Socioeconomic Characteristics and the Home Food Environment Are Associated With Feeding Healthful and Discretionary Foods During the First Year of Life in the Pregnancy Eating Attributes Study.

BACKGROUND: Feeding of nutrient-poor foods begins in infancy and may adversely influence long-term food preferences. OBJECTIVE: To examine associations of socioeconomic characteristics, childbearing parent eating behaviors, and home food environment with infant feeding characteristics. DESIGN: Secondary analysis of a prospective cohort study from first trimester of pregnancy through 12 months postpartum. PARTICIPANTS/SETTING: Participants with uncomplicated singleton pregnancies and no major chronic illness were enrolled from November 2014 through October 2016 from 2 university-based obstetrics clinics in Chapel Hill, NC. Of 458 enrolled, 321 were retained through 12 months postpartum. MAIN OUTCOME MEASURES: Participants completed infant food frequency questionnaires indicating age at introduction and frequency of consuming multiple food groups. Exposures included childbearing parent socioeconomic characteristics, hedonic hunger, addictive-like eating, Healthy Eating Index 2015 calculated from three 24-hour diet recalls, and home food environment fruit/vegetable and obesogenic scores. STATISTICAL ANALYSES PERFORMED: Multiple imputation using Heckman selection model; linear and logistic regressions examining associations with infant feeding characteristics. RESULTS: Lower education and income were associated with later infant age at introduction to, and lower frequency of consuming fruits and vegetables at age 12 months. Socioeconomic characteristics were not associated with age at introduction to discretionary solid foods; however, lower education and income were associated with greater infant frequency of intake of discretionary foods and greater odds of introducing fruit juice and sweetened beverages by age 12 months. Childbearing parent Healthy Eating Index 2015, hedonic hunger, and addictive-like eating were not consistently associated with infant feeding characteristics. A more obesogenic food environment was associated with greater frequency of intake of discretionary foods, lower frequency of intake of fruit, and greater odds of fruit juice introduction by age 12 months. CONCLUSIONS: Infant feeding characteristics may be important intervention targets for addressing socioeconomic disparities in child diet quality. Efforts to reduce routine feeding of discretionary foods across socioeconomic groups are needed; modifying the home food environment may promote healthful infant feeding.

Chronic disease outcome metadata from German observational studies - public availability and FAIR principles.

Metadata from epidemiological studies, including chronic disease outcome metadata (CDOM), are important to be findable to allow interpretability and reusability. We propose a comprehensive metadata schema and used it to assess public availability and findability of CDOM from German population-based observational studies participating in the consortium National Research Data Infrastructure for Personal Health Data (NFDI4Health). Additionally, principal investigators from the included studies completed a checklist evaluating consistency with FAIR principles (Findability, Accessibility, Interoperability, Reusability) within their studies. Overall, six of sixteen studies had complete publicly available CDOM. The most frequent CDOM source was scientific publications and the most frequently missing metadata were availability of codes of the International Classification of Diseases, Tenth Revision (ICD-10). Principal investigators' main perceived barriers for consistency with FAIR principles were limited human and financial resources. Our results reveal that CDOM from German population-based studies have incomplete availability and limited findability. There is a need to make CDOM publicly available in searchable platforms or metadata catalogues to improve their FAIRness, which requires human and financial resources.

Associations of ultra-processed food intake with maternal weight change and cardiometabolic health and infant growth.

