Faculty Bibliography

Nonalcoholic fatty liver disease (NAFLD) is an emerging global health concern. It is the most common form of chronic liver disease in Western countries, affecting both adults and children. NAFLD encompasses a broad spectrum of fatty liver disease, ranging from simple steatosis (NAFL) to nonalcoholic steatohepatitis (NASH), and is strongly associated with obesity, insulin resistance, and dyslipidemia. First-line therapy for NAFLD includes weight loss achieved through diet and physical activity. However, there is a lack of evidenced-based dietary recommendations. The American Diabetes Association's (ADA) recommendations that aim to reduce the risk of diabetes and cardiovascular disease may also be applicable to the NAFLD population. The objectives of this review are to: (1) provide an overview of NAFLD in the context of insulin resistance, and (2) provide a rationale for applying relevant aspects of the ADA recommendations to the nutritional management of NAFLD.

Examining the role of dietary protein and establishing intake guidelines among individuals with diabetes is complex. The 2013 American Diabetes Association (ADA) standards of care recommend an individualized approach to decision making with regard to protein intake and dietary macronutrient composition. Needs may vary based on cardiometabolic risk factors and renal function. Among individuals with impaired renal function, the ADA recommends reducing protein intake to 0.8-1.0 g/kg per day in earlier stages of chronic kidney disease (CKD), and to 0.8 g/kg per day in the later stages of CKD. Epidemiological studies suggest animal protein may increase risk of diabetes; however, few data are available to suggest how protein sources influence diabetes complications.

PURPOSE: Higher physical activity (PA) has been associated with greater attenuation of body fat gain and preservation of lean mass across the lifespan. These analyses aimed to determine relationships of change in PA to changes in fat and lean body mass in a longitudinal prospective study of postmenopausal women. METHODS: Among 11,491 women enrolled at three Women's Health Initiative clinical centers who were selected to undergo dual-energy x-ray absorptiometry, 8352 had baseline body composition measurements, with at least one repeated measure at years 1, 3, and 6. PA data were obtained by self-report at baseline and 3 and 6 yr of follow-up. Time-varying PA effect on change in lean and fat mass during the 6-yr study period for age groups (50-59 yr, 60-69 yr, and 70-79 yr) was estimated using mixed effects linear regression. RESULTS: Baseline PA and body composition differed significantly among the three age groups. The association of change in fat mass from baseline and time-varying PA differed across the three age groups (P = 0.0006). In women age 50-59 yr, gain in fat mass from baseline was attenuated with higher levels of PA. Women age 70-79 yr lost fat mass at all PA levels. In contrast, change in lean mass from baseline and time-varying PA did not differ by age group (P = 0.1935). CONCLUSIONS: The association between PA and change in fat mass varies by age group, with younger, but not older, women benefiting from higher levels of aerobic PA. Higher levels of aerobic activity are not associated with changes in lean mass, which tends to decrease in older women regardless of activity level. Greater attention to resistance training exercises may be needed to prevent lean mass loss as women age.

Regression calibration using biomarkers provides an attractive approach to strengthening nutritional epidemiology. We consider this approach to assessing the relationship of fat and total energy consumption with postmenopausal breast cancer. In analyses that included fat density data, biomarker-calibrated total energy was positively associated with postmenopausal breast cancer incidence in cohorts of the US Women's Health Initiative from 1994-2010. The estimated hazard ratio for a 20% increment in calibrated food frequency questionnaire (FFQ) energy was 1.22 (95% confidence interval (CI): 1.15, 1.30). This association was not evident without biomarker calibration, and it ceased to be apparent following control for body mass index (weight (kg)/height (m)(2)), suggesting that the association is mediated by body fat deposition over time. The hazard ratio for a corresponding 40% increment in FFQ fat density was 1.05 (95% CI: 1.00, 1.09). A stronger fat density association, with a hazard ratio of 1.19 (95% CI: 1.00, 1.41), emerged from analyses that used 4-day food records for dietary assessment. FFQ-based analyses were also carried out by using a second dietary assessment in place of the biomarker for calibration. This type of calibration did not correct for systematic bias in energy assessment, but may be able to accommodate the "noise" component of dietary measurement error. Implications for epidemiologic applications more generally are described.

BACKGROUND: To develop a positive aging phenotype, we undertook analyses to describe multiple dimensions of positive aging and their relationships to one another in women 65 years of age and older and evaluate the performance of individual indicators and composite factors of this phenotype as predictors of time to death, years of healthy living, and years of independent living. METHODS: Data from Women's Health Initiative clinical trial and observational study participants ages 65 years and older at baseline, including follow-up observations up to 8 years later, were analyzed using descriptive statistics and principal components analysis to identify the factor structure of a positive aging phenotype. The factors were used to predict time to death, years of healthy living (without hospitalization or diagnosis of a serious health condition), and years of independent living (without nursing home admission or use of special services). RESULTS: We identified a multidimensional phenotype of positive aging that included two factors: Physical-Social Functioning and Emotional Functioning. Both factors were predictive of each of the outcomes, but Physical-Social Functioning was the strongest predictor. Each standard deviation of increase in Physical-Social Functioning was accompanied by a 23.7% reduction in mortality risk, a 19.4% reduction in risk of major health conditions or hospitalizations, and a 26.3% reduction in risk of dependent living. CONCLUSIONS: Physical-Social Functioning and Emotional Functioning constitute important components of a positive aging phenotype. Physical-Social Functioning was the strongest predictor of outcomes related to positive aging, including years of healthy living, years of independent living, and time to mortality.

IGF-I shares structural homology and in vitro metabolic activity with insulin. Laboratory models suggest that IGF-I and its binding proteins IGFBP-1 and IGFBP-2 have potentially beneficial effects on diabetes risk, whereas IGFBP-3 may have adverse effects. We therefore conducted a prospective nested case-control investigation of incident diabetes (n = 742 case subjects matched 1:1 to control subjects) and its associations with IGF-axis protein levels in the Nurses' Health Study, a cohort of middle-aged women. The median time to diabetes was 9 years. Statistical analyses were adjusted for multiple risk factors, including insulin and C-reactive protein. Diabetes risk was fivefold lower among women with baseline IGFBP-2 levels in the top versus bottom quintile (odds ratio [OR](q5-q1) = 0.17 [95% CI 0.08-0.35]; P trend < 0.0001) and was also negatively associated with IGFBP-1 levels (OR(q5-q1) = 0.37 [0.18-0.73]; P trend = 0.0009). IGFBP-3 was positively associated with diabetes (OR(q5-q1) = 2.05 [1.20-3.51]; P trend = 0.002). Diabetes was not associated with total IGF-I levels, but free IGF-I and diabetes had a significant association that varied (P interaction = 0.003) by insulin levels above the median (OR(q5-q1) = 0.48 [0.26-0.90]; P trend = 0.0001) versus below the median (OR(q5-q1) = 2.52 [1.05-6.06]; P trend < 0.05). Thus, this prospective study found strong associations of incident diabetes with baseline levels of three IGFBPs and free IGF-I, consistent with hypotheses that the IGF axis might influence diabetes risk.

Obese and underweight women who develop breast cancer may have poorer survival compared with normal-weight women. However, the optimal weight for best prognosis is still under study. We conducted a prospective investigation of pre-diagnosis body mass index (BMI) and mortality among 14,948 breast cancer patients in the After Breast Cancer Pooling Project. Breast cancer patients diagnosed from 1990 to 2006 with AJCC Stage I-III breast tumors were drawn from four prospective cohorts. Hazard ratios (HR) and 95% confidence intervals (CI) representing the associations of BMI categories (World Health Organization international classifications) with recurrence and mortality were estimated using delayed entry Cox proportional hazards models. Obese (30 to < 35 kg/m(2)), severely obese (35 to < 40 kg/m(2)), and morbidly obese (>/= 40 kg/m(2)) were examined. After a mean follow-up of 7.8 years, 2,140 deaths and 2,065 recurrences were documented. Both underweight (HR = 1.59; 95% CI: 1.18, 2.13) and morbidly obese women (HR = 1.81; 95% CI: 1.42, 2.32) had the greatest risk of overall mortality compared with normal weight (18.5-24.9 kg/m(2)) women. Severe obesity (HR = 1.09; 95% CI: 0.88, 1.36) and obesity (HR = 1.11; 95% CI: 0.97, 1.27) were related to small non-significant increased risks. Overweight (25.0-29.9 kg/m(2)) was not associated with any excess risk compared with normal weight. Similar associations were found for breast cancer death and non-breast cancer death but not recurrence. Women who were underweight and morbidly obese before breast cancer diagnosis were at the greatest risk of all-cause mortality. Morbidly obese women were also at increased risk of death from breast cancer. These results suggest that degree of obesity confers differential risk on survival.

The 2008 Physical Activity (PA) Guidelines recommend engaging in at least 2.5 h (10 MET-hours/week) of moderate intensity PA per week (defined as 4 METs) to reduce risk of morbidity and mortality. This analysis was conducted to investigate whether this recommendation can be extended to breast cancer survivors. Data from four studies of breast cancer survivors measuring recreational PA from semi-quantitative questionnaires a median of 23 months post-diagnosis (interquartile range 18-32 months) were pooled in the After Breast Cancer Pooling Project (n = 13,302). Delayed entry Cox proportional hazards models were applied in data analysis with adjustment for age, post-diagnosis body mass index, race/ethnicity, menopausal status, TNM stage, cancer treatment, and smoking history. Engaging in at least 10 MET-hours/week of PA was associated with a 27% reduction in all-cause mortality (n = 1,468 events, Hazard Ratio (HR) = 0.73, 95% CI, 0.66-0.82) and a 25% reduction in breast cancer mortality (n = 971 events, HR = 0.75, 95% CI 0.65-0.85) compared with women who did not meet the PA Guidelines (<10 MET-hours/week). Risk of breast cancer recurrence (n = 1,421 events) was not associated with meeting the PA Guidelines (HR = 0.96, 95% CI, 0.86-1.06). These data suggest that adhering to the PA guidelines may be an important intervention target for reducing mortality among breast cancer survivors.

Little is known about the effects of diet after breast cancer diagnosis on survival. We prospectively examined the relation between post-diagnosis dietary factors and breast cancer and all-cause survival in women with a history of invasive breast cancer diagnosed between 1987 and 1999 (at ages 20-79 years). Diet after breast cancer diagnosis was measured using a 126-item food frequency questionnaire. Among 4,441 women without a history of breast cancer recurrence prior to completing the questionnaire, 137 subsequently died from breast cancer within 7 years of enrollment. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated for intake of macronutrients as well as selected micronutrients and food groups from Cox proportional hazards regression models. After adjustment for factors at diagnosis (age, state of residence, menopausal status, smoking, breast cancer stage, alcohol, history of hormone replacement therapy), interval between diagnosis and diet assessment, and at follow-up (energy intake, breast cancer treatment, body mass index, and physical activity), women in the highest compared to lowest quintile of intake of saturated fat and trans fat had a significantly higher risk of dying from any cause (HR = 1.41, 95% CI = 1.06-1.87, P trend = 0.03) for saturated fat; (HR = 1.78, 95% CI = 1.35-2.32, P trend = 0.01) for trans fat intake. Associations were similar, though did not achieve statistical significance, for breast cancer survival. This study suggests that lower intake of saturated and trans fat in the post-diagnosis diet is associated with improved survival after breast cancer diagnosis.

With aging, renal function tends to decline, as evidenced by reduced glomerular filtration rate. High-protein intake may further stress the kidneys by causing sustained hyperfiltration. To investigate whether dietary protein is associated with impaired renal function, we used data from 2 nested case-control studies within the Women's Health Initiative Observational Study (n = 2419). We estimated protein intake using a FFQ and estimated glomerular filtration rate (eGFR) from cystatin C. To account for the original study designs, inverse probability weights were applied. Self-reported energy and protein were calibrated using biomarkers of energy and protein intake. Associations between protein intake and renal function were estimated by weighted linear and logistic regression models. Average calibrated protein intake (mean +/- SD) was 1.1 +/- 0.2 g/(kg body weight.d).Twelve percent (n = 292) of women had impaired renal function. The odds of impaired renal function, defined as eGFR <60 mL/(min.1.73m(2)), was not associated with calibrated protein intake. When eGFR was modeled continuously, there was no association with calibrated protein when protein was expressed in absolute (g/d) or relative to energy (protein % energy/d), but protein relative to body weight [g/(kg body weight.d)] was associated with higher eGFR. There was no evidence for effect modification by age, BMI, or general health status. These data suggest higher protein intake is not associated with impaired renal function among postmenopausal women without a diagnosis of chronic kidney disease.