Biomarker-Calibrated Macronutrient Intake and Chronic Disease Risk among Postmenopausal Women

Diet and Chronic Disease Research in the Women's Health Initiative

The Women's Health Initiative (WHI) has been a major contributor to diet and chronic disease research among postmenopausal US women over its 30+ year history (1993 to present). The WHI program included full-scale randomized trials of a low-fat dietary pattern high in fruits, vegetables, and grains, and of calcium and vitamin D supplementation, each with designated primary and secondary chronic disease outcomes. The history of these trials will be briefly reviewed here, along with principal findings that included evidence for breast cancer-related benefits for each of the 2 interventions. In recent years, WHI investigators have developed an active research program in nutritional biomarker development and in the application of these biomarkers in WHI cohorts, among various other nutritional epidemiology uses of WHI observational study resources. The intake biomarker work, which primarily relies on blood and urine metabolomics profiles, lends support to the low-fat dietary pattern trial results, and supports chronic disease benefits of higher carbohydrate diets more generally, especially through the fiber component of carbohydrate.

Calibration of citrus intake assessed by food frequency questionnaires using urinary proline betaine in an observational study setting

BACKGROUND

Whether observational study can be employed to establish calibration equations for self-reported dietary intake using food biomarkers is unknown.

OBJECTIVES

This study aims to demonstrate the feasibility of obtaining calibration equations based on food biomarkers and 7-d diet records (7DDRs) to correct measurement errors of food frequency questionnaires (FFQs) in an observational study setting.

METHODS

The study population consisted of 669 males and 749 females from the Women's and Men's Lifestyle Validation Studies. In the training set, the biomarker-predicted intake derived by regressing 7DDR-assessed intake on urinary proline betaine concentration was regressed on the FFQ-assessed intake to obtain the calibration equations. The regression coefficients were applied to the test set to calculate the calibrated FFQ intake. We examined total citrus as well as individual citrus fruits/beverages.

RESULTS

Urinary proline betaine was moderately correlated with orange juice intake (Pearson correlation [r] = 0.53 for 7DDR and 0.48 for FFQ) but only weakly correlated with intakes of orange (r = 0.12 for 7DDR and 0.15 for FFQ) and grapefruit (r = 0.14 for 7DDR and 0.09 for FFQ). The FFQ-assessed citrus intake was systematically higher than the 7DDR-assessed intake, and after calibrations, the mean calibrated FFQ measurements were almost identical to 7DDR assessments. In the test set, the mean intake levels from 7DDRs, FFQs, and calibrated FFQs were 62.5, 75.3, and 63.2 g/d for total citrus; 41.6, 42.5, and 41.9 g/d for orange juice; 11.8, 24.3, and 12.3 g/d for oranges; and 8.3, 9.3, and 8.6 g/d for grapefruit, respectively. We observed larger differences between calibrated FFQ and 7DDR assessments at the extreme ends of intake, although, on average, good agreements were observed for all citrus except grapefruit.

CONCLUSIONS

Our 2-step calibration approach has the potential to be adapted to correct systematic measurement error for other foods/nutrients with established food biomarkers in a cost effective way.

Intake Biomarkers for Nutrition and Health: Review and Discussion of Methodology Issues

Metabolomics profiles from blood, urine, or other body fluids have the potential to assess intakes of foods and nutrients objectively, thereby strengthening nutritional epidemiology research. Metabolomics platforms may include targeted components that estimate the relative concentrations for individual metabolites in a predetermined set, or global components, typically involving mass spectrometry, that estimate relative concentrations more broadly. While a specific metabolite concentration usually correlates with the intake of a single food or food group, multiple metabolites may be correlated with the intake of certain foods or with specific nutrient intakes, each of which may be expressed in absolute terms or relative to total energy intake. Here, I briefly review the progress over the past 20 years on the development and application intake biomarkers for foods/food groups, nutrients, and dietary patterns, primarily by drawing from several recent reviews. In doing so, I emphasize the criteria and study designs for candidate biomarker identification, biomarker validation, and intake biomarker application. The use of intake biomarkers for diet and chronic disease association studies is still infrequent in nutritional epidemiology research. My comments here will derive primarily from our research group's recent contributions to the Women's Health Initiative cohorts. I will complete the contribution by describing some opportunities to build on the collective 20 years of effort, including opportunities related to the metabolomics profiling of blood and urine specimens from human feeding studies that approximate habitual diets.