BACKGROUND: Excessive intake of ultra-processed foods, formulated from substances extracted from foods or derived from food constituents, may be a modifiable behavioral risk factor for adverse maternal and infant health outcomes. Prior work has predominately examined health correlates of maternal ultra-processed food intake in populations with substantially lower ultra-processed food intake compared to the US population. This longitudinal study investigated relations of ultra-processed food intake with maternal weight change and cardiometabolic health and infant growth in a US cohort. METHODS: Mothers in the Pregnancy Eating Attributes Study were enrolled at ≤12 weeks gestation and completed multiple 24-Hour Dietary Recalls within six visit windows through one-year postpartum (458 mothers enrolled, 321 retained at one-year postpartum). The NOVA (not an acronym) system categorized food and underlying ingredient codes based on processing level. Maternal anthropometrics were measured throughout pregnancy and postpartum, and infant anthropometrics were measured at birth and ages 2 months, 6 months, and 1 year. Maternal cardiometabolic markers were analyzed from blood samples obtained during the second and third trimesters. RESULTS: Holding covariates and total energy intake constant, a 1-SD greater percent energy intake from ultra-processed foods during pregnancy was associated with 31% higher odds of excessive gestational weight gain (p = .045, 95% CI [1.01, 1.70]), 0.68±0.29 mg/L higher c-reactive protein during pregnancy (p = .021, 95% CI [0.10, 1.26]), 6.7±3.4% greater gestational weight gain retained (p = .049, 95% CI [0.03, 13.30]), and 1.09±0.36 kg greater postpartum weight retention (p = .003, 95% CI [0.38, 1.80]). No other significant associations emerged. CONCLUSIONS: Ultra-processed food intake during pregnancy may be a modifiable behavioral risk factor for adverse maternal weight outcomes and inflammation. Randomized controlled trials are needed to test whether targeting ultra-processed food intake during pregnancy may support optimal maternal health. TRIAL REGISTRATION: Clinicaltrials.gov. Registration ID - NCT02217462 . Date of registration - August 13, 2014.

Impact of the external school food environment on the associations of internal school food environment with high schoolers' diet and BMI.

OBJECTIVE: To examine associations of school food availability with student intake frequency and BMI, and whether the number of neighbourhood food outlets modifies these associations. DESIGN: Baseline assessment of a nationally representative cohort study of US 10th graders. Students reported intake frequency of fruits and vegetables (FV), snacks and soda. BMI was calculated from measured height and weight. Administrators of seventy-two high schools reported the frequency of school availability of FV, snacks and soda. The number of food outlets within 1 km and 5 km were linked with geocoded school addresses. Data were analysed using adjusted linear and logistic mixed models with multiple imputation for missing data. SETTING: US 2009-2010. PARTICIPANTS: Totally, 2263 US 10th graders from the Next Generation Health Study (NEXT). RESULTS: Greater school FV availability was positively associated with student FV intake. Food outlets within 5 km of schools (but not 1 km) attenuated the association of school FV availability with student intake; this was no longer significant at schools with > 58 food outlets within 5 km. School food availability was not associated with student BMI or student snack or soda intake. CONCLUSIONS: School food availability was associated with student intake of FV, but not with snacks, soda or BMI. Attenuation of the observed associations by the school neighbourhood food environment indicates a need to find ways to support healthy student eating behaviours in neighbourhoods with higher food outlet density.

Eating Patterns during Pregnancy and Postpartum and Their Association with Diet Quality and Energy Intake.

This study investigates the relationship between meal-specific eating patterns during pregnancy and postpartum with maternal diet quality and energy intake. Participants in a prospective cohort study completed 24-h dietary recalls three times throughout both pregnancy and 1 year postpartum (n = 420). Linear regressions estimated the associations of eating frequency (number of daily main meals and eating occasions), meal and energy regularity (meal skipping and variation of daily energy intake), and intake timing patterns (distribution of energy intake throughout the day, derived using principal component analysis) with daily energy intake and diet quality (Healthy Eating Index-2015, calculated daily and overall, across both pregnancy and postpartum). Eating frequency was positively associated with energy intake and daily diet quality. Irregular meals were associated with lower energy intake in pregnancy but not postpartum and with lower pregnancy and postpartum diet quality. Energy irregularity was not associated with energy intake or diet quality. Higher postpartum diet quality was associated with a morning energy intake pattern (versus late morning/early afternoon or evening). Differences in these associations between pregnancy and postpartum suggest that efforts to support optimal energy intake and diet quality by modifying eating patterns may require specific strategies for pregnancy and postpartum.

Using food network analysis to understand meal patterns in pregnant women with high and low diet quality.

