The Women's Health Initiative Randomized Trials and Clinical Practice: A Review.

Importance: Approximately 55 million people in the US and approximately 1.1 billion people worldwide are postmenopausal women. To inform clinical practice about the health effects of menopausal hormone therapy, calcium plus vitamin D supplementation, and a low-fat dietary pattern, the Women's Health Initiative (WHI) enrolled 161 808 postmenopausal US women (N = 68 132 in the clinical trials) aged 50 to 79 years at baseline from 1993 to 1998, and followed them up for up to 20 years. Observations: The WHI clinical trial results do not support hormone therapy with oral conjugated equine estrogens plus medroxyprogesterone acetate for postmenopausal women or conjugated equine estrogens alone for those with prior hysterectomy to prevent cardiovascular disease, dementia, or other chronic diseases. However, hormone therapy is effective for treating moderate to severe vasomotor and other menopausal symptoms. These benefits of hormone therapy in early menopause, combined with lower rates of adverse effects of hormone therapy in early compared with later menopause, support initiation of hormone therapy before age 60 years for women without contraindications to hormone therapy who have bothersome menopausal symptoms. The WHI results do not support routinely recommending calcium plus vitamin D supplementation for fracture prevention in all postmenopausal women. However, calcium and vitamin D are appropriate for women who do not meet national guidelines for recommended intakes of these nutrients through diet. A low-fat dietary pattern with increased fruit, vegetable, and grain consumption did not prevent the primary outcomes of breast or colorectal cancer but was associated with lower rates of the secondary outcome of breast cancer mortality during long-term follow-up. Conclusions and Relevance: For postmenopausal women, the WHI randomized clinical trials do not support menopausal hormone therapy to prevent cardiovascular disease or other chronic diseases. Menopausal hormone therapy is appropriate to treat bothersome vasomotor symptoms among women in early menopause, without contraindications, who are interested in taking hormone therapy. The WHI evidence does not support routine supplementation with calcium plus vitamin D for menopausal women to prevent fractures or a low-fat diet with increased fruits, vegetables, and grains to prevent breast or colorectal cancer. A potential role of a low-fat dietary pattern in reducing breast cancer mortality, a secondary outcome, warrants further study.

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.

Short-term Air Pollution Levels and Blood Pressure in Older Women.

BACKGROUND: Evidence of associations between daily variation in air pollution and blood pressure (BP) is varied and few prior longitudinal studies adjusted for calendar time. METHODS: We studied 143,658 postmenopausal women 50 to 79 years of age from the Women's Health Initiative (1993-2005). We estimated daily atmospheric particulate matter (PM) (in three size fractions: PM2.5, PM2.5-10, and PM10) and nitrogen dioxide (NO2) concentrations at participants' residential addresses using validated lognormal kriging models. We used linear mixed-effects models to estimate the association between air pollution concentrations and repeated measures of systolic and diastolic BP (SBP, DBP) adjusting for confounders and calendar time. RESULTS: Short-term PM2.5 and NO2 were each positively associated with DBP {0.10 mmHg [95% confidence interval (CI): 0.04, 0.15]; 0.13 mmHg (95% CI: 0.09, 0.18), respectively} for interquartile range changes in lag 3-5 day PM2.5 and NO2. Short-term NO2 was negatively associated with SBP [-0.21 mmHg (95%CI: -0.30, -0.13)]. In two-pollutant models, the NO2-DBP association was slightly stronger, but for PM2.5 was attenuated to null, compared with single-pollutant models. Associations between short-term NO2 and DBP were more pronounced among those with higher body mass index, lower neighborhood socioeconomic position, and diabetes. When long-term (annual) and lag 3-5 day PM2.5 were in the same model, associations with long-term PM2.5 were stronger than for lag 3-5 day. CONCLUSIONS: We observed that short-term PM2.5 and NO2 levels were associated with increased DBP, although two-pollutant model results suggest NO2 was more likely responsible for observed associations. Long-term PM2.5 effects were larger than short-term.

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.

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.

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.

Long-term exposure to ambient particulate matter and stroke etiology: Results from the Women's Health Initiative.