Practical Considerations for Sandwich Variance Estimation in 2-Stage Regression Settings

In this paper, we present a practical approach for computing the sandwich variance estimator in 2-stage regression model settings. As a motivating example for 2-stage regression, we consider regression calibration, a popular approach for addressing covariate measurement error. The sandwich variance approach has rarely been applied in regression calibration, despite its requiring less computation time than popular resampling approaches for variance estimation, specifically the bootstrap. This is probably because it requires specialized statistical coding. Here we first outline the steps needed to compute the sandwich variance estimator. We then develop a convenient method of computation in R for sandwich variance estimation, which leverages standard regression model outputs and existing R functions and can be applied in the case of a simple random sample or complex survey design. We use a simulation study to compare the sandwich estimator to a resampling variance approach for both settings. Finally, we further compare these 2 variance estimation approaches in data examples from the Women's Health Initiative (1993-2005) and the Hispanic Community Health Study/Study of Latinos (2008-2011). In our simulations, the sandwich variance estimator typically had good numerical performance, but simple Wald bootstrap confidence intervals were unstable or overcovered in certain settings, particularly when there was high correlation between covariates or large measurement error.

Biomarker-assessed total energy intake and its cohort study association with all-cause mortality in postmenopausal females

BACKGROUND

The association of total energy intake (EI) with all-cause mortality is uncertain as are the dependencies of this association on age and weight change history.

OBJECTIVES

To identify an EI biomarker suitable for use in epidemiologic association studies and to study EI associations with total mortality in a Women's Health Initiative (WHI) cohort of postmenopausal United States females (1993-present).

METHODS

EI biomarkers were developed based on doubly labeled water (DLW) total energy expenditure (TEE) and weight variation during the 2-wk DLW protocol period using the energy balance method in an embedded feeding study (n = 153). This along with 2 earlier WHI nutrition biomarker studies having TEE assessments (n = 1131 total), with 14.6 y (median) follow-up, constituted a prospective cohort for the study of EI and all-cause mortality.

RESULTS

An empirical biomarker for log(EI) was developed that had a correlation of 0.73 with log(feeding study-consumed EI). The overall association between EI and mortality was nonsignificant. The association, however, depended on age (P = 0.009), with lower EI associated with lower mortality at younger ages, and also on preceding weight change history (P = 0.03). Among participants with stable or increasing weight, mortality hazard ratios (95% confidence intervals [CIs]) for a 12% lower EI were 0.66 (95% CI: 0.51, 0.87) at age 60, 0.84 (95% CI: 0.72, 0.98) at age 70, and 1.06 (95% CI: 0.87, 1.29) at age 80. Corresponding values for participants having preceding weight loss were 0.83 (95% CI: 0.61, 1.12) at age 60, 1.05 (95% CI: 0.87, 1.26) at age 70, and 1.33 (95% CI: 1.08, 1.63) at age 80. A previously considered EI biomarker, using a theoretical model for variation in body fat and fat-free mass components over time, gave similar results following rescaling.

CONCLUSIONS

Lower EI is associated with lower all-cause mortality among younger postmenopausal females with stable or increasing weight and with higher mortality among older females with weight loss. This study was registered with clinicaltrials.gov as NCT00000611.

Measuring diet by metabolomics: a 14-d controlled feeding study of weighed food intake

BACKGROUND

Metabolomics has the potential to enhance dietary assessment by revealing objective measures of many aspects of human food intake. Although metabolomics studies indicate that hundreds of metabolites are associated with dietary intake, correlations have been modest (e.g., r < 0.50), and few have been evaluated in controlled feeding studies.

OBJECTIVES

The aim of this study was to evaluate associations between metabolites and weighed food and beverage intake in a controlled feeding study of habitual diet.