BACKGROUND: Little is known about how meal-specific food intake contributes to overall diet quality during pregnancy, which is related to numerous maternal and child health outcomes. Food networks are probabilistic graphs using partial correlations to identify relationships among food groups in dietary intake data, and can be analyzed at the meal level. This study investigated food networks across meals in pregnant women and explored differences by overall diet quality classification. METHODS: Women were asked to complete three 24-h dietary recalls throughout pregnancy (n = 365) within a prospective cohort study in the US. Pregnancy diet quality was evaluated using the Healthy Eating Index-2015 (HEI, range 0-100), calculated across pregnancy. Networks from 40 food groups were derived for women in the highest and lowest HEI tertiles at each participant-labeled meal (i.e., breakfast, lunch, dinner, snacks) using Gaussian graphical models. Network composition was qualitatively compared across meals and between HEI tertiles. RESULTS: In both HEI tertiles, breakfast food combinations comprised ready-to-eat cereals with milk, quick breads with sweets (e.g., pancakes with syrup), and bread with cheese and meat. Vegetables were consumed at breakfast among women in the high HEI tertile only. Combinations at lunch and dinner were more varied, including vegetables with oils (e.g., salads) in the high tertile and sugary foods with nuts, fruits, and milk in the low tertile at lunch; and cooked grains with fats (e.g., pasta with oil) in the high tertile and potatoes with vegetables and meat in the low tertile at dinner. Fried potatoes, sugar-sweetened beverages, and sandwiches were consumed together at all main meals in the low tertile only. Foods were consumed individually at snacks in both tertiles; the most commonly consumed food were fruits in the high HEI tertile and cakes & cookies in the low tertile. CONCLUSIONS: In this cohort of pregnant women, food network analysis indicated that food combinations differed by meal and between HEI tertiles. Meal-specific patterns that differed between diet quality tertiles suggest potential targets to improve food choices at meals; the impact of meal-based dietary modifications on intake of correlated foods and on overall diet quality should be investigated in simulations and intervention studies. TRIAL REGISTRATION: PEAS was registered with number NCT02217462 in Clinicaltrials.gov on August 13, 2014.

Chocolate and risk of chronic disease: a systematic review and dose-response meta-analysis.

PURPOSE: Evidence for the association between chocolate intake and risk of chronic diseases is inconclusive. Therefore, we aimed to synthesize and evaluate the credibility of evidence on the dose-response association between chocolate consumption with risk of all-cause mortality, coronary heart disease (CHD), stroke, heart failure (HF), type 2 diabetes (T2D), colorectal cancer (CRC), and hypertension. METHODS: Prospective studies were searched until July 2018 in PubMed, Embase, and Web of Science. Random-effects meta-analyses comparing highest versus lowest intake categories, linear, and non-linear dose-response analyses were conducted. The credibility of evidence was evaluated with the NutriGrade scoring-system. RESULTS: Overall, 27 investigations were identified (n = 2 for all-cause mortality, n = 9 for CHD, n = 8 for stroke, n = 6 for HF, n = 6 for T2D, n = 2 for hypertension and CRC, respectively). No associations with HF (RR 0.99, 95% CI 0.94, 1.04) and T2D (RR 0.94, 95% CI 0.88, 1.01) per each 10 g/day increase in chocolate intake were observed in the linear dose-response meta-analyses. However, a small inverse association for each 10 g/daily increase could be shown for the risk of CHD (RR 0.96, 95% CI 0.93, 0.99), and stroke (RR 0.90, 95% CI 0.82, 0.98). The credibility of evidence was rated either very low (all-cause mortality, HF, T2D, CRC or hypertension) or low (CHD, stroke). CONCLUSION: Chocolate consumption is not related to risk for several chronic diseases, but could have a small inverse association with CHD and stroke. Our findings are limited by very low or low credibility of evidence, highlighting important uncertainty for chocolate-disease associations.

Associations of food groups and cardiometabolic and inflammatory biomarkers: does the meal matter?

Increased attention has been paid to circadian patterns and how predisposition to metabolic disorders can be affected by meal timing. Currently, it is not clear which role can be attributed to the foods selected at meals. On a cross-sectional sub-cohort study (815 adults) within the European Prospective Investigation into Cancer and Nutrition-Potsdam study, we investigated whether the same foods (vegetables, fruits, refined grains, whole grains, red and processed meats) eaten at different meals (breakfast, lunch or dinner) show different associations with biomarkers of cardiometabolic risk. Meal-specific usual intakes were calculated from multiple 24-h dietary recalls. Multivariable-adjusted linear regression models showed that intake of vegetables at breakfast was associated with lower LDL-cholesterol (-0·37 mmol/l per 50 g; 95 % CI -0·61, -0·12) and vegetables at dinner was associated with higher HDL-cholesterol (0·05 mmol/l per 50 g; 95 % CI 0, 0·10). Fruit intake at breakfast was associated with lower glycated Hb (HbA1c) (-0·06 % per 50 g; 95 % CI -0·10, -0·01) and fruits at dinner with lower C-reactive protein (CRP) (-0·21 mg/l per 50 g; 95 % CI -0·42, -0·01). Red and processed meat intake at breakfast was associated with higher HbA1c (0·25 % per 50 g; 95 % CI 0·05, 0·46) and CRP (0·76 mg/l per 50 g; 95 % CI 0·15, 1·36). Our results suggest that by preferring fruits and vegetables and avoiding red and processed meats at specific meals (i.e. breakfast and dinner), cardiometabolic profiles and ultimately chronic disease risk could be improved. Lunch seemed to be a less important meal in terms of food-biomarker associations.

