Faculty Bibliography

PURPOSE OF REVIEW/OBJECTIVE:Poor diet quality is the leading risk factor related to the overall cardiometabolic disease burden in the USA and globally. We review the current evidence linking ultra-processed foods and cardiometabolic health risk and provide recommendations for action at the clinical and public health levels. RECENT FINDINGS/RESULTS:A growing body of evidence conducted in a variety of study populations supports an association between ultra-processed food intake and increased risk of metabolic syndrome, hypertension, type 2 diabetes, overweight and obesity trajectories, and cardiovascular disease. The strongest evidence is observed in relation to weight gain and obesity among adults, as this association is supported by high-quality epidemiological and experimental evidence. Accumulating epidemiologic evidence and putative biological mechanisms link ultra-processed foods to cardiometabolic health outcomes. The high intake of ultra-processed foods in all population groups and its associated risks make ultra-processed foods an ideal target for intensive health promotion messaging and interventions.

Refining existing dietary assessment methods to reduce measurement error and facilitate the routine evaluation of dietary quality is essential to inform health policy. Notable advancements in technology in the past decade have enhanced the precision and transformation of dietary assessment methods with applications toward both population health and precision nutrition. Within population health, innovative applications of big data including use of automatically collected food purchasing data, quantitative measurement of food environments, and novel, yet simplified dietary quality metrics provide important complementary data to traditional self-report methods. Precision nutrition is similarly advancing with greater use of validated biomarkers for assessing dietary patterns and understanding individual variability in metabolism. Concurrently enhancing our understanding of diet-disease relationships at the population health and precision nutrition levels provide tremendous potential to generate evidence needed to advance public health nutrition policy. This commentary highlights the importance of these advances toward progressing the field of dietary assessment and discusses the application of food purchasing data, data analytics, alternative dietary quality metrics, and -omics technology in population and clinical medicine.

Recent evidence suggests that 44.8% of women of reproductive age (WRA) in Tanzania suffer from anemia. Addressing this public health challenge calls for local evidence of its burden and determinants thereof for policy and tailored interventions. This secondary data analysis used Tanzania Demographic and Health Surveys (TDHS) 2004-2005 and 2015-2016 with a total of 23,203 WRA. Data were analyzed using descriptive statistics to characterize the burden of anemia, regression analyses to examine the adjusted change in the prevalence of anemia and remaining determinants thereof, and the Global Information System (GIS) to map the differences in the burden of anemia in Tanzania over the period of one decade. Considering the risk factors of anemia observed in our study, WRA in Tanzania should have been 15% less likely to suffer from anemia in 2015 compared to 2005. However, a small decline (3.6%) was not evenly distributed across the regions in Tanzania. Factors that remained significantly associated with anemia among WRA in the latest survey include age above 35 years (AOR = 1.564, p = 0.007), education level (AOR = 0.720, p = 0.001), pregnancy status (AOR = 1.973, p < 0.001), and use of contraception (AOR of 0.489, p < 0.001). Our findings suggest that WRA in Tanzania aged above 35 should be the target population to accept the more tailored interventions.

South Asians are among the fastest growing ethnic group in the USA yet remain understudied in epidemiologic studies. Due to their unique disease profile, identifying risk moderators and mitigators, such as dietary patterns and food intake, will help to determine the diet-disease relationship that is specific to this largely immigrant population group in the USA. The aim of this commentary is to highlight the dietary traditions and acculturated practices experienced by South Asians in the USA with a call for a diet assessment instrument that adequately captures their dietary diversity. Specifically, we call for (i) the inclusion of traditional food items, such as herbs and spices, that individualize diet assessment for participants; and (ii) leveraging technology that will enhance the experience of diet assessment for both researchers and participants, tailoring the collection of habitual dietary intake in this diverse population group.

This study explores a novel, mixed qualitative method to deconstruct the diet of second-generation South Asian Americans (SAAs). Online interviews of 32 second-generation SAAs were conducted usingintegrated free-listing and mind-mapping. Ranked free-lists were aggregated to identify salient drivers, while connections made within mind-maps were analyzed using social network analysis (SNA) methods. Overall, 34 distinct drivers and 247 unique connections were identified. Taste, family, and health had the highest adjusted rankings, while health displayed the strongest network centrality. Interventions aimed at second-generation SAA dietary behaviors may benefit from family-based or multi-level interventions, which consider the complex, unique dietary norms identified.