METHODS

Healthy postmenopausal females from the Women's Health Initiative (N = 153) were provided with a customized 2-wk controlled diet designed to emulate their usual diet. Metabolites were measured by liquid chromatography tandem mass spectrometry in end-of-study 24-h urine and fasting serum samples (1293 urine metabolites; 1113 serum metabolites). We calculated partial Pearson correlations between these metabolites and intake of 65 food groups, beverages, and supplements during the feeding study. The threshold for significance was Bonferroni-adjusted to account for multiple testing (5.94 × 10-07 for urine metabolites; 6.91 × 10-07 for serum metabolites).

RESULTS

Significant diet-metabolite correlations were identified for 23 distinct foods, beverages, and supplements (171 distinct metabolites). Among foods, strong metabolite correlations (r ≥ 0.60) were evident for citrus (highest r = 0.80), dairy (r = 0.65), and broccoli (r = 0.63). Among beverages and supplements, strong correlations were evident for coffee (r = 0.86), alcohol (r = 0.69), multivitamins (r = 0.69), and vitamin E supplements (r = 0.65). Moderate correlations (r = 0.50-0.60) were also observed for avocado, fish, garlic, grains, onion, poultry, and black tea. Correlations were specific; each metabolite correlated with one food, beverage, or supplement, except for metabolites correlated with juice or multivitamins.

CONCLUSIONS

Metabolite levels had moderate to strong correlations with weighed intake of habitually consumed foods, beverages, and supplements. These findings exceed in magnitude those previously observed in population studies and exemplify the strong potential of metabolomics to contribute to nutrition research. The Women's Health Initiative is registered at clinicaltrials.gov as NCT00000611.

Metabolomics Biomarkers for Fatty Acid Intake and Biomarker-Calibrated Fatty Acid Associations with Chronic Disease Risk in Postmenopausal Women

BACKGROUND

A substantial observational literature relating specific fatty acid classes to chronic disease risk may be limited by its reliance on self-reported dietary data.

OBJECTIVES

We aimed to develop biomarkers for saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acid densities, and to study their associations with cardiovascular disease (CVD), cancer, and type 2 diabetes (T2D) in Women's Health Initiative (WHI) cohorts.

METHODS

Biomarker equations were based primarily on serum and urine metabolomics profiles from an embedded WHI human feeding study (n = 153). Calibration equations were based on biomarker values in a WHI nutritional biomarker study (n = 436). Calibrated intakes were assessed in relation to disease incidence in larger WHI cohorts (n = 81,894). Participants were postmenopausal women, aged 50-79 when enrolled at 40 United States Clinical Centers (1993-1998), with a follow-up period of ∼20 y.

RESULTS

Biomarker equations meeting criteria were developed for SFA, MUFA, and PUFA densities. That for SFA density depended somewhat weakly on metabolite profiles. On the basis of our metabolomics platforms, biomarkers were insensitive to trans fatty acid intake. Calibration equations meeting criteria were developed for SFA and PUFA density, but not for MUFA density. With or without biomarker calibration, SFA density was associated positively with risk of CVD, cancer, and T2D, but with small hazard ratios, and CVD associations were not statistically significant after controlling for other dietary variables, including trans fatty acid and fiber intake. Following this same control, PUFA density was not significantly associated with CVD risk, but there were positive associations for some cancers and T2D, with or without biomarker calibration.

CONCLUSIONS

Higher SFA and PUFA diets were associated with null or somewhat higher risk for clinical outcomes considered in this population of postmenopausal United States women. Further research is needed to develop even stronger biomarkers for these fatty acid densities and their major components. This study is registered with clinicaltrials.gov identifier: NCT00000611.

Metabolomics-Based Biomarker for Dietary Fat and Associations with Chronic Disease Risk in Postmenopausal Women

BACKGROUND

The Women's Health Initiative (WHI) randomized, controlled Dietary Modification (DM) trial of a low-fat dietary pattern suggested intervention benefits related to breast cancer, coronary heart disease (CHD), and diabetes. Here, we use WHI observational data for further insight into the chronic disease implications of adopting this type of low-fat dietary pattern.