Intake of 12 food groups and disability-adjusted life years from coronary heart disease, stroke, type 2 diabetes, and colorectal cancer in 16 European countries.

Our aim was to estimate and rank 12 food groups according to disability-adjusted life years (DALYs) from coronary heart disease (CHD), stroke, type 2 diabetes (T2D), and colorectal cancer (CRC) in 16 European countries. De novo published non-linear dose-response meta-analyses of prospective studies (based on 297 primary reports), and food consumption data from the European Food Safety Authority Comprehensive European Food Consumption Database in Exposure Assessment, and DALY estimates from the Institute for Health Metrics and Evaluation were used. By implementing disease-specific counterfactual scenarios of theoretical minimum risk exposure level (TMRELs), the proportion of DALYs attributed to 12 food groups was estimated. In addition, a novel modelling approach was developed to obtain a single (optimized) TMREL across diseases. Four scenarios were analysed (A: disease-specific TMRELs/all food-disease associations; B: disease-specific TMRELs/only significant food-disease associations; C: single TMREL/all food-disease associations; D: single TMREL/only significant food-disease associations). Suboptimal food intake was associated with the following proportions of DALYs; Scenario A (highest-estimate) and D (lowest-estimate): CHD (A: 67%, D: 52%), stroke (A: 49%, D: 30%), T2D (A: 57%, D: 51%), and CRC (A: 54%, D: 40%). Whole grains (10%) had the highest impact on DALYs, followed by nuts (7.1%), processed meat (6.4%), fruit (4.4%) and fish and legumes (4.2%) when combining all scenarios. The contribution to total DALYs of all food groups combined in the different scenarios ranged from 41-52% in Austria to 51-69% in the Czech-Republic. These findings could have important implications for planning future food-based dietary guidelines as a public health nutrition strategy.

Correction to: Meal analysis for understanding eating behavior: meal- and participant-specific predictors for the variance in energy and macronutrient intake.

Following publication of the original article [1], the authors reported an error in Table 3. The correct Table 3 is provided below.

Meal analysis for understanding eating behavior: meal- and participant-specific predictors for the variance in energy and macronutrient intake.

BACKGROUND: Meals differ in their nutritional content. This variation has not been fully addressed despite its potential contribution in understanding eating behavior. The aim of this study was to investigate the between-meal and between-individual variance in energy and macronutrient intake as a measure of variation in intake and the meal type-specific relative importance of predictors of these intake variations. METHODS: Energy and macronutrient intake were derived from three 24 h dietary recalls in an EPIC-Potsdam sub-cohort of 814 German adults. Intra-class correlation was calculated for participants and meal type. Predictors of intake were assessed using meal type-specific multilevel regression models in a structural equation modeling framework at intake and participant levels using the Pratt Index. The importance of the predictor energy misreporting was assessed in sensitivity analyses on 682 participants. 95% confidence intervals were calculated based on 1000 bootstrap samples. RESULTS: Differences between meal types explain a large proportion of the variation in intake (intra-class correlation: 39% for energy, 25% for carbohydrates, 47% for protein, and 33% for fat). Between-participant variation in intake was much lower, with a maximum of 3% for carbohydrate and fat. Place of meal was the most important intake-level predictor of energy and macronutrient intake (Pratt Index of up to 65%). Week/weekend day was important in the breakfast meal, and prior interval (hours passed since last meal) was important for the afternoon snack and dinner. On the participant level, sex was the most important predictor, with Pratt Index of up to 95 and 59% in the main and in the sensitivity analysis, respectively. Energy misreporting was especially important at the afternoon snack, accounting for up to 69% of the explained variance. CONCLUSIONS: The meal type explains the highest variation in energy and macronutrient intakes. We identified key predictors of variation in the intake and in the participant levels. These findings suggest that successful dietary modification efforts should focus on improving specific meals.

Food Groups and Risk of Overweight, Obesity, and Weight Gain: A Systematic Review and Dose-Response Meta-Analysis of Prospective Studies.

This meta-analysis summarizes the evidence of a prospective association between the intake of foods [whole grains, refined grains, vegetables, fruit, nuts, legumes, eggs, dairy, fish, red meat, processed meat, and sugar-sweetened beverages (SSBs)] and risk of general overweight/obesity, abdominal obesity, and weight gain. PubMed and Web of Science were searched for prospective observational studies until August 2018. Summary RRs and 95% CIs were estimated from 43 reports for the highest compared with the lowest intake categories, as well as for linear and nonlinear relations focusing on each outcome separately: overweight/obesity, abdominal obesity, and weight gain. The quality of evidence was evaluated with use of the NutriGrade tool. In the dose-response meta-analysis, inverse associations were found for whole-grain (RRoverweight/obesity: 0.93; 95% CI: 0.89, 0.96), fruit (RRoverweight/obesity: 0.93; 95% CI: 0.86, 1.00; RRweight gain: 0.91; 95% CI: 0.86, 0.97), nut (RRabdominal obesity: 0.42; 95% CI: 0.31, 0.57), legume (RRoverweight/obesity: 0.88; 95% CI: 0.84, 0.93), and fish (RRabdominal obesity: 0.83; 95% CI: 0.71, 0.97) consumption and positive associations were found for refined grains (RRoverweight/obesity: 1.05; 95% CI: 1.00, 1.10), red meat (RRabdominal obesity: 1.10; 95% CI: 1.04, 1.16; RRweight gain: 1.14; 95% CI: 1.03, 1.26), and SSBs (RRoverweight/obesity: 1.05; 95% CI: 1.00, 1.11; RRabdominal obesity: 1.12; 95% CI: 1.04, 1.20). The dose-response meta-analytical findings provided very low to low quality of evidence that certain food groups have an impact on different measurements of adiposity risk. To improve the quality of evidence, better-designed observational studies, inclusion of intervention trials, and use of novel statistical methods (e.g., substitution analyses or network meta-analyses) are needed.

Food groups and risk of coronary heart disease, stroke and heart failure: A systematic review and dose-response meta-analysis of prospective studies.

BACKGROUND: Despite growing evidence for food-based dietary patterns' potential to reduce cardiovascular disease risk, knowledge about the amounts of food associated with the greatest change in risk of specific cardiovascular outcomes and about the quality of meta-evidence is limited. Therefore, the aim of this meta-analysis was to synthesize the knowledge about the relation between intake of 12 major food groups (whole grains, refined grains, vegetables, fruits, nuts, legumes, eggs, dairy, fish, red meat, processed meat, and sugar-sweetened beverages [SSB]) and the risk of coronary heart disease (CHD), stroke and heart failure (HF). METHODS: We conducted a systematic search in PubMed and Embase up to March 2017 for prospective studies. Summary risk ratios (RRs) and 95% confidence intervals (95% CI) were estimated using a random effects model for highest versus lowest intake categories, as well as for linear and non-linear relationships. RESULTS: Overall, 123 reports were included in the meta-analyses. An inverse association was present for whole grains (RRCHD: 0.95 (95% CI: 0.92-0.98), RRHF: 0.96 (0.95-0.97)), vegetables and fruits (RRCHD: 0.97 (0.96-0.99), and 0.94 (0.90-0.97); RRstroke: 0.92 (0.86-0.98), and 0.90 (0.84-0.97)), nuts (RRCHD: 0.67 (0.43-1.05)), and fish consumption (RRCHD: 0.88 (0.79-0.99), RRstroke: 0.86 (0.75-0.99), and RRHF: 0.80 (0.67-0.95)), while a positive association was present for egg (RRHF: 1.16 (1.03-1.31)), red meat (RRCHD: 1.15 (1.08-1.23), RRstroke: 1.12 (1.06-1.17), RRHF: 1.08 (1.02-1.14)), processed meat (RRCHD: 1.27 (1.09-1.49), RRstroke: 1.17 (1.02-1.34), RRHF: 1.12 (1.05-1.19)), and SSB consumption (RRCHD: 1.17 (1.11-1.23), RRstroke: 1.07 (1.02-1.12), RRHF: 1.08 (1.05-1.12)) in the linear dose-response meta-analysis. There were clear indications for non-linear dose-response relationships between whole grains, fruits, nuts, dairy, and red meat and CHD. CONCLUSION: An optimal intake of whole grains, vegetables, fruits, nuts, legumes, dairy, fish, red and processed meat, eggs and SSB showed an important lower risk of CHD, stroke, and HF.