BACKGROUND: Ambient particulate matter (PM) air pollution is a leading cause of global disability and accounts for an annual 2.9 million deaths globally. PM is established as an important risk factor for cardiovascular disease, however the evidence supporting a link specifically between long-term exposure to ambient PM and incident stroke is less clear. We sought to evaluate the association of long-term exposure to different size fractions of ambient PM with incident stroke (overall and by etiologic subtypes) and cerebrovascular deaths within the Women's Health Initiative, a large prospective study of older women in the US. METHODS: We studied 155,410 postmenopausal women without previous cerebrovascular disease enrolled into the study between 1993 and 1998, with follow-up through 2010. We assessed geocoded participant address-specific concentrations of ambient PM (fine [PM2.5], respirable [PM10] and coarse [PM10-2.5]), as well as nitrogen dioxide [NO2] using spatiotemporal models. We classified hospitalization events into ischemic, hemorrhagic, or other/unclassified stroke. Cerebrovascular mortality was defined as death from any stroke etiology. We used Cox proportional hazard models to calculate hazard ratios (HR) and 95% confidence intervals (CI), adjusting for individual and neighborhood-level characteristics. RESULTS: During a median follow-up time of 15 years, participants experienced 4,556 cerebrovascular events. The hazard ratio for all cerebrovascular events was 2.14 (95% CI: 1.87, 2.44) comparing the top versus bottom quartiles of PM2.5. Similarly, there was a statistically significant increase in events comparing the top versus bottom quartiles of PM10 and NO2 (HR: 1.17; 95% CI: 1.03, 1.33 and HR:1.26; 95% CI: 1.12, 1.42). The strength of association did not vary substantially by stroke etiology. There was little evidence of an association between PMcoarse and incident cerebrovascular events. CONCLUSIONS: Long-term exposure to fine (PM2.5) and respirable (PM10) particulate matter as well as NO2 was associated with a significant increase of cerebrovascular events among postmenopausal women. Strength of the associations were consistent by stroke etiology.

Sugar-Sweetened and Artificially Sweetened Beverages and Risk of Liver Cancer and Chronic Liver Disease Mortality.

Importance: Approximately 65% of adults in the US consume sugar-sweetened beverages daily. Objective: To study the associations between intake of sugar-sweetened beverages, artificially sweetened beverages, and incidence of liver cancer and chronic liver disease mortality. Design, Setting, and Participants: A prospective cohort with 98 786 postmenopausal women aged 50 to 79 years enrolled in the Women's Health Initiative from 1993 to 1998 at 40 clinical centers in the US and were followed up to March 1, 2020. Exposures: Sugar-sweetened beverage intake was assessed based on a food frequency questionnaire administered at baseline and defined as the sum of regular soft drinks and fruit drinks (not including fruit juice); artificially sweetened beverage intake was measured at 3-year follow-up. Main Outcomes and Measures: The primary outcomes were (1) liver cancer incidence, and (2) mortality due to chronic liver disease, defined as death from nonalcoholic fatty liver disease, liver fibrosis, cirrhosis, alcoholic liver diseases, and chronic hepatitis. Cox proportional hazards regression models were used to estimate multivariable hazard ratios (HRs) and 95% CIs for liver cancer incidence and for chronic liver disease mortality, adjusting for potential confounders including demographics and lifestyle factors. Results: During a median follow-up of 20.9 years, 207 women developed liver cancer and 148 died from chronic liver disease. At baseline, 6.8% of women consumed 1 or more sugar-sweetened beverage servings per day, and 13.1% consumed 1 or more artificially sweetened beverage servings per day at 3-year follow-up. Compared with intake of 3 or fewer servings of sugar-sweetened beverages per month, those who consumed 1 or more servings per day had a significantly higher risk of liver cancer (18.0 vs 10.3 per 100 000 person-years [P value for trend = .02]; adjusted HR, 1.85 [95% CI, 1.16-2.96]; P = .01) and chronic liver disease mortality (17.7 vs 7.1 per 100 000 person-years [P value for trend <.001]; adjusted HR, 1.68 [95% CI, 1.03-2.75]; P = .04). Compared with intake of 3 or fewer artificially sweetened beverages per month, individuals who consumed 1 or more artificially sweetened beverages per day did not have significantly increased incidence of liver cancer (11.8 vs 10.2 per 100 000 person-years [P value for trend = .70]; adjusted HR, 1.17 [95% CI, 0.70-1.94]; P = .55) or chronic liver disease mortality (7.1 vs 5.3 per 100 000 person-years [P value for trend = .32]; adjusted HR, 0.95 [95% CI, 0.49-1.84]; P = .88). Conclusions and Relevance: In postmenopausal women, compared with consuming 3 or fewer servings of sugar-sweetened beverages per month, those who consumed 1 or more sugar-sweetened beverages per day had a higher incidence of liver cancer and death from chronic liver disease. Future studies should confirm these findings and identify the biological pathways of these associations.

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.

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.

Variations in Dietary Patterns Defined by the Healthy Eating Index 2015 and Associations with Mortality: Findings from the Dietary Patterns Methods Project.

BACKGROUND: It is currently unknown if within high-quality dietary intake there exist distinct dietary patterns associated with health benefits that are identifiable with multidimensional dietary pattern analyses. The purpose of this study was to identify specific dietary patterns and groups therein and their associations with all-cause, CVD, and cancer mortality. METHODS: We conducted sex-specific k-means cluster analyses within Healthy Eating Index 2015 (HEI-2015) quintile 5 in 3 US cohorts [NIH-American Association of Retired Persons Diet and Health Study (AARP), the Multiethnic Cohort (MEC), Women's Health Initiative Observational Study (WHI OS)], clusters ranging from n = 1190 to n = 12,007. Characterizations incorporated HEI-2015 overall and component-specific percentage adherence goals, using untruncated and truncated radar graphs and shape analyses. Using cohort- and sex-specific Cox proportional hazards models, associations of quintile 5 clusters with all-cause, cardiovascular disease (CVD), and cancer mortality were evaluated relative to quintile 1. RESULTS: In each cohort sex-specific sample, 3 identified clusters included 16%-62% of participants, providing evidence for variation within high-quality dietary intake. Clusters revealed commonalities in total fruits and whole fruits intakes that exceeded goals and high sodium intake. Dairy and whole grain intakes oftentimes fell below goal. Some clusters were in addition characterized by total vegetables, greens & beans, and seafood & plant protein intakes exceeding goals. All high-quality dietary patterns were associated with a multivariable-adjusted significant 15%-26% lower risk of all-cause death than diet intake in quintile 1 (except for cluster 2 in WHI OS), and with a 16%-25% lower risk of CVD mortality in the AARP and MEC cohorts. Cancer mortality results were inconsistent. CONCLUSIONS: Multiple ways to achieve a high-quality diet were identified and significant associations with lower all-cause and CVD mortality were seen in some cohorts.

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-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.

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.

Accelerometer-Measured Daily Steps, Physical Function, and Subsequent Fall Risk in Older Women: The Objective Physical Activity and Cardiovascular Disease in Older Women Study.

Steps per day were measured by accelerometer for 7 days among 5,545 women aged 63-97 years between 2012 and 2014. Incident falls were ascertained from daily fall calendars for 13 months. Median steps per day were 3,216. There were 5,473 falls recorded over 61,564 fall calendar-months. The adjusted incidence rate ratio comparing women in the highest versus lowest step quartiles was 0.71 (95% confidence interval [0.54, 0.95]; ptrend across quartiles = .01). After further adjustment for physical function using the Short Physical Performance Battery, the incidence rate ratio was 0.86 ([0.64, 1.16]; ptrend = .27). Mediation analysis estimated that 63.7% of the association may be mediated by physical function (p = .03). In conclusion, higher steps per day were related to lower incident falls primarily through their beneficial association with physical functioning. Interventions that improve physical function, including those that involve stepping, could reduce falls in older adults.

