Fung ◽  
Frank B. Hu ◽  
Robert L. Barbieri ◽  
Walter C. Willett ◽  
Susan E. Hankinson
Keyword(s):  
Postmenopausal Women ◽  
Healthy Eating ◽  
Dietary Patterns ◽  
Healthy Eating Index ◽  
Sex Hormone

scholarly journals Mediterranean Diet and Healthy Eating in Subjects with Prediabetes from the Mollerussa Prospective Observational Cohort Study

Nutrients ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 252
Author(s):  
Mireia Falguera ◽  
Esmeralda Castelblanco ◽  
Marina Idalia Rojo-López ◽  
Maria Belén Vilanova ◽  
Jordi Real ◽  
...  
Keyword(s):  
Glucose Tolerance ◽  
Mediterranean Diet ◽  
Healthy Eating ◽  
Dietary Patterns ◽  
Normal Glucose Tolerance ◽  
Healthy Eating Index ◽  
Study Cohort ◽  
Cross Sectional ◽  
Normal Glucose ◽  
Multivariable Analyses

We aimed to assess differences in dietary patterns (i.e., Mediterranean diet and healthy eating indexes) between participants with prediabetes and those with normal glucose tolerance. Secondarily, we analyzed factors related to prediabetes and dietary patterns. This was a cross-sectional study design. From a sample of 594 participants recruited in the Mollerussa study cohort, a total of 535 participants (216 with prediabetes and 319 with normal glucose tolerance) were included. The alternate Mediterranean Diet score (aMED) and the alternate Healthy Eating Index (aHEI) were calculated. Bivariable and multivariable analyses were performed. There was no difference in the mean aMED and aHEI scores between groups (3.2 (1.8) in the normoglycemic group and 3.4 (1.8) in the prediabetes group, p = 0.164 for the aMED and 38.6 (7.3) in the normoglycemic group and 38.7 (6.7) in the prediabetes group, p = 0.877 for the aHEI, respectively). Nevertheless, women had a higher mean of aMED and aHEI scores in the prediabetes group (3.7 (1.9), p = 0.001 and 40.5 (6.9), p < 0.001, respectively); moreover, they had a higher mean of aHEI in the group with normoglycemia (39.8 (6.6); p = 0.001). No differences were observed in daily food intake between both study groups; consistent with this finding, we did not find major differences in nutrient intake between groups. In the multivariable analyses, the aMED and aHEI were not associated with prediabetes (odds ratio (OR): 1.19, 95% confidence interval (CI): 0.75–1.87; p = 0.460 and OR: 1.32, 95% CI: 0.83–2.10; p = 0.246, respectively); however, age (OR: 1.04, 95% CI: 1.02–1.05; p < 0.001), dyslipidemia (OR: 2.02, 95% CI: 1.27–3.22; p = 0.003) and body mass index (BMI) (OR: 1.09, 95% CI: 1.05–1.14; p < 0.001) were positively associated with prediabetes. Physical activity was associated with a lower frequency of prediabetes (OR: 0.48, 95% CI: 0.31–0.72; p = 0.001). In conclusion, subjects with prediabetes did not show a different dietary pattern compared with a normal glucose tolerance group. However, further research is needed on this issue.

Genetic and Environmental Influences of Dietary Indices in a UK Female Twin Cohort

2020 ◽  
Vol 23 (6) ◽  
pp. 330-337
Author(s):  
Olatz Mompeo ◽  
Rachel Gibson ◽  
Paraskevi Christofidou ◽  
Tim D. Spector ◽  
Cristina Menni ◽  
...  
Keyword(s):  
Healthy Eating ◽  
Menopausal Status ◽  
Healthy Eating Index ◽  
Healthy Diet ◽  
Nutritional Epidemiology ◽  
Best Fitting ◽  
Heritability Estimates ◽  
Fitting Model ◽  
Dietary Indices ◽  
Health Parameters

AbstractA healthy diet is associated with the improvement or maintenance of health parameters, and several indices have been proposed to assess diet quality comprehensively. Twin studies have found that some specific foods, nutrients and food patterns have a heritable component; however, the heritability of overall dietary intake has not yet been estimated. Here, we compute heritability estimates of the nine most common dietary indices utilized in nutritional epidemiology. We analyzed 2590 female twins from TwinsUK (653 monozygotic [MZ] and 642 dizygotic [DZ] pairs) who completed a 131-item food frequency questionnaire (FFQ). Heritability estimates were computed using structural equation models (SEM) adjusting for body mass index (BMI), smoking status, Index of Multiple Deprivation (IMD), physical activity, menopausal status, energy and alcohol intake. The AE model was the best-fitting model for most of the analyzed dietary scores (seven out of nine), with heritability estimates ranging from 10.1% (95% CI [.02, .18]) for the Dietary Reference Values (DRV) to 42.7% (95% CI [.36, .49]) for the Alternative Healthy Eating Index (A-HEI). The ACE model was the best-fitting model for the Healthy Diet Indicator (HDI) and Healthy Eating Index 2010 (HEI-2010) with heritability estimates of 5.4% (95% CI [−.17, .28]) and 25.4% (95% CI [.05, .46]), respectively. Here, we find that all analyzed dietary indices have a heritable component, suggesting that there is a genetic predisposition regulating what you eat. Future studies should explore genes underlying dietary indices to further understand the genetic disposition toward diet-related health parameters.

Abstract P109: Plasma Metabolomic Signature Of Healthy Dietary Patterns In The Chronic Renal Insufficiency Cohort (CRIC) Study

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Hyunju Kim ◽  
Cheryl A Anderson ◽  
Emily A Hu ◽  
Zihe Zheng ◽  
Lawrence J Appel ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Healthy Eating ◽  
Dietary Patterns ◽  
Healthy Eating Index ◽  
Plasma Metabolites ◽  
Dietary Intakes ◽  
Linear Regression Models ◽  
Omega 3 ◽  
Double Bonds ◽  
Disease Associations

Introduction: In individuals with chronic kidney disease (CKD), healthy dietary patterns are inversely associated with CKD progression. Metabolomics, an approach which measures many small molecules in biofluids, can identify biomarkers of healthy dietary patterns and elucidate metabolic pathways underlying diet-disease associations. Hypothesis: We hypothesized that adherence to 4 healthy dietary patterns would be associated with a set of known metabolites in CKD patients. Methods: We examined associations between 634 plasma metabolites assessed using the Broad platform at year 1 and Healthy Eating Index (HEI)-2015, Alternative Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, and alternate Mediterranean diet (aMED), and their food components in 1,117 participants in the CRIC Study. Usual dietary intakes were assessed using a food frequency questionnaire at baseline and year 2. We conducted multivariable linear regression models to study associations between diet scores and individual plasma metabolites, adjusting for sociodemographic characteristics, health behaviors, and clinical factors. Results: After Bonferroni correction, we identified a total of 362 diet-metabolite associations (HEI=78; AHEI=127; DASH=97; aMED=60), and 101 metabolites were associated with more than 1 dietary pattern. The most common metabolite categories were triacylglycerols and diacylglycerols. Most lipids were negatively associated with healthy dietary patterns, except for cholesterols esters and triacylglycerols with ≥7 double bonds. Triacylglycerols with high number of double bonds were positively associated with healthy fat intake (e.g., higher monounsaturated and polyunsaturated fatty acid, omega-3 fatty acid, fish) within HEI, AHEI, and aMED ( Table ). Conclusions: We identified many metabolites associated with healthy dietary patterns, indicative of food consumption. If replicated, they may be considered biomarkers of healthy dietary patterns in CKD patients.

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