Gaussian graphical models identified food intake networks and risk of type 2 diabetes, CVD, and cancer in the EPIC-Potsdam study.

PURPOSE: The aim of the study was to investigate the association between the previously identified Gaussian graphical models' (GGM) food intake networks and risk of major chronic diseases as well as intermediate biomarkers in the European Prospective Investigation into Cancer and nutrition (EPIC)-Potsdam cohort. METHODS: In this cohort analysis of 10,880 men and 13,340 women, adherence to the previously identified sex-specific GGM networks as well as principal component analysis identified patterns was investigated in relation to risk of major chronic diseases, using Cox-proportional hazard models. Associations of the patterns with intermediate biomarkers were cross-sectionally analyzed using multiple linear regressions. RESULTS: Results showed that higher adherence to the GGM Western-type pattern was associated with increased risk (Hazard Ratio: 1.55; 95% CI 1.13-2.15; P trend = 0.004) of type 2 diabetes (T2D) in women, whereas adherence to a high-fat dairy (HFD) pattern was associated with lower risk of T2D both in men (0.69; 95% CI 0.54-0.89; P trend < 0.001) and women (0.71; 95% CI: 0.53, 0.96; P trend = 0.09). Among PCA patterns, HFD pattern was associated with lower risk of T2D (0.74; 95% CI 0.58-0.95; P trend < 0.001) in men and bread and sausage pattern was associated with higher risk of T2D (1.79; 95% CI 1.29-2.48; P trend < 0.001) in women. Moreover, The GGM-HFD pattern was positively associated with HDL-C in men and inversely associated with C-reactive protein in women. CONCLUSION: Overall, these results show that GGM-identified networks reflect dietary patterns, which could also be related to risk of chronic diseases.

Comparative effects of different dietary approaches on blood pressure in hypertensive and pre-hypertensive patients: A systematic review and network meta-analysis.

BACKGROUND: Pairwise meta-analyses have shown beneficial effects of individual dietary approaches on blood pressure but their comparative effects have not been established. OBJECTIVE: Therefore we performed a systematic review of different dietary intervention trials and estimated the aggregate blood pressure effects through network meta-analysis including hypertensive and pre-hypertensive patients. DESIGN: PubMed, Cochrane CENTRAL, and Google Scholar were searched until June 2017. The inclusion criteria were defined as follows: i) Randomized trial with a dietary approach; ii) hypertensive and pre-hypertensive adult patients; and iii) minimum intervention period of 12 weeks. In order to determine the pooled effect of each intervention relative to each of the other intervention for both diastolic and systolic blood pressure (SBP and DBP), random effects network meta-analysis was performed. RESULTS: A total of 67 trials comparing 13 dietary approaches (DASH, low-fat, moderate-carbohydrate, high-protein, low-carbohydrate, Mediterranean, Palaeolithic, vegetarian, low-GI/GL, low-sodium, Nordic, Tibetan, and control) enrolling 17,230 participants were included. In the network meta-analysis, the DASH, Mediterranean, low-carbohydrate, Palaeolithic, high-protein, low-glycaemic index, low-sodium, and low-fat dietary approaches were significantly more effective in reducing SBP (-8.73 to -2.32 mmHg) and DBP (-4.85 to -1.27 mmHg) compared to a control diet. According to the SUCRAs, the DASH diet was ranked the most effective dietary approach in reducing SBP (90%) and DBP (91%), followed by the Palaeolithic, and the low-carbohydrate diet (ranked 3rd for SBP) or the Mediterranean diet (ranked 3rd for DBP). For most comparisons, the credibility of evidence was rated very low to moderate, with the exception for the DASH vs. the low-fat dietary approach for which the quality of evidence was rated high. CONCLUSION: The present network meta-analysis suggests that the DASH dietary approach might be the most effective dietary measure to reduce blood pressure among hypertensive and pre-hypertensive patients based on high quality evidence.

Potatoes and risk of chronic disease: a systematic review and dose-response meta-analysis.