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

Dietary guidance emphasizes healthy dietary patterns, but supporting evidence comes from self-reported dietary data, which are prone to measurement error. We explored whether nutritional biomarkers from the Women's Health Initiative Nutrition and Physical Activity Assessment Study Feeding Study (NPAAS-FS) (n = 153; 2010-2014) and the Women's Health Initiative Nutrition and Physical Activity Assessment Study Observational Study (NPAAS-OS) (n = 450; 2006-2009) could identify biomarker signatures of dietary patterns for development of corresponding regression calibration equations to help mitigate measurement error. Fasting blood samples were assayed for a specific panel of vitamins, carotenoids, and phospholipid fatty acids; 24-hour urine samples were assayed for nitrogen, sodium, and potassium levels. Intake records from the NPAAS-FS were used to calculate Healthy Eating Index 2010 (HEI-2010), Alternative Healthy Eating Index 2010 (AHEI-2010), alternative Mediterranean diet (aMED), and Dietary Approaches to Stop Hypertension (DASH) scores. Scores were regressed on blood and urine nutritional measures for discovery of dietary pattern biomarkers using a cross-validated model R2 ≥ 36% criterion (stage 1). Next, stepwise models (P ≤ 0.10 for entry/removal) using NPAAS-OS data were used to regress stage 1 dietary pattern biomarkers on NPAAS-OS self-reported dietary pattern scores using a food frequency questionnaire, a 4-day food record, and a 24-hour recall (stage 2). HEI-2010 and aMED analyses met the cross-validated R2 ≥ 36% criterion in stage 1, while AHEI-2010 and DASH analyses did not. The R2 values for HEI-2010 stage 2 calibration equations were as follows: food frequency questionnaire, 63.5%; 4-day food record, 83.1%; and 24-hour recall, 77.8%. Stage 2 aMED R2 values were 34.9%-46.8%. Dietary pattern biomarkers have potential for calibrating self-reports to enhance studies of diet-disease associations.

Alignment of Dietary Patterns With the Dietary Guidelines for Americans 2015-2020 and Risk of All-Cause and Cause-Specific Mortality in the Women's Health Initiative Observational Study.

Poor diet quality is a leading risk factor for death in the United States. We examined the association between Healthy Eating Index-2015 (HEI-2015) scores and death from all causes, cardiovascular disease (CVD), cancer, Alzheimer disease, and dementia not otherwise specified (NOS) among postmenopausal women in the Women's Health Initiative Observational Study (1993-2017). This analysis included 59,388 participants who completed a food frequency questionnaire and were free of cancer, CVD, and diabetes at enrollment. Stratified Cox proportional hazards models were fit using person-years from enrollment as the underlying time metric. We estimated multivariable adjusted hazard ratios and 95% confidence intervals for risk of death associated with HEI-2015 quintiles, with higher scores reflecting more optimal diet quality. Over a median of 18.2 years, 9,679 total deaths 3,303 cancer deaths, 2,362 CVD deaths, and 488 deaths from Alzheimer disease and dementia NOS occurred. Compared with those with lower scores, women with higher HEI-2015 scores had an 18% lower risk of all-cause death and 21% lower risk of cancer death. HEI-2015 scores were not associated with death due to CVD, Alzheimer disease, and dementia NOS. Consuming a diet aligned with 2015-2020 US dietary guidelines may have beneficial impacts for preventing overall causes of death and death from cancer.