OBJECTIVES

We aimed to use our earlier work on metabolomics-based biomarkers of carbohydrate and protein to develop a fat intake biomarker by subtraction, to use the resulting biomarker to develop calibration equations that adjusts self-reported fat intake for measurement error, and to study associations of biomarker-calibrated fat intake with chronic disease risk in WHI cohorts. Corresponding studies for specific fatty acids will follow separately.

METHODS

Prospective disease association results are presented using WHI cohorts of postmenopausal women, aged 50-79 y when enrolled at 40 United States clinical centers. Biomarker equations were developed using an embedded human feeding study (n = 153). Calibration equations were developed using a WHI nutritional biomarker study (n = 436). Calibrated intakes were associated with cancer, cardiovascular diseases, and diabetes incidence in WHI cohorts (n = 81,954) over an approximate 20-y follow-up period.

RESULTS

A biomarker for fat density was developed by subtracting protein, carbohydrate, and alcohol densities from one. A calibration equation was developed for fat density. Hazard ratios (95% confidence intervals) for 20% higher fat density were 1.16 (1.06, 1.27) for breast cancer, 1.13 (1.02, 1.26) for CHD, and 1.19 (1.13, 1.26) for diabetes, in substantial agreement with findings from the DM trial. With control for additional dietary variables, especially fiber, fat density was no longer associated with CHD, with hazard ratio (95% confidence interval) of 1.00 (0.88, 1.13), whereas that for breast cancer was 1.11 (1.00, 1.24).

CONCLUSIONS

WHI observational data support prior DM trial findings of low-fat dietary pattern benefits in this population of postmenopausal United States women.

TRIAL REGISTRATION NUMBER

This study is registered with clinicaltrials.gov identifier: NCT00000611.

Regression calibration utilizing biomarkers developed from high-dimensional metabolites

Addressing systematic measurement errors in self-reported data is a critical challenge in association studies of dietary intake and chronic disease risk. The regression calibration method has been utilized for error correction when an objectively measured biomarker is available; however, biomarkers for only a few dietary components have been developed. This paper proposes to use high-dimensional objective measurements to construct biomarkers for many more dietary components and to estimate the diet disease associations. It also discusses the challenges in variance estimation in high-dimensional regression methods and presents a variety of techniques to address this issue, including cross-validation, degrees-of-freedom corrected estimators, and refitted cross-validation (RCV). Extensive simulation is performed to study the finite sample performance of the proposed estimators. The proposed method is applied to the Women's Health Initiative cohort data to examine the associations between the sodium/potassium intake ratio and the total cardiovascular disease.

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.

The association of dietary macronutrient quality indices with depression and anxiety symptoms and quality of life in Iranian adults: The LipoKAP study

BACKGROUND

Macronutrients' quality may impact differently on mental health and quality of life (QOL). This study aimed to investigate the potential relationship between the carbohydrate quality index (CQI), fat quality index (FQI), protein quality index (PQI), the affective mental symptoms and QOL among Iranian adults.

METHODS

The LipoKAP is a cross-sectional study, conducted with 2456 adults in Iran. A validated food frequency questionnaire was used to evaluate usual dietary intakes. A validated Iranian version of the Hospital Anxiety and Depression Scale was used to assess the severity of anxiety and depression. QOL was assessed by EQ-5D.

RESULT

In the fully adjusted model, participants in the highest tertile of CQI had lower QOL than those in the lowest tertile (OR = 1.35; 95 % CI: 1.06, 1.73). Individuals in the top tertile of FQI (OR = 0.71; 95 % CI: 0.55, 0.91) and PQI (OR = 0.78; 95 % CI: 0.60; 1.01) were less likely to report lower QOL than those in the bottom tertile. An inverse association was found between PQI and depressive symptoms (OR = 0.72, 95 % CI: 0.55, 0.95), but not for CQI and FQI.

LIMITATIONS

The cross-sectional design of the study and the use of a memory-based dietary tool may limit the generalizability of our findings.

CONCLUSION

Higher PQI was associated with lower risk of depressive symptoms and having a low-quality life. Although CQI and FQI were not related to depressive and anxiety symptoms, higher values of FQI were associated with better QOL, while CQI showed an inverse association.