PURPOSE: We aimed to synthesize the evidence on the relation between different types of potato consumption with risk of all-cause mortality, coronary heart disease (CHD), stroke, type 2 diabetes (T2D), colorectal cancer (CRC), and hypertension. METHODS: Systematic searches until May 2018 were conducted in PubMed, Scopus, and Web of Science. Random effects meta-analyses comparing extreme categories, linear and non-linear dose-response analyses were conducted. RESULTS: Twenty-eight reports were identified. Only total potato consumption was available for some endpoints which showed no associations with all-cause mortality (RR: 0.88, 95% CI 0.69-1.12), CHD (RR: 1.03, 95% CI 0.96-1.09), stroke (RR: 0.98, 95% CI 0.93-1.03), and CRC (RR: 1.05, 95% CI 0.92-1.20) per one daily/serving (150 g/day) increase. Consumption of one daily serving of boiled/baked/mashed-potatoes was not associated with risk of hypertension (RR: 1.08, 95% CI 0.96-1.21), but slightly with the risk of T2D (RR: 1.09, 95% 1.01-1.18). Positive associations for the risk of T2D (RR: 1.66, 95% CI 1.43-1.94) and hypertension (RR: 1.37, 95% CI 1.15-1.63) were observed for each 150 g/day increase in French-fries consumption. The quality of evidence was rated mostly low (moderate quality of evidence for the risk-associations of French-fries). CONCLUSION: Total potato consumption is not related to risk for many chronic diseases but could pose a small increase in risk for T2D if consumed boiled. A clear risk relation was found between French-fries consumption and risk of T2D and hypertension. For several outcomes, the impact of different preparation procedures could not be assessed.

Food groups and intermediate disease markers: a systematic review and network meta-analysis of randomized trials.

Background: In previous meta-analyses of prospective observational studies, we investigated the association between food groups and risk of chronic disease. Objective: The aim of the present network meta-analysis (NMA) was to assess the effects of these food groups on intermediate-disease markers across randomized intervention trials. Design: Literature searches were performed until January 2018. The following inclusion criteria were defined a priori: 1) randomized trial (≥4 wk duration) comparing ≥2 of the following food groups: refined grains, whole grains, nuts, legumes, fruits and vegetables, eggs, dairy, fish, red meat, and sugar-sweetened beverages (SSBs); 2) LDL cholesterol and triacylglycerol (TG) were defined as primary outcomes; total cholesterol, HDL cholesterol, fasting glucose, glycated hemoglobin, homeostasis model assessment insulin resistance, systolic and diastolic blood pressure, and C-reactive protein were defined as secondary outcomes. For each outcome, a random NMA was performed, and for the ranking, the surface under the cumulative ranking curves (SUCRA) was determined. Results: A total of 66 randomized trials (86 reports) comparing 10 food groups and enrolling 3595 participants was identified. Nuts were ranked as the best food group at reducing LDL cholesterol (SUCRA: 93%), followed by legumes (85%) and whole grains (70%). For reducing TG, fish (97%) was ranked best, followed by nuts (78%) and red meat (72%). However, these findings are limited by the low quality of the evidence. When combining all 10 outcomes, the highest SUCRA values were found for nuts (66%), legumes (62%), and whole grains (62%), whereas SSBs performed worst (29%). Conclusion: The present NMA provides evidence that increased intake of nuts, legumes, and whole grains is more effective at improving metabolic health than other food groups. For the credibility of diet-disease relations, high-quality randomized trials focusing on well-established intermediate-disease markers could play an important role. This systematic review was registered at PROSPERO (www.crd.york.ac.uk/PROSPERO) as CRD42018086753.

Meal and habitual dietary networks identified through Semiparametric Gaussian Copula Graphical Models in a German adult population.

