Novel Application of Nutritional Biomarkers From a Controlled Feeding Study and an Observational Study to Characterization of Dietary Patterns in Postmenopausal Women

"We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021

Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.

Enhancing Capacity for Food and Nutrient Intake Assessment in Population Sciences Research

Nutrition influences health throughout the life course. Good nutrition increases the probability of good pregnancy outcomes, proper childhood development, and healthy aging, and it lowers the probability of developing common diet-related chronic diseases, including obesity, cardiovascular disease, cancer, and type 2 diabetes. Despite the importance of diet and health, studying these exposures is among the most challenging in population sciences research. US and global food supplies are complex; eating patterns have shifted such that half of meals are eaten away from home, and there are thousands of food ingredients with myriad combinations. These complexities make dietary assessment and links to health challenging both for population sciences research and for public health policy and practice. Furthermore, most studies evaluating nutrition and health usually rely on self-report instruments prone to random and systematic measurement error. Scientific advances involve developing nutritional biomarkers and then applying these biomarkers as stand-alone nutritional exposures or for calibrating self-reports using specialized statistics.

A nutritional biomarker score of the Mediterranean diet and incident type 2 diabetes: Integrated analysis of data from the MedLey randomised controlled trial and the EPIC-InterAct case-cohort study

BACKGROUND

Self-reported adherence to the Mediterranean diet has been modestly inversely associated with incidence of type 2 diabetes (T2D) in cohort studies. There is uncertainty about the validity and magnitude of this association due to subjective reporting of diet. The association has not been evaluated using an objectively measured biomarker of the Mediterranean diet.

METHODS AND FINDINGS

We derived a biomarker score based on 5 circulating carotenoids and 24 fatty acids that discriminated between the Mediterranean or habitual diet arms of a parallel design, 6-month partial-feeding randomised controlled trial (RCT) conducted between 2013 and 2014, the MedLey trial (128 participants out of 166 randomised). We applied this biomarker score in an observational study, the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study, to assess the association of the score with T2D incidence over an average of 9.7 years of follow-up since the baseline (1991 to 1998). We included 22,202 participants, of whom 9,453 were T2D cases, with relevant biomarkers from an original case-cohort of 27,779 participants sampled from a cohort of 340,234 people. As a secondary measure of the Mediterranean diet, we used a score estimated from dietary-self report. Within the trial, the biomarker score discriminated well between the 2 arms; the cross-validated C-statistic was 0.88 (95% confidence interval (CI) 0.82 to 0.94). The score was inversely associated with incident T2D in EPIC-InterAct: the hazard ratio (HR) per standard deviation of the score was 0.71 (95% CI: 0.65 to 0.77) following adjustment for sociodemographic, lifestyle and medical factors, and adiposity. In comparison, the HR per standard deviation of the self-reported Mediterranean diet was 0.90 (95% CI: 0.86 to 0.95). Assuming the score was causally associated with T2D, higher adherence to the Mediterranean diet in Western European adults by 10 percentiles of the score was estimated to reduce the incidence of T2D by 11% (95% CI: 7% to 14%). The study limitations included potential measurement error in nutritional biomarkers, unclear specificity of the biomarker score to the Mediterranean diet, and possible residual confounding.

CONCLUSIONS

These findings suggest that objectively assessed adherence to the Mediterranean diet is associated with lower risk of T2D and that even modestly higher adherence may have the potential to reduce the population burden of T2D meaningfully.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12613000602729 https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=363860.

Associations of Biomarker-Calibrated Healthy Eating Index-2010 Scores with Chronic Disease Risk and Their Dependency on Energy Intake and Body Mass Index in Postmenopausal Women

BACKGROUND

Prior studies examined associations between the Healthy Eating Index (HEI) and chronic disease risk based on self-reported diet without measurement error correction.

OBJECTIVE

Our objective was to test associations between biomarker calibration of the food-frequency questionnaire (FFQ)-derived HEI-2010 with incident cardiovascular disease (CVD), cancer, and type 2 diabetes (T2D) among Women's Health Initiative (WHI) participants.

METHODS

Data were derived from WHI postmenopausal women (n = 100,374) aged 50-79 y at enrollment (1993-1998) at 40 US clinical centers, linked to nutritional biomarker substudies and outcomes over subsequent decades of follow-up. Baseline or year 1 FFQ-derived HEI-2010 scores were calibrated with nutritional biomarkers and participant characteristics (e.g., BMI) for systematic measurement error correction. Calibrated data were then used in HR models examining associations with incidence of CVD (total, subtypes, mortality), cancer (total, subtypes, mortality), and T2D in WHI participants with approximately 2 decades of follow-up. Models were multivariable-adjusted with further adjustment for BMI and doubly labeled water (DLW)-calibrated energy.

RESULTS

Multivariable-adjusted HRs modeled a 20% increment in HEI-2010 score in relation to outcomes. HRs were modest using uncalibrated HEI-2010 scores (HRs = 0.91-1.09). Using biomarker-calibrated HEI-2010, 20% increments in scores yielded multivariable-adjusted HRs (95% CIs) of 0.75 (0.60, 0.93) for coronary heart disease; 0.75 (0.61, 0.91) for myocardial infarction; 0.96 (0.92, 1.01) for stroke; 0.88 (0.75, 1.02) for CVD mortality; 0.81 (0.70, 0.94) for colorectal cancer; 0.81 (0.74, 0.88) for breast cancer; 0.79 (0.73, 0.87) for cancer mortality; and 0.45 (0.36-0.55) for T2D. Except for cancer mortality and T2D incidence, results became null when adjusted for DLW-calibrated energy intake and BMI.

CONCLUSIONS

Biomarker calibration of FFQ-derived HEI-2010 was associated with lower CVD and cancer incidence and mortality and lower T2D incidence in postmenopausal women. Attenuation after adjustment with BMI and DLW-calibrated energy suggests that energy intake and/or obesity are strong drivers of diet-related chronic disease risk in postmenopausal women. The Women's Health Initiative is registered at clinicaltrials.gov at NCT00000611.

Development of a Multibiomarker Panel of Healthy Eating Index in United States Adults: A Machine Learning Approach

BACKGROUND

Dietary and nutritional biomarkers are objective dietary assessment tools that will enable a more accurate and precise determination of diet-disease relations. However, the lack of established biomarker panels for dietary patterns is concerning, as dietary patterns continue to be the focus of dietary guidelines.

OBJECTIVES

We aimed to develop and validate a panel of objective biomarkers that reflects the Healthy Eating Index (HEI) by applying machine learning approaches to the National Health and Nutrition Examination Survey data.

METHODS

Cross-sectional population-based data (eligible criteria: age ≥20 y, not pregnant, no reported supplement use of dedicated vitamin A, D, E, or fish oils; n = 3481) from the 2003 to 2004 cycle of the NHANES were used to develop 2 multibiomarker panels of the HEI, 1 with (primary panel) and 1 without (secondary panel) plasma FAs. Up to 46 blood-based dietary and nutritional biomarkers (24 FAs, 11 carotenoids, and 11 vitamins) were included for variable selection using the least absolute shrinkage and selection operator controlling for age, sex, ethnicity, and education. The explanatory impact of selected biomarker panels was assessed by comparing the regression models with and without the selected biomarkers. In addition, 5 comparative machine learning models were constructed to validate the biomarker selection.

RESULTS

The primary multibiomarker panel (8 FAs, 5 carotenoids, and 5 vitamins) significantly improved the explained variability of the HEI (adjusted R² increased from 0.056 to 0.245). The secondary multibiomarker panel (8 vitamins and 10 carotenoids) had lesser predictive capabilities (adjusted R² increased from 0.048 to 0.189).

CONCLUSIONS

Two multibiomarker panels were developed and validated to reflect a healthy dietary pattern consistent with the HEI. Future research should seek to test these multibiomarker panels in randomly assigned trials and identify whether they have broad application in healthy dietary pattern assessment.

Mortality Associated with Healthy Eating Index Components and an Empirical-Scores Healthy Eating Index in a Cohort of Postmenopausal Women

BACKGROUND

Studies of diet and chronic disease include a recent important focus on dietary patterns. Patterns are typically defined by listing dietary variables and by totaling scores that reflect whether consumption is encouraged or discouraged for listed variables. However, precision may be improved by including total energy consumption among the dietary variables and by scoring dietary variables empirically.

OBJECTIVES

To relate Healthy Eating Index (HEI)-2010 components and total energy intake to all-cause and cause-specific mortality in Women's Health Initiative (WHI) cohorts and to define and evaluate an associated Empirical-Scores Healthy Eating Index (E-HEI).

METHODS

Analyses are conducted in WHI cohorts (n = 67,247) of healthy postmenopausal women, aged 50-79 y, when enrolled during 1993-1998 at 40 US clinical centers, with embedded nutrition biomarker studies. Replicate food-frequency assessments for HEI-2010 ratio variables and doubly labeled water total energy assessments, separated by ∼6 mo, are used as response variables to jointly calibrate baseline dietary data to reduce measurement error influences, using 2 nutrition biomarker studies (n = 199). Calibrated dietary variables are associated with mortality risk, and an E-HEI is defined, using cross-validated HR regression estimation.

RESULTS

Of 15 dietary variables considered, all but empty calories calibrated well. Ten variables related significantly (P < 0.05) to total mortality, with favorable fruit, vegetable, whole grain, refined grain, and unsaturated fat associations and unfavorable sodium, saturated fat, and total energy associations. The E-HEI had cross-validated total mortality HRs (95% CIs) of 0.87 (0.82, 0.93), 0.80 (0.76, 0.86), 0.77 (0.72, 0.82), and 0.74 (0.69, 0.79) respectively, for quintiles 2 through 5 compared with quintile 1. These depart more strongly from the null than do HRs for HEI-2010 quintiles, primarily because of total energy.

CONCLUSIONS

Mortality among US postmenopausal women depends strongly on diet, as evidenced by a new E-HEI that differs substantially from earlier dietary pattern score specifications.

Metabolomics Meets Nutritional Epidemiology: Harnessing the Potential in Metabolomics Data

Traditionally, nutritional epidemiology is the study of the relationship between diet and health and disease in humans at the population level. Commonly, the exposure of interest is food intake. In recent years, nutritional epidemiology has moved from a "black box" approach to a systems approach where genomics, metabolomics and proteomics are providing novel insights into the interplay between diet and health. In this context, metabolomics is emerging as a key tool in nutritional epidemiology. The present review explores the use of metabolomics in nutritional epidemiology. In particular, it examines the role that food-intake biomarkers play in addressing the limitations of self-reported dietary intake data and the potential of using metabolite measurements in assessing the impact of diet on metabolic pathways and physiological processes. However, for full realisation of the potential of metabolomics in nutritional epidemiology, key challenges such as robust biomarker validation and novel methods for new metabolite identification need to be addressed. The synergy between traditional epidemiologic approaches and metabolomics will facilitate the translation of nutritional epidemiologic evidence to effective precision nutrition.