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.

Biomarker-Calibrated Red and Combined Red and Processed Meat Intakes with Chronic Disease Risk in a Cohort of Postmenopausal Women

BACKGROUND

The associations of red and processed meat with chronic disease risk remain to be clarified, in part because of measurement error in self-reported diet.

OBJECTIVES

We sought to develop metabolomics-based biomarkers for red and processed meat, and to evaluate associations of biomarker-calibrated meat intake with chronic disease risk among postmenopausal women.

METHODS

Study participants were women who were members of the Women's Health Initiative (WHI) study cohorts. These participants were postmenopausal women aged 50-79 y when enrolled during 1993-1998 at 40 US clinical centers with embedded human feeding and nutrition biomarker studies. Literature reports of metabolomics correlates of meat consumption were used to develop meat intake biomarkers from serum and 24-h urine metabolites in a 153-participant feeding study (2010-2014). Resulting biomarkers were used in a 450-participant biomarker study (2007-2009) to develop linear regression calibration equations that adjust FFQ intakes for random and systematic measurement error. Biomarker-calibrated meat intakes were associated with cardiovascular disease, cancer, and diabetes incidence among 81,954 WHI participants (1993-2020).

RESULTS

Biomarkers and calibration equations meeting prespecified criteria were developed for consumption of red meat and red plus processed meat combined, but not for processed meat consumption. Following control for nondietary confounding factors, hazard ratios were calculated for a 40% increment above the red meat median intake for coronary artery disease (HR: 1.10; 95% CI: 1.07, 1.14), heart failure (HR: 1.26; 95% CI: 1.20, 1.33), breast cancer (HR: 1.10; 95% CI: 1.07, 1.13) for, total invasive cancer (HR: 1.07; 95% CI: 1.05, 1.09), and diabetes (HR: 1.37; 95% CI: 1.34, 1.39). HRs for red plus processed meat intake were similar. HRs were close to the null, and mostly nonsignificant following additional control for dietary potential confounding factors, including calibrated total energy consumption.

CONCLUSIONS

A relatively high-meat dietary pattern is associated with somewhat higher chronic disease risks. These elevations appear to be largely attributable to the dietary pattern, rather than to consumption of red or processed meat per se.

Criteria for Acceptable Dietary Intake Biomarkers

Dietary intake biomarkers that can be written as actual intake, plus 'error' that is independent of actual intake and confounding factors can substitute for actual intake in disease association analyses. Also, such biomarkers can be used to develop calibration equations using self-reported diet and participant measures, and biomarker-calibrated intakes can be calculated in larger cohorts for use in disease association analyses. Criteria for biomarkers, and for biomarker-calibrated intakes, arise by working back from properties needed for valid disease association analyses. Accordingly, arguments for a potential biomarker are strengthened if error components are small relative to actual intakes, and important sources of reduced sensitivity or specificity are not apparent. Feeding study biomarker development can then involve regression of actual intake on putative biomarkers, with regression R2 values playing a role in biomarker evaluation. In comparison, 'predictive' biomarker status, as argued in this issue by Freedman and colleagues for 24-hour urinary sucrose plus fructose as biomarker for total sugars, involves regression of potential biomarker on actual intake and other variables, with parameter stability across populations and limited within-person variability as criteria. The choice of criteria for biomarkers and for biomarker-calibrated intakes, is discussed here, in the context of total sugars intake. See related article by Freedman et al., p. 1227.

Four-Day Food Record Macronutrient Intake, With and Without Biomarker Calibration, and Chronic Disease Risk in Postmenopausal Women

