Periconceptional Dietary Patterns and Adverse Pregnancy and Birth Outcomes.

BACKGROUND: The periconceptional period is a critical window for the origins of adverse pregnancy and birth outcomes, yet little is known about the dietary patterns that promote perinatal health. OBJECTIVE: We used machine learning methods to determine the effect of periconceptional dietary patterns on risk of preeclampsia, gestational diabetes, preterm birth, small-for-gestational-age (SGA) birth, and a composite of these outcomes. METHODS: We used data from 8259 participants in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (8 US medical centers, 2010‒2013). Usual daily periconceptional intake of 82 food groups was estimated from a food frequency questionnaire. We used k-means clustering with a Euclidean distance metric to identify dietary patterns. We estimated the effect of dietary patterns on each perinatal outcome using targeted maximum likelihood estimation and an ensemble of machine learning algorithms, adjusting for confounders including health behaviors and psychological, neighborhood, and sociodemographic factors. RESULTS: The 4 dietary patterns that emerged from our data were identified as "Sandwiches and snacks" (34% of the sample); "High fat, sugar, and sodium" (29%); "Beverages, refined grains, and mixed dishes" (21%); and "High fruits, vegetables, whole grains, and plant proteins" (16%). One-quarter of pregnancies had preeclampsia (8% incidence), gestational diabetes (5%), preterm birth (8%), or SGA birth (8%). Compared with the "High fat, sugar, and sodium" pattern, there were 3.3 to 4.3 fewer cases of the composite adverse outcome per 100 pregnancies among participants following the "Beverages, refined grains and mixed dishes" pattern (risk difference -0.043; 95% confidence interval -0.078, -0.009), "High fruits, vegetables, whole grains and plant proteins" pattern (-0.041; 95% confidence interval -0.078, -0.004), and "Sandwiches and snacks" pattern (-0.033; 95% confidence interval -0.065, -0.002). CONCLUSIONS: Our results highlight that there are a variety of periconceptional dietary patterns that are associated with perinatal health and reinforce the negative health implications of diets high in fat, sugars, and sodium.

Periconceptional Dietary Quality and Metabolic Syndrome at 3 Years Postpartum.

BACKGROUND: The period around pregnancy is a critical window in the primordial prevention of cardiovascular disease, but little is known about the role of dietary patterns in cardiometabolic health. Our objective was to determine the association between alignment of periconceptional diet with the 2020 to 2025 Dietary Guidelines for Americans and the risk of metabolic syndrome. METHODS AND RESULTS: We used data from the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-Be Heart Health Study, a pregnancy cohort study that followed pregnant participants to a median of 3 years postpartum (n=4423). Usual dietary intake in the 3 months around conception was estimated from a Food Frequency Questionnaire. Alignment with the Dietary Guidelines was measured using the Healthy Eating Index-2020, where higher scores represent greater alignment. Postpartum metabolic syndrome was defined using the American Heart Association/National Heart, Lung, and Blood Institute guideline. The prevalence of metabolic syndrome at 3 years postpartum was 20%. After adjusting for confounders, the prevalence of metabolic syndrome was flat up to a periconceptional Healthy Eating Index-2020 total score of ≈60, and then declined steeply as scores increased. Compared with a Healthy Eating Index-2020 score of 60, having scores of 70, 80, and 90 were associated with 2, 4, and 7 fewer cases of metabolic syndrome per 100 individuals, respectively (prevalence differences: -0.02 [95% CI, -0.03, 0]; -0.04 [-0.08, -0.1]; -0.07 [-0.13, -0.02]). CONCLUSIONS: Dietary interventions around conception and systems-level changes to support high diet quality may be important for improving postpartum cardiometabolic health, and helping to reverse or slow the decline in women's cardiometabolic health.

Development, validity and reliability assessment of the Canadian Food Literacy Measure.

Food literacy is a growing area of interest given its potential to support healthy and sustainable diets. Most existing food literacy measures focus on nutrition and food skills but fail to address food systems and socio-environmental aspects of food literacy. Further, measures developed and tested in the Canadian context are lacking. The objective of this project was to develop and test the validity and reliability of a brief self-administered measure, in French and English, designed to assess multiple dimensions of food literacy among adults living in Canada. The 23-item Canadian Food Literacy Measure was developed through an iterative process that included assessment of face and content validity through expert review (n=20) and cognitive interviews (n=20), and construct validity and reliability, i.e., internal consistency through an online survey (n=154). The results indicate that the measure is well understood by both English- and French-speaking adults. The measure's construct validity is demonstrated by the observed differences in total scores in hypothesized directions by gender (p=0.003), age (p=0.007), education level (p=0.002), health literacy (p<0.001) and smoking status (p=0.001) and the significant positive correlation (r = 0.29; p=0.002) between total scores and fruit and vegetable intake. The measure also has high internal consistency with a Cronbach's coefficient alpha of 0.80. This measure can be used in surveillance studies to provide insight into the food literacy of adults living in Canada and in epidemiologic research that aims to explore how food literacy is associated with a variety of health outcomes.

Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study.

BACKGROUND: Technology-assisted 24-h dietary recalls (24HRs) have been widely adopted in population nutrition surveillance. Evaluations of 24HRs inform improvements, but direct comparisons of 24HR methods for accuracy in reference to a measure of true intake are rarely undertaken in a single study population. OBJECTIVES: To compare the accuracy of energy and nutrient intake estimation of 4 technology-assisted dietary assessment methods relative to true intake across breakfast, lunch, and dinner. METHODS: In a controlled feeding study with a crossover design, 152 participants [55% women; mean age 32 y, standard deviation (SD) 11; mean body mass index 26 kg/m2, SD 5] were randomized to 1 of 3 separate feeding days to consume breakfast, lunch, and dinner, with unobtrusive weighing of foods and beverages consumed. Participants undertook a 24HR the following day [Automated Self-Administered Dietary Assessment Tool-Australia (ASA24); Intake24-Australia; mobile Food Record-Trained Analyst (mFR-TA); or Image-Assisted Interviewer-Administered 24-hour recall (IA-24HR)]. When assigned to IA-24HR, participants referred to images captured of their meals using the mobile Food Record (mFR) app. True and estimated energy and nutrient intakes were compared, and differences among methods were assessed using linear mixed models. RESULTS: The mean difference between true and estimated energy intake as a percentage of true intake was 5.4% (95% CI: 0.6, 10.2%) using ASA24, 1.7% (95% CI: -2.9, 6.3%) using Intake24, 1.3% (95% CI: -1.1, 3.8%) using mFR-TA, and 15.0% (95% CI: 11.6, 18.3%) using IA-24HR. The variances of estimated and true energy intakes were statistically significantly different for all methods (P < 0.01) except Intake24 (P = 0.1). Differential accuracy in nutrient estimation was present among the methods. CONCLUSIONS: Under controlled conditions, Intake24, ASA24, and mFR-TA estimated average energy and nutrient intakes with reasonable validity, but intake distributions were estimated accurately by Intake24 only (energy and protein). This study may inform considerations regarding instruments of choice in future population surveillance. This trial was registered at Australian New Zealand Clinical Trials Registry as ACTRN12621000209897.

Advances in methods for characterizing dietary patterns: A scoping review.

There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns.