This cross-sectional study investigated the relationship between dietary patterns and body mass index among 514 women with different ethnic backgrounds who completed a validated food-frequency questionnaire. An exploratory factor analysis with orthogonal rotation started with 23 food items and resulted in four factors that accounted for 93% of the total variance. Confirmatory factor analysis with the 16 items that had factor loadings of at least 0.60 validated the four dietary patterns. The most significant dietary pattern, “meat,” was characterized by high intake of processed and red meats, fish, poultry, eggs, fats and oils, and condiments. The “vegetable” pattern loaded high on different vegetables, whereas the third pattern named “bean” was high in legumes, tofu and soy protein. The major components of the “cold foods” pattern were fruit, fruit juice and cold breakfast cereals. Although the “meat” pattern was predominant among Hawaiians and the “bean” pattern very common among Chinese and Japanese women, factors two and four were not related to ethnicity. After adjustment for daily energy intake, the “meat” pattern was positively associated with body mass index (r = 0.17, P = 0.0001), whereas the other three patterns showed negative relationships to body mass index (r = −0.076, P = 0.084, r = −0.13, P = 0.003, and r = −0.13, P = 0.003) for vegetables, beans and cold foods, respectively. The associations were similar in direction and magnitude for all ethnic groups. The study results support the ideas that choosing the right foods may be important in weight control and that food-based dietary patterns may be useful in dietary counseling.

Mammographic densities are hypothesized to reflect the cumulative exposure to risk factors that influence breast cancer incidence. This report analyzed percent densities over time and explored predictors of density change in relation to age. The study population consisted of 607 breast cancer cases and 667 frequency matched controls with 1,956 and 1,619 mammographic readings, respectively. Mammograms done over >20 years and before a diagnosis of breast cancer were assessed for densities using a computer-assisted method. Using multilevel modeling to allow for repeated measurements, we estimated the effect of ethnicity, case status, reproductive characteristics, hormonal therapy, body mass index, and soy intake on initial status and longitudinal change. After integrating the area under the percent density curve, cumulative percent density was compared with age-specific breast cancer rates in Hawaii. Percent densities decreased ∼5.6% per 10 years but a nonlinear effect indicated a faster decline earlier in life. Cumulative percent densities and age-specific breast cancer rates increased at very similar rates; both standardized regression coefficients were >0.9. Japanese ancestry, overweight, estrogen/progestin treatment, and, to a lesser degree, estrogen-only therapy predicted a slower decline in densities with age. Case status and adult soy intake were related to higher densities whereas overweight and having any child were associated with lower densities at initial status. Risk factors that influence the decline in mammographic densities over time may be important for breast cancer prevention because cumulative percent densities may reflect the age-related increase in breast cancer risk. (Cancer Epidemiol Biomarkers Prev 2006;15(4):732–9)

Background Breast cancer incidence is considerably lower among Japanese and Chinese women than among Caucasian and Native Hawaiian even in second and third generation migrants. Mammographic densities, which refer to the radiological appearance of the healthy female breast, are related to breast cancer risk. The purpose of this project was to explore the hypothesis that women from ethnic groups at high breast cancer risk are more likely to have high levels of densities than women from low breast cancer risk groups. Methods In a cross-sectional design, 514 pre- and post-menopausal women recruited at mammography screening clinics completed a self-administered questionnaire. We used a computer-assisted method to measure the dense and the total areas of the breast and to compute per cent breast density. Student's t-tests and multiple linear regression were applied to examine ethnic differences and to explore determinants of mammographic densities, respectively. Results The unadjusted mean dense area was 15% smaller in Chinese and Japanese women than in the Caucasian/Hawaiian group. However, because of their smaller breast size, the per cent of the breast occupied by dense tissue in Chinese and Japanese women was 20% higher than in Caucasian women. Body mass index, age, menopausal status, parity, and oestrogen therapy were associated with mammographic densities, but they did not account for all ethnic differences. Conclusions Whereas this study detected some ethnic differences in mammographic densities, the importance of dense areas and per cent densities as indicators of breast cancer risk in ethnically diverse populations remains to be clarified.

The influence of dietary fiber, magnesium (Mg), and glycemic load (GL) on diabetes was examined in the Hawaii component of the Multiethnic Cohort. Th

Mechanisms linking a proinflammatory diet to obesity remain under investigation. The ability of diet to influence the gut microbiome (GM) in creating chronic low-grade systemic inflammation provides a plausible connection to adiposity. Assess whether any associations seen between the Energy-Adjusted Dietary Inflammatory Index (E-DII score), total fat mass, visceral adipose tissue (VAT), or liver fat (percentage volume) operated through the GM or microbial related inflammatory factors, in a multiethnic cross-sectional study. In the Multiethnic Cohort–Adiposity Phenotype Study (812 men, 843 women, aged 60–77 y) we tested whether associations between the E-DII and total adiposity, VAT, and liver fat function through the GM, LPS, and high-sensitivity C-reactive protein (hs-CRP). DXA-derived total fat mass, MRI-measured VAT, and MRI-based liver fat were measured. Participants provided stool and fasting blood samples and completed an FFQ. Stool bacterial DNA was amplified and the 16S rRNA gene was sequenced at the V1–V3 region. E-DII score was computed from FFQ data, with a higher E-DII representing a more proinflammatory diet. The associations between E-DII score, GM (10 phyla, 28 genera, α diversity), and adiposity phenotypes were examined using linear regression and mediation analyses, adjusting for confounders. There were positive total effects (c) between E-DII and total fat mass (c = 0.68; 95% CI: 0.47, 0.90), VAT (c = 4.61; 95% CI: 2.95, 6.27), and liver fat (c = 0.40; 95% CI: 0.27, 0.53). The association between E-DII score and total fat mass was mediated by LPS, Flavonifractor, [Ruminococcus] gnavus group, and Tyzzerella. The association between E-DII score and ectopic fat occurred indirectly through Fusobacteria, Christensenellaceae R-7 group, Coprococcus 2, Escherichia-Shigella, [Eubacterium] xylanophilum group, Flavonifractor, Lachnoclostridium, [Ruminococcus] gnavus group, Tyzzerella, [Ruminococcus] gnavus group (VAT only), and α diversity (liver fat only). There was no significant association between E-DII score and adiposity phenotype through hs-CRP. Associations found between E-DII and adiposity phenotypes occurred through the GM and LPS.