We recently evaluated associations of biomarker-calibrated protein intake, protein density, carbohydrate intake, and carbohydrate density with the incidence of cardiovascular disease, cancer, and diabetes among postmenopausal women in the Women's Health Initiative (1993-present, 40 US clinical centers). The biomarkers relied on serum and urine metabolomics profiles, and biomarker calibration used regression of biomarkers on food frequency questionnaires. Here we develop corresponding calibration equations using food records and dietary recalls. In addition, we use calibrated intakes based on food records in disease association estimation in a cohort subset (n = 29,294) having food records. In this analysis, more biomarker variation was explained by food records than by FFQs for absolute macronutrient intake, with 24-hour recalls being intermediate. However, the percentage of biomarker variation explained was similar for each assessment approach for macronutrient densities. Invasive breast cancer risk was related inversely to carbohydrate and protein densities using food records, in analyses that included (calibrated) total energy intake and body mass index. Corresponding analyses for absolute intakes did not differ from the null, nor did absolute or relative intakes associate significantly with colorectal cancer or coronary heart disease. These analyses do not suggest major advantages for food records or dietary recalls in comparison with less costly and logistically simpler food frequency questionnaires for these nutritional variables.

Biomarkers for Components of Dietary Protein and Carbohydrate with Application to Chronic Disease Risk in Postmenopausal Women

BACKGROUND

We recently developed protein and carbohydrate intake biomarkers using metabolomics profiles in serum and urine, and used them to correct self-reported dietary data for measurement error. Biomarker-calibrated carbohydrate density was inversely associated with chronic disease risk, whereas protein density associations were mixed.

OBJECTIVES

To elucidate and extend this earlier work through biomarker development for protein and carbohydrate components, including animal protein and fiber.

METHODS

Prospective disease association analyses were undertaken in Women's Health Initiative (WHI) cohorts of postmenopausal US women, aged 50-79 y when enrolled at 40 US clinical centers. Biomarkers were developed using an embedded human feeding study (n = 153). Calibration equations for protein and carbohydrate components were developed using a WHI nutritional biomarker study (n = 436). Calibrated intakes were associated with chronic disease incidence in WHI cohorts (n = 81,954) over a 20-y (median) follow-up period, using HR regression methods.

RESULTS

Previously reported elevations in cardiovascular disease (CVD) with higher-protein diets tended to be explained by animal protein density. For example, for coronary heart disease a 20% increment in animal protein density had an HR of 1.20 (95% CI: 1.02, 1.42) relative to the HR for total protein density. In comparison, cancer and diabetes risk showed little association with animal protein density beyond that attributable to total protein density. Inverse carbohydrate density associations with total CVD were mostly attributable to fiber density, with a 20% increment HR factor of 0.89 (95% CI: 0.83, 0.94). Cancer risk showed little association with fiber density, whereas diabetes risk had a 20% increment HR of 0.93 (95% CI: 0.88, 0.98) relative to the HRs for total carbohydrate density.

CONCLUSIONS

In a population of postmenopausal US women, CVD risk was associated with high-animal-protein and low-fiber diets, cancer risk was associated with low-carbohydrate diets, and diabetes risk was associated with low-fiber/low-carbohydrate diets.

Biomarker-Based Methods and Study Designs to Calibrate Dietary Intake for Assessing Diet-Disease Associations

BACKGROUND

Dietary biomarkers measured in biospecimens can play an important role in correcting for random and systematic measurement error in self-reported nutrient intake when assessing diet-disease associations. To date, high-quality biomarkers for calibrating self-reported dietary intake have only been developed for a few nutrients.

OBJECTIVES

To investigate new study designs and regression calibration approaches for calibrating self-reported nutrient intake for use in disease association analyses.

METHODS

We studied 3 regression calibration approaches: 1) an existing approach built on a calibration cohort assuming the existence of an objective biomarker (i.e., biomarker with random independent measurement error), 2) a proposed approach using a biomarker development cohort, and 3) a proposed 2-stage approach using both cohorts. We conducted simulation studies to compare the performance of different study designs/methods for estimating diet-disease associations and applied suitable methods to examine the association of sodium and potassium intake with cardiovascular disease (CVD) risk in Women's Health Initiative cohorts.

RESULTS

Simulation studies showed that the first approach can lead to biased association estimation when the objective biomarker assumption is violated; the second and third proposed approaches obviate the need for such an objective biomarker. Precision for estimating the association depends critically on sample size of the biomarker development cohort and the strength of the self-reported nutrient intake. Analyses based on the second and third approaches support previously reported significant findings using the first approach about associations of the ratio of sodium to potassium intake with CVD risk while providing efficiency gain for some outcomes.