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

BACKGROUND: Knowledge about macronutrient intake and chronic disease risk has been limited by the absence of objective macronutrient measures. Recently, we proposed novel biomarkers for protein, protein density, carbohydrate, and carbohydrate density, using established biomarkers and serum and urine metabolomics profiles in a human feeding study. OBJECTIVES: We aimed to use these biomarkers to develop calibration equations for macronutrient variables using dietary self-reports and personal characteristics and to study the association between biomarker-calibrated intake estimates and cardiovascular disease, cancer, and diabetes risk in Women's Health Initiative (WHI) cohorts. METHODS: Prospective disease association analyses are based on WHI cohorts of postmenopausal US women aged 50-79 y when enrolled at 40 US clinical centers (n = 81,954). We used biomarker intake values in a WHI nutritional biomarker study (n = 436) to develop calibration equations for each macronutrient variable, leading to calibrated macronutrient intake estimates throughout WHI cohorts. We then examined the association of these intakes with chronic disease incidence over a 20-y (median) follow-up period using HR regression methods. RESULTS: In analyses that included doubly labeled water-calibrated total energy, HRs for cardiovascular diseases and cancers were mostly unrelated to calibrated protein density. However, many were inversely related to carbohydrate density, with HRs (95% CIs) for a 20% increment in carbohydrate density of 0.81 (0.69, 0.95) and 0.83 (0.74, 0.93), respectively, for primary outcomes of coronary heart disease and breast cancer, as well as 0.74 (0.60, 0.91) and 0.87 (0.81, 0.93) for secondary outcomes of heart failure and total invasive cancer. Corresponding HRs (95% CIs) for type 2 diabetes incidence in relation to protein density and carbohydrate density were 1.17 (1.09, 1.75) and 0.73 (0.66, 0.80), respectively. CONCLUSIONS: At specific energy intake, a diet high in carbohydrate density is associated with substantially reduced risk of major chronic diseases in a population of US postmenopausal women. This trial was registered at clinicaltrials.gov as NCT00000611.

An approximate quasi-likelihood approach for error-prone failure time outcomes and exposures.

Measurement error arises commonly in clinical research settings that rely on data from electronic health records or large observational cohorts. In particular, self-reported outcomes are typical in cohort studies for chronic diseases such as diabetes in order to avoid the burden of expensive diagnostic tests. Dietary intake, which is also commonly collected by self-report and subject to measurement error, is a major factor linked to diabetes and other chronic diseases. These errors can bias exposure-disease associations that ultimately can mislead clinical decision-making. We have extended an existing semiparametric likelihood-based method for handling error-prone, discrete failure time outcomes to also address covariate error. We conduct an extensive numerical study to compare the proposed method to the naive approach that ignores measurement error in terms of bias and efficiency in the estimation of the regression parameter of interest. In all settings considered, the proposed method showed minimal bias and maintained coverage probability, thus outperforming the naive analysis which showed extreme bias and low coverage. This method is applied to data from the Women's Health Initiative to assess the association between energy and protein intake and the risk of incident diabetes mellitus. Our results show that correcting for errors in both the self-reported outcome and dietary exposures leads to considerably different hazard ratio estimates than those from analyses that ignore measurement error, which demonstrates the importance of correcting for both outcome and covariate error.

Evaluation of potential metabolomic-based biomarkers of protein, carbohydrate and fat intakes using a controlled feeding study.

PURPOSE: Objective biomarkers of dietary exposure are needed to establish reliable diet-disease associations. Unfortunately, robust biomarkers of macronutrient intakes are scarce. We aimed to assess the utility of serum, 24-h urine and spot urine high-dimensional metabolites for the development of biomarkers of daily intake of total energy, protein, carbohydrate and fat, and the percent of energy from these macronutrients (%E). METHODS: A 2-week controlled feeding study mimicking the participants' habitual diets was conducted among 153 postmenopausal women from the Women's Health Initiative (WHI). Fasting serum metabolomic profiles were analyzed using a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for aqueous metabolites and a direct-injection-based quantitative lipidomics platform. Urinary metabolites were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy at 800 MHz and by untargeted gas chromatography-mass spectrometry (GC-MS). Variable selection was performed to build prediction models for each dietary variable. RESULTS: The highest cross-validated multiple correlation coefficients (CV-R2) for protein intake (%E) and carbohydrate intake (%E) using metabolites only were 36.3 and 37.1%, respectively. With the addition of established dietary biomarkers (doubly labeled water for energy and urinary nitrogen for protein), the CV-R2 reached 55.5% for energy (kcal/d), 52.0 and 45.0% for protein (g/d, %E), 55.9 and 37.0% for carbohydrate (g/d, %E). CONCLUSION: Selected panels of serum and urine metabolites, without the inclusion of doubly labeled water and urinary nitrogen biomarkers, give a reliable and robust prediction of daily intake of energy from protein and carbohydrate.