Gaussian graphical models (GGMs) are exploratory methods that can be applied to construct networks of food intake. Such networks were constructed for meal-structured data, elucidating how foods are consumed in relation to each other at meal level. Meal-specific networks were compared with habitual dietary networks using data from an EPIC-Potsdam sub-cohort study. Three 24-hour dietary recalls were collected cross-sectionally from 815 adults in 2010-2012. Food intake was averaged to obtain the habitual intake. GGMs were applied to four main meals and habitual intakes of 39 food groups to generate meal-specific and habitual dietary networks, respectively. Communities and centrality were detected in the dietary networks to facilitate interpretation. The breakfast network revealed five communities of food groups with other vegetables, sauces, bread, margarine, and sugar & confectionery as central food groups. The lunch and afternoon snacks networks showed higher variability in food consumption and six communities were detected in each of these meal networks. Among the central food groups detected in both of these meal networks were potatoes, red meat, other vegetables, and bread. Two dinner networks were identified with five communities and other vegetables as a central food group. Partial correlations at meals were stronger than on the habitual level. The meal-specific dietary networks were only partly reflected in the habitual dietary network with a decreasing percentage: 64.3% for dinner, 50.0% for breakfast, 36.2% for lunch, and 33.3% for afternoon snack. The method of GGM yielded dietary networks that describe combinations of foods at the respective meals. Analysing food consumption on the habitual level did not exactly reflect meal level intake. Therefore, interpretation of habitual networks should be done carefully. Meal networks can help understand dietary habits, however, GGMs warrant validation in other populations.

Evaluating Mediterranean diet and risk of chronic disease in cohort studies: an umbrella review of meta-analyses.

Several meta-analyses have been published summarizing the associations of the Mediterranean diet (MedDiet) with chronic diseases. We evaluated the quality and credibility of evidence from these meta-analyses as well as characterized the different indices used to define MedDiet and re-calculated the associations with the different indices identified. We conducted an umbrella review of meta-analyses on cohort studies evaluating the association of the MedDiet with type 2 diabetes, cardiovascular disease, cancer and cognitive-related diseases. We used the AMSTAR (A MeaSurement Tool to Assess systematic Reviews) checklist to evaluate the methodological quality of the meta-analyses, and the NutriGrade scoring system to evaluate the credibility of evidence. We also identified different indices used to define MedDiet; tests for subgroup differences were performed to compare the associations with the different indices when at least 2 studies were available for different definitions. Fourteen publications were identified and within them 27 meta-analyses which were based on 70 primary studies. Almost all meta-analyses reported inverse associations between MedDiet and risk of chronic disease, but the credibility of evidence was rated low to moderate. Moreover, substantial heterogeneity was observed on the use of the indices assessing adherence to the MedDiet, but two indices were the most used ones [Trichopoulou MedDiet (tMedDiet) and alternative MedDiet (aMedDiet)]. Overall, we observed little difference in risk associations comparing different MedDiet indices in the subgroup meta-analyses. Future prospective cohort studies are advised to use more homogenous definitions of the MedDiet to improve the comparability across meta-analyses.

Nordic diet, Mediterranean diet, and the risk of chronic diseases: the EPIC-Potsdam study.

BACKGROUND: The Mediterranean Diet (MedDiet) has been acknowledged as a healthy diet. However, its relation with risk of major chronic diseases in non-Mediterranean countries is inconclusive. The Nordic diet is proposed as an alternative across Northern Europe, although its associations with the risk of chronic diseases remain controversial. We aimed to investigate the association between the Nordic diet and the MedDiet with the risk of chronic disease (type 2 diabetes (T2D), myocardial infarction (MI), stroke, and cancer) in the EPIC-Potsdam cohort. METHODS: The EPIC-Potsdam cohort recruited 27,548 participants between 1994 and 1998. After exclusion of prevalent cases, we evaluated baseline adherence to a score reflecting the Nordic diet and two MedDiet scores (tMDS, reflecting the traditional MedDiet score, and the MedPyr score, reflecting the MedDiet Pyramid). Cox regression models were applied to examine the association between the diet scores and the incidence of major chronic diseases. RESULTS: During a follow-up of 10.6 years, 1376 cases of T2D, 312 of MI, 321 of stroke, and 1618 of cancer were identified. The Nordic diet showed a statistically non-significant inverse association with incidence of MI in the overall population and of stroke in men. Adherence to the MedDiet was associated with lower incidence of T2D (HR per 1 SD 0.93, 95% CI 0.88-0.98 for the tMDS score and 0.92, 0.87-0.97 for the MedPyr score). In women, the MedPyr score was also inversely associated with MI. No association was observed for any of the scores with cancer. CONCLUSIONS: In the EPIC-Potsdam cohort, the Nordic diet showed a possible beneficial effect on MI in the overall population and for stroke in men, while both scores reflecting the MedDiet conferred lower risk of T2D in the overall population and of MI in women.