Frank B. Hu

Bio: Frank B. Hu is an academic researcher from Harvard University. The author has contributed to research in topics: Type 2 diabetes & Diabetes mellitus. The author has an hindex of 250, co-authored 1675 publications receiving 253464 citations. Previous affiliations of Frank B. Hu include Southwest University & Brigham and Women's Hospital.

Development and validation of anthropometric prediction equations for lean body mass, fat mass and percent fat in adults using the National Health and Nutrition Examination Survey (NHANES) 1999-2006.

Abstract: Quantification of lean body mass and fat mass can provide important insight into epidemiological research. However, there is no consensus on generalisable anthropometric prediction equations to validly estimate body composition. We aimed to develop and validate practical anthropometric prediction equations for lean body mass, fat mass and percent fat in adults (men, n 7531; women, n 6534) from the National Health and Nutrition Examination Survey 1999-2006. Using a prediction sample, we predicted each of dual-energy X-ray absorptiometry (DXA)-measured lean body mass, fat mass and percent fat based on different combinations of anthropometric measures. The proposed equations were validated using a validation sample and obesity-related biomarkers. The practical equation including age, race, height, weight and waist circumference had high predictive ability for lean body mass (men: R 2=0·91, standard error of estimate (SEE)=2·6 kg; women: R 2=0·85, SEE=2·4 kg) and fat mass (men: R 2=0·90, SEE=2·6 kg; women: R 2=0·93, SEE=2·4 kg). Waist circumference was a strong predictor in men only. Addition of other circumference and skinfold measures slightly improved the prediction model. For percent fat, R 2 were generally lower but the trend in variation explained was similar. Our validation tests showed robust and consistent results with no evidence of substantial bias. Additional validation using biomarkers demonstrated comparable abilities to predict obesity-related biomarkers between direct DXA measurements and predicted scores. Moreover, predicted fat mass and percent fat had significantly stronger associations with obesity-related biomarkers than BMI did. Our findings suggest the potential application of the proposed equations in various epidemiological settings.

Abstract P282: Dairy Fat and Risk of Cardiovascular Disease in 3 Cohorts of US Adults

Abstract: Background: Few prospective studies have examined dairy fat in relation to cardiovascular disease (CVD). Objective: We aimed to evaluate the association between dairy fat and incident CVD in US adults. Design: We followed 41,852men in the Health Professionals Follow-Up Study (1986-2010), 70,636women in the Nurses’ Health Study (1980-2010), and 90,056 women in the Nurses’ Health Study II (1991-2011). Dairy fat and other fat intakes were assessed every 4 years by validated food-frequency questionnaires. Results: During 4,324,477 person-years of follow-up, we documented 10,834 incident CVD cases including 5,107coronary heart disease (nonfatal myocardial infarction or fatal coronary disease) and 5,843 stroke cases. In multivariate analyses, dairy fat intake was not significantly related to the risk of total cardiovascular disease (for a 5% increase in energy from dairy fat, the relative risk (RR) was 1.00, 95% CI: 0.95-1.04), or coronary heart disease [RR: 1.02; 95%CI: 0.95-1.09] or stroke [RR: 0.98; 95%CI: 0.92-1.05]. In models estimating the effects of exchanging different fat sources, compared with equivalent energy from dairy fat, a 5% replacement in energy intake of dairy fat with vegetable fat was associated with a 10% (95%CI: 8%-12%) reduction in the risk of CVD. Substitution of other animal fat with dairy fat was not significantly associated with CVD risk. Conclusion: These results suggest that replacing dairy fat with carbohydrates or other animal fat was not associated with CVD risk. However, replacing dairy fat with vegetable sources of fat was associated with a lower risk of CVD. Figure 1. RR and 95% CI for cardiovascular disease associated with isocaloric substitutions (5% of energy) of vegetable fat (A) polyunsaturated fat (B) and other animal fat (C) for dairy fat. ![][1] [1]: /embed/graphic-1.gif

Prepregnancy Dietary Protein Intake, Major Dietary Protein Sources, and the Risk of Gestational Diabetes Mellitus A prospective cohort study