CONCLUSIONS

Self-reported dietary intake needs to be calibrated for measurement error correction in diet-disease association analyses. When there are no existing objective biomarkers that can be used for calibration purpose, controlled feeding studies can be used to develop new biomarkers for use in calibration or can be used to calibrate self-reported dietary intake directly.

Whole-diet interventions and cardiovascular risk factors in postmenopausal women: A systematic review of controlled clinical trials

OBJECTIVES

Menopause is accompanied by many metabolic changes, increasing the risk of cardiometabolic diseases. The impact of diet, as a modifiable lifestyle factor, on cardiovascular health in general populations has been well established. The purpose of this systematic review is to summarize the evidence on the effects of whole diet on lipid profile, glycemic indices, and blood pressure in postmenopausal women.

METHODS

Embase, Medline, Cochrane Central Register of Controlled Trials, and Google Scholar were searched from inception to February 2021. We included controlled clinical trials in postmenopausal women that assessed the effect of a whole-diet intervention on lipid profile, glycemic indices, and/or blood pressure. The risk of bias in individual studies was assessed using RoB 2 and ROBINS-I tools.

SUMMARY OF EVIDENCE

Among 2,134 references, 21 trials met all eligibility criteria. Overall, results were heterogenuous and inconsistent. Compared to control diets, some studies showed that participants experienced improvements in total cholesterol (TC), low-density lipoprotein cholesterol (LDL), systolic blood pressure (SBP), fasting blood sugar (FBS), and apolipoprotein A (Apo-A) after following fat-modified diets, but some adverse effects on triglycerides (TG), very low-density lipoprotein cholesterol (VLDL), lipoprotein(a) (Lp(a)), and high-density lipoprotein cholesterol (HDL) concentrations were also observed. A limited number of trials found some effects of the Paleolithic, weight-loss, plant-based, or energy-restricted diets, or of following American Heart Association recommendations on TG, TC, HDL, insulin, FBS, or insulin resistance.

CONCLUSION

Current evidence suggests that diet may affect levels of some lipid profile markers, glycemic indices, and blood pressure among postmenopausal women. However, due to the large heterogeneity in intervention diets, comparison groups, intervention durations, and population characteristics, findings are inconclusive. Further well-designed clinical trials are needed on dietary interventions to reduce cardiovascular risk in postmenopausal women.

Nutritional epidemiology and the Women's Health Initiative: a review

The dietary modification (DM) clinical trial, within the Women's Health Initiative (WHI), studied a low-fat dietary pattern intervention that included guidance to increase vegetables, fruit, and grains. This study was motivated in part from uncertainty about the reliability of observational studies examining the association between dietary fat and chronic disease risk by using self-reported dietary data. In addition to this large trial, which had breast and colorectal cancer as its primary outcomes, a substantial biomarker research effort was initiated midway in the WHI program to contribute to nutritional epidemiology research more broadly. Here we review and update findings from the DM trial and from the WHI nutritional biomarker studies and examine implications for future nutritional epidemiology research. The WHI included the randomized controlled DM trial (n = 48,835) and a prospective cohort observational (OS) study (n = 93,676), both among postmenopausal US women, aged 50-79 y when enrolled during 1993-1998. Also reviewed is a nutrition and physical activity assessment study in a subset of 450 OS participants (2007-2009) and a related controlled feeding study among 153 WHI participants (2010-2014). Long-term follow-up in the DM trial provides evidence for intervention-related reductions in breast cancer mortality, diabetes requiring insulin, and coronary artery disease in the subset of normotensive healthy women, without observed adverse effects or changes in all-cause mortality. Studies of intake biomarkers, and of biomarker-calibrated intake, suggest important associations of total energy intake and macronutrient dietary composition with the risk for major chronic diseases among postmenopausal women. Collectively these studies argue for a nutrition epidemiology research agenda that includes major efforts in nutritional biomarker development, and in the application of biomarkers combined with self-reported dietary data in disease association analyses. We expect such efforts to yield novel disease association findings and to inform disease prevention approaches for potential testing in dietary intervention trials. This trial was registered at clinicaltrials.gov as NCT00000611.