Meat analogs are processed foods designed to mimic meat products. Their popularity is increasing among people seeking foods that are healthy and sustainable. Animal-sourced protein products differ in both their environmental impact and nutritional composition. The protein sources to produce meat analogs come from different plants. There is a lack of published research data assessing differences in these two aspects of meat analogs according to the plant protein source. This study compared the greenhouse gas (GHG) emissions of different types of meat analogs according to their main source of protein (wheat, soy, wheat and soy, or nuts), and their nutritional composition. We also compared totally plant-based products with those containing egg. We performed life cycle analyses of 56 meat analogs from ingredient production to the final commercial product. The nutrient profile of the meat analogs was analyzed based on ingredients. Descriptive statistics and differences between means were assessed through t-test and ANOVA. No differences in GHG emissions were observed among products with different major sources of protein. However, egg-containing products produced significantly higher amounts of GHG (p < 0.05). The nutritional composition of all meat analogs was found to be quite similar. Altogether, total plant-based meat analogs should be the choice for the sake of the environment.

Objective: To validate a 204-item quantitative FFQ for measurement of nutrient intake in the Adventist Health Study-2 (AHS-2). Design: Calibration study participants were randomly selected from the AHS-2 cohort by church, and then subject-within-church. Each participant provided two sets of three weighted 24 h dietary recalls and a 204-item FFQ. Race-specific correlation coefficients (r), corrected for attenuation from within-person variation in the recalls, were calculated for selected energy-adjusted macro- and micronutrients. Setting: Adult members of the AHS-2 cohort geographically spread throughout the USA and Canada. Subjects: Calibration study participants included 461 blacks of American and Caribbean origin and 550 whites. Results: Calibration study subjects represented the total cohort very well with respect to demographic variables. Approximately 33 % were males. Whites were older, had higher education and lower BMI compared with blacks. Across fifty-one variables, average deattenuated energy-adjusted validity correlations were 0·60 in whites and 0·52 in blacks. Individual components of protein had validity ranging from 0·40 to 0·68 in blacks and from 0·63 to 0·85 in whites; for total fat and fatty acids, validity ranged from 0·43 to 0·75 in blacks and from 0·46 to 0·77 in whites. Of the eighteen micronutrients assessed, sixteen in blacks and sixteen in whites had deattenuated energy-adjusted correlations ≥0·4, averaging 0·60 and 0·53 in whites and blacks, respectively. Conclusions: With few exceptions validity coefficients were moderate to high for macronutrients, fatty acids, vitamins, minerals and fibre. We expect to successfully use these data for measurement error correction in analyses of diet and disease risk.

Objective: To assess race-specific validity of food and food group intakes measured using an FFQ.Design: Calibration study participants were randomly selected from the Adventist Health Study-2 (AHS-2) cohort by church, and then by subject-within-church. Intakes of forty-seven foods and food groups were assessed using an FFQ and then compared with intake estimates measured using six 24 h dietary recalls (24HDR). We used two approaches to assess the validity of the questionnaire: (i) cross-classification by quartile and (ii) de-attenuated correlation coefficients.Setting: Seventh-day Adventist church members geographically spread throughout the USA and Canada.Subjects: Members of the AHS-2 calibration study (550 whites and 461 blacks).Results: The proportion of participants with exact quartile agreement in the FFQ and 24HDR averaged 46 % (range: 29–87 %) in whites and 44 % (range: 25–88 %) in blacks. The proportion of quartile gross misclassification ranged from 1 % to 11 % in whites and from 1 % to 15 % in blacks. De-attenuated validity correlations averaged 0·59 in whites and 0·48 in blacks. Of the forty-seven foods and food groups, forty-three in whites and thirty-three in blacks had validity correlations >0·4.Conclusions: The AHS-2 questionnaire has good validity for most foods in both races; however, validity correlations tend to be higher in whites than in blacks.

Relying on self-reported anthropometric data is often the only feasible way of studying large populations. In this context, there are no studies assessing the validity of anthropometrics in a mostly vegetarian population. The objective of this study was to evaluate the validity of self-reported anthropometrics in the Adventist Health Study 2 (AHS-2). We selected a representative sample of 911 participants of AHS-2, a cohort of over 96,000 adult Adventists in the USA and Canada. Then we compared their measured weight and height with those self-reported at baseline. We calculated the validity of the anthropometrics as continuous variables, and as categorical variables for the definition of obesity. On average, participants underestimated their weight by 0.20 kg, and overestimated their height by 1.57 cm resulting in underestimation of body mass index (BMI) by 0.61 kg/m2. The agreement between self-reported and measured BMI (as a continuous variable), as estimated by intraclass correlation coefficient, was 0.97. The sensitivity of self-reported BMI to detect obesity was 0.81, the specificity 0.97, the predictive positive value 0.93, the predictive negative value 0.92, and the Kappa index 0.81. The percentage of absolute agreement for each category of BMI (normoweight, overweight, and obese) was 83.4%. After multivariate analyses, predictors of differences between self-reported and measured BMI were obesity, soy consumption and the type of dietary pattern. Self-reported anthropometric data showed high validity in a representative subsample of the AHS-2 being valid enough to be used in epidemiological studies, although it can lead to some underestimation of obesity.

Background: Accurate assessment of diet in study populations is still a challenge. Some statistical strategies that use biomarkers of dietary intake a