Abstract: OBJECTIVE Dietary protein is an important modulator of glucose metabolism. However, studies regarding the association between dietary protein intake and gestational diabetes mellitus (GDM) risk are sparse. This study was to examine the association. RESEARCH DESIGN AND METHODS Our study included 21,457 singleton pregnancies reported among 15,294 participants of the Nurses9 Health Study II cohort between 1991 and 2001. Included pregnancies were free of chronic diseases before pregnancy or previous GDM. Generalized estimating equations were used to estimate the relative risks (RRs) and 95% CIs. RESULTS After adjustment for age, parity, nondietary and dietary factors, and BMI, multivariable RRs (95% CIs) comparing the highest with lowest quintiles were 1.49 (1.03–2.17) for animal protein intake and 0.69 (0.50–0.97) for vegetable protein intake. The substitution of 5% energy from vegetable protein for animal protein was associated with a 51% lower risk of GDM (RR (95% CI), 0.49 (0.29–0.84)). For major dietary protein sources, multivariable RRs (95% CIs) comparing the highest with the lowest quintiles were 2.05 (1.55–2.73) for total red meat and 0.73 (0.56–0.95) for nuts, respectively. The substitution of red meat with poultry, fish, nuts, or legumes showed a significantly lower risk of GDM. CONCLUSIONS Higher intake of animal protein, in particular red meat, was significantly associated with a greater risk of GDM. By contrast, higher intake of vegetable protein, specifically nuts, was associated with a significantly lower risk. Substitution of vegetable protein for animal protein, as well as substitution of some healthy protein sources for red meat, was associated with a lower risk of GDM.

Dietary Patterns and Risk of Prostate Cancer in U.S. Men

Abstract: We prospectively investigated the associations between dietary patterns and risk of prostate cancer in the Health Professionals Follow-up Study. Between 1986 and 2000, 3,002 incident prostate cancer cases were identified in our cohort. Using factor analysis, two major dietary patterns were identified, a prudent and a western dietary pattern. Dietary patterns were not appreciably associated with risk of total prostate cancer. For the highest versus the lowest quintiles, the multivariable relative risk (RR) for the prudent pattern was 0.94 [95% confidence interval (CI), 0.83-1.06], and for the western pattern, the multivariable RR was 1.03 (95% CI, 0.92-1.17). Neither were these associated with risk of advanced prostate cancer [highest versus lowest quintile, prudent pattern (RR, 1.01; 95% CI, 0.68-1.49); western pattern (RR, 1.13; 95% CI, 0.77-1.67)]. Higher western pattern scores were suggestively associated with a greater risk of advanced prostate cancer among older men [highest versus lowest quintile (RR, 1.35; 95% CI, 0.97-1.90)], but not after adding processed meat to the model [highest versus lowest quintile (RR, 1.11; 95% CI, 0.75-1.65)]. We did not find any evidence for a protective association between prudent pattern and risk of prostate cancer. The lack of association between a western dietary pattern as identified by factor analysis in our cohort and prostate cancer risk suggests that dietary risk factors for prostate cancer are likely to differ from those for other conditions, such as cardiovascular disease and type 2 diabetes, that have been associated with a western dietary pattern in this cohort.

Vitamin D Deficiency

Abstract: Once foods in the United States were fortified with vitamin D, rickets appeared to have been conquered, and many considered major health problems from vitamin D deficiency resolved. But vitamin D deficiency is common. This review considers the role of vitamin D in skeletal and nonskeletal health and suggests strategies for the prevention and treatment of vitamin D deficiency.

Diagnosis and Management of the Metabolic Syndrome An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement

Abstract: The metabolic syndrome has received increased attention in the past few years. This statement from the American Heart Association (AHA) and the National Heart, Lung, and Blood Institute (NHLBI) is intended to provide up-to-date guidance for professionals on the diagnosis and management of the metabolic syndrome in adults. The metabolic syndrome is a constellation of interrelated risk factors of metabolic origin— metabolic risk factors —that appear to directly promote the development of atherosclerotic cardiovascular disease (ASCVD).1 Patients with the metabolic syndrome also are at increased risk for developing type 2 diabetes mellitus. Another set of conditions, the underlying risk factors , give rise to the metabolic risk factors. In the past few years, several expert groups have attempted to set forth simple diagnostic criteria to be used in clinical practice to identify patients who manifest the multiple components of the metabolic syndrome. These criteria have varied somewhat in specific elements, but in general they include a combination of both underlying and metabolic risk factors. The most widely recognized of the metabolic risk factors are atherogenic dyslipidemia, elevated blood pressure, and elevated plasma glucose. Individuals with these characteristics commonly manifest a prothrombotic state and a pro-inflammatory state as well. Atherogenic dyslipidemia consists of an aggregation of lipoprotein abnormalities including elevated serum triglyceride and apolipoprotein B (apoB), increased small LDL particles, and a reduced level of HDL cholesterol (HDL-C). The metabolic syndrome is often referred to as if it were a discrete entity with a single cause. Available data suggest that it truly is a syndrome, ie, a grouping of ASCVD risk factors, but one that probably has more than one cause. Regardless of cause, the syndrome identifies individuals at an elevated risk for ASCVD. The magnitude of the increased risk can vary according to which components of the syndrome are …