OBJECTIVE:To explore associations between food insecurity, health behaviors, and academic performance among undergraduates at a private, urban US university. METHODS:A cross-sectional web-based survey was conducted among a convenience sample of New York University undergraduates. Multivariable logistic regression estimated associations of food security (using the 6-item US Household Food Security Survey Module) and health behaviors (fruit/vegetable, beverage and alcohol intakes, and sleep), self-rated health, and academic performance. RESULTS:Of the 257 students who completed the survey, 41% reported food insecurity. Food insecurity was associated with approximately 2-fold higher odds of sugar-sweetened beverage consumption (adjusted odds ratio, 1.97; 95% confidence interval, 1.14-3.41) and fair/poor health (adjusted odds ratio, 2.29; 95% confidence interval, 1.23-4.25). CONCLUSIONS AND IMPLICATIONS/CONCLUSIONS:Increased awareness of food insecurity and associated health behaviors among students has implications for higher education's provision of on-campus food support programs.

BACKGROUND:Accumulating evidence links ultra-processed foods to poor diet quality and chronic diseases. Understanding dietary trends is essential to inform priorities and policies to improve diet quality and prevent diet-related chronic diseases. Data are lacking, however, for trends in ultra-processed food intake. OBJECTIVES/OBJECTIVE:We examined US secular trends in food consumption according to processing level from 2001 to 2018. METHODS:We analyzed dietary data collected by 24-h recalls from adult participants (aged >19 y; N  = 40,937) in 9 cross-sectional waves of the NHANES (2001-2002 to 2017-2018). We calculated participants' intake of minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed foods as the relative contribution to daily energy intake (%kcal) using the NOVA framework. Trends analyses were performed using linear regression, testing for linear trends by modeling the 9 surveys as an ordinal independent variable. Models were adjusted for age, sex, race/ethnicity, education level, and income. Consumption trends were reported for the full sample and stratified by sex, age groups, race/ethnicity, education level, and income level. RESULTS:Adjusting for changes in population characteristics, the consumption of ultra-processed foods increased among all US adults from 2001-2002 to 2017-2018 (from 53.5 to 57.0 %kcal; P-trend < 0.001). The trend was consistent among all sociodemographic subgroups, except Hispanics, in stratified analyses. In contrast, the consumption of minimally processed foods decreased significantly over the study period (from 32.7 to 27.4 %kcal; P-trend < 0.001) and across all sociodemographic strata. The consumption of processed culinary ingredients increased from 3.9 to 5.4 %kcal (P-trend < 0.001), whereas the intake of processed foods remained stable at ∼10 %kcal throughout the study period (P-trend = 0.052). CONCLUSIONS:The current findings highlight the high consumption of ultra-processed foods in all parts of the US population and demonstrate that intake has continuously increased in the majority of the population in the past 2 decades.

There is a growing need to better capture comprehensive, nuanced, and multi-faceted qualitative data while also better engaging with participants in data collection, especially in virtual environments. This study describes the development of a novel 3-step approach to virtual mind-mapping that involves (1) ranked free-listing, (2) respondent-driven mind-mapping, and (3) interviewing to enhance both data collection and analysis of complex health behaviors. The method was employed in 32 virtual interviews as part of a study on eating behaviors among second-generation South Asian Americans. Participants noted the mind-mapping experience to be (1) helpful for visual learners, (2) helpful in elucidating new ideas and to structure thoughts, as well as (3) novel and interesting. They also noted some suggestions that included improving interpretability of visual data and avoiding repetition of certain discussion points. Data collection revealed the adaptability of the method, and the power of mind-maps to guide targeted, comprehensive discussions with participants.

Ultra-processed foods are industrially manufactured ready-to-eat or ready-to-heat formulations containing food additives and little or no whole foods, in contrast to processed foods, which are whole foods preserved by traditional techniques such as canning or pickling. Recent epidemiological studies suggest that higher consumption of ultra-processed food is associated with increased risk of cardiovascular disease (CVD). However, epidemiological evidence needs to be corroborated with criteria of biological plausibility. This review summarizes the current evidence on the putative biological mechanisms underlying the associations between ultra-processed foods and CVD. Research ranging from laboratory-based to prospective epidemiological studies and experimental evidence suggest that ultra-processed foods may affect cardiometabolic health through a myriad of mechanisms, beyond the traditionally recognized individual nutrients. Processing induces significant changes to the food matrix, for which ultra-processed foods may affect health outcomes differently than unrefined whole foods with similar nutritional composition. Notably, the highly degraded physical structure of ultra-processed foods may affect cardiometabolic health by influencing absorption kinetics, satiety, glycemic response, and the gut microbiota composition and function. Food additives and neo-formed contaminants produced during processing may also play a role in CVD risk. Key biological pathways include altered serum lipid concentrations, modified gut microbiota and host-microbiota interactions, obesity, inflammation, oxidative stress, dysglycemia, insulin resistance, and hypertension. Further research is warranted to clarify the proportional harm associated with the nutritional composition, food additives, physical structure, and other attributes of ultra-processed foods. Understanding how ultra-processing changes whole foods and through which pathways these foods affect health is a prerequisite for eliminating harmful processing techniques and ingredients.