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Little is known about the association of specific nutrients, especially proteins, on the age-related gut dysbiosis.To determine the associations between the quantity and sources (vegetable and animal) of dietary protein intake and gut microbiome composition in community-dwelling older men.We performed a cross-sectional analysis on 775 older men from the Osteoporotic Fractures in Men (MrOS) Study (age 84.2 ± 4.0 years) with available dietary information and stool samples at visit 4 (2014-16). Protein intake was estimated from a brief food frequency questionnaire and adjusted to total energy intake. The gut microbiome composition was determined by 16S (v4) sequencing (processed by DADA2 and SILVA). 11,534 amplicon sequence variants (ASVs) were identified and assigned to 21 phyla with dominance of Firmicutes (45%) and Bacteroidetes (43%). We performed α-diversity, β-diversity, and taxa abundance (by ANCOM-BC) to determine the associations between protein intake and gut microbiome.Median protein intake was 0.7 g/(kg body weight · d). Participants with higher energy-adjusted protein intakes had higher Shannon and Chao1 α-diversity indices (P 0.05). For β-diversity analysis, participants with higher protein intakes had a different center in weighted and unweighted UniFrac PCoA vs. those with lower intake (P 0.05), adjusted for age, race, education, clinical center, batch number, fiber and energy intake, weight, height, and medications. Similarly, higher protein consumptions from either animal or vegetable sources were associated with higher gut microbiome diversity. Several genus-level ASVs, including Christensenellaceae, Veillonella, Haemophilus, and Klebsiella were more abundant in participants with higher protein intakes, whereas Clostridiales bacterium DTU089 and Desulfovibrio were more abundant in participants with lower protein intake (Bonferroni corrected P 0.05).We observed significant associations between protein intake and gut microbiome diversity in community-living older men. Further studies are needed to elucidate the mediation role of gut microbiome on the relationship between protein intake and health outcomes in older adults.

BACKGROUND: Few studies have evaluated whether plant-centered diets prevent progression of early stage chronic kidney disease (CKD). OBJECTIVES: We examined the association between plant-centered diet quality and early CKD markers. METHODS: We prospectively examined 2869 black and white men and women in the Coronary Artery Risk Development in Young Adults Study free of diagnosed kidney failure in 2005–2006 [examination year 20 (Y20); mean age: 45.3 ± 3.6 y]. CKD marker changes from Y20 to 2015–2016 (Y30) were considered, including estimated glomerular filtration rate (eGFR; serum creatinine), urinary albumin-to-creatinine ratio (ACR), and both. Diet was assessed through interviewer-administered diet histories at Y0, Y7, and Y20, and plant-centered diet quality was quantified with the A Priori Diet Quality Score (APDQS). Linear regression models were used to examine the association of APDQS and subsequent 10-y changes in CKD markers. RESULTS: After adjustment for sociodemographic, behavioral, and diet factors, we found that higher APDQS was related to less adverse changes in CKD markers in the subsequent 10-y period. Compared with the lowest APDQS quintile, the highest quintile was associated with an attenuated increase in lnACR (−0.25 mg/g; 95% CI: −0.37, −0.13 mg/g; P-trend

Prior studies examined associations between Healthy Eating Index (HEI) and chronic disease risk based on self-reported diet without measurement error correction.Our objective was to test associations between biomarker-calibration of 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. WHI is registered at clinicaltrials.gov: NCT00000611.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., body mass index (BMI)] for systematic measurement error correction. Calibrated data were then used in hazard ratio (HR) models examining associations with incidence of CVD (total, subtypes, mortality), cancer (total, subtypes, mortality), and T2D in WHI participants with approximately two decades of follow-up. Models were multivariable-adjusted with further adjustment for BMI and doubly-labeled water (DLW)-calibrated energy.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): coronary heart disease; 0.75, (0.61.-0.91): myocardial infarction; 0.96 (0.92-1.01): stroke; 0.88 (0.75-1.02): CVD mortality; 0.81 (0.70-0.94): colorectal cancer; 0.81 (0.74-0.88): breast cancer; 0.79 (0.73-0.87): cancer mortality; and 0.45 (0.36-0.55): T2D. Except for cancer mortality and T2D incidence, results became null when adjusted for DLW-calibrated energy intake and BMI.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 suggest that energy intake and/or obesity are strong drivers of diet-related chronic disease risk in postmenopausal women.

Background Prospective evidence on associations between diet quality indices and lung cancer risk is limited, particularly among older women. Objectives We investigated associations between 4 diet quality indices [Healthy Eating Index-2015 (HEI-2015), Alternate Healthy Eating Index 2010 (AHEI-2010), alternate Mediterranean Diet (aMED), and Dietary Approaches to Stop Hypertension (DASH)] and lung cancer incidence and mortality in the Women's Health Initiative Observational Study. Methods Postmenopausal women aged 50-79 y at baseline (1993-1998) self reported their diet intake and information on relevant covariates. We used Cox proportional hazards regression models to estimate HRs and 95% CIs after controlling for age, smoking, and other relevant covariates. Results During ∼17 y of follow-up among 86,090 participants, 1491 lung cancer cases and 1393 lung cancer deaths were documented. Dietary indices were not associated with overall lung cancer incidence but were protective against squamous cell carcinoma (12.8% of total lung cancer) cases (HEI-2015: HR: 0.85; 95% CI: 0.76, 0.96; AHEI-2010: HR: 0.87; 95% CI: 0.78, 0.98; aMED: HR: 0.90; 95% CI: 0.81, 0.99; DASH: HR: 0.87; 95% CI: 0.77, 0.98). Among the indices, only HEI-2015 showed an inverse trend (P-trend = 0.02) with overall lung cancer mortality. Smoking status or participant age at baseline did not modify the association between dietary indices and lung cancer incidence or mortality. Conclusions After comprehensive control of smoking exposure, we found that diet quality was not associated with overall lung cancer among postmenopausal women. However, a high-quality diet was inversely related to incident lung cancer of the squamous cell subtype. Future studies in populations with diverse age, smoking history, and dietary intake may further elucidate the relation between diet quality indices and lung cancer, especially by histological subtype.

Some previous studies have suggested that consuming dairy products, particularly the low-fat variety, lowers the incidence of type 2 diabetes. However, no study to our knowledge has focused on an ethnically diverse group of postmenopausal women, a population with a high risk of this disease. We conducted a prospective cohort study of 82,076 postmenopausal women enrolled in the Women’s Health Initiative Observational Study who did not report diabetes at enrollment. Total, low-fat, and high-fat dairy product and yogurt intakes were estimated from FFQ at baseline and 3 y of follow-up. Treated diabetes incidence was ascertained from annual follow-up questionnaires. During 8 y of follow-up, 3946 cases of incident treated diabetes were reported (annual incidence, 0.73%; cumulative incidence, 4.8%). After multivariable adjustment, low-fat dairy product consumption was inversely associated with the risk of type 2 diabetes. RR was roughly 0.5–0.6 in the upper quintiles compared with the lowest quintile (median servings/d, 2.8 in the 5th quintile and 1.5 in the 4th quintile vs. 0.05 in the first quintile; P-trend , 0.001). The inverse relationship was more pronounced in women with a higher BMI. High yogurt consumption was associated with a significant decrease in diabetes risk, whereas there was no relationship between high-fat dairy product consumption and diabetes risk. A diet high in low-fat dairy products is associated with lower diabetes risk in postmenopausal women, particularly those who are obese. J. Nutr. 141: 1969–1974, 2011.

Stroke mortality rates and prevalence of several chronic diseases are higher in Southern populations and blacks in the US. This study examined the relationships of race (black, white) and region (Stroke Belt, Stroke Buckle, other) with selected nutrient intakes among black and white American men (n = 9229). The Block 98 FFQ assessed dietary intakes and multivariable linear regression analysis was used to examine whether race and region were associated with intakes of fiber, saturated fat, trans fat, sodium, potassium, magnesium, calcium, and cholesterol. Race and region were significant predictors of most nutrient intakes. Black men consumed 1.00% lower energy from saturated fat compared with white men [multivariable-adjusted β: 1.00% (95% CI = −0.88, −1.13)]. A significant interaction between race and region was detected for trans fat (P < 0.0001), where intake was significantly lower among black men compared with white men only in the Stroke Belt [multivariable-adjusted β: −0.21 (95% CI = −0.11, −0.31)]. Among black men, intakes of sodium, potassium, magnesium, and calcium were lower, whereas cholesterol was higher, compared with white men (P < 0.05 for all). Comparing regions, men in the Stroke Buckle had the lowest intakes of fiber, potassium, magnesium, and calcium compared with those in the Stroke Belt and other regions; men in both the Stroke Buckle and Stroke Belt had higher intakes of cholesterol compared with those in other regions (P < 0.005 for all). Given these observed differences in dietary intakes, more research is needed to understand if and how they play a role in the health disparities and chronic disease risks observed among racial groups and regions in the US.

Background: Healthy obese individuals may be protected against adverse health outcomes. Diet and race might influence healthy obesity, but data on their roles and interactions on the phenotype are limited. Objective: We compared the food intake of metabolically healthy obese men to those of other weight status‐metabolic health phenotypes. Methods: Men (n = 4855) aged $45 y with BMI $18.5 kg/m 2 and free of cardiovascular diseases, diabetes, and cancer were evaluated in a cross-sectional study of the REGARDS (REasons for Geographic And Racial Differences in Stroke) study cohort. Food intake was collected with the use of a food frequency questionnaire. Weight status‐metabolic health phenotypes were defined with the use of metabolic syndrome (MetS) and homeostasis model assessment of insulin resistance (HOMA-IR) criteria. Mean differences in food intake among weight status‐metabolic health phenotypes were compared with the use of linear regression. Results: MetS-defined healthy obesity was present in 43.9% of white obese men and 58% of black obese men; the healthy obese phenotype, based on HOMA-IR, was equally prevalent in both white (20%) and black (21%) obese men. Among white men, MetS-defined healthy and unhealthy obesity were associated with lower wholegrain bread intake and higher consumption of red meat (P < 0.001), whereas HOMA-IR‐defined healthy and unhealthy obesity were associated with lower red meat intake (P < 0.0001) compared with healthy normal weight in multivariable-adjusted analyses that adjusted for sociodemographic, lifestyle, and clinical confounders. However, results were attenuated and became nonsignificant after further adjustment for BMI. Healthy and unhealthy overweight, defined by both criteria, were associated with lower whole grain bread intake (P < 0.001) in all models. Among black men, weight status‐metabolic health phenotypes were not associated with food intake in all models. Conclusion: Healthy obesity in men was not associated with a healthier diet. Future studies need to consider dietary patterns, which may better inform the holistic effect of diet on healthy obesity, in prospective analyses. JN utrdoi: 10.3945/jn.115.221283.

Background: Metabolically healthy obesity may confer lower risk of adverse health outcomes compared with abnormal obesity. Diet and race are postulated to influence the phenotype, but their roles and their interrelations on healthy obesity are unclear. Objective: We evaluated food intakes of metabolically healthy obese women in comparison to intakes of their metabolically healthy normal-weight and metabolically abnormal obese counterparts. Methods: This was a cross-sectional study in 6964 women of the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. Participants were aged 45–98 y with a body mass index (BMI; kg/m2) ≥18.5 and free of cardiovascular diseases, diabetes, and cancer. Food intake was collected by using a food-frequency questionnaire. BMI phenotypes were defined by using metabolic syndrome (MetS) and homeostasis model assessment of insulin resistance (HOMA-IR) criteria. Mean differences in food intakes among BMI phenotypes were compared by using ANCOVA. Results: Approximately one-half of obese women (white: 45%; black: 55%) as defined by MetS criteria and approximately one-quarter of obese women (white: 28%; black: 24%) defined on the basis of HOMA-IR values were metabolically healthy. In age-adjusted analyses, healthy obesity and normal weight as defined by both criteria were associated with lower intakes of sugar-sweetened beverages compared with abnormal obesity among both white and black women (P < 0.05). HOMA-IR–defined healthy obesity and normal weight were also associated with higher fruit and low-fat dairy intakes compared with abnormal obesity in white women (P < 0.05). Results were attenuated and became nonsignificant in multivariable-adjusted models that additionally adjusted for BMI, marital status, residential region, education, annual income, alcohol intake, multivitamin use, cigarette smoking status, physical activity, television viewing, high-sensitivity C-reactive protein, menopausal status, hormone therapy, and food intakes. Conclusions: Healthy obesity was not associated with a healthier diet. Prospective studies on relations of dietary patterns, which may be a better indicator of usual diet, with the phenotype would be beneficial.

Consumption of foods with a high glycemic index (GI) or glycemic load (GL) is hypothesized to contribute to insulin resistance, which is associated with increased risk of diabetes mellitus, obesity, cardiovascular disease, and some cancers. However, dietary assessment of GI and GL is difficult because values are not included in standard food composition databases. Our objective was to develop a database of GI and GL values that could be integrated into an existing dietary database used for the analysis of FFQ. Food GI values were obtained from published human experimental studies or imputed from foods with a similar carbohydrate and fiber content. We then applied the values to the Women's Health Initiative (WHI) FFQ database and tested the output in a random sample of previously completed WHI FFQs. Of the 122 FFQ line items (disaggregated into 350 foods), 83% had sufficient carbohydrate (>5 g/serving) for receipt of GI and GL values. The foods on the FFQ food list with the highest GL were fried breads, potatoes, pastries, pasta, and soft drinks. The fiber content of foods had very little influence on calculated GI or GL estimates. The augmentation of this FFQ database with GI and GL values will enable etiologic investigations of GI and GL with numerous disease outcomes in the WHI and other epidemiologic studies that utilize this FFQ.